geographic information systems

1

OXFORD

Geographic Information Systems

Applications in Natural Resource Management

Second Edition



Michael G. Wing

Pete Bettinger

OXFORD UNIVERS ITY PRESS

2

Second Edition



Michael G. Wing

Pete Bettinger

OXFORD UNIVERSITY PRESS

OXFORD U:-:lvr RSITV l'Rt'SS

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Li brary and Archives Canada Cataloguing in Publication

Wing. M ichael G

Geographic information synems: applications in forestry and nafUr.l.1

resources management / M ichael G. W ing & Pele Beninger.- 2nd cd .

Previous cds. by Pete Beninger and Michael G. Wing.

ISBN 978-0- 19-5426 10-6

I . Forests and forestry- Remme sensing. 2. alura.! resources-Remme

sensing. 3. Geographic information systems. I. Ben inger. Pete. 1962- II. Title.

SD387. R4W562008 634.9·028 C2008-902309-9

Cover image: Philip & Karen Smith/Geny Images.

345- 12 11 10

This book is primed on permanent (acid·free) paper e. Printcd in C anada 3

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Librnry ~nd Archives Canada Cataloguing in Puhlic:1 lioll

Wing. Mid13d G

Gcogr.tphic information SYSWllS : applications in forestry and n:1IlIral resources management J Michael G. Wing & Pete I3cltinger.-lnd c:d .

Pr('ViollS: t'd<. by Pete Beninger and i\'li chad C. Wing. ISBN 978-0- 19-542610-6

1. Forests and for~try-Remote )cllSing. sensing. J . Geographic information systellls.

2. ;uur.aJ r('~urccs-Rcmou:

I. Beltinger. rete. 1962- II. Title:..

SD387. R4W562008 634.9 '028 C2008-902309-9

Cover image: Philip & KJreu milhfGercy Images.

345 - 121110

This book is primed on permant'nt (ac.id, rree) p:tpt:r c::. .

Primed in Canada

Contents

List of Tables XIV

Preface xv

Part 1 Introduction to Geographic Information Systems. Spatial Databases. and Map Design I

Chapter 1 Geographic Information Systems 2

Objectives 2

What is a Geographic Infonnation System? 3

A Brief History of GIS 4

Why Use GIS in Natural Resource Management Organizations? 7

GIS Technology 8

Data collection processes and inpuc devices 8

Manual map digirizing 10

Scanning 10

Remme sensing II Phologrammerry 13

Field data collection 1 G Dara s[Qrage rechnology 19

D.ta ma nipulation and display 19

O utput Devices 20

Prinrers and ploners 20

Screen displays 21

Graphic images 22

Tabular outpur 22

G IS software programs 22

Summary 24

Applications 24

References 25 4

vi Contents

Chapter 2 GIS Databases: Map Projections, Structures, and Scale 27

Objectives 27

The Shope and Size of the Earth 27

Ellipsoids, Geoids, and Datums 28

The Geographical Coordinate System 30

Map Projections 32

Common Types of Map Projections 33

Planar Coordinate Systems 34

GIS Database Structures 38

Raster data srruc[Ure 38

Sa«llite imagery 38

Digital elev'Hion models 39 Digital orthophotographs 39

Digital raster graphics 40

ar ional Map Accuracy Standards 43

Vector data structure 44

Topology 45

Comparing raster and vector data structures 47

Alternative data S(fuc£ures 4 8

Triangular Irregular Nerwork 48 D ynam ic segmentat ion ofli near nerworks 49

Regions 50

Metadata 50

Obtaining Spatial Data 50

Scale and Resolution of Spatial Databases 51

Summary 51

Applications 52

References 53

Chapter 3 Acquiring, Creating, and Editing GIS Databases 54

Objectives 54

Acquiring GIS Databases 55

Creating GIS Databases 57

Editing GIS Databases 59

Editing anribuccs 60

Editing spatial position 6 1

5

Chapter 4

Checking for missing da,a 62

Checking for inconsistent data 62

Sources of Error in GIS Databases 63

Types of Error in GIS Databases 64

Summary 67

Applications 67

References 70

Map Design 71

Objectives 71

Map Components

Symbology 72

Direction 73

Scale 74

Legend 74

Locational inset

Neadine 76

Annotation 76

Typography 76

72

75

Color and contrast 77

AnciHary information 78

Caveats and disclaimers 79

Map Types 80

Reference maps 80

Thematic maps 81

O,her types of maps 84

The DeSign Loop 85

Common Map Problems 85

Summary 86

Applications 87

References 88

Contents vii

Part 2 Applying GIS to Natural Resource Management 89

Chapter 5 Selecting Landscape Features 90

Objectives 90 6

viii Contents

Selecting Landscape Features from a GIS Database 91

Selecting one feature manually 91

Selecting many features manually 91

Selecting all of the features in a GIS database 92

Select ing none of the features in a GIS database 92

Selecting features based on some database cr iteria 92

Single critcrion queries 94

Multiple criteria queries 94

Selecting features from a previously selected set of features 95

Inverting a selection 97

Example 1: Find the landscape features in one GIS database by using single and mulriple criteria queries and by selecting features from a

previously selected set of features 98

Selecting features within some proximity of other features 99

Example I: Find rhe landscape featu res in one G IS database (har are

inside landscape features (polygons) contai ned in anmher G IS database 99

Example 2: Find rhe landscape fea[tlres in one GIS database that are

close to the landscape features contained within another GIS database 100

Exam ple 3: Find landscape featu res from one G IS darabase that are adjacent ro other landscape featu res in the sa me G IS database 101

Advanced query applications 102

Syntax errors 102

Summary 102

Applications 103

References 105

Chapter 6 Obtaining Infonnation about a Specific Geographic Region 106

Objectives 106

The Process of Clipping Landscape Features 107

Obtaining information about vegetation resources within riparian zones 109

Obtaining information about soil resources within an ownership 110

Obtaining information about roads within a forest III

Obtaining information about streams within a forest I 13

The Process of Erasing Landscape Features I 14

Obtaining information about vegetation resources outside of riparian zones 115

Summary 116

Applications I 16

References 118

7

Chapter 7 Buffering Landscape Features 119

Objectives 119

How a Buffer Process Works 120

Buffering Streams and Creating Riparian Areas 122

Fixed-width buffers 123

Variable-width buffers 123

Buffering Owl Nest Locations 124

Buffering the Inside of Landscape Features 125

Buffering Concentric Rings around Landscape Features 125

Buffering Shorelines 126

Other Reasons for Using Buffering Processes 127

Summary 128

Applications 128

References 130

Chapter 8 Combining and Splitting Landscape Features, and Merging GIS Databases 132

Objectives 132

Combining Landscape Features 132

Contiguous, similar landscape features 135

Contents ix

Multiple spatial rep resentations within a single landscape feature or record 136

Overlapping polygons 137

Splitting Landscape Features 138

Merging GIS Databases 140

Determining how much land area is unrestricted 140

Summary 142

Applications 142

References 143

Chapter 9 Associating Spatial and Non-spatial Databases 144

Objectives 144

Joining Non-spatial Databases with GIS Databases 145

One-to-one join processes 145

One-to-many joins 147

8

x Contents

Many-to-one (or many-to-many) joins 147

Example 1: Determining [he number of hardwood sawm ills in a stare 148

Example 2: Determining sawmill em ploymenr in a counry 149

Joining Two Spatial GIS Databases ISO

Making Joined Data a Permanent Part of the Target (Destination) Table 153

Linking or Relating Tables 153

Summary 154

Applications ISS

References 156

Chapter 10 Updating GIS Databases 157

Objectives 157

The Need for Keeping GIS Databases Updated 158

Example 1: Updating a forest stand GIS database managed by a forest management company 159

Example 2: Updating a streams GIS database managed by a state agency 160

Updating an Existing GIS Database by Adding New Landscape Features 161

Updating a stands GIS database 161

Updating a trails GIS database 162

Updating an Existing GIS Database by Modifying Existing Landscape Features and Attributes 166

Ed_iring the spatial position of landscape features using digitaJ ortbophotographs 166

Updating the tabular 3rrribures using a join process 167

Summary 167

Applications 168

References 169

Chapter 11 Overlay Processes 170

Objectives 170

Intersect Processes 171

Identity Processes 174

Union Processes 175

Incorporating Point and Line GIS Databases into an Overlay Analysis 178

Applying Overlay Techniques to Point and Line Databases 179

Additional Overlay Considerations 180 9

Summary 181

Applications 182

References 183

Contents xi

Chapter 12 Synthesis of Techniques Applied to Advanced Topics 184

Objectives 184

Land Classification 185

Recreation Opportunity Spectrum 188

Habitat Suitability Model with a Road Edge Effect 191

Summary 193

Applications 194

References 19S

Chapter 13 Raster GIS Database Analysis 197

Objectives 197

Digital Elevation Models (DEMs) 197

Elevation Contours 198

Shaded Relief Maps 200

Slope Class Maps 20 I

Interaction with Vector GIS Databases 202

Viewshed Analysis 20S

Watershed Delineation 208

Summary 210

Applications 211

References 212

Chapter 14 Raster GIS Database Analysis II 213

Objectives 213

Raster Data Analysis 213

Raster Analysis Software Parameters 213

Distance Functions 214

Statistical Summary Search Functions 21S

Density Functions 216

10

Summary 181

Applications 182

References 183

Contents xl


Objectives 184

Land Classification 185

Recreation Opportunity Spectrum 188

Habitat Suitability Model with a Road Edge Effect 191

Summary 193

Applications 194

References 195


Objectives 197

Digital ElevaHon Models (DEMs) 197

Elevation Contours 198

Shaded Relief Maps 200

Slope Class Maps 20 I

Interarnon with Vector GIS Databases 202

Viewshed Analysis 205

Watershed Delineation 208

Summary 210

Applications 211

References 212


Objectives 213

Raster Data Analysis 213

Raster Analysis Software Parameters 213

Distance Funrnons 214

Statistical Summary Search Funrnons 215

Density Functions 216

xii Contents

Raster Reclassification 217

Raster Map Algebra 218

Database Structure Conversions 218

Getting Started with the ArcGIS Spatial Analyst 219

Determining the Most Efficient Route to a Destination 220

Creating a Density Surface for the Number of Trees Per Acre 221

Summary 223

Applications 223

References 224

Part 3 Contemporary Issues in GIS 225

Chapter 15 Trends in GIS Technology 226

Objectives 226

Integrated RasterNector Software 226

Linkage of GIS Databases with Auxiliary Digital Data 227

High Resolution GIS Databases 228

Distribution of GIS Capabilities to Field Offices 229

Web-based Geographic Information Systems 230

Data Retrieval via the Internet 230

Portable Devices to Capture, Display, and Update GIS Data 231

Standards for the Exchange of GIS Databases 231

Legal Issues Related to GIS 232

GIS Interoperability and Open Internet Access 234

GIS Education 234

Summary 235

Applications 235

References 235

Chapter 16 Institutional Challenges and Opportunitie s Related to GIS 237

Objectives 237

Sharing GIS Databases with Other Natural Resource Organizations 237

Sharing GIS Databases within a Natural Resource Organization 239

11

xii Contents

Raster Reclassilication 217

Raster Map Algebra 218

Database Structure Conversions 218

Getting Started with the ArcGIS Spatial Analyst 219

Determining the Most Efficient Route to a Destination 220

Creating a Density Surface for the Number of Trees Per Acre 221

Summary 223

Applications 223

References 224

Part 3 Contemporary Issues in GIS 225


Objectives 226

Integrated Raster/Vector Software 226

Linkage of GIS Databases with Auxiliary Digital Data 227

High Resolution GIS Databases 228


Web-based Geograpruc Information Systems 230

Data Retrieval via the Internet 230

Portable Devices to Capture, Display, and Update GIS Data 231

Standards for the Exchange of GIS Databases 231

Legal Issues Related to GIS 232

GIS Interoperability and Open Internet Access 234

GIS Education 234

Summary 235

Applications 235

References 235

Chapter 16 Institutional Challenges and Opportunities Related to GIS 237

Objectives 237

Sharing GIS Databases with Other Natural Resource Organizations 237

Sharing GIS Databases within a Natural Resource Organization 239


Technical and Institutional Challenges 241

Benefits of Implementing a GIS Program 243

Successful GIS Implementation 243

Summary 243

Applications 244

References 244

Chapter 17 Certification and Licensing of GIS Users 245

Objectives 245

Current Certification Programs 246

The NCEES Model Law 247

The Need for GIS Certification and Licensing 248

GIS Community Response to Certification and Licensing 249

MAPPS Lawsuit 249

Summary 251

Applications 251

References 251

Appendix A GIS Related Terminology 253

Contents xiii

Appendix B GIS Related Professional Organizations and Journals 260

Appendix C GIS Software Developers 263

Index 264

12

List of Tables

1.1 Common sizes of map Outpur fro m plQners 2 1

1.2 Common rypes of graphics image Output fi les 22

2. 1 Map scales and associated ar ia na! Map Accuracy Standards fo r hori zonta l accu racy 44

2.2 Comparison of raster and vector dara srruc(Ures 47

3.1 Typical informatio n assoc iated with a GIS database request 55

3.2 Auribures of stands in rhe Daniel Pickerr sra nds GIS darabase 6 1

3.3 Exa mple Roor Mea n Square Erro r (RMSE) calcularion fo r Grs coordinares 66

5.1 A rimber srand darabase 93

6. 1 A subser of rhe rabular da,. contained in the GIS database rhar resulred from cl ipping Brown T racr stands within 50-merer stream buffers 110

6.2 Lengrh and rype of road within rhe roads GIS darabase developed fo r rhe Brown Traer I 12

6.3 Lengrh and rype of road with in rhe boundary of ,he Brown T racr I 12

6.4 Length and Type of srrea ms within the SHearns GIS database used by rhe Brown T rac( 11 3

6.5 Length and rype of strea ms within the boundary of rhe Brown T racr 11 4

7 .1 Ten hypmherical streams and their st rea m class. length , and width 12 1

7.2 T en hypor herical streams and their stream class, length, width, and buffer distance 122

7.3 Stale of Oregon riparia n managemem area policy 124

7 .4 Sample stream reaches represenred in the Brown Trdc[ strea ms GIS database, their cha racreristics. and resulring buffer width 124

8. 1 Resul ts of co mbi ning rwo sra nds 136

9.1 A non-spa ri al database in ASCII rexr file fo rmat illustrari ng com ma-del imired data 145

9.2 Spadal join oprions by targer and source fea ture (ype 151

10.1 A sam pling of reasons fo r updat ing GIS databases 158

10.2 I np urs and process thar ca n be used to ass ist a GIS database update 159

10.3 Attribures o r sra nds in a 32.38 hecra re (80 acre) land purchase adjacenr 10 [he Daniel Pickert foresr 16 1

11.1 Frequency distriburion of land allocation categories in resea rch plot locarions within rhe Brown T ract 179

11.2 Frequency disrriburion ofland allocation categories in relarion ro srrea m segmenrs wi thi n the Brown Tract 180

12. 1 An exam ple of a managemenr-related land classi fica rion sysrem 185

12.2 A subset of rules with spatia l co nsiderations fo r delineat ing recreational opportunity specrrum (ROS) classes 189

13. 1 OutpUt of percent slope va lues for managemenr units 203 13

List of Tables

1.1 Common sizes of map OUtP'" from plouers 21

1.2 Common rypes of graph ics image OUtpUt files 22

2.1 Map scales and associa ted ational Map Accu racy Standards fo r horizontal accuracy 44

2.2 Comparison of raster and vector doH3 Stru Ctures 47

3. J TypiC'JJ informarion associated with a GIS d;nabase request 55

3.2 Attributes of sl.lI1ds in rhe Daniel l' ickeoo Stands GIS database 6 1

3.3 Example Roor Mean quare Erro r (R.'v1SE) ealculaoion for GI'S coordinates 66

5.1 A lim ber . t.lnd da tabase 93

6.1 A subS« of the tabular da ta contained in the .IS database that resulted from clipping

Brown T ract stands within 50-meIer stream buffers 110

6.2 Length and rype of road wi thin the roads GIS database developed fo r the Brown Tract 11 2

6.3 Length and rype of road wirhin the boundary of Ihe Brown T raer I 12

6.4 Lengrh and rype of sore.ms within rhe Streams GIS cb rabas. used by Ihe Brown Traer 11 3

6.5 Lenglh and rype of meams withi n the boundary of the Brown T (3Ct I 14

7.1 Ten hyporher i al soreams and rheir meam class. length. and width 12 1

7.2 Ten hypothetical meams and their stream class. lengrh . widd,. and buffer disrancc 122

7.3 State of O regon riparian managemem area policy 124

7.4 Sample stream reaches represented in the Brown Trace strcarns GIS darabase. their characreristics. and n:sulring buffer width 124

8.1 Results or combi ning rwo sta nds 136

9.1 A non-spatial darabase in AS IItcxt file format i1lustr:oti ng com ma-delimited ,bra 145

9.2 Spatial join oprions by mget and sou rce fea rure type 15 1

10.1 A sam pling of reasons for updaring GIS databases 158

10.2 Inpurs and process rim ca n be used to assist a GI database update 159

10.3 Attributes of sm nds in a 32.3 hectare (80 acre) land pur h. < adjacent to the Daniel Pickett foreSt 16 1

I 1.1 Frequency dist ribution or land allocation categories in research plol locacions within rhe Brown T tact 179

11.2 Frequency distribution orla nd allocarion caregorit;S in retll ion to stream segments within tile: Brown T tact 180

12.1 An ex.m pIe of a management-related land c1assiflcarion sYStem 185

12.2 A su bsel of rules wi th sp3rial considerations for deline-J.ling recreational opportunity spectrum (ROS) classes 189

13. 1 O UtPUt of percell[ stope val ut::s for managemem units 203

Preface

T his second edition of Gtogmphic Infonnatioll Sysums: Applications ill Natural Rtsourct

Management is intended for inrroducrory courses in geographic information systems

or computer applications that add ress copies related [0 natural resource management. The emphasis of rhe book is on geographical informacion systems (C IS) app lications in naru ~

fal resource management. GIS lOols are now considered core technologies for natural resource organizations and have become pare of day-co-day activities in many pans of rhe world. In addition , many natural resource programs in higher education require [hat S[U

dents comple[(~ at least one course tha t contai ns significant rrearmenr of GIS. We provide derailed discussions and examples orGIS o perat ions such as querying, buffering, cl ipping, and overlay analysis (and others), as well as background information on the history of GIS,

database creation, editing, acquisition. and map development. The applicarions provided in this book can be extended (0 any region in the world, although the primary emphasis is on North America, as portrayed by numerous examples of natural resource management scenariOS.

The contents of this book were determined largely through our experiences in natural resource management and research, as well as our extensive instructional experience over rhe previous decade. Many applications similar ro (he oncs we present in this book have been performed by natural resource professionals (as well as by the authors) as parr of their

normal job (asks in private o rganizations and public agencies. The goa l of this book is to introduce students, field personnel, biologists, and orher

natural resou rce professionals ro the most common GIS applica tions and principles associated with managing natural resou rces. Therefore, the book focuses mainly on GIS applications r:uher than o n GIS theory. We would be remiss, however, if we did nor provide

some background on the history, technology, and theory that defines GIS. Consequen tly,

the first pan of the book provides a b rief background on many of those areas as well as map development; it is nor all- inclusive. however, as we wish (0 focus rhe text o n GIS

applicarions in natural resource managemenL For a broader rrearmem of GIS concepts. other resources are recommended , including more general GIS books or User Gu ides specific [0 GIS sofrware packages.

Wirh that in mind, who comprises rhe audience of this book? Students. field personnel. biologists. and other natural resource professionals (and their managers) who work in narural resou rce- related fields. but where GIS is perhaps nor their primary job responsibility. People who already serve as GIS analysts, coordinators. or technicians will likely find many of rhe topics presented in this book co be familiar. We rry co focus on topics and applications used common ly by field professionals, [hose rhal are essemial ro their management needs. There are a variety of resources {hat delve deeper into various subject areas of GIS [har will undoubtedly be of va lue to GIS analysts. coordinators. and technicians. In our experiences these resources rare ly consistently focus on narural resource

14

Preface

r-r-his second edition of G~ogrtlphic Infonnatiol1 SysuJns: AppLicatiolls ill NOITlml Rtsourre 1. Mnnagtmt!1lt. is inrended for inrroducrory courses in geographic information systems

or campmer applications Ihat address (opics relared TO narural resource manage-mem. The emphasis of the book is on geogrdphical information systems (G IS) applications in narural resource managemelli. GIS rools are now considered corC' technologies for natural

resource organiZ.1rions and have b«ome parr of daY-fo-day acrivities in many pans of dlC

world. In addirion. many narural resou rce programs in higher education require [hat srudems comple[e at leasl one course rhal comai ns signific.1nt rre'llmem of GIS. We provide derailed discussions and examples of GIS opera rions such as querying, buffering, clipping, and overlay analysis (and 01 hers), as well as background information on the history of GIS. database creation , ediring, acquisition. and map development. T he 3pplicarions provided in rhis book can be exrended to any region in rhe wo rld. although rhe primary emphasis is on Nonh America. 3S portrayed by numerous examples of narural resource management scenarios.

The conreOiS of rhis book were determined largely (hrough our experiences in na[Ural resource management and research. as well as our ex(ensive instructional experience over lhe previous decade. Many appiic.1lions similar to the ones we presenr in this book have been performed by natural resource professionals (as well as by the aurhors) as parr of their

normal job tasks in private organizadons and public ag('ncies. The goal of this book is to introduce Sludent . field personnel. biologists, and other

n31l1ral resource professionals to [he mOSt common GIS applica rions and principles 3SS0-

ciared with managing naturaJ resources. Therefore. the book focuses mainly on GIS applical-ions J'<uher Ihan on GIS theory. We would be remiss, however, if we did not provide some background on the history. technology, and theory that defines GIS. Consequen tly. the first pan of the book provides a brier background on many of those areas as well as map development; it is nOl all- inclusive. however. as we wish (0 focus the [ext on GIS applications in natural resource management. For a broader rreatmem of GIS conceprs. other resources are recommended. including more general ,IS books or User Guides s~cific (0 CI software packages.

With that in mind. who comprises rhe audience of this book? Student.s, field personnel, biologis[s. and other natural resourc(' professionals (and their managers) who work in narural resource-relared fidds. but where GIS is perhaps not (heir primary job responsibil· icy. People who al ready serve as GIS analystS. coordinacors, or technicians wililikdy hnd many of the topics presenred in this book to be F..miliar. We try to focus on topics and applic3t'ions used commonly by fidd professionals, dlOse rhar are essemiallO lhdr man · agemem needs. There are a variecy of resources char delve deeper inro various subjecl areas of GIS rhal will undoubledly be of va lue ro GIS analysrs. coordi mllor . and technicians. In ou r experienc('!\ these resources rarely eonsis[enriy focus 011 naruml resoure('

xvi Preface

applicarions typical [0 field protessionals associated with tederal, stare, p rovincial, or privare narural resource organizations.

To illustrare the applicarions otG IS to narural resource managemelH, we have provided tour sets ot GIS databases. The flrsr ser reterences rhe hypotherical Daniel Picken toresr, one thar may be fa miliar ro {"hose who have taken cou rses in foresr m3nagemenr, as ir is one of the landsca pes used to illustrare managemem alternatives in the book Foust Mfl1Jflgtmt11l (Davis et aI., 2001) . The second set reterences a fictional foresl called the Brown T racr. The Brown Tract represems a more realistic landscape and includes a digi

[al onhophorograph so that users can acrually see rhe resources being managed. The thi rd dara ser represents land uses in Saskarchewan, while rhe founh rep resents milliocarions and counties of the southern US. These databases were derived from acrual GIS data, bur were modified signi fican tly by the authors m make them suitable for li se in th is texr. Each

ot these sets of GIS data can be accessed through a websire hosred by Oregon State University (hrrp:/ /www.foresrry.o regollsrare.edu/gisbook).

Parr 1 provides readers nor on ly with rhe h isrory and development of GIS, bur also with

a common la nguage and perspecrive on GIS. Too otten people us ing GIS have lirrle formal training; instead , they gain knowledge and skills through lrial-and-error applications, shorr courses, o r through other means. We do nor want [0 discourage rhe effons ot seltmOl iva ted CIS users; however, they usually have an abridged perspective on rhe history of GIS, how and why dara srructures are diffe rent, and in other reiared mpies. We hope rhat communication among naruml resource professionals as ir reiares to GIS processes and requests will thus be imp roved with a more thorough perspective, allowing work rasks [0

be accomplished more efficienrly. Pan 2 emphasizes GIS operations and introduces readers ro many ot the most power

ful and commonly lIsed GIS applications in narura l resource ma nagemenr. Each chaprer in Parr 2 inr roduces GIS tech niques, and then provides applications relared to the rechniques. T he concepts introduced in Part 2 are initially related (Q the managemenr and use of vec(Qr GIS darabases. The concepts build upon themselves, and culminare in a synthesis of advanced analyses presemed in chapter 12. Chaprers 13 and 14 provide [rearments of raste r GIS database uses in naru ral resource managemenl.

Parr 3 of rhe book introduces a number of wpies related w the trends in the use ot GIS in narural resource management, {he challenges and opponunities Faced by those o rganiza[ions desiring to use GIS to ass isr in decision-mak ing processes, and the o ngoing and contentious issues relared to cert ification and licensing of GIS users. The appendices ot rhe book provide users wirh a glossary of terms, a summary of organ izations and academic journals associated wirh (he use of GIS, and references (0 lhe developers ot the most com

mon GIS softwa re programs. This book is dedicated (Q those students who have challenged LIS to develop course

wo rk rhat relares directly to rhe GIS tasks (hat they will likely perform as natural resource

managers.

References

Davis. L.S .• Johnson. K.N .• Berringer. P.S .• & Howard. T.E. (2001). Form mOllogelllellf:

To slistain tcological. tconomic, and socifllvfllues (4th ed .). New York: McGraw-H ilI.

15

xvi Preface

applica tions typica l [0 field pro fessionals associated with federal. sta te. provi ncial. or pri

V(ltC' natural resource organizations.

To illustrate the applications of . IS to natural resource management. we have: provided

fOll r sets of GI databases. The first set re ferences the hypotimical Daniel Pickett lo rest. one rhar may be fami liar to those who )ulve taken courses in foresr managemelH. as it is

ont' of the landsca pes lIsed to iliustr.Hc management alrern atives in the book Fort'st Mal/agflllcm (Davis e[ al.. 2001) . The second set references a fictional fores, called the

Brown Trace The Brown Tract representS a more realistic landscape and includes a digi

,al onhopho togrn ph so thar users can acrllally so< the resources being managed . T he third

da(:l Set represems land uscs in askarchewan. while rhe founh represents mill locations

and cou m ies of rhe sourhern U . These databases were derived from aemal GIS data. bur

were modified significantly by dl C' authors to make them suitable fo r use in this tocr. E...1ch of these sets of Gl data can be accessed through a websi(e has red by regan [ate

Univers ity (lmp:llwww.forestry.o tegonstate.edu/gisbook). Parr I provides readers not on ly wirh ,he history and development of GIS. bur also with

a co mmon language and perspecrive on C IS. Too o ften people using GIS have lirrle tor

mal rraining; insre:ld . they gain knowledge and skil ls through Irial-and-erro r applications.

shorr courses, or th rough orher means. \Vlc do nor wam to discourage the efion s of self· ma l iva red GIS IIsers; however. rhey usually have an abridged perspective on the his lOry of

C I . how and why data strucrures are difFerenr. and in other related topics. We hope (hat

com munication among naru ral resource professionals as iI' rda tes ro GIS processes and

rC'1uests will rhus be improved with a mo re rhoro ugh perspecrive. allowi ng work lasks to

be accomplished more efficiently.

Pan 2 emphasizes GIS operarions and inrroduccs r~.ldc rs lO many of the: most power

ful and com monly used GIS applica tions in namral resource ma nagemenr. Each chapu~r

in Pan 2 im roclllces GIS tech niques, and [hen provides applicatio ns related ro rhe tech

niques. The conceprs introduced in Pan 2 are inirially related to the management and use

of vector GIS d:uabases. The concepts build upo n themselves. and culm inate in a symhc~

sis of advanced analyses presented in chaprer 12. C hapters 13 and 14 provide rreat ments

of raster GIS database uses in narural resource m:m agemenr.

Pan 3 of the book introduces a number of ropics relared to rhe rrends in the use of Gl

in na lUra) resou rce management. rhe chaJlenges and oppo rruni lies faced by those organi

'lat io ns desiring to use ,IS to ass isr in dec isio n-making processes, and rhe ongoing and

cOl1len[ious issues related 10 cerriflcarion and licensing of CIS users . The append ices of the

book provide users with :'1 glossary of [erms, a summary of organ iz.u ions and academic

journals associated with Ihe lise of GIS. and references [0 {he devdopers of rhe mOSt com

mon GIS sofrwa re progra ms. Tbis book is dedicated to those studems who have challenged us to d evelop cOll r.se

wo rk thar reiates directly ' 0 [he GIS r .. ks tha t they willlikcly perform .5 narural resource

managers.

References

Davis. L.S .• Johnson. K.N .• Bellinger. r ... & H oward. T.E. (200 1). Form lIIaul/gem",/:

To sustain ~colqgicfll, t rvl/omie, find socia/ llaluts (4(h ed. ). New York: McG raw-HilI.

Part 1

Introduction to Geographic Information Systems, Spatial Databases, and Map Design

W e hope Pa re 1 of GIS AppiicatiollS in Natural Resouras provides readers wirh a com

mo n language and pe rspecrive o n geographic in fo rma t io n sys tems (G IS).

Frequently, people using G IS have li rrle fo rmaJ tra ining, and they ga in rheir knowledge

and skills either through shorr courses or rh rough [hei r own ini{iarive. While these self

mociv3 rcd effo rts 3rc laudable, they lIsually result in an abridged perspecrive on lhe histOry o f GIS. how and why data srrllc{tl res di ffer. and m her related topics. Communicat ion

among naruml resource p rofessionals as it relates [0 GIS processes and requcs[s should be improved with an informed perspecrive of GIS, encouraging work tasks to be accom

plished mo re e ffect ively.

In chapte r I , rhe historical development of GIS and the va rious tools you might use ro

creare C IS da tabases are exa m ined . The focus of chapte r 2 is an essential ropic fo r C IS:

data. C haprer 2 begins by describing the ways in which we ca n quant ify and measure the

Ea rth 's size and shape, and how resulrs from these methods can be incorpo rated inco C IS.

C hapter 2 also includes materia l o n how data can be structured within C IS and outlines

[he options (har a re avai lable fo r t h~e pu rposes. In additio n, some o f rhe more com mon

sources of dara for developing o r augment ing spat ial databases are presented in chapte r 2.

C hapter 3 builds upon the data theme imroduced in chapter 2 by examining how organiza rio ns might acquire or develop darabases, and d iscuss ing issues related ro database ed it

ing and pment ial errors. C hapter 4 delves into ca rtogra phy; o ne of many supporting d is

ci plines from which GIS has evolved bur also one of rhe cemral ways in which C IS resul ts

ca n be comm un icated ro others. In addit ion, chapte r 4 includes a tho rough d iscussio n of

rhe concepts and compo nems that may lead to a successful map. while at the same time identifyi ng some commo n pi tfa lls lO avoid in rhe map crea tion process.

16

Chapter 1


Geographic Information Systems (G IS) are now core tech

nology fo r many natu ral resource o rganizations and are

also app lied in disciplines throughout society. The initial

applicat ions of GIS that demo nstrated some of rhe power and potential of (his spacial technology, however, were

within namra! resou rce applications (Wing & Beninger,

2003). In one ofrhe firsr papers on the use of GIS in narural resource managemenr, de Sreiguer and Giles (1981)

describe the potenrial uses of GIS in naru ra l resource managemenr. In adapting one of their inrroducrory remarks [0

rhe presenr day, you will find rhe releva nce of GIS ro nar

lIfal resou rce management clearly stared:

A natural resource manager is often ca lled upon to

selecr an area of land to designate as cririca l wildlife habir3r, as a pmenria l area to implemem a timber

harvest, or as an area to recommend a silviculrural

rrear mem, o r to evaluate a landscape under a lterna

rive managemem policies. The manager describes

(0 (he GIS the charac teristics of the ideal a rea in

terms of forest s(fucwrai cond itions, soils, or

(Opography. Within seco nds the manager receives

gra phic and rabu lar informatio n ro loca re the

appropriate manage ment areas, or to compare alre rnative policies. (de Steiguer & G iles, 198 1,

p. 734)

Obviollsly, natura l resource managers ca n perfo rm the

same task of idenrifYi ng appropriate managemenr areas o r

of analyzi ng policies by examining se ts of paper o r mylar maps, bur rhe process beco mes much mo re efficienr 'lOd

accurate when perfo rmed with G IS. Furrher, analyses of

the impact of alternat ive policies are faci litated , allowing

managers {Q consider rhe impacts of d ifferenr policies o r

act ions in a more efficienr manner, usually savi ng time

and money.

For many na[Ural resou rce management o rganiza

tions, G IS has become an irreplaceable rool to assis t in

[he day-to-d ay manage menr of land, wate r, and other

resources. The applica tio ns of GIS vary widely amo ng

o rganiza tions and may range from using GIS primarily as

a mapping tool to lIsing C IS to model pol icy aire rnatives

rhat may impact landscape fearures during rhe nexr 100

years and beyond. Rega rdless of how a narural resource

managemenr o rganizat io n plans to use G IS, understand

ing rhe potential appl ica tions of C IS to na tural resource

managemenr is essenrial for natural resource profession

als. This text is designed to int roduce readers to GIS con

cepts and princi ples and to provide examples of how [0

apply rhis knowledge in a narural resource management

conrex[. The introductory chapler begins by describ ing

the rools and technology thar comprise a GIS, and illus

rrari ng why GIS has beco me so impo rtalll for many

o rganizat ions. A brief hislOry of the evolu tion of G IS and

identification of significanr conrribmors to G IS develop

ment is then provided. Toward the end of this chapter

rhe key compo nent of any successful GIS (sparial dara ) is discussed.

Objectives

This chapter represenrs an introduction to GIS concepts,

roo ls, technology, history, and significant co ntribu rors.

Given that the focus of this book is on rhe appl ications of C IS ro natu ral resource management, what is provided in

(his chap te r is a co ndensed version of these [opics.

17

Nevenheless, ar rhe co nclusion of this chapter, readers

should understand and be able to discllss the perrinent

as peas of the following ropics:

I. [he reasons why GIS use IS prevalenr In natural

resource management.

2 . rhe evolution of the development of GIS technology and key figu res.

3. the common spatial data collecrion tech niques and

input devices rhat are available,

4. the common GIS omput processes thar are rypical in

natural resource managemenr, and

5. rhe broad rypes of G IS sofrwa re rhar are avai lable.

What is a Geographic Information System?

A geographic information system consisrs of the necessary

rools and services ro allow you to collect, organize. manip

ulate, inrerprer, and display geographic information. A GIS is more than JUSt rhe hardware and software familiar (Q

mosr people; it extends to the staff who operate the sysrem, r.he databases, [he physical facilities, and the o rgan i

zational commitmelll necessary ro make it all work. A GIS

can be defined by how it is used (e.g., a land informarion

system, a narural resou rce managemenr information system), by what it contains (sparially distinct features, activ

ities. or events defined as points, lines, polygons. or raster

grid cells), by irs capabilit ies (a powerful set of 1001s for

co llecting. sroring. retrieving. transforming, and displaying

spa rial dara) , or by its role in an organization (a map pro

duction syste m. a spat ial analysis system. a system for

assisting in making decisions regarding basic geograph ic

quesrions such as: Where is it? What is it? Why is it

mere?). The core component of a GIS however is a data

base thar contains a geographic componenr. We wi ll dis

cuss geographic data in more detail shordy.

GIS can also be defined as geographic information science (GIScience). GIScience involves the identification

and srudy of issues that are rdated ro GIS use, affect its

implementation, and that arise from irs applicarion

(Goodchi ld, 1992). In short, GIScience borh encourages

users (0 understand the benefits of GIS technology in pro

viding a powerful set of ana lysis tools and encourages

users ro view rhe technology as pan of a broader discipline rh:H prommcs geographical th inking and problem solving

strategies as being useful to society. The deveiopmem of

GIScience is an outgrowth of the faCt that GIS technology

is avai lable ro more users today than ever before, and that

Chapter 1 Geographic Informafion Systems 3

spatial categorization and analys is is applicable to many

societal issues and problems. Regardless of how a GIS is perceived or used. it is the

imegrar ion of rhe variolls (ools and services rhat leads to

a successful GIS. Although other software programs per

form GIS-like tasks (e.g., darabase management, graphics,

or computer assiSted drafting [CAD) software), a GIS is

unique in its ability (0 allow users ro crC'dtC, maintain.

and analyze geographic o r spat ial data. The term spatial

d ata implies thar a database nor only describes landscape

features (e.g., candidon, composition, structure of

forests), but also includes a geographic reference co where features can be found. A GIS allows you to manipulate

and display spatia l data so that questions regarding a

resource and its conditions can be answered. A GIS. when

used properly, is capable of analyzing a large volume of

spatial data quickly and providing graphical and tabular

results. A GIS stores spatial data in a digital database file;

rhe database file may be referred to using a number of

terms including themes. maps, covers, rabies, layers, or

GIS databases. The terminology for referring to a GIS

database varies depending on the GIS software program

being used and, in some cases, the vers ion of the sofrware

being used. In most GIS software programs, similar land

scape featu res are mainrained in a single GIS database. For

instance. YOli might have a soils GIS database thar con

tains rhe soil characteristics of a landscape, a hydrographic

database [hat shows the locations of rivers and lakes, or a

wildlife GIS database that contains me nest locatio ns of a

single species or a group of species of animals. GIS allows

the integration and simultaneous examination of mulriple

G IS databases through a process described as overlay

analysis. Overlay analyses allow us to determine how fea

tures in one darabase: relate spatially to fearures in another

database, and provide us a powerful means of supporting landscape mapping and investigation. Overlay analysis.

described in further dem il )Her in this book, represenrs

[he essence of whar many co nsider to be {he main role of

GIS in natural resource management: the abiliry to com

b ine tWO or more GIS databases inco a si ngle database (hat

demonstrates {heir spa rial connecriviry.

GIS is related to a number of other Relds and disci

plines, including computer aided drafring (CAD), com

puter carrography. darabase managemem, sraristics, and

remOte sensing. In FaCt, GIS both comains and relies on certain aspects from each of these fields. and thus is closely

related to each of rhem. However, the difference bef\veen GIS and any of these o ther a llied Relds is notable. For

example, most CAD software programs have rudimentary

18

Nevertheless. at the co nclusion of this cha pter. readers

should understand and be able to discuss the perrinent

aspeclS of the following topics:

1. the reasons why GIS usc: is pr~valenl in natural


2. the evolution of the development of GIS technology and key figures.

3. the common spatial data collecrion techniques and

inpur devices rhat are available.

4. the common GIS OUlptH processes thar are typical in

narural resource man:lgemellt. and

- rhe broad rypes orGI software tim are avai la ble.

What is a Geographic Information System?

A geog'raphic information sy tern consisrs of the necessary

{ools and services lO allow you to collecr, organize. manip

ulate, interpret. and display geogrJphic information. A GIS

is more {han JUSt tlit': h'lrdware and softwa re hlmiliar to

most people; it extends to lhe staff who operate the system. the dat.bases. the physical fuci lities. and lhe o rgani

zational commirment necessary {O make ir all work. A GI

can be defined by how it is used (e.g .• a land information

sysrcm. a natural resource management information system), by what it comains (spadaJly distincr fearurcs. acriv

iciest or events defined as points. lines. polygons, or raster

grid cells). by its capabiliries (a powerful SCI of tools for

collccling. storing, retrieving. rransforming. and displ:tying

sparial data). or by ils role in an organizarion (~ map pro

duction sysrem. a spadal analysis system. a sysl~m for

assisting in making decisions regard ing basic geographic

questions such as: Where is it? What is il? Why is it (here?). The core component of a GIS however is ... dara

base Ihar conmins a geographic camponelll. We will dis

cuss geographic dara in more detail shordy.

GIS can also be defined as geograph ic information

science (GIScience). GIScience involves the idemiflcation

and srudy of issues that are rdated 10 .1 use, affect ils

implemenr:uion. and lhar arise from iu applic;l(ion

(Goodchild. 1992). In short. GIScience both encourages

lIsers 10 IInderstand the benefits of GIS rechnology in pro

viding a powerful set of ana lysis (ools and encourages

users to view Ihe rechnology as pan of a broader discipline that promotes geogmphiC'JI thinking and problem solvi ng

strJtegies as being useful to sociery. The development of GIScience is an outgrowth of the FaCt rhac GIS technology

is avai lable 10 more users roday than ever before. and [hat

Chapler 1 Geographic Inlormabon Systems 3

sparial categorizarion and analysis is applicable (Q many

societal issues and problems. Regardless of how a GIS is perceived or used. il is (he

inlegralion of Ihe various rools and services ,hat leads (0

a successful GIS. Although other sofrware programs per

form liS-like rasks (e.g., darabase manilgemCnt, graphics.

or compurer ass isred drafting [CAD] sofrwa re). a GIS is

unique in irs abiLiry to allow lIsers to create. maintain,

:lnd analyu geographic or spa rial dara. The term spatial

d ata implies that a database not only describes landscape

fe-dlures (e.g .• condition. composirion. SUUClUre of

forests). bur also includes a geographic reference (0 where

Features can be found. A GIS allows you to manipulare :tnd display sp:nial d:lIa so fllat quesrions regarding a

resource and its conditions am be answered. A GIS. when

used properly. is cap.ble of analyzing a la rge volume of

spa rial data quickly and providing graphical and tabular

results. A GIS stores spa rial data in a digital database file;

the darabase tile may be ref<rred to using a number of {erlllS including (hemes. maps. covers. rabies, layers. or

GIS databases. The: terminology for referring (Q a GIS

darabase varies depending on the GI soFrware program

being used and. in some cases, the: ve rsion of me sofrware

being used. In most .IS software programs. similar landscape fearures are mainraincd in a single ,IS da[abasc. For

insrance. YOli mighr have a soils GIS database that con~

rains rhe soil characterisrics of:1 landscape. it hydrographic

database rhal shows rhe 10C3rions of rivers and lakes. or a

wildlife GIS daraba e Iha( contains the nest ioc31ions of a

single species or a group of species of animals. CIS allows

the integration :lnd simultaneous examination of multiple

GIS databases rhrough a process described as overlay

analysis. Overlay analy~s allow us (0 determine how fea~

(Ures in onc darabase relate spatially (Q fearures in anorher

da rabase, and provide liS <1 powerful means of supporting

landscape mapping and invatigation. Overlay analysis. described in further derail lal er in this book, represems

[he essence of what many consider ro be Ihe main role of

GIS in nalUral resource management: rhe abiliry 10 com

b ine (wo or more Gl databases inlo a si ngle datab~lse I hat

demonsu:ues (heir spatial connccriviry.

GIS is related to a number of other lields dnd disci

pline • including computer aided drafting (CAD). om

pucer cartography. database m<lnagement. srarislics. and

remole sensing. In filet. GIS bolh coma_ins and rdies on cerr .. in aspec(s rrom each of these fields. and thus is closely

related (0 each of them. However. the diflerence between GIS and dny of these orher allied fields is notable. For

example. mosl CAD software programs have rudimentary

4 Part 1 Introduclion to Geographic Information Systems, Spatial Databases, and Map Design

In many ways. college and university srudems are

examples of a living, breathing GIS. Each day YOli ven

{Ufe from your home inro rhe world. and make deci

sions about where you are going. how you will get rhere, and what YOLI will do when you arrive. For

instance, as a rypical srudenr. you probably have a route

that you usually rake [0 campus. Chances 3rc that you

have designed this route over rime and based on your

experiences. so thar you can arrive as quickly and easily

as possible. Perhaps you have included a stop ar your

favorite coffee shop in your roure. If you have a e.u,

rhen you might e1eer to dr ive, and depending on the

rime of day. you might alter your usual route (0 avoid

traffic. Road consrrllcrion may force you to alter your

route for a few days or weeks. You will make orher

adjustments (Q avoid unfo reseen delays. Once you

links [0 a database management system and are often lim

ired in their abiliry (Q srore and analyze descripdve

information abom fearures, whereas GIS software pro

grams generally have srrong links (Q a database manage

menr system. CAD spadal modell ing capabili ties are also

limited, whereas GIS conrains a wide variery of spatial

modelling capabi liries (rhese will be examined in larer

chaprers of this book). The field of compurer cartography

emphasizes map production, and while rhe databases used

may be similar to those lIsed in GIS, computer cartogra

phy generally purs less emphasis on rhe non-graphic arrribures of spadal landscape fearures than does GIS.

Database managemelH software programs have rhe abiliry

to store and manage locarion and attribure data of land

scape fearures, bur rhey genera lly lack the power ro display

the locations and characte risdcs of feamres. Visua l capa

bi liries are fundamelHal qualities within most GIS, CAD,

and canography soff\vare programs. Statistical programs

are lIsll<llly designed so thar users can quickly develop

summaries of data, such as averages, standard deviations,

or correlations that allow us to describe a large amount of

data or to describe rhe relationship of a single variable to

another. GIS soff\vare can usually not only accommodate

basic srarisrical operations, bm can show where rhe resu lts

of statistical operations are cenrered or located, and can help visually determine whar other var iables may be of

interest in an analys is, Inte resti ngly, several of the more

powerful and commonly used stat istical packages now

ar rive on campus, you will have to find a parking space,

and then walk another roure to get to your hrsr class.

Of course, you might have decided rhat the rroubles

wirh parking make riding a bike (Q campus more

attractive, bur then you will still need to design a rome

for the bike trip. The choices you make just to get to

school in the morning require you to analyze muhiple

layers of spatial informarion abom you r presenr loca

tion, you r desti nation, and rhe interven ing influential

factors. In shon, as you solve your daily rransponation

challenge, you are acring as a GI S. This rype of exam

ple, in which location is a key component in decision

making, can be applied (Q many activities that people

engage in, ranging from how best (Q cross the street, to

navigating a downhill skiing or snowboarding course,

ro arranging trips to other counrries.

integrate GIS-like funcrional ity in some of their modules.

Finally, rem ore sensing-related software programs gener

ally focus on the manipulation and management of rasrer GIS dara derived from satellites, scanners, or Olher photo.

graph ic devices; rhey have a limired capabiliry ro handle vector GIS databases, which tend to be more commonly

used within natural resource management organizations.

A Brief History of GIS

As previously mentioned, GIS is unique from other software

programs in its inregrat ive ability that enables you to

process, catalog, map, and analyze spalial data. Spatial data

have been collected and maintained for millennia, with

records of pro perry boundary surveys for raxarion purposes

in Egypr daring back ro abour 1400 Be. Ir is only wid,in

the pasr 40 years, however, th:u sociery has learned how to digitally capture. mainrain, and analyze spatial dara.

Although the term 'geographic information system' was firsr

used in the 1 960s, overlay analysis has been demonsrrared

through manual techniques for over 200 years. Overlay

analysis is the process of analyz,ing mu ltiple layers of infor

mation simultaneously to address management issues. The

layers rep resent different rypes of information bur are reiared

to each orher in that the informadon is drawn from a com

mon landscape area (Figure 1.1). GIS allows you to drape, or overlay, rhe layers on top of one anorher and to combine all

pans into a new, imegrared layer thar contains all or some of

19

Stand Types Hydrology Roads

Figure 1.1 G IS lhC'lne overlay.

rhe pans of the original layers. depending on rhe rype of

overlay selected by the user.

The new inregrared layer allows liS to examine the spa

rial relationsh ips of rhe in formation contained in rhe o rig

inal layers. Although digitally-based GIS has been available for a relatively shorr period in his[Qry. (here is a

significanr hisrory of ana lysts using the overlay approach

th rough manual techniques. During rhe American

Revolution, rhe French carrographer Louis-Alexandre

Berrhier overlaid multiple maps to analyze (roop move

menrs (Wolf & Ghi lani, 2002). In 1854, Dr John Snow

conducted a spa rial analysis by compari ng rhe locations of

cholera deaths ro well locations in London. His analysis

revealed rhar well warer drawn from specific wells was a

means of spreading cholera infecrions. The first wr inen

description of how ro precisely combine multiple maps

rhrough a manual overlay process appea red in a 1954 rex(

titled Town and Country Planning Textbook by Jacqueline

Tyrwhitt (Ste initz et aI. , 1976). In 1964, Ian McHarg

described how ro lise a series of rransparent overlays m

derermine the suirabiliry of areas for developmenr in New

York 's Staren Island. By using a transparent overlay fo r

each layer of interest (soi ls, forests, parks, erc.) and black

ing-our rhe areas on each overlay rhar presented develop

ment impedimems. the layers could be overlaid and rhe

final suitable areas defined. McHarg (1969) later pub

lished examples of his overlay rechniques in his sem inal

book, D~sign with Natllr~, which continues ro be sold throughour the world.

in rile early stages of the development of GIS rechnol

ogy, rwo fac rs were evidenr: {here was little geograph ic or

spada I dam to work with, and rhe rechnology ro srore and manipulate rhe dara was rudimentary (by mday's stan

dards), Some may argue thar GIS technology has nor evolved very much in the passing years simply because


" ", . . ... -........ .

Topography Composite Layers

many of the compurarional processes used roday were inirially developed in rhe 1980s, however, advancements in

computer technology and rhe increasing availabil iry of GIS

darabases indicare orherwise. In addirion, a growing num

ber of people throughollr sociery have heard of GIS {even

though rhey may often confuse irs purpose wirh that of a

similar acronym, GPS [global positioning systems]} . We provide a brief history below of rhe developmenr

of'digitally-based GIS, and note that many of irs advance

ments were made by innovarors and scienrisrs throughout

North America, During rhe 1960s, organizarions in the

Un ited States (i ncluding the US Geological Survey and

rhe US Deparrment of Agriculture's Natural Resource

Conservarion Service) began m create GIS darabases of

topography and land cover (Lon gley et aI. , 200 I).

Srudents and resea rchers began ro write computer pro

grams and design hardware devices (such as the precursor

ro roday's digitizing tab le) rhar would allow you (0 rrace

the outl ines of landscape fearures on hardcopy thematic

maps and rransfer them inro a digital formaL These early

programs were designed ro handle specific [asks and were

ofren limited in scope. As programmers began ro bring

these algorirhms rogerher ro creare more versatile, power

fu l software programs. rhe era of com purer mapping

applications began. Early examples of mapping programs include IMGRID, CAM, and SYMAP (Clarke, 2001).

In conjuncrion with rhe developmenr of sofrware pro

grams. other organizarions began ro assemble GIS darabases for mapping and analyzing fearures of inreresr to

public agencies, The firsr example was rhe GIS darabase

created by the US Centra l Intelligence Agency (CIA) and was called rhe 'World Dara Bank' , Sparial layers in the

GIS database included coas tlines. major ri ve rs. and poliri

cal borders from arollnd the world, The US Census

Bureau designed a merhodology for linking census infor- 20

6 Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design

maUDn to loca tions in preparation for rhe 1970 US census. The 1970 US CenSllS was the first census that was

mai led, and rhe only piece of informatio n that was

returned to reference rhe lacarion of rhe respondem was

rhe address. The Census Bureau, however. was faced with

rhe challenge of marching rhe response addresses to a map

so that rhe spatia l disrriburions of responses co uld be

mapped and analyzed. The Census Bureau developed a

system known as DIME (Dual Indepe ndent Map

Encoding) in response to this challenge. which nm only

created digi ta l records of all streets, bm also associated

addresses ro st reet locations. The DIM E system allowed

rhe Censlls Bureau (Q understand which st reets were con

nected to which other streets, and whar landscape features

were adjacent to each streer. T his method of associating

the digital represemarions of landscape features to orher

landsca pe featu res was a c ritical advance because if

enabled the idenrificado n of spacial relationships within a

digital enviro nment.

The description or characterization of the spat ial rela

t io nships between landscape features in a GIS database is

referred to as topology. Topology is an importam con

cept with respect to GIS applications and will be discussed

in more detail in chapter 2. T o pology manages objects

and requires objects to be organized and analyzed accord

ing to their locat ion and with respect widl proximiry to

other objecrs. The topological characteristics of data structu res allow a determinatio n, for example, of how

water travels through a stream network. the con nectiviry

of roads in a forest to other roads. or dle idemification of

forest stands that share a border with other fores t stands.

These relationships form rhe basis of many resource

analyses that take locationa l position imo account in

problem solvi ng techniques. The DIME system was rhe predecessor to T IGER

(To pologically Integrated Geographic Encod in g and

Referencing System) files. which were inrroduced by the US Census Bureau in 1988, and are st ill used today [0

distribute spacially-referenced census and boundary data.

The avai labil ity of TIGER fi les was instrumental in pro

mo ting GIS use in the US. The US Geologica l Survey

(USGS) made an additional important contri bution to

spatial dara availabiliry when they began digitizing fea

tu res from its I: I 00,000 scale ha rd copy maps in rhe early 1980s. Spatial data from these maps were made ava ilable

as digital line graphs (DLGs) thar, like the TIGER and

DIME systems. were also stored in a file format that

allowed the [Opology of objectS [0 be characterized. The

file format was restructured in the early 1990s, and the

USGS has made features from finer-resolution 1 :24,000

scale maps ava ilable for small portions of rhe COUntry .

The USGS has since become a worldwide leader in map

ping land cover resources and making maps ava ilable in

borh hardcopy and digital format.

To manage and analyze spat ial data for their jurisdic

£ions. Canadian and US organizations began to develop

sofn. ... are programs in the 1 960s. One of the most ambi

tious and noteworthy of these systems was the Canada

Geographic Informat io n System (eGIS), which, in 1964,

was created under the guidance of Roger Tom linson. A

chance meeting. on a plane. between Tom li nson and

Canada 's Minister of Agriculture resulted in Tomlinson

overseei ng rhe creation of a narional effort ro inventory

Canada's land resources. and developing a sofrwa re pro

gram ro quanr ifY existing and potential land lIses. The

CG IS is recognized as being the first na t ional-level GIS.

and thus Tomlinson continues to receive recogn irion as

a GIS pioneer for his efforts. Other early landmark efforrs

in the evolution of GIS include the developmem of the

Land Use and Natural Resou rce In ventory System

(LUN R) in New York in 1967, and the development of

the Minnesota Land Management System (MLM IS) in

1969.

The success of these early systems and need for furrher

refinements were recognized by a group of faculry and students 3r Harvard University's Laborarory for

Computer Graphics and Spa tial Analysis. The group set forth to create a versati le GIS that would map and track

locations like the DIME system, wh ile possessing rhe land

measuremenr strengths of rhe CG IS. From rhis effort rhe

Odyssey GIS (containing modules named after parts of

Homer's epic work, The Odyssey) emerged in 1977, and

pioneered rhe use of a data Structure known as the

arc/node. or vector data structure. We w ill discuss the

vector data structure in more detail in the neX[ chapter.

however, it is importanr to nOte that rhe specifics of the

Odyssey vecror structure were first published by Peucker

and Chrisman (1975) and the S[fuc rure co ntinues to

influence the design of modern GIS sof('\.vare programs.

Jack D angermond, a H arvard Lab student, founded the

Environmental Systems Resea rch Insr iwte (ESRI) in

1969. and earlier versions of ArcView and Arclnfo-the most widely used desktop and workstarion GIS software

programs-were based on the Odyssey vector da ta st ruc

ture. Arcln fo. in fact. was introduced in 1981 , marking

the first major commercial venrure into the developmenr GIS technology. Both of these GIS packages have been sig

nifica ndy rewrinen in terms of com purer code support

21


mauon to locatio ns in prepararion for the 1970 US

census. The 1970 US Censlis was rhe first census tbar was

mailed, and rhe o nly piece of information (har was

rerurned [0 refe rence rhe location of rhe respondem was

rhe address. The Census Bureau. however. was faced with

rhe challenge of matching [he response add resses (Q a map

so char rhe spa rial disrributions of responses could be

mapped and analyzed. The Census Bureau developed a

sYSlem known as DIME (Dual lndepe nden r Map

Encoding) in response (Q rhis challenge, which nor only

creared digiral records of all srreets, bur also associared addresses [a srreer locarions. The DIM E system allowed rhe Censlis Bureau [Q understand which streets were con

necred to which orher srreets, and whal lanclsc;-tpe fealUres

were adjacent to each s tree!. This method of associating

the digital n:presemarions of landsca pe features (Q other

landscape featu res was a c ri rical advance because it

enabled the idenrifica [ion of spadaJ relationships within a

digital envi ronment.

The description or characterization of the spat ial rela

tio nships berween landscape feat ures in a GIS database is

referred [Q as topology. Topology is an imporranr COI1-

cept with respect to GIS applicatio ns and will be discussed

in more detail in chaprer 2. Topology manages objects

and requ ires objecrs to be orga nized and analyzed accord

ing ro their location and with respecr with proximity ro

other objects. The topological characteristics of dara

Structures -allow a determinacion. for example. of how

warer rravels through a stream necwork, the con nectivity

of roads in a forest ro orher roads, or the idcnrificar ion of

forest sta nds [hal share a border with other forest stands.

These relationships form the basis of many resource

analyses thaI take locational posidon into account in

problem solving rcchniques.

The DIME system was the predecessor to T IGER

(To pologically Integrated Geogra phic Encod ing and

Referencing ysrem) files. which were inr roduced by the

US Census Bureau in 1988, and are sdJl used today to

d istribute spatially- re ferenced census and boundary data.

The avai lability of TIGER fl ies was insrrumemal in pro

mOting GIS use in the U . The US Geological Surv<y

(USGS) made an addirional imporram conrribution {Q

spatial data availabiliry when they began d igitizi ng Fea

lUres from irs I : I 00,000 scale hard copy maps in the early

1980s. Sparia l data fro m rhese maps were made available

as digital line graphs (DLGs) tha t, like the T IGER and

DIME sysrems, were also sto red in a file format rhat

allowed the topology of obje", to be characterized. The

file formal was restructured in rhe early 1990s, and the

USGS has made features from finer-resolution 1 :24,000

scale maps ava ilable for small ponions or rhe coumry.

The USGS has si nce become a worldwide leader in map

ping land cover resources and making maps avai lable in

both hardcopy and digital format.

To manage and ana lyze spatial dara for their jurisdi -

lions. Canad ian and US orga nizations began co develop

sofrwa re programs in rhe 1 960s. One of the mosr ambi

rious and noteworthy of rhese systems was rhe Canada

Geograph ic Informat io n System (eGIs), which, in 1964,

\vas creared under (he guidance of Roger Tomlinson. A

chance meering, on a p!;H1e, berween Tomlinson and

Canada 's Minister of Agriculrure resulted in Tomlinson

overseei ng the creation of a national etTon ro inventory

Canada's land resources, and developing ~I software pro

gram to 'luantiry existing and porelllia l land uses. The

GIS is recognized as being the first na t io nal -level GIS. and thus Tomlinson continues ra receive recognition as

a GIS pioneer for his eilorts, Other early landmark efforts

in the evolution of GIS include the developmem of the

Land Use and Narural Resource Invenrory System

(LUNR) in New Yo rk in 1967, and rhe development of

the MinneSOta La nd Management System (MLM IS) in

1969.

The success of rhese early sysrems and need for further

refinemenrs were recognized by a grou p of faculty <lnd

scudem s ar Ha rvard University's Laborarory for

Com puter Graphics and patial Analysis. The group Set

forth to create a versadle IS that would map and track

locat ions like the DIME system, while possessing the land

measmemclH srrengclts of the eGIs. From [his effon the

Odyssey GI (containing modules named after parts of

Homer's epic work, The Odyssey) emerged in t 977, dnd

pioneered rhe lise o f a dara strucrure known as rhe

a rc/node, or vecrar data Slructure. We wi ll discuss the

vector data structure in marc detail in rhe next chapter,

however, it is important [ 0 nore mat the specifics of the

Odyssey vector structure were first published by Peucker

and Ch risman (1975) and the structure conrinues to

inAuence the design of modern GIS software programs.

Jack Dangermond, a H arv-d rd Lab student, founded the

Envi ronmental Systems Research Insritllte (ESRI ) in

1969. and earlier versions of ArcView and Arclnfo-the

mosr widely used deskrap and workst'Jtion GIS software

programs-were based on the Odyssey vecror data st ruc

[Ure. Arclnfo. in fact, was introduced in 198 1, markjng

the first major commercial venlu re inro the developrnel1l

GIS technology. Both of rhese GIS packages have been sig

nificantly rewrinen in terms of com puter code supporr

a nd use r ilHerface; rhey are now offered as differenr

licenses wirhin ArcGIS.

The 1980s also witnessed [he proliferation of [he micro

com purer, coday's version of rhe personal compucer (PC).

In response, sofrware manufacmrers began [0 produce GIS

softwa re programs rhar could operare on rhe microcom

purer (see Appendix C for a Ii" of GIS sofrware manuF..c

[urers). In 1986, Maplnfo Corporation was formed, and

subsequemly developed [he world's firsr major deskrop vec

tor GIS sof[Ware program for the Pc. Soon afre rwards,

rasrer GIS sof£\vare programs, such as IORISI, began co

appea r. Some software programs, such as the raster GIS

program GRASS, utilize a software archirecmre thai was

developed for works[3rion computer platforms.

Orher significant developments in GIS included rhe

emergence of GIS-relared conferences and publications.

The first AuroCano Conference was held in 1974 and helped [0 esrablish ,he GIS research agenda. One of [he

firsr compilarions of available mapping programs was

published by the Internationa l Geographical Union in

1974. Basic Readings in Geographic Information Systemsa collection of papers thar discussed GIS rechnology-was

published in 1984, and inl 986 [he firsr textbook wrinen

specifically for GIS, Principles of Geographic Infonnanoll Systtms for Land Resources Assessmelll was published

(Bu rrough, 1986). Finally, [he firsr GIS- rela[ed academic

journal, the In ternational Journal of Geographic Information Scinlce, was published in 1987.

More recenriy, Interner rechnology has adva nced co

[he poim where people worldwide can ;Iccess and use

rudimemary forms of GIS fo r free. Google Eanh is per

haps rhe best example of (he inregra rion of remote sensi ng [echnology (digi[al onhophorographs and sa[elli[e

imagery) with rransponarion networks and other land

scape features rhar is available on line. A limited number

of geographical processing rools a re available: however,

Google Earth represelHs a significanl advancemenr in

allowing rhe general public ro visualize rhe landscape. Microsoft 's TerraServer is similar in rhis respect.

MapQuesr , perhaps [he most widely used geographic locator o nl ine. is now similar in rhis respect as well.

The history of GIS conrinues to evolve. with GIS users

provid ing a number of chal lenges. GIS users, for example, have the abi li ty ro influence [he development of GIS soft

wa re program features. As new and challenging narural

resource managemenl issues ari se, users identi fY and pro

pose processes and funct ions rhat will make rhe (ask of analyzing porenrial narural resource decisions more effi

ciem and accurare. In addirion, GIS users increasingly

Chapter t Geographic Information Systems 7

expecr suppOrt and training relared ro specific GIS soft

ware programs, and expecr (ha[ software will be mosdy

perrecred by [he rime of irs release [Q [he general publ ic.

Further, as GIS darabases a re sha red amongsr organiza

tions, rhe need ro standardize dara fo rmars is evident, because dara transformations can require an exrensive

commirmenr of resou rces and may lead ro Aawed results

if nO{ done correcdy.

Society is forrunate roday, on one hand. ro have a vari

ery of GIS software programs from which to choose. On

the orher hand, evaluari ng which of these programs best su its rhe needs of a narural resource management organi

zarion is problema ric. This mct posed a significam chal

lenge even in the creario n of (his book. Since each organ

izarion (nar ural resource managemem , as well as

academic) may use a different GIS sof£\vare program, we decided (0 design rhis book as a general reference for

describing, in general , [he ry pical types of GIS applica

rions Faced by field-level professionals assoc ia red wirh nat

ural resource management organizarions. Therefore, spe

cifi C examples of how ro address each application

described in rhis book, rhose rhar are related (0 specific

GIS sof£\va re programs, will be made ava ilable through orher means (e.g., a book-related websire at W\,VW.

fores[ry.oregonsra[e.edu/gisbook).

Why Use GIS in Natural Resource Management Organizations?

It is commonplace to see GIS used to assisr managers make

decisions in roday's natural resource managemenr environ

ment. For exam ple, maps are requ ired co be submiaed ro

srate agencies in [he wesrern US in supporr of forest man

agement plans. In most areas ofNo rrh America, pesticide

plans requi re a map to derail rhe proposed acriviry. as well

as the nearby homes and water resources. While maps may

st ill be hand-drawn in a handful of narural resource o rga n

izariol1s, GIS allows map producrion processes ro be amo

mated and repeated. reducing a lengrhy drafting exercise

[0 on ly a few sho rr minmes and likely producing far more

reliable resuirs. In addir ion, GIS allows some processes to be accomplished rhat would normally [ax a person's ana

lytica l abilir ies. For example, a narural resource manage

menr organization in the southern US considering a fe rcil

izadon project, yet operd(ing wirh a limited budget, may need ro loc3re those forested areas (stands) rhar would ben

efir mosr in rerms of rhe growrh of rhe foresr from a fert ilization applicarion in order ro make efficiem use of their budget. I f you were to assume rhar rhe s[a nds musr be

22

tlnd user illlerfacc; they are now ofTered as differenr licenses within ArcGI .

The 1980s also wimessed the proli femion of rhe microcompmer. mday's vers ion of the personal computer (PC). In response. sofrware manufacrurers began to produce GI. sofrwa re programs thar could operate on the microcom· purer (see Appendix for a list of GI sofrware manu",crurers) . In 1986. Maplnfo Corporarion was formed. and subsequently developed rhe world's firsr major desktop vecmr GI software program for rhe PC. Soon afterwards. raster GIS soff\vare programs. such as IDRJ I. began to

appear. orne software programs, such as me raster GIS program GRA S. utilize: a software architectu re rhal was developed for workstation computer platforms.

Other significant deveiopmelHs in GIS included rhe cmergence of Gl -related conferences and publications. The first AuroCano Conference was held in 1974 and helped to establish rhe GIS research agenda. ne of lhe first compilations of avajlable mapping programs was published by rhe International Geographical Union in 1974. Bmic Rmdillgs ill G~ograpbic Infomuuion Sysum.s-a collecrion of papers rhat discussed GI technology-\va_ published in 1984. and in 1986 rhe first textbook wrinen spe ifically for GIS. Principln of C<ogrllphic Informnrioll

'YSUIIIS for Land R~souras ASJ(JJm~m was published (Burrough. 1986). Fi nally. the firsr GIS- related academic journal. rhe In ternational Journal of G~ograp"i('

Information Sci~I1U. \ V'dS publishc:d in 1987. More recently. In te rnet technology has advanced (0

Lhe- point where people:: worldwide can access and use

rudimel1lary forms or .IS for free. Google Earth is perhaps the best example of the imcgrarion of rel"l1Q(e sensing rechnology (digital orthophotographs and satellite imagery) with rransportacion networks and other landscape fc:acures that is available online. A limited number of geographical processing rools are available: however. Google Earlh represents a significant advancemenr in allowing rhe general public co visualize [he landscape. Microsoft's Terra crver is similar in this respect. MapQuesr. perhaps the most widely used geographic locator online. is now similar in {his respect as well.

The history orGI continues to evolve. with GIS uscrs providing a number of challenges. GIS users. for example. have the abi lity to inlluenc< the development of .IS softW3rc program features. As new and challenging natural resource management issues arise. users identify and propose processes and fu nctions that will make [he task of .111;tlyz.ing pOlcmia l nalUr:.ll resou rce decisions more eAlciCIll and aCClIr:He . In addir ion. GIS IIsers increasingly

Chapter 1 Geographic InformaMn Systems 7

expecr suppOrt and lrdining rdated to specific (;15 software programs. and expect [hat sofnvare will be mosdy perfected by rhe rime of irs release ro rhe general public. Funher, as .1 dalabases are shared amongsr organizalions. rhe need ro standardiu da[a fo rmars is evidem, because dara Iransformarions ca n require an exrensive commirmem of resou rces and may lead to flawed results if nor done correctly.

Sociery i fonunare today. on one hand. ro have a va rieryof .IS sofrware programs from which to choose. On Ihe mher ha nd. evaluating which of these programs beSt su its the needs of a naru ral resource managemenr organization is problematic. This faCt posed a significant chal· lenge even in the cre-cllion of this book. Since each organ~

izatio l1 (narural resOllrce managemenl. as well as academic) may lise a different GIS software program. we decided (0 design {his book as a general reference for describing. in genera l. lhe rypical types or GIS :1pplic:ltions ",ced by field-level professionals associa,ed with natural resource management organizations. T herefore, specific examples of how to address each applica rion described in rhis book. [hose lha, are relared to specific GIS software programs. will be made 3vailabl< through other means (e.g .• a book-relared websire al www.

roresrry.oregonsrare.edu/gisbook).

Why Use GIS in Natural Resource Management Organizations?

It is commonplace [0 see GI usC'd [Q assist managers make decisions in loday's nalural rcsource managemC'J1[ environ~ ment. For example. maps are required ro be submined LO

smre agencies in the western U in support of forest managemem plans. In mosr areas ofNonh America. pcsricide plans requi re a map to dClai l rhe propos~d OIcciviry. as well as Lhe nearby homes and warer resources. While maps may sri ll he hand-drawn in a handful of natu ral resollfce orga niza[ions. GIS allows map producrion pro C'Sses ro be 3U[Q

mated and repeated, reducing a l~nglllY drafting exercise to only a few short minUles and likely producing fa r more reliable resuils. I n addition. GIS allows some processes (0

be accomplished that wou ld normally taX a person's analyTi ca l abiliries. For example. ~ n:ttur..tl resource management organization in the sourhern US considering a fertilization pro ject, yet operaring with a limited budget. may need to locate dlOSC forested arca5 (srands) thal would ben efit mosr in rerms of (he growth of rhe forest From a fen i1-ization application in order to make efficient use of their budget. I r you were to :1$Sume lh:H rhe sta nds must be


dominated by pine [ree species. and located on cenain soil

rypes, you can imagine the enormous [ask fitced by a large

landowner (> 500,000 acres) if paper maps (soils and

sta nds) were rhe only resource available for analysis. A process such as [his would have required several days to

complete with paper maps, but might require only a fe\v

minures when performed within GIS. The application of GIS in natural resource ma nage

men[ organizations has become standard practice duri ng the lasr 10 yea rs (Wing & Berringer, 2003) . rarr of the

reason for this widespread lise is because of rhe efficiencies

hinred at above, bur also a resu lt of cami ll ucd tech no logical advances in computer hardware and software.

Compurer prices conti nue [0 decline whi le processing power and smrage efficiency have continued {Q grow. A

wide variety of GIS software progrAms have also emerged, and rhe [rend in GIS sof[\vare program design has been to

make programs more user-friendly while. perhaps. sacri

fic ing efficiency of operarions.

One of the primary reasons for rhe growth of GIS use

in natural resource management organizations is rhat rhe

collection and analys is of landscape measuremenrs is fun

damenral for mosr naru ral resource analysis and manage

ment acriviries. GIS allows you to work with measurement

information lO facilitate mapping and modelling land

scape featu res o r {Q suppor[ rhe eval uat ion of manage

menr policies. For example. you m ighr be inrerested in

determining the extenr of vegetation resou rces with in a watershed, rhe amounr of wildlife habitar within a naru

ral area, or the potenriai impacts of cha nges in ripar ian

managemenr pol ic ies. GIS facilitates an efficient explo

ration of rhe information relared to natural resources.

Although many narural resou rce managemenr organ

izations employ a GIS expert (guru, manager) ar a cenrral

ized GIS office, rhese people are often overloaded wirh

work and unable to offer sustained assistance {Q field per

sonnel. I n some cases, rhe experts, particularly if they have

a computer background as opposed ro a narural resource

background, may be unaware of rhe common rypes of

GIS applicat io ns in natural resource management. When you additionally consider thar many natural resource

management organizations desire new employees to have

a GIS background, rhe advanrages for those involved in

nalural resource managemem to be familiar w ith the

pQ[ential uses of GIS are clear. This fami liariry shou ld

include [he ability to communicate us ing basic GISreiared terminology and the abi liry to perform basic CIS

processes, such as viewing data and making maps of fea

mres of interest.

Recenr graduates of many university-level natural

resource management programs com plete at leasr one course

involving GIS. Geospar ial skills are currently in-demand in

narural resource management, and are seen as part of an

emerging industry thar will experience conrin~ l ed growth for

the near futu re (Wing & essions, 2007). One survey of nat

ural resource managemenr employers (Brown & Lassoie, 1998) supporrs these assertions, while another indicates rhar

nea rly half of industrial employers expected new employees

ro have obtained GIS experiences during their undergraduate

education (Sample et al., 1999). What skills should students

develop a proficiency prior to graduating? Merry et al.

(2007) surveyed recent graduates who were employed in nat

ural resource managemenr-related posirions and found that ESRJ's ArcMap and ArcView sof[\va re products were the

mOSt commonly used GIS software packages. Others rypes of software products were also being used (e.g., Maplnfo,

Google Earth, Delorme, Landmark Systems' SoloFidd CE,

and commercial software packages from Davey Resources).

and learning how to use at least one program wi ll make

adapting to the use of others reiariveiy easy. Bas ic GIS oper

adons (heads-up digirizing, manual editi ng of arrributes,

manuaJ editing of spatial posirions, and querying of tabular

artribures) were lhe most frequen tly used GIS processes.

More complex processes, such as combining and erasing fea

[tires. and spatia l queries, were also mode rarely used by

recem graduates. Of the rypes of products recem graduates

created with GIS. basic locational maps, managemenr decision-related maps (i.e., planting maps) , and GIS databases

(e.g., prescribed fire locations. conservation easemems. inva

sive species distriburions, soil maps) were me mosr common.

GIS Technology

Operating a GIS requires working with a wide range of

technology and possessing. or acquiring. the skills neces

sa ry to understand and manipulate the rechno logy.

During the next part of this chapter. a desc ription of rhe

various technological components of GIS is presenred.

Although it may not be important for GIS users [Q be con

sidered expens in all GIS-related technologies, fam iliariry

with the various componenrs may help you understand

how the componenrs are integrated.

Data collection processes and input devices

Despite (he rapid advances of GIS hardware and sofrwa re ,

one of the primary challenges for organizations using GIS

23

8 Part 1 Introduction to Geographic Information Systems. Spatial Databases. and Map Design

dominated by pine rree species, and located on cerrai n soil

rypes, you can imagine [he enormOuS (ask faced by a large landowner (> 500.000 acres) if paper maps (soils and

stands) were rhe only (eSOlIrCe available for analysis. A

process such as (his would have required severa) days to

complete with paper maps. bur mighr require only a few minmcs when performed within GIS.

The application of GIS in natural resource manage

ment organizadons has become standard pracrice during

rhe las, 10 years (Wing & Beninger. 2003). ran of ,he

reason ror this widespread lise is because of rhe efficiencies

hinted m above, bUt a lso a result' of comillucd technolog

ical advances in computer ha rdware and software.

Com pUler prices conri nue {Q decline whi le processing

power and srorage efficiency have conLinued ( 0 grow. A

wide variety of GIS sofrvvare programs have also emerged.

and the (rend in GIS softwa re program design has been to

make programs more user-friendly while. perhaps. sacri

fic ing efficiency of operacions.

One of lhe primary reasons for the growrh of GIS u",

in natural resource management organizations is thaI the

collection and analysis oflandscape measuremems is fun

damemal for most namraJ resource analysis and manage

menl acciviries. GIS allows )'ou to work with measuremCni

informarion to facilitate mapping and modelling land

scape featu res or to supporr Ihe eval uat ion of manage

mem policies. For example, you might be interested in

determining the exrcnr of vegerarion resources within a

watershed . the amOllnt of wildlife habilar wirhin a nalu

ral area , or the potencial impacts of cha nges in riparian

managemelll policies. GIS facil itates an dTicient explo

r.nion of rile informarion relaled to narural resources.

Alrhough many narural resource managemenr organ

izadons employ a GIS expert (gllru . manager) ar a central

ized GI S office, these people arc oFten overloaded wirh

work and unable 10 oHer stls rained 3ssiS[ance ro field per

sonnel. In some cases, the expcn s, parricularly iF they have

a computer background as opposed ro ~I natural resource

background , may be unaware of (he common types of

G IS applications in na tu ral resource management. When

you 3dditiona lly consider that many I1:ltural resource

management organizations desire new employees to have

a G IS background. lbe advantages for ,hose involved in

naLUral resource m ~magemenl ro be fami liar w idl the

potential uses of GI are clear. This F..miliariry shou ld

include the ability to communicare using basic GI ~

rei31ed terminology and [he abi lity ro perform basic GI

processes. such as viewing dara and making maps of teacures of interesr.

Recem graduates of many university-level natural

resource management programs complete alle;)sr one course

involving GI . GeospariaJ skills are currend)' in-demand in

narural resource managemcnr, and a re sC'en as pan of an

emerging industry thar wi ll experience: conrinued growth for

,he near future (Wing & Sessions, 2007). One survey of nal

ural resource management employers (Brown & L.assoie.

1998) supportS ,hese assenions, while anomer indicales cllat ne-drly half of industrial employers expeCted new employees

to have obtained GlS experiences during their undergraduare

educalion (Sample e, al., 1999). Wha, skills should students

develop a proficiency prior 10 graduating? Merr), el al.

(2007) surveyed reeent graduares who were employed in na,

ural resource man3gemenr-relared positions and found rhar

ESRJ's ArcMap and ArcView soFrwa re products were the

mOSI commonly used GIS sof,ware packages. Olhers rypes of

sof"V'dre producls were also being used (e.g. , Maplnfo.

Google Eanh. Delorme, Landmark Sysrems' SoloFieid CEo

(U1d commercia.l software packages from Davey Resoufces).

and learning how to use at leasr one program will make

Jdapring '0 the use of others relatively easy. Basic GIS oper

arions (heads~up digitizing, manual editi ng of anribures.

manual editing of spatial positions. and querying of tabular

anributes) were the moSl frequently lIsed GIS processes.

More complex processes, such as combining and erasing fea

(Ures. a nd spatial queries, were also moderately used by

recelll graduates. Of the rypcs of products recem graduates

creat~ with ,IS. basic 1000donai maps. management deci

sion-reialed maps (i.e .. planting maps). and GIS databases

{e.g .. prescribed fire locadons. conservarion easemenlS. inva

sive species disrriburions. soil maps} were rhe mosr common.

GIS Technology

Operating a G I requires working with a wide range of

rechnology and possessing. or acquiring, rhe skills neces

sary CO understand and manipulare rhe techno logy.

During rhe next part of this chapter, a description of rhe

various technological omponenls of G IS is presemed.

Although it may nor be imporr:mr for GIS users (0 be con

sidered experts in all GIS-related !echnologies. F..milia ri ry

with the various components may hdp you undersrand

how rhe components are integrated.

Data collection processes and input devices

Despire the rapid advances of GIS hardware and sofrware.

one of rhe primary ch311enges fOf organizations using I

relines ro the developmenr and maimenance ot G IS da[abases. Collecting spatial data, preparing the data for GIS

use, and documenting rhese processes conri nue [0 com

prise the majoriry ofbudgers allocated fo r GIS processes. Sparial data qualiry is cemral [0 successtul GIS implementarion and analysis.

Dara are often described in re rms ot thei r precision and accuracy, (wo terms that are otren confused.

Precision relates ro rhe degree ot specificity {Q which a measuremenr is desc ribed. A measurement that is

described with mulr iple decimal places, such as an area measu rement ot 2.6789 hectares, is co nsidered a very precise measurement. It rhis measuremenr were derived trom a property bou ndary survey where distances were gathered by cou ncing paces. and angles were measured

using a handheld compass, you might quesrio n the accuracy ot [he measuremem; however, it is ina rguably pre

senred in a highly precise manner. Precision can also be described in rerms ot rhe relative consistency among a set of measu rements. For insrance, if the measurements relared ro a property bou ndary were measured multiple rimes wilh a sophisticated surveying insrrumenr and rhe resu lting variarion among measu rements was small , YOli

could rhen describe rhe measuremems as being relarively

precise. Accu.racy refers ro rhe abiliry of a measurement to

describe a landscape feature 's [rue lacarion. size, or condi

[ion. Accuracy is typically described in rerms of a range o r varia nce [hat derails a dueshold within which we would expect to find the likely value. The assessmem of accuracy anemprs to answer rhe fo llowing question: How close are the measurements to their t rue val ue? Examples of accuracy levels include distance measuremems of ± 0.5 m or angle measuremenrs of ± 1 second. You can have meas

uremenrs rhat are borh highly precise and accurare (Figu re 1.2, rarr A), highly precise without being very

accu rare (Figure 1.2, Pan B), nor very precise. bm accurare (Figure 1.2. Pan C), or neirher precise nor accurate (Figu re 1.2, rart D).

Accuracy and precision may also be stared in relative terms. Suppose rhe lengrh of a sr ream is measured (\vice wirh a I ~O-foo t meral rape, resulr ing in measurements of 232.7 and 232.5 feet. The average length of the srream is 232.6 feet. If the meta l tape was previously broken, say at the I O-foot mark. and spl iced back rogerher, reducing rhe dfecr ive lengrh of the rape (Q 99.9 feel , (he relat ive aCCll racy and precision of (he measuremenrs can be calcu lated. Since rhe tape was used about 2.3 rimes when measuring Ihe stream, and the broken part of rhe rape was used each


A B

c D

Figure 1.2 Examples of accuracy and precision. Pan A shows accur:HC and precise locations of €lara around rhe circle centcr; Pan B shows predse bUi nor vcry accurare dar. ; Pan C shows accurau~ bur not very precise data; and Part D sho .... , neither precise nor :accurate d:ata around the circle center.

rime, rhe accuracy of rhe measuremenrs is aboUt 2.3 X

0 .1 foot = 0.23 foot off of the value you might have expecred wirh an unbroken rape. You could exp ress the relative precision of rhe measurements as (232.7 - 232.5) 1 232.6 = I: 1,163 and the relative accuracy of [he tape as 0.1 I 100.0 = I: 1,000. One advantage of using relative

accuracy is rhar it provides an assessmenr of rhe expecred porrion of erro r given some measu red amount. This allows for lhe reladve comparison of rhese errors berween differenr measured irems and locations. Relative accuracy can also provide a means of stat ing o r assessing re<Juired o r mi nimal mappin g accuracies. The U Federal Geodetic Contro l Subcommittee (FG e S) (J 984) and Natural Resources Canada (J 978) fo llow this practice whereby rhe relar ive precision ca n be carego ri zed as

acceptable or unacceprable. given a desired measuremenr accuracy.

It is important rhar G IS users are aware of [he disrinc

tion berween precision and accu racy. parricularly when considering rhe value of using a C IS database in an analysis fh ar leads lO a management decis ion . These terms,

despire [heir com mon usage. imply information about differenr qualif ies of a measuremen( o r acriviry. Alrhough

accura cy and precisio n imply differenr cha racrerisrics,

24

10 Part 1 Introduction 10 Geographic Information Systems, Spatial Databases, and Map Design

many lise these te rms inrerchangeably and. as a result ,

incorrecdy.

There are many ways (0 create and co llect data, and all

methods require va rying degrees of skill and organiza

donal commit menr, The following secrions describe some

of rhe most commo n medlOds for creating GIS databases.

Manual map digitizing The abi lity (0 manually encode vecto r maps using a digitizing table (Figure 1.3) and associated software has been ava ilable since the late I 960s. Paper or mylar maps are raped down to a digitizing rabie, in which is embedded a fine mesh of copper wire. Known reference points on rhe maps are idclHified using rhe digitizing table 's 'puck'

(s imilar ro a compurer mouse), which sends a signal ro rhe

wire mesh within rhe rable. Once rhe reference poincs

have been idemiried, all orher landscape fearures can be encoded in a Cartesian coord inare sysrem and related ro

rhe reference poinrs. For poim fe-drures. rhis requires lin

ing up the cross-hairs of the puck wirh the poim locarions

and identifying rhe points. For line and polygon fearures.

ir involves rracing rhe boundaries of rhe lines or polygon

boundaries. noring each change in a line's direcrion.

Features can be recorded with either 'srream mode' o r

' poinr mode' referencing processes. In stream mode. the

spa rial locatio n of the digitizing puck is recorded ar either

regular time intervals (e.g., every second) or regular dis

ranees inrervals (e.g., every 0.25 inch). In poinr mode, rhe spalia llocarion of rhe digirizing puck. and hence rhe loca

rion oflandsca pe fe'drures. is recorded every time a button

on rhe puck is pushed.

Manual digitizing of maps can be a redious process

and. like many orher tasks thar are done by hand. subjecr

Figun 1.3 Digitizing table:.

ro human error and variarion. Today, digitizing is sti ll a

necessary function for many namral resource manage

menr organizations bur reliance on this technique has dra

matically decreased as orher dara collection merhods have

emerged or become refined.

Scanning Scann ing involves rhe exam ination of maps by a com

puter process dlat seeks to ident ify (a nd convert to digital

form) changes in map colo r or rone. which idemi fy land

scape fearures. Flat-bed scanners allow a picture or map,

such as an aerial pho(Qgraph o r a to pographic map. to be

co nvened ro a digita l form. The resuhing images a re

described by rhe rasrer data srrucCllre, and include pixels

or grid cells that may be encoded (or attribured) dilterenriy. depending on how the scanner imerprets the color

or rone of each feature. Scanners (Figure 1.4) generally move systematically across a picture or map. and record rhe reAecrance values of the (Ones or colors for each grid

cell. Scanned images rend ro look very much like rhe pic

tures or maps that were scanned, yer there is lIsually some

difference in qualifY due [0 the size of the grid cells

assumed in rhe scanning process, rhe qualiry of the pic

(Ure or map. or the qualiry of rhe scan ner.

- --- ~-~ .

Figure 1.4 Small format JCln n~r.

25

A second method of scan ni ng involves the use of d ig

iral cameras. An array of phoroderectors located within

digital cameras allows you to caprure and store an image.

The images are saved with a raster data srructure and can

be transferred ro a computer system and rhen used in a

manner similar to the sca nned images mentioned above.

DigitaJ cameras can be synchron ized with CPS receive rs so

that a coordinare value and elevation a re potenrially asso

ciared with each image.

Remote sensing Remote sensing involves rhe use of a sensor that is nOt in

physical contact with its subject of interest (Avery & Berlin , 1992). Ir can include a wide variery of rechniques,

and in faCt, capturing images with a digitaJ camera rheorer

ically uses remme sensing technology, since the camera is

nor necessarily in contact with the image being co llecred

(the landscape). However, when discussing remore sensing

technology in natural resource managemem, the use of

satellites or cameras mounted on airplanes is frequently

referenced. Remore sensing devices capture electromag

netic energy, generated by the sun or perhaps by some

other device, such as a radar eminer, that is reflected off of

landscape features. Most sa tellite senso rs a re designed to

record the reflectance of light or heat from objects on the

Earth 's surface. These e1ectromagneric reflecrances are

recorded by rhe sensors in terms of their wavelength of

energy, as described by rhe e1ectromagneric specrrum. The

electromagnetic wavelengths are then convened to a digi

(al formar and transmirred back to a computer for process

in g and interpolation. Satelli tes such as rhe Landsat

On-board computer

Camera field of view ---.....

Figure 1.5 Digi tal camera mounled on airpla ne.


Thematic Mapper™ series can capture wide swaths of rhe

Earth 's surface (185 km, or 115 miles), and rhus have rhe

potencial to record vast amounts of informarion over a

shorr time period. The launching and operation of sa tel

lires for dara collection has increasingly been conducted by

private o rganizations, and as a result many different forms

ofhighly accurate and precise data are becoming available,

such as the I In resolurion IKONOS satell ite data (Land

Info Worldwide Mappi ng, LLC, 2006). Alrhough rhese

advances in remote sensing technology have increased the

variety of products available to consumers, the cOSt of

acquiring and processing sa tellite-collected data is sri II pro

hibirive for many organizarions.

D igiral cameras ca n be mounred on airplanes (Figu re

1.5) , and can genera lly p rovide higher reso lurion images

than that provided by satellites, yet rhis distincrion is get

ring less clear with each passing year. It is also possible to

mount digiral cameras on smaller, remore controlled a ir

crafr, and to synch ro nize color or infrared photography

with CPS measuremems. This coupli ng of technology

may become more widespread in the futu re.

A relarively new rechnology called LiDAR (light derec

tion and ranging) has emerged that allows for rhe collection of ropographic or elevation data . LiDAR sysrems are

rypically mounred on an aircralT (although ground-based

plat fo rms are also used), and include a laser, an inertial

navigation sysrem, a CPS receiver, and an on-board com

purer fo r dara processing. LiDAR measuremenr rechnol

ogy allows scienriscs [0 remotely sense and create digital

models oflandscape features such as vegera tion, ropogra

phy, and strucru res. LiDAR technology has been applied

26

12 Part 1 Introductioo to Geographic Information Systems, Spatial Databases, and Map Design

[0 natural resources (Q measure foresl ca no py strucrure,

inventory, and biomass (Reutebuch et aI., 2005). LiDAR

airborne lase r sca nning invo lves directing discrete pulses

of light onro a landscape in o rder ro rerurn rhe posirions

and dimensions orJandscape features.

A LiDAR lighr pulse is emined from a u ansmi [(er as rhe aircraft moves and rravds unli l it reaches a solid object

(Figure 1.6). Depending on the type, density, and reAecciviry of 3n object, the light pulse is either refl ected back ro an airborne sensor o r conrinues (0 deflect off of other

objeclS unril it reaches a solid surface. such as [he ground.

T ypica lly, up [Q fou r reflecred values can be retu rned from

a si ngle pulse. T he combi nation of repear (e(Urns can be

fused with orher multiple reru m pulses [Q create a rhreedimens io nal visualiza cio n of landscape fearures. The

round-trip lravel rime of individual lighr pulses is meas

ured and srored by an airborne sensor rhar is coordinared

wirh an o n-board globa l posi t ioning system (C PS) . By

comparing rhe rerum rime ro rhe speed of light, rhe d is

rance ro the ground or the landscape fearure ca n be calcu

laled. The coupling of pu lse sensor and CPS measurements

results in the geo-referencing of return pulses so thar coor

dinates (longilllde and latitude) and height (elevat ion) are

associared with each returned pulse. In addition, rhe iner-

laser ~ scanner

..

tial naviga tion sysrem [racks [he irregularities of rhe ai r

crdft's Aight path and att itude (yaw, pitch, and roll) and all

informarion is collected and processed by the on-board

computer. Up to 150,000 pulses per second ca n be gener

ated with contemporary LiDAR systems (YII et aI., 2006).

This rapid pulse rate leads to LiDAR databases of hundreds

of gigabytes for even modest sized areas (e.g. 4,000 hal .

Image p rocessing sofr""are can convert rhe millions of rerurn pulses rhat are ryp ical of LiDAR da ta projects into

twO and rhree-dimensional represenrarions of landscape

characrerisrics including streams, roads, and vegetation.

In addition ro posirion and heigh t measuremelHs, rhe

reflecrance inrensiry of each LiDAR pulse is measured and

sro red with rhe geo-referenced information. Researchers

have recenrly recognized (har rhe sr rengdl of reflecrance

inrensiry values can pOlenrially provide descriptive info r

marion abom landscape features. Reflecra nce intensity is

the rat io o f srrengdl of ('he reflected pulse ro thal of the

emined pulse. The reAecra nce inrensiry informarion is a spectral signa[ure and ca n be used [0 derermine the na[ure

of landscape objens. There are few published studies of usi ng LiDAR reflectance inrensiry values for na rural

resou rce appl ications bur researchers have used LiDAR ro

investigare differences between con iferous and deciduous

28

24

20

§: 16 ;: ~ 12 '"

8

4

0

0 50 100 Number of laser shots

Figun 1.6 LiDAR system on ai rcraft (cou rtes)' Dr Jason Drake. US Forest Service ).

27

In ou r sociery, electromagnetic energy is generated by a va ri ety of sou rces, including su nl amps, fi res, microwaves, radio [Owers, radar detectors. and lasers. Devices or techniques for capturing this energy can ~ characterized as passive or act ive. Passive data cap[Ure techn iques. such as aer ial photography o r LandsarTM, record electromagnetic energy that is nat

urally emitted or reAected. The most obvious narural producer of e1ecrromagneric energy is the sun. The

sun produces eiecrromagneric energy at multiple wavelengrhs, some of which are visible to the human

eye. Devices that employ radar or laser rechno logy (fansmir elecrromagnecic energy and record rhe

rrees (Song et aI., 2002) and <0 determin e rree healrh

(McCombs et aI. , 2003). While these prior research findings have had modeSt success in app lyi ng LiDAR reAectance intensities, the more powerful emitters and

sensors that are rypical of contemporary LiDAR equipmell[ may provide descriptive information such as tree

species and healrh, land cover rype, o r eype of structure fo r mapped positions.

LiDAR has shown great porenrial in foresrry and narural resou rce applicarions, nOt only in generating high-reso lution digital elevation models (DEMs), but also in measuring stand strucrural cond itions. Although the COSt of acquiring LiDAR data is st ill prohibitive for many o rganizations. large areas can lx Aown ar a COSt of abom $ 1 per acre; COS[5 are expected to decrease in the furure.

Photogrammetry Phorogram metry is perhaps the primary method used for rhe creation of spacial data in forestry and narural resource management, alrhough LiDAR acquisition is gaining steadily in its application. Within the US, many of the

products produced by the US Geological Survey (USGS), including elevation surfaces and other represemarions of natural resources, were derived from phorogrammerric techniques. Photogrammetry can be defined as the act of

collecting measuremenrs from rhe image of an object o r resource. T his tech nique dates back to the mid-nine

teemh century. soon after the first phorograph was created (Wolf & Dewitt, 2000). Through va ri ous techniques, pholOgrammetry fac ilitates the interpretation and


amounr of time ir rakes for rhe energy (Q rerurn; rhese are defined as acrive techn iques. The enti re range of electromagnetic energy is known as rhe electromagneric spectru m. The range of electromagneric energy humans can see is ca lled the visible porrion of the elecrromagneric specrrum , which co nta ins wave

lengrhs between 0.4 and 0.7 mm. Other portions of the spectrum that are nOt visible to humans include rhe cosmic, ultravioler , infrared, microwave, and radar wavelengths. Mosr digital imagery developed from remore sensi ng devices makes use of rhe visible and infrared portions (0.4- 0.9 mm) of the elecrromagnetic spect rum .

meas urement of features captured on photographs. Phorogrammetry requires a firm understanding of photography, strong quantit3rive skills, and ar rimes, creariviry; successful interpretacion sometimes becomes an art.

Phorogrammerry offers several advantages ove r ground-based dara collection techniques. Usi ng airc rafts, phorographs ca n be taken of areas at heiglHs that might ordinarily be inaccessible by orher devices. Large land

scape areas can be capru red. creati ng a permanent record of a resource ar the time of data collection. Photographs can also be used for hisrorical research because it is relatively easy [0 reexamine a ph otograph, as opposed to

reviewing a field survey of a resource, which genera lly is d ifficulr to reproduce. The accuracy. speed of acquisir ion, and cosr of photogrammerric products are consra nriy

improv ing, thus phorogrammerry remains a popular mer hod for collecting spatial dara and for the creation of GIS darabases. Digital methods ofcapruring images, however, are steadily replacing those rhat use phowgraphic

film. The mOSt crucial physical componenr of phorogram

merry is the photographic system utilized. Single lens cameras are most common, and a rypical frame measures 23 X 23 cm (9 X 9 in.). The camera lens is held ar a fixed distance, o r focal length, from rhe frame. Knowing this distance is critical in facili tating furu re measu rements from photOgraphs (Figure 1.7) . The most common focal

length is 152.4 mm (6 in.), bur orher lengths are also used (90, 210, and 305 mm). Photogra phic images are cap(ured when a shuner near (he lens is opened, momemarily

28


Principal point

Film surface +

Ground Surface

Figurr: 1.7 A~rial photog~phy geometry.

f ocal length (I)

Height (H-h)

allowi ng lighr ro srrike rhe fi lm su rface . Fiducial ma rks

usually in the form of four or eight markings located in the sides or corners of the photograph margins-are pro

jecred onto the film during rhe exposure of rhe phoro-

Figur~ J.8 A~riaJ photograph.

graph [Q allow you [0 define the geometric cenrer of the

phorograph.

Aerial phorographs are described by rhe angle in which they were capmred: vertica l o r oblique. A venical aer ial

phmograph is o ne where rhe position of the camera axis is

a nearly perpendicu lar orientat ion [0 rhe grou nd su rface.

An oblique aerial pholOgraph is one where the positio n of

rhe camera axis is located somewhere between a vertica l

and horizontal or ientat io n ro rhe ground . For measu re

ment purposes. most phOlogram merrisrs prefer verr ica l

phorographs (Figure 1.8) while oblique phorographs are

mos rl y useful fo r imerprerive purposes.

Verrical aeria l phorographs usually capture images at

regular imervals along a consisrenr heading. known as a

flight line. This systematic data collecrion approach is fol

lowed ro ensure tora l coverage of a resource. Flight lines

are usually designed so rhat subsequent photographs will

have an overlap of 60 per cenL In addirio n. phorographs

captured on adjacent Aighr lines should have an overlap

or 30 per cem. An advamage or crearing overlapping pho

rographs is thar YOll can see landscape features in Stereo

with a stereoscope (Figure 1.9) when s illluhaneously

viewing phorographs ca prured of rhe same area yet a[ di f-

29

Figure: 1.9 Mirror $le:re:osc:opt.

ferem angles. In addition. phorograph ic mosaics can be more eas ily created when using overlapping phorographs.

T he scale of an aerial photograph is described by the rario o f phmo dista nces ro grou nd distances. A scale can be calculated for any point on a photOgraph by usi ng the following formula:

s= f (H -h)

where 5 is Ihe scale, l is the ca mera focal length. H is the height of rhe ca mera above a comrol surface. such as mean sea level (Figure 1.7), and IJ is the poinr's e1evalion.

Variables f H. and h. all need to be stated in [he same uni ts of measuremen t. If the focal length were 6", the height of the ca mera 2.000'. and the height of the point in question 450'. the absol ute scale would be 0.5 / (2000 - 450) = 0.000323. Expressed as a rel at ive sca le (the inverse of rhe abso lure scale) . these measuremenrs represent 1:3 100.

If a map is ava ilable of the photographed area. scale can be derived withom using the focallengrh and ca mera height. bur instead by comparing the photo distance with the map disrance berween twO points. T he fo llowing formu la can then be used:

(photo disca nce

photo scale = -----,---- ) X map scale. map dista nce

The distances lIsed in this formula must be in the same units, and the phoro scale wi ll reAecr rhe average elevation between rhe tw O points. Once rhe phoro scale is known ,


phoro distances can be convened to ground dislances by multiplying the photo distance by the scale. For exa mple. assu me that the distance between rwo points o n a phmo distance was 2.5 inches, rhe distance berween rhe same [\-Yo points o n a map was 4 .5 inches, and thar the map scale was I :24.000. The scale of the photo is (2.5 /4 .5) X

24.000 = 13.333. or expressed as a ratio I: 13.333. Analytical phmogrammerry involves the use of math

emat ics to precisely defi ne the locarions of landsca pe features on srereo pairs of phorographs. Srereoplotters are often used in analytical photogrammetry 10 register and measure photOgraphs (Figu re 1.10). There are several di fferenr rypes of srereoploners; newer models inrerfilce wirh a computer ro increase the speed of d:Ha creat io n and correcrio n. Once rhe pho rographic images are placed o n the

ste reoplo ner, lights projecred from differem angles are directed through the photOgrdphs. The lights are adjusted so thar a srereomodel is formed from rhe overlapping areas of me projected images on the photOgraphs. Once rhe srereo plotrer o peraro r brings the srereo model into

focus, landscape featu res can be measured and mapped, and a porenrial GIS database is created. The accu racy of measurements obtained rhrough anaJytica l phorogrammerry is usually expressed as a rario of the camera heigh I involved in rhe imaging process. Accuracy levels of arou nd 1/12.000 of the camera height are typica l. For camera heiglHs of 12,000 fr , [his rranslares into an accu

racy of about ± I ft. A relarively new product thar is developed from aerial

photographs is a digital orthophotogtap h. While an orthophotograph is derived fro m aerial photographs. the

Figure: 1.10 S l e: re:op l otl~ r.

30


relief displacemenr inherent in rhe phorographs is minimized. and measurements of landscape fearures can be

taken direcdy from rhe orrhophomgraph withom the

need ror displacement corrections. To create a digital orthophomgraph, scanned aerial phomgraphs and digital

e leva tion models (OEMs) are required . Orrhophoro

graphs and DEMs a rc discussed in mo re derail in

cha pter 2.

Field data collection

Field collection techniques for the c rearion of G IS data

bases have advanced tremendously over rhe past 20 years

and a re now fully enmeshed in rhe digital age.

Increasingly, field dara collection processes in natural

resource environmenrs are using digital data collection

techniques (Wing & Kellogg, 2004) . Field collection

techniques were once limited [0 m anua l [ools that

required physical skill on the part of the operamr and,

depending on rhe instrument, technica l competency

equivalenr to that possessed by a p rofessiona l land sur

veyor (Kavanagh & Bird, 2000) . Field c rews would lise

meral or synthetic rapes co measure distances between

objects. and clinometers or level guns [0 determine gradi

ents and elevation differences. Approximate angles could

be derermined from compass readings, and more precise

a ngle measurements were calcula ted from rransirs or

rheodolites. Measurements were recorded in field nore

books and processed in an office sening. Post-processing

and adjustment of the data were almost always necessary

{Q ensure [har data collection and insrrumenr errors were

acco tllHed for and balanced (hroughom rhe measure

ments. These practices a re sri ll com mon and appropriate

tOday fo r many field crews who are involved in collecting

spada l data for forestry and natural resource purposes.

Although technica l competency with d igita l instru

mentarion and an understanding of measuremenr error

and corrections a re necessa ry skills for field crews using

this techno logy, spatia l data can be collected and

processed wiLh an efficiency and precision thar far sur

passes other manual field measu remem techniques.

Electronic distance measu ring devices (EDMs) were first

developed abour 50 years ago and represented a major

breakthrough in data collection (Wolf & Ghilani, 2002).

These devices measure the amounr of time ir rook a beam

of electromagnetic energy to (ravel from an instrument, (0

a reflective surface. and back. With (his information, a

distance can be calculated. Currenr technology includes

rhe abiliry IO nor only ca pture distance measurements.

bur also the angles berween objects. In addirion, the

measurementS a re s{Qred in a digiral dambase. In some cases, these measuremenrs can be highly accurate, provid

ing positions that are within centimeters, or less , of their

[rue locarions va lues. Total S[3rions and laser range find

ers (Figure 1.11) are examples of tools that make ir possi

ble for field crews to sigh t and 'shoor' d istant o bjects.

Typically, these insrrumenrs require thar a reflective sur

face be placed on the object of inrerest so that a beam can

be projeC[ed onro the surface and returned for measure

ment. Measurements include not only horizontal dis

rances and angles, bur also rhe elevation difference from

rh e instrument's position. Auromarica lly storing rhe

measuremenrs within the surveying instruments elimi

nares rhe potenrial errors rhar may a rise when data

recorded by hand on field forms is transferred to a G IS

database.

Another technology [har has become bmh more

affordable and more useable in recent yea rs is that of

global positio ning systems (GPS). GPS requires [har a

receiver, locared on the Earth's surface, collect and record

signals transmitted by satellites o rbiting the Earth (Figure

1.12). Many narural resource professionals consider GPS

receivers ro be a source of frusrrar ion but recenr evidence

suggests (har some GPS receivers are capable of reliably

collecting measuremelHs under ca nopy. The com mon

limiring factor for GPS appl ica tions in narural resources

has been that lines-of-s iglu between GPS receivers on-(he

ground and space-based satellite sysrcms have been

obscured by ca nopy conditions. [Opographic barriers. or

some combi nation thereof.

A GPS receiver calculates a position by being ab le to

receive signals from at leas t four sa tellites, wid1 more sa rel-

Fig ure 1.11 Laser rangt" fi ndc r.

31

Satellite

Figure 1.1 2 GPS schenl2lic.

lites leading ro berrer data collection opponu nities. GPS

receivers (Figure 1.13) calculate rhe amou nt of time it takes each signal to travel from the satel li re. The GPS receiver uses information contained in the signals ro calculare rhe range (dis£ance) berween the receiver and all satell ites in com munication. Ranges arc lIsed to estimate a posirio n through rrilare ration. Satellite signal quality

and rel iabiliry for measuremeIH determinacion depends on sarellite availabil ity and geometry of avai lable satell ites in relarion ro the GPS receiver. Satellite signal qua li ry is esrimated as a Posirion Dilution of Precision (POOr) sta

tisric. Mission planning software is designed ro idenrify the potemiaHy best or preferred data collection rimes for GPS. Mission planning softwa re ca n calculate an expected POOl> statist ic and potemially ava ilab le number of satellires fo r a field sire. La rger va lues of POOP (> 8) infer diminished satell ite geometry and measu rement reliability with values of 6 or below being preferred for data collecrion (Kennedy. 2002).

Measuremenr variability and error for GPS receivers

can be inrroduced by atmospheric interference of satellite signa ls, ti min g errors between sarellires and rhe CPS receiver, the rotation of the Eanh, and satellire orbiral


FigUR 1.13 GPS receiver and antenna.

parrerns (Leick. 2004). A porrion of these errors can be es timated and removed through the process of differential correction. Differential correcdon uses a fixed CPS base station at a known location rhat co nrinuously co mpares calculated CPS-derived posirions to its own location. C PS

base stalion locations are derermined through repeated measurements tha t lead ro an accurate and precise dete rmination. Calculated differences between rhe known location and the GPS-derived locations serve as a correction facror that can also be applied to other GI)S receivers

that are collecting measurements nearby. Anmher potem ial source of error for GPS receivers is

that of mulriparh. Multipath errors occur when satell ite

signals reAect off of Olher objects before reaching a C PS receiver. This can inrroduce positional er rors into the

measuremems (Figure 1.14). These errors genera lly must

____ --------------~L.------~-:I-li~-t-h-er-r~----~~ Trail

location

Figure 1.14 Example of multipath error in da12 collccttd through GPS.

32


be removed manually bur some GPS manufacru rers ofTer

software romines mat 3re designed [0 defect and reduce mulriparh errors.

Anorher way in which to reduce CPS receiver measure

ment error is ro collect muhiple measurements at single

locations. a process known as precise po int positioning.

Statistical probability tells us that a coordinate determina

tion based on the average of multiple measurements sho uld be more reliable than that based o n a single measurement.

In addition to rhe difficulty in receivi ng satellite signals in natural resource senings in fo rested terrai n, until

relatively recendy there has been on ly one satellite system

available to users worldwide. This primary sarell ire system is the NAVSTAR (Navigarion Satellite Tracking and Ranging) system operated by the US Department of Defense (0 00). NAVSTAR became avai lable in the early 1980sj it has a minimum of 24 o perational satell ites at

anyone cime and makes satellite signals freely and condn

uously ava ilable to GPS users worldwide. Until 200 I, rhe DoD, in the interest of national security, inrenrionally

scrambled CPS satell ite signals so rhat random errors pre

vented accurate locario n informarion from being col

lecred. The scrambl ing process was known as selective

availability (SA) and could lead to measuremem e rrors of

100 III o r more. The random errors could be removed by

mapping- and survey-grade C PS receiver sofrware duough

differential correcrion. S ince 200 1, select ive ava ilabi li ty

has been removed and several new satellire systems have

become o perational. There is no guarantee, however, thar

selecrive ava ilability will remai n off in the futu re.

In addition to NAVSTAR, (here are now Space Based

Augmentarion Systems (SBAS) thar can provide conventional real-time differential corrections to CPS receivers as

rhey collect data. Convenrional real-ri me differential correc

tion uses the more easily access ible coarse/acquisi tion (CIA) sa rellire s ignals rathe r rhan phase code. AI(hough phase

code signals have grearer potenrial for accurate CPS meas

uremems, conri nuous and uni nterrupted satellite signals are

required. Conrinuous and uninterrupted satellire signals are

often difficulr to maimain under forest ca nopy and in

uneven rerrain. SBAS derives measurement correction fac

tors for several potenrial CPS error sources includi ng atmos

pheric inrerference of signals. time sequences for satellite

signal range (distance) estimates. and satell ite o rbital pat

terns. T he primary SBAS for many lIsers in North America

is Ihe US Federal Aviat ion Administ ration's Wide Area

Augmenrarion System (WAAS). In 2008, four WAAS sarellites were in o rbi( with at least (wo being operational, with

more satellires expected in furure years. Although a single

WAAS sate llite signal is required fo r a CPS receiver to apply

real -rime co rrection factors, receptio n from additional

WAAS satellite signals ca n provide a backup should receprion from one satellite become unavailable. Other SBAS

include the European Geostationary Navigation Overlay

Sysrem (EGNOS) and the Japanese MTSAT Sarell ite-based Augmenration System (MSAS). Some conremporary GPS rece ivers can operate w ith all SHAS.

Orher GPS satellite systems include GLONASS (Global Navigar ion S .. e1lire System), which was developed by the Russian military. Although GLO ASS was made fu lly operat ional with 24 satellites in 1996, it has been incon

sisrcnr with regard (0 rhe number of operational satellites.

In addition, anorher system , ca lled Ga lil eo. is under

development through the European Space Agency and is expected to be operational in 2008.

The key componem o f a CPS from a user's perspective

is (har of the CPS receiver. CPS receivers can be separated

by measu remem accuracy and price into three broad

grades or catego ries: survey, mapping, and consumer

(Wing & Kellogg, 2004). Accuracy in this distinction is the di ffe rence berween a CPS-collected measuremenr and

the true location of rhe CPS receiver when it collected the

measuremem. The most accura te and ex pens ive C PS

receivers are survey grade and ca n calculate positions to

within o ne cm of true locarion when used co rrectly.

Survey-grade CPS receivers are rhe mosr full-fearu red of

rhe th ree receiver grades and enable users to differentially

co rrect collected dat;}. I n addition ro the relatively high

COst of survey-grade GPS (rypically greater than $10,000) o perato r profic ie ncy wirh {he hardwa re and so ftware

applications is necessary. Survey-grade CPS rece iver use

in natural resou rce applica tio ns has bee n very limited

because of the del icate na[Ure o f the equipmenr and rhe

requiremcnc for sustained satellite reception in o rder (Q

derive measurements efficiently. urvey-grade CPS accu

racies arc likely g rearer than thar required for many na[U

ral resource applicarions.

Mapping- or resource-grade GI'S falls into rhe seco nd level of the three CPS categories and can be purchased for

$1 ,500-$10,000, depending on the features and the manu facturer. These GPS receivers are also so metimes

ca lled GiS-grade GPS. Many mapping-grade GPS have associa ted software for diffe renrial correction.

Manufacrucer esr imatcs of posilional accuracy are 1-5 III

depending on the receiver configura tion and mapping

application. Accuracy esrimares often reAecr rhe best-case

data co ll ecrion scenarios, which may nor be possible in

33


be removed manually bur some CPS manufacru rers offer software romines mar are designed [0 delee, and reduce multipath er rors.

Anorher way in which [0 reduce GPS receiver measure

menr error is (0 collecr multiple measurements al single

locations, a process known as precise point positioning. Statistical probability [dis us mal a coordjnarc dete rm ination based on rhe average of multiple measuremenrs should be more reliable (han rhar based on a s ingle

measuremc:nt.

In addition to the difficulry in receivi ng sa<elli (. sig

nals in natural resource settings in fo res ted teridi n. until

relatively recently the re has been only one sarellirc sysrem

availab le to uscrs worldwide. This primary satdlite sys

<em is ,he AVSTAR (Navigation Satellite Tracking and

Ra nging) sYStem opera red by rhe US Depanme nt of

D efense (000). NAV TAR became avai lable in the eady

19805; it has :I minimum of 24 o perational satell ires at

anyone rime and ma kes sarellire signals freely a nd contin

uously available ro GPS users worldwide. U ntil 2001, rhe

000, in me imeres{ of narional secu riry, ilHenr ionally

scrambled GPS sate ll i« signals so that random errors pre

ve illed accurate locatio n information rrom being col

lected . The scrambling process was known as sel~ctive

availabili(y ( A) and could lead to measurement e rrors of

100 m or more. The random errors could be removed by

mapping- and survey-grade CP ' receiver software through

difTerenrial correction. Since 200 1, selective ava ilabi lity

has been removed and several new sa tellite sys tems have

become operational. There is no guaramee, however, that

selective availabiliry will remain off in rhe fumre.

In addit ion to NAV TAR, there are now Space Based

Augmentarion Systems (SBA ) (hat can provide conven

tional re-dl-rime diff'ereOlial corrt:crions to GPS receivers 3S

they collect dara. Convenrional real-dme differential correc

tion uses the more easily accessible coarse/acq uisition (C/A)

","e1l itc signa ls r3rher than phase code. Although phase

code signals have grearer porenda l fo r accurate CPS meas

urements, conrinuous and uni merrupted sarellite signals are

requi red. onri nuous and uni nrerrupted satellite signals are

o f len difficlllr fO maintain under forest canopy a nd in

Ilneven termin. BAS derives measurement correcrion fac

tors ror several potemial (;1'5 error sOllTces including armos·

pheri inte rrerence of signals. time sequences for satelli te

signal range (disrancc) enim:ues. and sardli te orbital pat

terns. T he primary SBAS for mallY users in North America

is the US Federal Aviation Administrarion's Wide Area

Augmentation System (WAAS). In 2008, four WAAS sa tel

Iires were in orbir with ar least tWO being opermionai. with

more s3fel lites expected in fumr. yea rs. Although a single

WAA satell ire signal is required for a GI'S (eceiver to apply

rea l-ti me co rrec tion factors. reception from additional

WAAS sarellite signals can provide a backup should recep

tion rrom one satellite become unavailable. Other SBAS

include the European Geostationary Navigation Overlay

)'Stem (EG OS) and the Japanese MTSAT Satel lite-based

Augmentation System (MSAS). orne contemporary GPS

receivers C,ln opera te w ith all SBAS.

Orher GPS satellite systems include GLO ASS (Global

Navigation atellite System), which was developed by ,he

Russ ian military. Although G LONASS was made fully

operarional wirh 24 satellites in 1996. it has been incon

sistent wich regard to the number of operationalsatdlites.

In addidon. 'Inorher sys tem , ca lled Galileo. is under

development th rough the European pace Agency and is

expected to be operational in 2008. The key com ponent of a GPS from a user's perspective

is that of the' GP receiver. GPS receive rs can be separated

by measurement accuracy and price into three broad

grades or categories: survey. mapping, and consumer

(Wing & Kellogg, 2004). Accuracy in this distinction is

[he difference between a CPS-collected measurement and

me true location of rhe CPS receiver w hen it collected the

measuremenc. The most accurate and expensive CPS

receivers are survey grade and can calcu late posi tions to

within one em ot true location when used correctly.

Survey-grade GP receivers are the most full-fearured of

the three receiver grades and enable users to differentially

correcr collected data. In addirion to the relatively high

COSt of survey-grade GPS (rypicaliy greater than $10,000) operaro r proficiency with rhe hardwa re a nd sofrwa re

applications is necessary. urvey-grade G PS receiver use

in natural resource ap plicalions has been very limited

because of [he del icare I'w[U re or rhe equipmenl and rhe

requi rement for sustained sarellire reception in o rder ro

derive measurements efflciendy. Survey-grdde GI)S ;ICCU

racies are likely g rearcr rhan rhat required for ma llY mllu

ral resource applications.

Mapping- or resource-grade CPS falls into the second

level of ,he three GPS categories and ca n be purchased for

$1,500-$10,000, depending o n t he features a nd rhe

rn:lOu r.,ctll rer. T hese GPS receivers a re a lso so merimes

ca ll ed G iS-grade GPS. Ma ny mapping-grade GPS have

associared software for diffe rcmial correction.

Manufacrurer eSlima[es of posilional accu racy are 1-5 m

depending on the receiver configurarion and mapping

applicadon. Accuracy estimates oftt'n reAecr rhe bc=st~casc data co llection scenarios, wh ich may nO( be possible in

forested environmems; there have been several studies on ,his issue. Sigrisr et al. (1999) found positional accuracies be"veen 3.8 and 8.8 m during leaf-off and between 12.3 and 25.6 m during leaf-on conditions within a mixedhardwood forest during selective availability. Naesset and Jonmeister (2002) reported positional errors be[\veen 0.5 and 5.6 m in sitka spruce (Piun sitch",;;s). Liu (2002) tested several mapping grade receivers under dense hardwood canopy and reponed average positional errors of 4.0 ITI. Wing and Karsky (2005) found measurement

accuracies berween 1 and 4 m depending on the amount of canopy closu re and the type of CPS configu ration. Bolstad et aJ. (2005) twed a variety of mapping-grade GPS receiver configurations and found accuracies between 2.4 and 4.5 m under forest canopy in deciduous and redpine forests . Wing el al. (in press) tesred several mappinggrade GPS configurations and determined accuracies from post-processed data of 0 .1 and 1.2 m in young foresr and closed canopy conditions, respectively.

Consumer-grade GPS receivers are the leaS! accu rate

and most affordable of rhe GPS g rades with receivers costing between $50-$750. This price range may be att ractive for many porenrial lIsers bur several d isadvantages must be considered. Consumer-grade GPS receivers don't allow operators ro ser minimum rhresholds for

sa tellite signal qua li ty through the establishmel1l of a minimum POOP level as a quality camrol. Mission plan

ning software is usually nor included with consumer CPS receivers and some do not enable users to conduct poim averaging ro determine a single posicion. While most consumer GPS afford users the ability to srore measuremenrs individually, a common srorage limir of 500

poinrs can limir rhe amount of rime a consumer CPS receiver can be used in the field befo re rhe receiver memory is ful l. Differentia l correction capab il ities th rough data posr-processing techniques are not generally available to consumer grade CPS.

Like survey grade GPS receiver accuracy. consumer CPS receiver accuracy in forested settings has been reported in previous studies. Wing et aJ. (2005) tested the posirional accuracies and reliabiliry of six consumer grade GPS receivers within several different forest rypes and reponed measurement accuracies wirhi n 10 meters of true posirion under dense conifer canopy and within 5 meters under partial canopy. depending on the type of consumer grade CPS receiver. Average accuracies of consu mer GPS receivers be[\veen 6.5 and 7.1 m under dense primarily hardwood canopies were reponed by Bolstad er al. (2005) . Although the typical reported average accuracies


reported by these studies (S to 10m) may be acceptable

for many nalllral resource applications. consumer CPS receiver limitations, including the inability to set mini

mum sa tel lite qual ity standards, the possibility of poinr averaging. and rhe lack of different ial correccion procedures, must be co nsidered.

D ata storage technology Commonly, GIS databases consisr of large quantir ies of

data that muSt be Stored and r<plicated ('backed-up') in a system [hat allows easy access for CIS managers and users at nafUral resource managemenr organization field offices.

JUSt a few years ago magnetic tapes and magnetic disks were commonly used ro store computer files on networks and personal compurers (Burrough & McDonnell, 1998)

bur srorage technology continues to develop at a rapid pace.

Many people use oprical storage devices such as compact discs (CD) and digital versatile discs (DVD) as Standard Storage technology. CDs generally can hold about 650 MB of data. and many personal computers now conrain CD drives that allow users ro both read and wrire ro

CDs. The read and write speeds of CDs are proh ibitive for GIS lIsers who work wirh large GIS databases. DVDs look

very similar to CDs, also require a com purer drive ro operare. and are sim ilar in speed. Many manufacrurers now ofTer 'combo' drives which can read and write both CD

and DVD fo rmats. A DVD can hold approximately 4.5 GB of dam, wi dl some va rieties bei ng able to stOre even more. Recently, rapid read and write speeds (-10 ms) have been achieved by many brands of portable exrernal hard drives. These external hard drives can be quickly connected to a computer's universal serial bus (USB) port. They provide

fast backup and recovery, and can also serve as an addi[ional hard drive if needed. USB keys. or what some refer to as ' thumb drives' are very popular. inexpensive solu

rions for rransponi ng 'smaller' « 2 GB) databases. These devices are comparable in size to a key-chain and less,rhan $50 in price for a large capacity (2 GB) version. It wou ld not seem unreasonable for a GIS worksrat ion ro include the following data disk drives: CD and DVD combo drive, large (100-200 GB) external USB drive, additional USB drives for data transfer, and a 100-200 GB inrerna l hard drive.

D ata manipulation and display Persona l computers and worksrations are now nearly synonymous in meaning, although JUSt 10 years ago the term worksration implied rhe use of a UNIX {unip lexed infor-

34

rorested environments; rhere have been several studies on

this issue. igrist er 3J. (1999) fo und positional accuracies

belween 3.8 and 8.8 m during leaf-ofT and berween 12.3 and 25.6 m during leaf-on conditions within a mixed

hardwood forest during selective availabiliry. aesser and

Jonmdsler (2002) reported posidonal errors berween 0.5 a nd 5.6 m in sitka spruce (Pian sitchmsis). Liu (2002) tested several mapping grade receivers under dense hard

wood canopy and reponed averdge posidonal errors of

4.0 m. Wing and Karsky (2005) found measuremenr

accurf1cies belWeen I and 4 m depend ing o n the amount

of ca nopy closure and the type of CPS configu rarion. BolSlad et oj. (2005) tesled a va riery of mapping-grade Grs receiver configurAtions :Ind found accuracies bcrween

2.4 and 4.5 m under forest ca nopy in deciduous and red

pine foresls. Wing et al. (i n press) resred severn I mappinggrade GPS con figura tions and determ ined accuracies from

POst-processed dar. of 0.1 and 1.2 m in you ng forcsl and

closed canopy conditions, respect ively. Consumer-grade GP receivers are rhe least accurare

and most affordable of rhe C PS g rades with receivers

cosring berween $50- $750_ This price range may be anracrive for many potential users but several disadvan

rages must be considered. Consumer-grade CPS receivers don 't allow o perators LO set minimum rluesholds for

sa relli re signal qua li ty rhrough rhe establishment of a minimum POOP level as a quality conlrol. Mission plan

ning software is usually nor included wirh consumer GP receivers and so me do not enable users to conduct poilH averaging to determine a single position. While mosl

con urner GPS affo rd users the ability LO store mc:asure

me ll( S individually, a common srorage limit of 500 poims can limit the amount of rime .1 co nsumer GPS receiver ca n be used in the field befo re the receiver mem

ory is full. Differential correclion capabi lities through data po (-processing techn iques are not generally ava il

able lO consumer grade CPS. Like survey grade GPS receiver accuracy. consu mer

G P receiver accu racy in forested sc::rtings has been

reponed in previous srudies. Wing er al. (2005) lesled rhe positionJI accuracies and reliabil ity of s ix consumer grade

CPS receivers within several dirrerenr lorest types and

reponed me;:asurement accuracies wi thin 10 meters of true

position under dense co nifer GIIlOPY and wi thin 5 merers

under partial ca nopy. depending on [he:: type of consumer grade CPS receiver. Average accumcies o f' co nsumer CPS

receivers between 6.5 and 7. 1 m under dense primarily

hardwood c.mopies were reponed by Bolsrad er al.

(200S). Although lhe rypical reported average accuracies

Chapler I Geographic Information Syslems 19

reported by I hese srudies (5 to 10m) may be acceptable for many natural resou rce applications. consumer GP receiver limirarions, including the inabiliry ro set mini

mum sa lel lite qual iry standards, rhe possibiliry of poinr averaging. and rhe lack of dilTerenrial correction proce

dures. mllsr be considered.

Data slorage technology COlllmonly. GIS darabases co nsist of large quanrities of

dala thal muSt be slored and replicaled {'backed-up') in a system thar allows easy access for I managers an d users

at nawraJ resource O1anagemem organization field offices.

Jusr .1 few years ago magnetic rapes and magneric disks were: commonly used ro store compmcr files on nerworks

and persona l co mputers (Burrough & McDonnell, 1998)

bur srorage rechnology cominuc.s to develop at a rapid

pace.

Many people use optical s{Qrage devices such as com

pad discs (CD) and d igiral versalile discs (OVO) as standard slorage tech nology. CDs generally can hold about 650 MB of dala. and many personal compurers now conram 0 d rives that allow users to both read and write to

Ds. The read and wrile speeds of CDs are proh ibirive for CIS users who work wirh large I databases. DVDs look very imilar lO Os. al 0 require a computer drive co oper

ale, and are sim ilar in speed. Many manufacturers now offer 'combo' drives which can read and write bo(h CD

and OVo formalS. A DVO can hold approximarely 4.5 CB of di1ta , with some varicries being able [0 store even more.

Recently, rnpid rC'ad and write speeds (- 10 ms) have been achieved by many brands of port"db le external hard drives.

These enernal hard drives can be quickly connecled to a

com purer's universal serial bus (U B) port. They provide fust backup and recovery, and ca n also serve as an addi

rional hard drive ir needed. U B keys, o r whal some rerer to as 'rhumb dri ves' are very popular, inexpensive solu

rions for rransp rring 'smaller' « 2 CB) darabascs. These devices are comparable in size (0 a key-chain and iess,rhan

$50 in price for a large ca paciry (2 C B) "crsion. I r wou ld nor seem unreasonable tor a GI works larion lO inchlde ,he rollowing dara disk drives: CD and DVD combo drive, large (100-200 CB) eXlernal USB drive, add irional USB drives lor d3[a rransfer, and a 100-200 B internal hard drive.

Data manipulatio n and display Persona l compurers and workstations are now nearly syn

onymous in mean ing. although jusr 10 ye'J rsago [he term worksration implied rhe use of a UN IX (uniplexed infor-


marion a nd comp uting system } operating system .

Personal computers a re now rhe primary plarfo rm upon

which to urilize GIS sofr\,l<u e programs and manipulate GIS databases. Even though rhe distinctio n between per

sonal computers and worksrario ns has beco me blu rred ,

co mpu te rs ca n be chamcrerized by crite ria [hat constan dy

change widl advances in compme r technology, such as

speed , memory, and rhe o perating system. Key com po

nems within a compu te r include random access memory

(RAM), central processing unitS (C PUs), and storage

devices (hard drives o r oprical discs). Windows" and

Linu operating systems have become much more co mmon (han U IX- run computers. Most GIS software designers now focus t hei r development effo n s on

Windows<i'l-com p3lible sys tems and no longer develo p

UN IX-based sofrware.

The resolution and memory o f computer monirors is

also imporranr fo r the quick and clear display of GIS dara

bases. Video memory cards are installed in most compur

ers to manage the level of pixel resolmio n of raster GIS

databases. to display a wider a rray of colors, a nd ro

inc rease rhe speed at which images are displayed o n a

compu te r monitor. A bel ow-a verage video card ca n

reduce the effectiveness of an otherwise fiIsr- processing

computer. Video ca rds are often rated by the amOUIH of

RA.t\4 insta lled on rhe ca rd. At rhe rime of w riting of this

book, 256 MB would be an adequa re amou nr of RANI for

sta ndard G IS processes. For those working with larger GIS

databases, a video ca rd with 5 12 MB or more memory

should be co nsidered.

1- •

A GIS workstatio n usually includes a higher-end com

purer with a fast processor and large amountS of RAM

a nd imernai disk sto rage space. However, fo r the type

of wo rk pe rformed by field foreste rs and natural

resource managers, a typical personal com puter (PC),

or even a laptop compmer, will generally suffice to

facil irate rhe use of desktop G IS softwa re such as

ArcG1S or Mapln fo. What are [he desired cha racrer is

rics of a PC com puter [hat might allow you to use

deskrop GIS sofrwa re ' Obviously a fasr processor

Output devices

G IS darabases a nd [he resulrs of G IS analyses can be pre

sented in a varie[), of manners , both in graph ical and tab

ula r fo rm. For example, when examining the impacts of

alternative riparia n management policies on a landscape,

ir may be important to present data in tabular form (Q

describe the potential econo mic impacts of alte rnatives,

and in graphical form to visually display th e area each

policy affects. A number of co mmon ompur devices thar

ca n be used to presenr GIS analys is results are described in

the nex t few sections.

Printers and p lotters

T he most obvious output devices associa ted with GIS are

primers and pio((ers. Fifteen yea rs ago, line printers were

common peripherals to computer systems. Their output

qualiry was low and [he range of symbols and colors was

limited. Today, a va riety of color and black and white

prinrers are avai lable (hal' ca n produce high qua li ty maps.

Thus the cartographic creariviry of G IS users is virtual ly

unhindered. Printers are generally class ified as laser o r in k

jer, depending on how ink is transfe rred (Q paper. Laser

printers a re mo re expensive ($ 100-$2,000) rhan ink jer

prinrers « $500), and rhey require reusable laser car

tridges. H owever, (hey generally produce OutpUt products

that are more stab le. with regard (Q exposure ro moisture,

than ink jet printers. Ink jet printers require disposable

ink ca rtridges and the o mput products produced are more

easi ly affected by exposure to mo isture.

wou ld be beneficia l: rhe faster rhe processor, t he

quicker a process is complered. such as buffering o r

overlaying. In addir ion, 1 GB of RAJ"t is perhaps rhe

m inimum RAM necessary to easily handle large pro

cessing rasks. And finally, a 100-140 G B ha rd drive is

perhaps the minimum size necessary to srore GIS data

bases YO ll might develo p and use over rhe useful life of

a personal compllter (2-3 years). If LiDA R o r o rhe r

la rger-size G IS databases are to be used , the minimum

ha rd drive size mighr begin ar 200 GB.

35

The main drawback with moSt primers is the format of

the ourpm. which is generally limited to BIh" X II " or

1 I" X 14" media. Plouers allow GIS users to produce maps

of a variery of sizes (Table 1.1 ). Plo([ers, however, are gen

era lly more expensive than primers. {hough they are now

ava ilable for prices beginning at $ \000; they also typically

require special paper and ink cartridges. One way {Q care

gorize planers is to use a vector/ras ter analogy. Venor plot

ters include (hose termed as Aad>ed or drum. Vector plot

ters draw lines using planing pens of differelll colors and

can produce some very precisely d rawn maps. Raster plot

ters include those termed electrostatic, laser, ink jel, and

warm wax. Electrostatic ploners use an a rray of elect ric

contaClS (> 100 per inch) that apply a charge ro paper,

which then comes in CO IHacr with negari vely charged toner

to produce images. The technology that is used in laser and

ink jet plotters is similar (0 thar in laser and ink jet prim

ers-each featu re on rhe map is drawn pixel by pixel.

Warm wax ploners are simila r to ink jet plouers bur rhe

resulting producrs have a glossy appearance. Some of these

primers and planers. in co njunction with h igh-quality

TABLE l.l

Map siu

ANSI A

ANSI B

ANSI C

ANSID

ANSIE

ANSI ~

ISOM

ISOAJ

ISOA2

ISOAI

ISOAO

ISO B4

ISO B3

ISO B2

ISOBI

Common sizes of map output from plotte rs

D imt:RsiORs

805''' X 11.0" (2 16 mm x 279 mm)

11.0" x 1 7.0~ (279 mm x 432 nun)

17.0" X 22.0" (432 mm X SS9 mm)

22.0" X 34.0" (559 mm X 864 nun)

34.0" X 44.0" (864 mm X 1118 mm)

28.0" X 40.0" (7 11 mm X 1016 mm)

8.3" X 11.7" (210 mm X 297 mm)

11.7" X 16S (297 m m X 420 mm )

16.5" X 23.4" (420 mill X 594 mm)

23.4" X .n. I" (594 mm X 841 mm)

33. 1" X 46.8" (S4 1 mm X IIS9mm)

9.S" X 13.9" (250 mm X JS3 mm )

13.9" X 19.7" (353 mm X 500 mm)

19.7" X 27.S" (500 mm X 707 mOl)

27.S" X 39.4" (707 mm X 1000 mm)

ANSI . Ametican Nalional SI:lndards InSfiWIt:

ISO", International Standards Organizalion


paper, enable G IS users ro produce photographic-qual ity

oucpur products. A tho rough examinarion at the needs at

an organization is warranted prior ro making a decision

rega rd ing an investment in primers or plotters.

Screen displays

A more rudimemary set of outpm products trom C IS are

those relared to rhe image displays YO ll can view on a computer screen. These processes co nsist of capturing

informatio n (data o r maps) d isplayed on [he screen at a

computer and temporarily sroring {he information in a

digita l database. A number of methods a re avai lable to

ca pcu re co mputer screen images. The resolution and

derail of rhe resulting captu red image, however, depends

on the method used for image capture. Screen displays are

someti mes ca ptured and saved as image flies, and ar other

ti mes simply stored in the com purer's 'clipboard', and

thus a re available for past ing into a variety of other soft

ware programs. Fo r exam ple, mOSt personal compmers

allow users [0 save what may be displayed on a compmer

screen by pressing (all at o nce) the Alt and Pmt Scm but

rons on a compute r keyboa rd . This Stores (he entire

image displayed o n th e sc reen ro the compu ter's clip

board, JUSt like if you were copying text in a word pro

cessing program. Then, YO ll ca n paste the captu red image

imo either a word processing or graphics software program (Figure 1.15). One potential drawback is thar screen

~"'J.o .. , ..... _ .. __ ... ,_ ,::,x f"(.Oo~ __ ~ __

._...;;0 . --, o

r

~ .. "_. -- -------------" Fig urt: 1.15 Scrt:c:n d is play.

36

22 Part t Introduction to Geographic Information Systems, Spatial Databases, and Map Design

ca prures a re raster images. eve n tho ugh YO li may be

3ncmpling {Q caplU re a represenration of a vector GIS

darabase displayed on [he screen.

Graphic images

In addition {Q screen caprures , most GIS sof~\Ia re pro

grams allow lIsers (Q direcrly store images viewed on the

screen as inclependenr compmcr files. These products are

also raster images. yet they 3rc slighdy difFerem ('han rhe screen displays desc ri bed above in [hac, generally, only rhe

map image is caprured and stored (Figure 1.16) , and nor

everything else fhar may be vis ible on [he screen. These

images can be sro red in a wide va riety of fo rmats (Table

1.2) depending on rhe availabili ry within [he GIS sofrware

program bein g used . H oweve r, transferring graphic

images from one system (e.g., GIS) ro another (gra phics

edit ing programs) or vice versa ca n somerimes be prob

lema dc because offormar inconsistenc ies. In addition, [he

size of rhe resu lring graphic image files will vary depend

ing on the formar lIsed ro save rhe image.

Tabular output

As you might assu me, rabula r ourpuc consists of tables or sets of data (numbers, rex ,) derived direcrly from a GIS

database or from rhe resul, of a GIS analysis. While maps

are engagi ng-rhey draw people in and allow rhem ro

visua lize rhe qualifies and conditions of a landscape-rab

ular dara are also importanr fo r illustra ring non-spa rial

information. For exam ple, while developing a map of

habita, qualiry fo r the sporred owl (Strix occidmln/i,) may

provide an in re resfing and compelling view of a land-

Figure 1. 16 Graphic imag~.

TABLE 1.2

FiI~ CIt~nsion

cgm

emf

'P' gif

jps

PC'

png

,g, tif

wmf

wpg

Common types of graphics image output files

Description

Windows® bilmap formal

Corel DtaW® formal

Computer graphics metafile fo rmal

AutoCad@ digiml exc hange f..le format

Windows® Enhanced Metafile fornu.t

Encapsu lated PostScript format

Graphics interchange format

J PEG Fil~ interchange format

Macill{Qsh® PICT formal

PC paintbrush formal

Ilo rtable network graphics form:J1

Targa format

Tagged image file forl11 :1.1

Windows@ metafile formal

WordPerfcct«l graphics format

scape, decision-makers might also be inreresred in how

much land of h igher quality habirat ex ists. O r, as alrerna

rive riparian managemem policies are evaluated, [he effect

(e.g .• rhe area or timber volume wirhin rhe riparian man

agement areas) will likely be of inrerest [Q decision mak

ers. Tabula r da ta fro m GIS analyses ca n be displayed

direcrly on a map. or d rawn rogerher imo an independelll

table for incorporation wirhin a report.

GIS software programs

There are many GIS software programs available to natu

ra l resource managemenr orga niza rio ns raday. The com

meI1[S provided in rhis secrion are general in natu re, how

ever a lisr of dle common GIS so ftware programs is

provided in Appendix C. GIS software programs are categorized in a number of

ways. One characterizat ion is based on which of rhe twO co mmon dara slrucm res (raster or vecto r) is accommo~

dated. Rasrer and vecror data st ructures is discussed in

more deprh in chapler 2. GIS software programs have also

been characrer ized by rhe operat ing sys tem used . For

example, GIS sofrwa re programs developed for UNIX

worksta tions were once considered 'workstario n GIS sofrware\ and GIS softwa re programs developed for pes were

37

considered 'PC GIS sofrware' . This diS[incrion has essen

tial ly d isappea red as workstarion CIS sofrwa re is now used o n com purers using both Windows® and UNIX operating systems. A comemporary distinction amo ng GIS software progra ms has emerged that also makes use of the term 'workstation ' and comrasts it with 'desktop' systems. In {his categorization, workstation GIS software programs

are rhose rhat include the full range of GIS p roc<sses that a ll ow users to create, edi t, and analyze spatia l data. Examples of full-featured workstation GIS soflWare programs include Arclnfo and MGE Microstat ion. Desktop GIS software programs are co nsidered to be scaled-down

versions of workstation GIS sofrware programs, possessing a po rtion of the rools o r GIS processes found in fullfeatured wo rkstation GIS sof('\.vare programs. Examples

of deshop GIS software programs include ArcView, Maplnfo, and GeoMedia. A primary disrinct ion benveen workstation and desktop GIS programs (beside their asso

ciared cose) has been the ability to ensure that spatial relatio nships berween the locadons described in GIS databases (the topology) remains rrue o r correc£. Some desktop GIS sofrware programs are limited in their abi li ty to maimain topological relationships without rhe use of (and, therefore . pu rchase of) add itional software modules.

In choosi ng a GIS sofnva re program. purchasers need to consider the price, the GIS databases ro be managed. and the Aexibility of rhe soflWa re to perform the likely

analyses required to suppOrt managemenr decisions. The price for most GIS soflWare programs ranges from $500 for a ras ter-based desktop GIS sofnvare program to well

over $ 15,000 for a fu ll -featured workstation GIS softwa re program. Those on a tighr budger mighr consider a recenr artic le by Berna rd and Prisley (2 005) tided ' Digital Mapping Alternatives: GIS for rhe Busy Forester'. This article compared the abilities of nine GIS software products cost ing less than $500.

The types of spatial databases that are access ible by a GIS softwa re program should be carefu lly eva luated . For exa mple, if primarily raster GIS databases will be used, perhaps a rasrer-based GIS sofrware program is more appropriate (as opposed [0 a GIS software program tha t focuses o n the developmenr and mai nrena nce of vector GIS databases). Finally, a considerat io n of the rypes of GIS processes likely to be perfo rmed is imporranr in making an informed purchase decisio n. For exa mple, GIS users may need to geo-reference, interpret, and classify satellite imagery, and may need to create vecror C IS databases (e.g., roads or srreams) from measuremenrs collecred dur-


ing field data collection processes, o r to perform water

shed delineation and analysis processes with a OEM.

If, afrer pondering these issues, the choice of a GIS

sofnvare program remai ns unclear. perhaps choosing a GIS sofnvare program that seems ro have the capabili ty to expand with the needs of an orga niza tion would be a good decisio n. Increasingly. GIS software programs are designed in a modular manner, which allows users to

purchase separate software modules intended [0 work

with a base GIS progra m. Users then purchase the base GIS program and o nly those modules that they deem necessary.

Maintenance charges are also becoming more typical fo r GIS soFtware programs, and are often overlooked

when narural resource managemem organizations create rheir budgets. Users can either purchase annual maime!lance suppOrt for a GIS software program, o r pay for technical suppOrt as issues arise. An annual fee is easier to

predict , and to include in the budger, bur a per-incidenr fee may result in a lower [Oral cOSt (depending on the amount of supporr a user needs). For some G IS software

programs, maimenance fees cover product upgrades, and so as new vers io ns of rhe sofrware are released. they may be available af no cosr ro rhose wirh annual maimenance

agreements. As an al terna t ive, online GIS user suppOrt groups a re avai lable for users with li mired access ro technical support from the sofrware developer. Reliance on these grou ps ca n resulr in an inexpensive and often rapid response resou rce for [hose with so Frware and hardware d ifficulties.

When eva luati ng GIS software programs, o rganizations sho uld also consider the projected longevity of the use of the software. Ir is expensive ro implement and maintain a GIS system. Once a software selectio n has been made and a commitment by rhe o rgan izatio n to the sofrware has

been institutio nalized, changing ro a different GIS software program is difficult and expensive. O rganizations may nor have a cho ice in (his maner- there are many examples of

the obsolescence of GIS softwa re programs, and the subsequent elimination of support for GIS software programs (from rhe software developer) as new products are developed . However, organizations should consider the length of time that a GIS developer has been in business, and attempt to gauge its future prospects. GIS software programs thar have been avai lable for at leas t five yea rs, demonstrate com inually growing sales. and have welI

es tablished . active user support groups, might serve as criteria to consider.

38

considered 'I)C GIS sofrware', This distinction has essen~

riaJly disappeared as wori<sration GIS sofrware is now used

on computer using both Windows® and U IX opeT<lt~ ing systems. A contemporary diSlincrion among Gl software programs has emerged [hat" also makes use of the

term 'workstation' and contraslS i[ with 'desktop' systems.

In [his categorizacion, worksrarion GIS software programs

are ,hose ,ha, include ,he full range ofGI processes ,ha,

allow lIsers (0 c reare. edit. and analyze spadal data.

Examples of full-fea,ured worksr;"ion GIS sof,ware programs include Arclnfo and MGE Microsral ion. Desklop GIS sofrware programs are considered 10 be SCAled-down

versions of worlu(ation GIS software programs. possessing

a portion of ,he roo Is or GIS processes found in fullfeatured worksradon GI sonware programs. Examples

of desktop GIS solTware prog rams incl ude ArcView. Maplnfo. and GeoMedia. A primary disrincrion belwccn worksCdlion and desktop GIS programs (beside rheir asso

ciared COSt) has been rhe abiliry to ensure rh:H spatial reb~

donships between rhe locuions described in GI databases

(the topology) remains trlle o r correct. Some desktop .IS sofrware programs are limited in their abi li ry 10 maintain

lopological relalionships wilhoul ,he use of (and, rherefore. purchase of) addi ,ional sofrware modules.

I n choosing a GIS sof[ware program. purchasers need ro consider rhe price. ,he GIS databases ro be managed. and ,he nexibiliry of the softwa re ro perform ,he likely

analyses required to SUppOf[ management decisions. The

price lor most GIS sonware programs ranges from $500 for a raSler-based deskrop GIS sofrware program ro well

over $ 15.000 for a fnll-featured worksr.,ion GIS sonwa re progrJm. Those on:l eight budgel might cons ider a re cm

,micle by Bernard and Pri sley (2005) ,ided ' Digiral Mapping Aherna,ives: GIS for the Busy Foresrer'. This anicle compared the abiliries of nine I software prod~

nClS cosring less lhan $500.

The types of spalial darab .. es LilOl are access ible by a GIS software program shou ld be carefu lly evaluared . For example. if primarily raster GI daraba es will be usod.

perhaps a raslt"r-bdsed GIS software program is mort'

appropri"'" (as opposed ro a GI sofrwa re program rhar foclIses on {he development and maintenance of vcc lor

.1. darabases). Finally. a considerarion of the rypes of CIS processes likely 10 be performed is impof[am in making

an informed purchase decision. For example. GIS users may need to geo-reference, inrcrprcr, and classify satellite

imagery. Jnd m:ly need to c reate Vector GIS d3rabases (e.g., roads or streams) from measurements co llected dur~


ing field dara collecrion processes. o r La perfo rm water

shed delinearion and ana lysis processe wirh aDEM. If •• fter pondering rhese issues. rhe choice of a GIS

software program r('mains unclear. perhaps choos ing a GIS software program (har seems co have the capabiliry to

txpand wirh tht needs of an o rganizarion would be a

good de ision. Increasingly, ,IS so ftware programs are

designed in a modular manner. which allows users co purchase separate sof['\vare modules intended to work

with a base GI program . Users [hen purchase rhe base

GIS program and only those modules Iha, they deem necessary.

Mainrenan e cha rges are also becoming more typicaJ

for CIS software program s. :Hld are often overlooked

when natura) resource managemenr organizations create

rheir budgets. Users can either purchase annual mainre

nance SUppOfl for 3 GIS software program, or pay for

technical suppOrt as issues arist. An annual fee is eas ier to

predict, and to include in rhe budger, bur a per-incident

fee may resuh in a lower roral COSt (depending on rhe

;1mounr of support a user needs). For some GIS software

programs, mainrenance fees cover product upgrades, and

so as nC\'V versions of the soflware are released. rhey may be availdble :H no cosr ro [hose wirh annual maimenance

agreemems. As an alternative, online GIS user suppOrt

groups are available for users with limiled access ro rech

nica1 suppOrt from the sofrware developer. Reliance on

these groups can result in an inexpensive and ofte n rapid

response resource for those wirh sofrware <lnd hardware

difficulries. When eval uating GIS software programs. organizations

should also consider the projected longevity of rhe lise of rhe sofrwa re. Ir is expensive lO implement and maimain a

GIS system. Once a sofrware selection has been made and a commirment by tht' organ iza(ion LO rhe softwa re has

been illsrirU[ionalized , changing ro a di£fercl1l GIS software

program is difficuh and expensive. Organizations may not

have a choice in (his matter- there.He many examples of

the obsolescence of GI sonware programs. and rhe subsequenr elimination or supporr (or GI software programs

(from rhe sofrwar< developer) as new product are developed. However, orga niz.1tions should consider [he length

of rime [hal a GI developer has been in business. and

artempt to gauge irs furure prospe (5. GIS sofrware pro~ grams thar have been ava ilable for ,H least five yea rs , demonstrate conri nuaJly growing sales. aod have wcll

esrablished, acdve user supporr groups. might serve as cri· ceria [0 consider.


Summary

This introducrory chapter described the history of GIS

developmenr. why GIS is imporranr in natural resource

management, and rhe wide variety of inpur and output

devices associated with GIS. In addidon. a num ber of

issues related (Q rhe selection and purchase of GIS sof{~

ware programs were ourlined; hopefully (his summary of

issues will stimulate discussion among those considering

rhe development of a GIS system within a narural resource

organization. The applications of GIS to narural resource

Applications

1.1 Developing the specifications for a GIS system.

You have been asked by the Distr ict Manager of you r nat

ural resource management o rganization. Jane Lerner, ro

develop the specificat ions for a computer (Q be purchased

and used for, among other things, GIS analysis and map

production in a narura l resource managemenr orga niza

tion field office. Use resources avai lable on the Internet to

design a com purer sysrem (har would be capable of run

ning deskrop GIS software in a forestry or natural resource

field office.

a) What are the specificat ions of rhe computer system

[hat you would recommend. and how much might

it cost?

b) If yoll r budget were limited to $2,500 (max imum),

how might your recommendation change?

c) You have been asked to decide whether marc RAM

or video memory wou ld be a berrer investment for

a GIS com purer system. What is the difference

between these (wo types of memory and which

would you select?

1.2 Terminology. The District Manager of you r nam

ral resource managemenr o rganization, Steve Sm ith, is

unEulliliar with a number of terms related to GIS. He has

heard these terms distribllIed freely during staff meetings,

and during one of your weekJy reviews he asks you to help

him understand what they mean. Briefly describe for him

GIScience, [Opology. and overlay analysis.

1.3 C haracterizing GIS software systems. Your super

visor, John Darling, has heard rhe terms 'workstation ' and

'desktop ' GIS sofrware. bur remains confused about how

they diffe r. Explain to him the differences between the

twO types of GIS software.

managemenr issues will vary widely among organizations,

however, understanding the capabilities and porcnria l use

of GIS is essenrial fo r na tura l resou rce management pro

fessionals. The following applications, as with those in

subsequenr chapters, are inrended ro provide srudents

with a taste of the typical types of GIS requestS posed to

field fo resters. biologists. and other professionals fami liar

with GIS. who work in narural resource management field

offices.

1.4 History of GIS. While on vacation and VISlfll1g

your relatives. you find that the conversation around rhe

dinner table has rurned to the rypes of work you perform

in your role as a narural resource manager. Describe for

your relatives, many of whom have never heard of GIS,

the origin of GIS. how GIS has evolved into irs current

form. and how YOll mighr use GIS in narural resource

management.

1.5 GIS pioneers. IdentifY and list the noteworrhy con

tribut ions of someone who has made significant contri bu

tions to the development of GIS.

1.6 GIS data. Identi fY and describe o ne of the GIS databases described in [his chapter thar contained data ror

the resources of an entire counrry or for a portion of the

world.

1.7 A question of scale. YOll measure the distance

between twO owl nests o n a 1 :24,000 scale ropographic

map to be 6 cm. Whar is (he ac(Ual ground disrance

between the nesrs?

1.8 Scale revis ited . YOli have measured (he distance

bel"ween two campgrou nds on a ropographic map to be

2 cm. From a field vis it, you know that rhe corresponding

grou nd distance berween rhe campgrounds is 1 km. What

is the sca le of the topographic map'

1.9 GIS software. List and briefly describe three GIS softwa re packages (hat were avai lable prior to 1990.

1.10 Relat ive error. YOll measure the perimeter of a

field plor wirh a meta l rape and derermine a tota l 39

perimeter of 134.5 meters. Your instrllcror tells you that he used a (Otal station and determined rhar rhe (Oral

perimeter is actually 136.2 meters. a) Whar is rhe closure error between your measure~

menr and your insrrucror's? b) Whar is rhe relative precision of your meraJ tape

measurements?

1.11 GIS d ata fro m above. You have been asked (Q

develop a database of your counry that contains elevation and landform information. Whar are three remore sens~

ing data co llectio n tech niques [har would be used (0

develop rhis database and what are rhe relative strengths and weaknesses of each ?

1. 12 GIS data from the ground. You have been asked (0 create a GIS database that conrains the boundaries of a set of tree stand boundaries in a research forest. Describe th ree approaches (0 co llecting this data and the relative strengths and weaknesses of each approach.

1. 13 GPS considerations. A friend of you rs has recendy purchased a nC\v GPS receiver from a local departmelH Store for $75 and has told YOll that she is excited thar she will be able ro collect coo rdinates of fearures that repre~

References

Avery, T.E, & Berlin , G.L. ( 1992). Fllndammtais of remote Jeming and airphoto interpretation (5 th ed .). New York: Macmillan Publishing Company.

Berna rd, A.M. , & Prisley, S.P. (2005). Digital mappi ng alternatives: GIS for the busy forester. journal of Formry, 103(4), 163-8.

Bolsead, P., Jenks, A., Berkin, J., Horne, K. , & Reading,

W.H. (2005) . A compa rison of autonomous, WAAS,

realtime, and post~p rocessed global posit ioning sys~ terns (GPS) accuracies in nonhern forests. Northern jOllmal of Applitd Formry, 22( I ), 5- 1 \.

Brown, T.L., & Lassoie, J.P. (1998). Entry-level competency and skill requiremenrs for foresters . journal of Forestry, 96(2), 8-14.

Burrough , P. (1986). Principl.s of geographical information sysums for land resourus assessment. Oxford: Oxford Universiry Press.

Burrough , P.A. , & McDonnell , R.A. (1998). Principk, of geographical information sysums. Oxford: Oxford Universiry Press.

Chapler 1 Geographic Information Syslems 25

sem 'exact loeadons' on the Earth's surface. What advice

would you offer her aboUT rhe measu rement accuracy of a $75 GPS receiver?

1.14 Data input devices. The Bu reau of Land Management has hired you as a fo restry technician. Your supervi

sor is aware that you have a background in GIS. and asks for your input regarding the technology that can be used to develop a vegetation GIS database. Describe th ree

options. and their srrengrhs and weaknesses in terms of collecting data and developing a G IS database.

1. 15 Data dhplay options. It is Friday afternoon in a narural resou rce organization's field office. As you are day~ dreaming abour the fo rthcoming weekend's events, your

supervisor enters your office and rells you rhat he has a meeting Monday morning with a neighboring landowner [0 describe rhe management alrernatives for a pordon of

the forest you r organ ization manages. A fC\v graph ics that desc ribe rhe alrernatives under consideration would be

beneficial to the meeting, and maps are the obvious choice of ourpur products to engage [he public. However, the color plo[[er in your office is not working. Describe three m her methods for examin ing ourpur from GIS that mighr be useful for your supervisor's Monday meeting.

Clarke, K.C. (200 I). Getting "aned with geographic information sy"",u (3rd ed.). New Jersey: Premice Hall, Inc.

de Steiguer, J.E., & Giles, R.H. (1981). Introduction to compute ri zed la n d~i n fo rmarion systems. JournaL of

Formry, 79,734-7. Goodchild, M.F. (1992) . Geograph ical informatio n sci

ence,lmernatiollaL jOlirnaL ofGeographiCtll lnformation

Sy"",u, 6(1),31--45. Kava nagh, B.F. , & Bird, S.J.G. (2000). SlIrvqing:

Principl.s and practim (5th ed.). New Jersey: Prentice Hall , Inc.

Kennedy, M. (2002). The global positioning system and GIS. London and New York: Taylor and Francis.

Land Info Worldwide Mapping, LLC. (2006) . IKONOS

high-ruollllion ,aftllift imagery. Highlands Ranch , CO: Land Info Worldwide Mapping, LLC. Retrieved April 21 , 2007 , from http: //www.lan dinfo. com/ s(lrprices.htm.

Leick, A. (2004). CPS ,at"li" survqing. Hoboken , NJ:

John Wiley & Sons. 40


Liu , c.J. (2002). EffectS of selective ava ilability on CPS

pos irioning accu racy. Sou/bun Journal of Appli~d Formry, 26(3), 140-5.

Longley, P.A. , Goodchi ld, M.F. , Magui re, D.J ., & Rhind, D.W. (200 1). Ceographic information 'ystems find science. New York: John Wi ley and Sons, Inc.

McHarg, I.L. ( 1969). Design with Ilfltllrt. New York: Joh n Wiley and Sons, Inc.

McCombs, J.W., Roberts, S.D., & Evans, D.L. (2003). Influence of fusing LiDAR and ll1uhispecrral imagery on remorely sensed esrim ates of srand density and mean tree heigh I in a managed loblolly pine planration . Form Sci",,,, 49(3),457-66.

Merry, K.L., Beninger, P. , Cl uner, M., Hepinstall, J., & Nibbelink, N. P. (2007) . An assessment of geogra ph ic informarion sysrem (C IS) skills used by field-level nallIral resource managers. journal of Forestry. 105(7), 364-70.

NaesSCI, E. , & Jonmeister, T. (2002). Assessing poinr accu racy of DGPS under fo rest ca no py before data acqu isition, in rh e fie ld , and afrer postprocessing. Scandinavian journal of Forest ReunrriJ, 17, 351-8.

Narural Resources Ca nada. (1978) . SptcijicariollS and reconmundntiom for control surveys and survey markers. Onawa, ON: Canada Cemre fo r Remore Sensing.

Peucker, T.K. , & Ch risman, N. (1975). C.nographic dara structures. Am"imll Cartograph", 2(1), 55-69.

Reutebuch, .E., Andersen, H.-E., & McGaughey, R.J. (2005). Lighr detection and rangi ng (Ll DAR): An emerging rool for multiple resource invenrory. Journal

ofFomtry, 103(6),286--92. Sample, V.A., Ringgold, P.c., Block, N.E., & Gil rmier,

J. W. (1999). Forestry educarion: adapting ro rhe changing dem.nds. jOllrnal of Forestry, 97(9), 4- 10.

Sigrist, P., oppin, P. , & Hermy, M. (1999). Impacr of forest canopy on qualiry and accuracy of CPS measu rements. Imt'rnational Journal of Remou Sensing. 20( 18), 3595-610.

Song, J.- H ., Han, S.- H ., Yu , K., & Kim , Y.-I. (2002) . Assessing rhe possibi li ty of land-cover classifica tion using LIDAR inrensiry data. Inrernational Society of

Phorogram metry and Remore Sensing (ISP RS),

Commission III Symposium. 9-13 September, G raz, Ausrria, Vo!' XXXIV, B-259-62.

Steinitz, c. , Parker, P., & Jordan, L. ( 1976). H anddrawn overlays: Their history and prospecrive uses.

LflIu/scap' Architectllre, 66(5),444-55. US Federal Geoderic Control Committee (FCeS).

(1984). Stalldards alld 'p"ijicariollS for geodetic cOlltrol networks. Rockvi lle. MD: National Geodetic

J nformarion Branch. Wing, M.G., & Berringer, P. (2003). C IS: An updared

primer o n a powerful management tool. Jo urnal of Formry, 101(4),4-8.

Wing, M.G. , & Kellogg, L.D . (2004). Locating and mobile mapping rechniques for forestry applicarions. C,ographic IlIformatioll Scimm, 10(2), 175-82.

Wing, M.G., Eklund , A., & Kellogg, L.D . (2005). onsum er grade global pos itio nin g syste m (CPS)

accuracy and rel iability. jOllmal of Fomtry, 103(4), 169-73.

Wing, M.G ., & Karsky, R. (2006). Srandard and realtime accuracy and reliability of a mapping-grade CPS

in a coniferous western Oregon foresr. Western Journal

of Applied Fomtry, 2 1(4), 222-7. Wi ng, M.G., & Sessions, J. (2007). Geosparial rechnol

ogy education. jOlinUlI of Formry. 105(4), 173-8 . W ing, M.G., Eklund , A., & Karsky, R. (In press).

H orizonra l measuremenr performance of five mappin g-grade GPS rece iver co nfi gllradons in seve ral foresred se((ings. W",,," jOllmal of Applied Fomtry.

Wolf, P.R., & Dewitt, B.A. (2000). EI,mmts of photogrfllllm,try: With applicatiollS ill GIS (3 rd ed.). New York: McGraw-HilI.

Wolf, P.R., & Ghilani, .0. (2002). Elemmtary mrvrying: An introduction to geomntics (1 O[ h ed.). Englewood Cliffs, NJ: Prenrice Hall , Inc.

Yu , X., H yy ppa, J. , Kukko, A. , Malramo, M., & Kaaninen , H. (2006). Cha nge detection techniques fo r canopy height growth measuremems using airborne laser scanner dara. Photogrammetric Engineering

alld Remote S""ing, 72(12), 1339--48.

41

26 Part 1 Introduclion to Geographic Information Systems, Spatial Databases, and Map Design

Liu , ,j. (2002). Effecrs of seleerive availability on GPS positioning accu racy. Sourban Journal of Appli~d Forestry, 26(3), 140- .

Longley, P.A .. Goodchild, M.F., Magui re, D.j .. & Rhind. D.W. (200 1). CeogroplJir illformolioll systems flnd s(i~nct. New York: John Wiley and Sons. Inc.

M Harg. I.L. (1969). Dnigll wi," 1II1I/m. New York: john Wiley and ons. Inc.

Me ombs, j .W., Robe"s, S.D .• & Evans. D.L. (2003). Innuence of fusing LiDAR and multispectral imagery o n remotely sensed eS(im ~HCS of stand dens ity Jnd

mean lfee height in a managed loblolly pine plantation, Form Sci",u. 49(3), 457-66,

Merry. K.L., Berringer. P .• Cluner. M .• Hepinstall. j .• & Nibbelink, N.P. (2007) . An assessment of geographic informalion system (G IS) skills used by field-Ievd na(ural resollrce managers. journal of Forestry, 105(7)'

364-70. Naesset. E .. & jonmeister. T. (2002). Assessing poilll

accuracy ot DGPS under foresr ca nopy before dara

acqui ilion. in th e field. an d after postprocessing.

S{(lIIdi1lllVioll jot/mol of Foml R",orrh. 17. 3 1-8. arural Resources Canada. (1978). Sptcificnli01lS olld rec

OlmlltlldllliollJ for control surll9s Ilnd Sf/TVt] markus.

OWlwa. ON: Canada Centre for Remote Sensing.

Peucker, T,K .. & Chrisman. N. (1975). Cartographic data structures. Americoll Cllrrogrllpher. 2(1), 5--69.

Reutebuch. .E.. Andersen, H.-E., & McG.ughey, R.j. (2005). Ligh t detectio n and ranging (L1DAR): An emerging lool for muhiple resource invclHory.Joumal

ofFomrry, 103(6),286-92. Sample. V.A., Ringgold , P.c.. Block. N.E., & Giltmier.

j.W. (1999). Forestry education: .dapting to the ch.nging demands. jOlmlll1 of Foresrry. 97(9), 4-10.

igrisr, P.. oppin. P .• & Hermy. M. (I 999). Impact or Forest canopy on qualiry and accuracy ofcp. measure

ments. IIIf~r/lllfiolllll journal of Remote ~nsillg. 20( 18). 3595-610.

ong. j.-H .• Han. S.-H .• Yu, K .. & Kim , Y.-I. (2002) . Assessing lhe possibiliry or land-cover classification

using UDAR intensiry dala. IlHern:uional ociery of

Photogrammerry and Remote Sensing (ISI' R5) . Commission 111 ymposium,9-13 eptember, Gra •• Austria, Vol. XXXiV, B-259-62.

teinitz. c., Parke r, P .• & jordan. L. ( 1976) . H anddrawn overlays: Their history and prospective uses.

LOlldSCIlp' IlrriJirechrr,. 66(5) ,444-55. U Federal eodetic Control Committee (FGeS).

(1984). Siamiords I11ld Iptcificoriolll for g,odnic cOlltrol lI<l'work,. Rockville. MD: National Geodetic I nformarion B ... dnch.

Wing, M.G .• & Bettinger. P. (2003). GI : An updated primer on a powerful managemel1l rool. journal of

Fomtry. 101(4),4-8. Wing. M.G., & Kell ogg. L.D. (2004). Locating and

mobile mapping techniques for forestry applic.1rions.

C,ograpiJic IlIformOlioll Srimm. 10(2). I 5--.s2. Wing, M.G .. Eklund , A .• & Kellogg, L.D. (2005).

onsumer grade global positioning system (GI'S) accuracy and reliabili ty. jOllrlllri of Fomtry. 103(4) , 169-73.

Wing, M.G .• & Karsky. R. (2006). Standard and realrime accumey and reliability of a mapping-grade GI'S in a coniferous wes[ern Oregon forest. \Y/~Slem journal of Applied FomlrJ'. 21(4), 222-7.

Wing. M.G .. & Sessions, j. (2007). Geospatialleehnology educllion. jourtlol of Fowtry. 105(4), 173- 8.

Wing, M.G .. Eklund. A .. & K.rsky, R. (In pres ). Horizonta l measurement performance of five mapping-grade CPS receiver co nfigurations in s(,vc:ral fore ted se((ings. \\'It'SltrJI jOlmlol of Appli_d Fomtry.

Wolf. P.R .• & Dewitt, B.A. (2000). Elm,,,,,, of pho-10grfllllllJelry: \\'Iilh applicfllioJlS ill GIS (3rd ed.). New York: McGraw-H ilI.

Wolf. P.R .. & Ghil.n;' .0. (2002) . Elmtmurry ",roryillgo' An iutToduClioll to geolnlllics (I Olh cd.). Engle

wood ClilTs. j: Prentice Hall, Inc. Yu. X,. Hyyppii. j., Kukko. A .. Maltamo, M .. &

Kaarlinen, H. (2006). Change dete rion techniques fo r canopy heighl growrh measurel1lC'I1[S using air

borne laser scanner dat:t. Phologrllmmtfrir Engillt'l'ring fwd RtII,olt Stilling. 72( 12), 1339-48.

Chapter 2

GIS Databases: Map Projections, Structures, and Scale

Objectives

This chaprer imrocluces rhe concepts of map projections

and dara srrucru res. After compiering (his chaprer. readers should have an understanding of the following top ics

rdared ro rhe sr rucru re and composition of GIS darabases:

I . the defi nirion of a map projection, and rhe compo

nents rhat comprise a projeccion.

2. the components and characrcri sri cs of a ras rer dara

structure,

3. the componems and characrerisrics of a vec[Q r clara

structu re,

4. the purpose and srrucrure of meradar3,

5. the likely sources of GIS databases that describe natu

ral resources with in North America,

G. rhe rypes of info rmation available on a typical [OPO

graphic map. and

7. the definirion of scale and reso lmion as rhey relate to

GIS databases.

Perform ing GIS processes and analyses in suppo rt of

natural resource management decisions requires obta ining

and wo rking with spatial databases. Many GIS lIsers find

rhar they spend a grear deal of time and efforr acquiring

and modifying GIS darabases ro ensu re rhar rhe most su itable and approp riate data is being used in subsequenr

analyses. One of the great challenges in working wirh fea

(Ures locared on rhe su rface of the Earth is thar the Earth

is very irregularly shaped, and is in a constant sta re of

change. When you arrempr to create a n.vo-dimensional

represenration of the Earth (as is typically represenred on

maps), rhe Earrh 's irregu larities musr be addressed.

Di fferent map project ions and projecr ion components

have been creared so {hat data from the Earth 's surfa,ce

ca n be displayed on ma ps and othe r fl at su rfaces.

Understanding [har spar ial dara can be represen ted

through any numb<r o f different map projections. and

that data can be transfo rmed from one map projection to

anmher, is a very imponanr component in rhe process of

learning to manage GIS databases successfully. This chap

rer is intended ro introduce readers to commo n GIS dara

base formats, [he ways in which G IS data can be srruc

tured and adjusted to represenr rhe Earth 's surface, and

how GIS databases are documented and described. Some

direction is also prov ided to allow you to begin to think

about sources of GIS databases. although more detailed

trearmem on {his subject is provided in chapter 3.

The Shape and Size of the Earth

GIS software programs are designed to work with data

describing rhe Eanh's featu res. (Q provide merhods fo r

fearu re measurements, and ro allow comparisons of fea

rures of interest. A number of options exist by which you

can collect, structure, and access GIS data. Spatial data users, however, must always be cognizant that represenra

tion of landscape fearures on n.vo-dimensional surfaces.

such as maps or co mputer monitors, are subject to disror

tio n based on the sp herical shape or the Earth. These 42


poi nr loeadons within a datum , rhe greater the potential of rhe darum [Q acr as a reliable surface upon which you

can reference other landscape features.

Hundreds of darums have been developed to describe the Earth, many o f which are specific [ 0 a panicular cou n

try or region. Within Noreh America, [wo darums are prominenr: the arc h American Datum of 1927 ( AD2? ) and rhe Norrh American Darum of 1983 (NA D83). Another, WGS84, is co mmonly used in con

ju ncrion wirh GPS dara collecrion effom. The NAD83 and WGS84 are very si milar, and are sometimes used

interchangeably, although this practice may nor be suitable for applicarions that require high data accu racy levels; rhe NAD83 was designed fo r Norrh America whereas rhe WGS84 rakes a global approach in representing rhe Earlh . Differences berween AD83 and WGS84 are in rhe

neighborhood of 1 co 2 m within rhe conrerminous US. The primary differences berween NAD2? and NAD83

darums are rhe number of longirude and latitude locations (hat were measured (0 creare each darum , and rhe

way in wh ich rhe measured locario ns are referenced (0 rhe

surface of rhe Ea rth . Abour 25,000 poinr locarions were lI sed to creare rhe NAD27 datum , each of which was ref

erenced to a cemrallocarion-the Meades Carde Ranch

located in Kansas. Some 270,000 locations were used (Q

c reate rhe NAD83 datum. Instead of referencing loca rions

to a cemralloca rion on the Earrh 's surface, loeadons are

referenced to the cemer of rhe Ea rth 's mass . The NAD83

datum has become the preferred datum for use in North

America alrhough many GIS databases cominue to con

ra in landscape fearures described by rhe NA D2? datum. Geoids and ellipsoids are ohen associared with a par

ricular datum. For insrance, rhe Cla rke Ellipsoid of 1866 was designed [0 describe rhe landsca pe fea tures of Nonh

Ame rica, and is commonly used in conju ncr ion with rhe

NAD2? darum. Borh GRS80 and WGS84 rake a more global approach and are thus bener sui led for describing

worldwide surFd.ces . Whereas GRS80 is commonly associared wirh NAD83, WGS84 can be rhoughr of as barh an

ell ipsoid and horizomal datum.

D atums are sometimes updared [Q renecr additio nal

comrol measuremems, shifrs in rhe Earrh's landmasses, or

dara co rrecrio ns. When a da tum is updated, the most

recenr year in which data were coHecred is often appended

ro rhe datum name. As an exa mple. AD83/91 would indical< rim rhe NAD83 darum has been adj usted wirh addirional dara rhar were collecred rhrough 199 1. These darum adjusrmenrs are o ften small (on rhe order of cenrimerers, o r

less) and are somerimes referred ro as darum realizatio ns.

Many agencies and organ izations that are involved in

working with spatial dara in onh America use NAD27,

NAD83, o r an adj usred NAD83 darum. A co mmon error among users of GIS, especial ly those who have acqui red

clara from a number of difference sources, is in forgercing

to convert their darabases to a common da(Um. In rerrns

of comparison, in the US, landscape fe-d. rures re ferenced in

barh AD2? and NAD83 will appea r up ro 40 m offser from each other in latitude and as many as 100 m off in

longitude. T hese differences vary by region and may be ha rd to derecr visually when using G IS to view large

resource areas. Th is overs ighr can obviously lead to inac

curate analys is resulrs .

The discussion of da(U ms, {Q rhis poim , has focused

on rhose relared to horizomal surfaces. When worki ng

wim elevation dara, such as a DEM , GIS users musr also be

aware thar darums have also been developed [Q descri be

rhe verrical dimension . A verrical datum allows us ro

derermine where '0' eievarion begins and rhe heighrs of

orher objecrs locared eirher above o r below this poinr. The Narional Geoderic Verrical Darum of 1929 ( GVD29) was esrablished from 26 gauging srarions in

rhe US and Ca nada and was a direcr efTorr in determining

rhe posido n or mean sea level. The Norrh America n

Vert ical Darum of 1988 (NAVD88) used addirional meas

uremenrs from a large number of elevarion pro files (Q c re

ate a single sea level comcol surface. Between 1929 and

1988, over 600,000 km oflevel profiles were com plered,

and changes that had occurred ro ex iring elevation benchmarks were (aken inro accounr. These addirionai meas

uremenrs and adjusrmen rs provided a mo re reliable

mea ns of esrabli shing eleva tion surfaces and fo r rhis rea

son NAVD88 has become (he preferred verrical darum.

Elevarion d ifferences berween NGVD29 and NAVD88 in

rhe US could di ffe r by as much as 1.5 m in some areas.

The Geographical Coordinate System

N ow thar a desc riptio n of [he size and shape or rhe

Earth's surface has been presented, an enrio n is rurned (Q

rhe merhods by w hich landseape fea rures are locared on

rhe Earrh's surface. Rene D escartes. a sevenreenrh-cen

lU ry French marhematician and philosopher, devised one

of rhe fi rsr wrinen methods fo r locaring landscape fe-d

(Ures o n a planar surface. D escarres superi mposed rwo

axes, o rienred perpendicular ro one anorher, with gradations along bot h axes to create equal distance inrervals (Figure 2.4). The horizonral axis is rermed {he x-axis and

43


9

8

7 2,6

6

y 5

4

3

2

6,1

o 2 3 4 5 6 7 8 9

x

Figur~ 2.4 Exampl~ of point locations as idcmtific=d by Cartesian coordinate geometry.

[he ve rr ical is [he y-axis. The (ocarian of any po int o n rhe

planar surface covered by rhis rype of gr id can be defined

wirh respec( to rhe inrerval lines that it inrersec(s or chat ir most closely neighbors. This basis of determining loca

rion is known as a Ca rtes ian coorcl inare sysrem .

The most common coordinate system is rhe sysrem of

larirude and longirude. someti mes refer red ro as rhe geo

graph ic coord inare sys tem . Although [his system can be rega rded as having x- and y-axes. angular measurements

miller than distance-orienred imervals. establish rhe axes

and imcrsecrions. The geographic coordinate sysrem has

an or igi n at rhe cenrer of the Earth and concains a ser of

perpendicular Jines running rhrough the cemer [Q approx

imate the x- and y-axes of rhe Cartes ian coo rd inare sysrem. The o rienrarion of rhe perpendicular lines is based

on the rota rian of rhe Earth . The Earth sp ins on an axis

rhat, if exrended, coi ncides very closely wirh rhe Norrh

Sta r (Polaris}-th is ax is is called the axis of rotarian. Th is

ro rario n axis divides rhe Earrh in half [Q creare a line of

longitude that approximares rhe y-axis. A line perpendi

cu lar to the line o f longitude fa lls a lo ng the equatOr

(Ea rth 's widest exrenr) ro crea re a line of lar itude that is

conceprually si milar ro rhe x-axis. Laritudes are expressed

ro a maxi mum of 90°, in a no[(h or sourh direction from Ihe equaror with the equamr deno[ing 0° (Figure 2.5) .

Trave ll ing 90° norrh from the equaro r would leave YO ll ar

rhe mosr norrhern poi nt of rhe Earth and would be noted

as 90· N. Sim ilarly a position half'vay between the South

90· North lalilude

0" latitude

30' 5,60' E

90' 5001h la1i1ude meridian

Figure 2.5 GC'ogr"phic coordinalC's as determ ined from angular distance from the cenler of the Earth and referenced to the equator and prime meridian.

Pole and rhe equato r would be referenced as 45· S. The

equator and orher lines oflatitude that parallel the equa

tor a re also called parallels.

Altho ugh the axis of roration splits the Earth in half, a

reference line must be established from which coord inares

can srart. This reference line is referred ro as [he prime

meridian, and altho ugh there are dozens in exisrence, rhe

most widely recognized prime meridian circles rhe globe

while passing across the Brirish Royal Observdtory located

in Greenwich, England. Longiwde measuremems a re

made from this reference line and a re designared from 0°

ro 180°, in a wesrern o r easrern di reccion. North America

is locared in a regio n (har is wesr of rhe prime meridian

and is correcrly described as falli ng inro an area o f nega

rive longi tude (a ll areas rhar ex tend wirh in 180° west of

rhe prime meridian) , alrhough many ma ps rhal are

regional o r loca l with lIsually omir rhe negarive s ign.

Other lines tim pass through the Norrh and South Po les

(0 acr as guides and mark prominent longirude differences

from rhe prime meridian are simply called meridians. The

conceprual co llecrion o f meridians and parallels superim

posed on the Ea rth 's surface is known as a grar.icule.

The geogra phi c coo rdina[e system ca n be used to

locate any po int on [he Ea rth 's surFace. To achieve a high

level of precis io n when locating landscape fea rures, degrees are funher subdivided imo minures and seconds.

There are 60 minutes (noted by ') wirhin each degree,

and 60 seconds (nored by") within each minme. A loca

[ion rhar is described as 38°30' laritude would indicare a

44

34 Part t Introduction to Geographic Information Systems, Spatial Databases, and Map Design

LZI ~ LL \~

LZ/ \~ L! \~

'\\ II ""'" \. /L/ '\:\ 17 ""'" \. /L/

a. Mercator b. Transverse Mercator

Figure 2.8 Thf: o ricnU.lio n of the Mercator and Tra nsverse MUC2tOr to the p rojection cylinder.

related [Q ae rial navigation. meteorological uses, and

[Opographic maps. The emphasis is usually placed on

mid-Iarlrude features of the world, such as those found in

rhe conterminolls US. Detailed applicarions of this pro

jection system should focus on smaller land areas, since

mainraining angular imegriry across large areas is difficult. Equal area or equ ivalent projections are wel l suited for

mainraining rhe relative size and shape of landscape fea

tures when size comparisons are of imerest. Equal area

projections preserve the siz.e and shape of landscape fea

tu res bur sacrifice linear or d isrance relationships in doing

so. A tenet of map projection techniques and an importam distinction berween equal area and conformal projec

(ions is rhat areas and angles cannor be maimained simul

taneollsly-you mUSt decide which is more important [0

your work. One example of (he equal area projection is

the Albers ' equal a rea projecrion. T his projection is widely

used and is typica lly based on a secam conic map surface.

Similar (0 rhe Lambert's conformal co nic projection.

mid-latitude areas, which have extensive east-wesr orien

[adons, are bener candidates. This projection system has

been selected by many US agencies as a base map projec

tion. The Lambert equal area projection is anomer co m

monly used equal area projection , however, i[ is based on

an az.imuthal map surFace.

Azimurhal projecrions are llseful for maintain ing

direction on a mapped surface. Azimmhal project io ns can

be based on one (ra llgem) or [wo (secam) points of refer

ence. Wirh one poilU of reference. distortion will occur

radially from the reference point but directions near the

reference poim should remain rrue. For this reason, the

az imutha l projectio n is appropriate for maps that have reladvely the same amoum of area in nonh-sourh and

east-west orientations. When using two points of refer-

ence. direcrions emanating from either reference point

should be true. The azimuthal equidistant project ion

offers rhe unique abi lity of maintaining uniform direc

tion and distance from reference poims. Azimuthal pro

jecrions are usefu l for demonstrating the shortest route

between tWO poims (Robinson et aI., 1995). Applications

include those related to air navigation romes, radio wave

ranges, and the description of celestial bodies. Azimuthal

projecrion approaches include Lambert's equal area, stere

ographic, orthographic, and gnomic.

\'Q'hen pondering which projection sysrem ro use to

descr ibe GIS databases , you should consider the size of

the area being managed, and whether maimaining direc

tion o r area is more important (Cla rke, 2001). ProjeClion

distortions and the resulring analytical errors can become

magnified as the size of a management area increases. A

conformal o r azimuthal projection should be considered

when navigational or other directional properties are

importanr. If maimaining the size, shape, and dis rribu

rion oflandscape featu res is important. an equal area pro

jection should be employed.

Planar Coordinate Systems

Now that the process of taking shapes located on the sur

face of a sphere and projecti ng rhem on a flat su rface has

been discussed. it is rime to explore the coordinate sys

tems that are useful in order (0 locare landscape features

on a flat surface. These systems are known as planar

coordinates. or rectangu lar coordi nates. Previously, [he

framewo rk for examining plane coordinates was intro

duced with [he concep( of the Cartesian coordinate sys

[em . Th is same framework appl ies to planar coordina res,

wirh a few minor mod ifications. For example, depending

on the type of planar coordinate systems. coordinates are

sometimes referred to as east in gs or nonhings. An eaSl

ing measu res distance easr of the coordi nate sysrem's ori

gin whi le a northi ng measures distance north of the ori

gin. These are usually specified by following the ' right-up'

approach; easrings are numerically organized so rhal pos

itive measuremems begin ar the o rigin and increase to [he

right (to rhe east) of the origin, while northings a re

numerically o rga n ized so that positive measu rements

begin ar the origin and increase up (to the north) of the

origin. One inconvenience of this approach is rhar if a

coordinate system 's origin is in the middle of a landscape, negarive eas tings and northings may occur, since some of (he landscape is (0 the west' and somh of the origin. These

negative coordinares might complicate the calculation of

45


distances and areas with in GIS softwa re. and rhey also make manual calculations more cha1lenging. As a remedy to these ci rcums[3nces. false origins or False castings can be constructed to prevent nega tive coordinates. This involves shifting rhe coo rdinate grid's numeric origin from the cemcr of a landscape ro rhe lower lefr corner (the fdrrhes( poim west and sourh locatio n o f rhe landscape), o r JUSt

outside rhe lower left corner, so that all areas of rhe landscape are IOC3ted east and north of rhe origi n. and can be represenred by posit ive coordinate va lues.

The most common coordinarc system in the US and Ca nada is that of lhe Universal Transverse Mercato r (UTM). which has even been lI sed to describe rhe surface of Mars (Cla rke, 200 I). T he UTM system has been used

for remOle sensi ng, foresrry, and topographic map applications. and if has been used in many ocher countries duc. in parr co its world-wide applicabili ty and relative simplicity. The UTM system d ivides rhe Earth into 60 vertical

lones, each zone covering 6° oflongirude. The zones 3re numbered I-GO starring at 1800 longirude (rhe inrernadonal dare line) and proceeding eastward. The ren zones lhac cover rhe conterminous US and Canada are illusrrared in Figure 2.9. The system ex rends norrhwa rd (0

84°N iarirude, and sourhward 800 S lari[t1cle. A universal polar stereographic (UPS) grid system is used for the polar regions. Coord inares for each zone scan af rhe equator for areas covering rhe nonhern hemisphere and at 800 S laritude fo r areas in rhe southern hemisphere. A fa lse origin

," ,'<.' ... '.' '.

is established for each zone so that the central meridian of each zone has an easting of 500,000 meters. This arrangemenr ensures rhar all easrings are positive. and that areas of zones can overlap, if needed. As the name implies, the UTM coord inate system uses the Mercaror projection ro minimize distortion. The level of accu racy in the sysrem is assumed ro be one part in every 2,500 (Robinson er aI. , 1995). Another version of the UTM is the military grid

version. The mili ta ry grid version utilizes many princi ples of the UTM, yet divides each zone inro rows, and each row covers 8° oflatirude. Rows are denoted usi ng rhe letters C ro X wirh X occupyi ng the no nhern lati tude between 72° and 84° latitude. The military UTM can be used to further define blocks of zones into 100,000 meter

sq uares. The state plane coordinate system (SPC) was devel

oped in the 1930s by the US Coast and Geoderic Survey (known today as the US C hart an d Geodetic Survey),

which created a unique set of planar coordinates for each of the 50 Uni ted Srates. The SPC was originally designed

fo r land surveying purposes, so that location monumems could be permanently established. Under this system,

mosr stares are spli t inro a smaller set of zones depending on rhe size and shape of rhe stare. For insta nce, Florida has twO zones and Ca lifornia has four. The SPC system uses either a Lambert's conforma l conic or Transverse Mercaror projection , the choice of which is usually influenced by rhe dimensions of the sta le (Lamben is lIsed for

• •

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

'. .• • FigUJ"~ 2.9 UTM "lon~s and longitud~ lin~s for North Am~rica.

46


large wcsr-eaS( dimensions relative to north-south) and rhe dimensions of the zones of each srare. The level of

accuracy of rhe SPC system is approximately olle parr in 10,000.

Mosr of rhe o riginal surveys in the US and Canada

were described by metes (rhe act of metering. measuring, and ass igning by measure a straigh t course) and bounds (a reference [0 general property boundaries), These were systems of describing real esrare (hat wefe based on English

ammon Law, and involved describing rhe boundaries of a property by physical land feafllres such as streams, trees,

a. First Standard Parallel North

Baseline

and so on. The metes and bounds sysrem is inadequate

roday because of the subjecrive and transiem narure of physical features; however, many of the original surveys were described in this manner and can be seen in properry maps associated with land surveyed prior ro abom 1830. In some of [he original US colonies. metes and bounds were used in [he Head right systems char were developed [Q distribure land ro setders. Other more regular systems of describing land, such as rhe lorrery system used [0

describe abom two-thirds of the State of Georgia, followed (Cadle. 1991). Unforeseen problems wirh rhese

T2N R3E

I~ c

" Innial ~

:2 a; Point ,. ~ u .s 0:

First Standard Parallel South

b. T2N R3E c. NW 114 , NE 1/4, Seclion 17

6 5 4 3 2 1 NW1I4 NE 1/4 NE 1/4 NE 1/4

7 8 9 10 11 12 NW 114

SW 1/4 SE 1/4

18 17 16 15 14 13 NE 1/4 NE 1/4

19 20 21 22 23 24 N 1/2 SW 1/4

30 29 28 27 26 25 WII2 E 112 SE 114 SE 114

31 32 33 34 35 36 S 112 SW 1/4

Figur~ 2. 10 Origin (a), township (b). and ~ction (c) compon~nu of lh~ Public Land Su("\'~Y Syst~m.

47


early surveying systems included extensive fraud . survey

ing errors, a lack of consistency in the surveying processes,

hostility fro m nat ives. and remoteness of rhe terrain. As a

resu lt. several rectangular systems of describing land were

proposed, ranging from 6 or 7 square miles to 1,000 o r 2,400 acres each (Ladell , 1993). These rectangular town

ships were meanr CO fac ilitate the development of communities (Stewart, 1979).

Th e recrangular US Public Land Survey System

(PLSS) system was established in 1785 by ,he US

Congress as a national system for the measuremenr and subdividing of public lands. Approximately 75 per cent of the US was su bject to measurement by rhe PLSS. The origina l 13 colonies, which com posed a significant holding, were not included in this system because rhey had

al ready been inventoried through meres and bounds systems. In 1872 a similar sysrem , the Dominion Land

Survey, was creared for adm inisrrarion of the Dominion

Lands of western Canada. The objectives of ,he PLSS and

Dominion Land Survey were ro quanrirariveiy measure

previously non-planed land , c reate land port io ns {hat

could be so ld o r discribured , and provide a means of

recording ownersh ip informatio n. Neirher sysrem is asso

ciared wirh a map projection and therefore neirher o ne

can be considered a coord inate system, ahhough you can

use rhem to specifY the general locar io ns of prope rty

boundaries or other features. These rectangular systems

begin wirh an in iriaJ po inr for a region, province, o r srate

rhro ugh which a prin cipal meridian is asrrollom ica lly derived (Figure 2.10). A baseline is also esrablished, and ir

intersects a principal poine at a righe angle ro the princi

pal meridian. For exam ple. 37 principal points exisr

wirhin (he US . In (he US, at 24-m ile interva ls north and

south of each b .. e1ine, standard parallels are established that extended east and west of the principal meridian

(thus parallel ro rhe baseline). Parallels are numbered, and

are referred to as either being north o r somh from rhe

baseline (e.g., 2nd parallel north or 7th para llel south of a baseline) . Guide meridians were also escabl ished at 24-

mile inrervals easr and west of the principle meridian. The

guide meridians were esrablished astronomically and are

numbered relative ro thei r posicion east o r wesr of rhe

principle meridian (e.g., 4th meridian e'dSt, 8th meridian

west of the princi ple meridian). The grid of meridia ns

and parallels crea tes blocks, each nominally 24 miles square.

Townships are created by fo rming range lines (run

ni ng north and south) and township lines (ru nn ing east

and wesc) boch ar six-mi le imervals. Each [Qwnship is six

mi les squa re, and is fu rther divided ineo secrions. with

each section measuring o ne square milei {here are 36 sec

[ions wirhin a rownship. Sect ions are numbered 1-36

with the 36th sect ion being ar rhe lower riglu-hand cor

ner of a [Qwnship. Sectio ns ca n be appo rrioned in to

smaller componems such as quarter sectio ns. half sectio ns, or quarrer quarrer sections. In {he naming co nven

tion, rhe smaJlesr component is named nrsc, scarr ing with

the porrion of a section rhar a piece of land res ides, then

the rownship, range, and name of the principal meridian.

An example would be the NWI/4, NE 114, Section 17, T2N, R3W , Mt Diablo Meridian. Use of ,he rectangular

systems is limired within GIS to visualizing survey sysrem

themes. but it is likely , especia lly in narural resource

applicar ions, that you will encoumer this system as your

wo rk wirh GIS progresses. T his is particu larl y true fo r

projects that involve properry ownersh ip issues , as most

properry locations and boundaries in areas covered by me

rectangu lar sysLems are described us ing sections, tOwn

sh ips, ranges. and principal meridians. At some po int YO LI

may be required ro re-p roject ow nersh ip boundaries

derived from rhe rectangular sysrems so [hat rhey march

the projectio n systems used in mher GIS databases.

Mismatched map projections have been the bane of

many spa rial analys is effof{s and do ubrless ly, [here are

many published and repof{ed smdy results rhat suffer from th is malady. The likel ihood is ,hat there will be

many future srudies and wrinen products {hat will also

be subject to map projection problems. One of rhe rea

sons fo r projections problems is thar many GIS users are

unaware of the intent of projec tions and fail ro realize

thar there are sub-com ponents, such as coo rdinate systems and datums, which need to be considered when

working with a projection. Anmher contribu tOr to rhis

problem is the inabili ty of many desktOp GIS softwa re programs to manipu late sparial database projections.

Although proj ect io n algo rithms are becoming more common in desktop CIS sofrware programs. they are typ

ica lly more robust in a fu ll -featu red GIS software pro

grams. As a CIS user or analys t, you must be cognizanr of

the projections that are associated wirh spatia l databases.

Wh en obtaining GIS databases , either from wi rhin or

from o U[side an organ ization, it is critical to obtain as

much information as possible abou r rhe structu re of rhe

data , wh ich should at leaSt minimally include info rmarion aoour rhe map projecrion. Information aboU[ spatial

databases ca n be stored in a metadata documenr. We'll

disc llss meradara in more deraillarer in this chaprer bur

turn ou r anemion now to GIS data structu res.

48

Chapter 2 GIS Databases: Map Projections. Structures, and Scale 37

early surveying systems included ex((nsive fraud. surveying errors, a lack of consisrency in the surveying processes, hostil ity from n~tives. and remoteness of the (ccrain. As a resulr, several reCtangular sysrems of describing land wore proposed, ranging from 6 or 7 square miles ro 1,000 or 2,400 acres each (Ladell, 1993). Thes< recrangular lOWI1-ships were meant fO facilitate rhe development of communities (Srew:m, 1979).

The rectangular US Publ ic La nd Survey System (PLSS) system was CSlablished in 1785 by the US Congress as a nalionaJ system for the measuremenl and

subdividing of public lands. Approximarely 75 per cem of the U was subjeCt to measuremenr by rhe PLSS. The

origin.1 13 colonies, which composed a significanl holding, were nor included in [his sysrem because they had

already been invenroried ,hrough meres and bounds syslems. In 1872 a similar system, rhe Dominion Land

urvey. was creared for adminisrration of the Dominion L1nds of weSlern Ca nada. The objec' ives of Ihe PLSS and Dominion Land Survey were [0 quantitatively measure

previously non-planed land. create land pardons th<lr could bc sold or disrribuecd. and provide a means of r~cording ownership information. Neither system is associated wi d1 a map projection and therefore neieher one can be considered a coordinaTe SYSTem , although you can use rh~m to specify [he general locations of pro perry

boundaries or other feacures. These r~clangular systems begin widl an inidal point for a region. provin e, or state through wh ich .1 principal meridian is astronomically derived (Figure 2. I 0). A baseline is also established, and il incersecls a principal point at a righe angle to the principal meridian. For example, 37 principal poin,s exist wilhin Ihe US. In ,he US, at 24-mile inlcrva ls north and sou,h of each baseline, Slandard parallels are eSl3blished that extended e:lst and west of rhe principal meridian (Ihus parallel to the baseline). Parallels are numbered, and :Ire referred to ~lS either being north or south From the basdine (e.g., 2nd parallel norrh or 71h paralld sou lh of a baseline). Guide meridians were also established al 24-mile intervals cast and west of the pri nciple meridian. The guide meridians were established astronomically and are numbered relarive to their posicion east or wesr of the principle meridian (e.g .. 4rh meridian east. 8th meridian west of the principle meridian). The grid of meridi:ans and pa rall els creales blocks, each nominally 24 miles square.

Townships are creared by forming mnge lines (run· ning north and soulh) .md (Ownship lines (running easT . Jnd west) borh :H six-mi le imervals. Each township is six

miles square, and is further divided into sections, with each section measuring one square mile; there are 36 seccions within a wwnship. Seccions are numbered 1-36 with [he 361h secrion being ar ,he lower righr-hand corner of a [Ownship. Sections can be apportioned inca smaller components such as quaner sections. half sections, or quarrer quarter seCTions. In the naming convention, the smallest component is named first, starting with rhe portion of a section thai a piece of land res ides, then the rownship. range, and name of the principal meridian. An example would be Ihe NWII4, NE 114, Sec,ion 17, T2N. R3W, M, Diablo Meridian. Use of [he rec,angular system is limited within CIS to visualizing survey sysrem themes, but it is likely. esp~cia ll y in namra l resource applicadons. that you will encounter th is system as your work wilh GIS progresses. This is particularly (Cue for projects rhat involve property ownership issues. as most properry locarions and boundaries in areas covered by We rectangular systems are described using sections. tOWI1-ships. ranges. and principal merid ians. At some poim you may be reqllired co re~p rojecr ow nership boundaries derived from the recrangular sysrems so [haT (hey match rhe projection systems used in Olher GIS dar-abases.

Misma(ched map projections h3ve been [he b"1I1e of many sparial analysis efrons and doubtlessly, ,here are many publ ished and reported study resules that suffer from Ihis malady. The likelihood is ,ha, rhere will be many furu re sruclies and wrirten products (har will also be subjec, ro map projecrion problems. One of Ihe reasons ror projc rions problems is [hal many GIS users are unaWate of the intent of projections and filii co realize that chere are sub-componellls. such as coordinate ys[CI11S and datums. which ne-cd to bl! considered when working with a projecrion. Another conrriburor (Q This problem is ,he inabiliry or many deskrop GIS soliware programs [0 manipuhne spatia l darabase projections. Although projection .t!gorithms are becoming more common in deskmp ~IS software programs, they are rypically more robuS! in a full-realured GIS sof,ware progr3ms. As a GIS user or ana lyst, you musr be cogniz31H of the projections that are associated wirh spatial databases. \Xlhen obtai ning GI. databases. either from within or from outside an organization. it is critical to obrain as mll h info rmarion as possible aboul the srrucrure of the data . which should at leas[ minimally include informalion .Jbour rhe map projection. Informadon 300m sprlli~1

darabases can be s[Qred in a metadara document. We'll discllss meradar3 in more detail laler in this chapter bur rurn OUt anC'mion no\ .... ro GIS data s(ru turd .

38 Part 1 Introduc1ioo 10 Geographic Informalion Syslems, Spalial Dalabases, and Map Design

GIS Database Structures

Like moS[ digilal fi les, spa(ial databases muS[ be con

S[ruc(ed so rim (hey can be recognized and read by a GIS

software program. Although GIS manufacmre rs have developed the ir own data fo rmats. there are st ill [wo com

monly lIsed data structures for GIS clara: rasrer and vector.

Many GIS manufacrurers have created their own spatial

dam formats bue al most all make lise of ras ter or veeror

formar principles. T he ras te r and vector data structures

are as diffe renr as n ight and day, and borh have strengths

and weaknesses to be considered for use in va rious appli

cations. Ahhough many of the applications in this book involve vector databases, most GIS users will evenrually

find themselves using a combinatio n of both raster and

veem f databases.

Raster data structure

Ras te r dara Strucrures are co nsrructed by whar can be

considered grid cells or pixels (picture dements) that are

organized and referenced by their row and column posi

tion in a darabase file. Raster data st ructures arremp{ (Q

divide up and represenr the landscape duough rhe use of

regu lar shapes (Wolf & Ghilani, 2002). The shape (har is

almost exclusively used is (he square (Figure 2. 11), yer

other shapes can also cover the Earth completely and in a

regular fashion. such as {riangles and octagons. For each

ce ll or shape in a rasrer database. an atrribure va lue [har

... r- Raster or grid cell

Columns

Figure 2.11 G~n~ric ra.Sler dala SlrUClure.

S(Qres information abom the resources or characrerisrics is

associated with rhe cell. These values can be numeric (e.g.

remperam re, e1evarion) o r ca n be descrip tive (e.g. fo rested,

prai rie) and are used to desc ribe all areas represented by

each cell. Some common rasrer GIS databases include

rhose rela red (Q satellire imagery, digiral elevation models.

digi(a l orthophotographs, and digita l raSler graphics.

AI(hough (he particular fo rmal {how they are stored digi

rally} d iffers among raster databases and the tech niques

used to creare raster databases may also diffe r, rhe raster

approach co scoring spatial information is consistem: a sys

rem of cells (usually square) that covers a landsca pe.

Satellite imagery Satellite imagery is a term used ro describe a wide array

of products generated by remore sensors comained widlin satellites (Figure 2.12). Satellites are either posit ioned sta

tio nary above some loca tio n on rhe Ea rrh , o r ci rcumnav

igate (he Earth using a fixed orbit. Although satellites

have been sent inro deep space, and have returned

imagery co Earth. nam ral resource management is gener

ally concerned only wirh imagery rhar provides informa[ion abo ur pla neta ry fearures. When view ing sare ll i[e

imagery of the Earth. ir may seem as if [here is no relief

associared with the landscape. since the images were col

lected from a very high e1evarion (l00+ miles), however,

you ca n assoc iate elevation data (OEMs) w ith raster

images, and subsequently view them in rhree dimensions.

Figure: 2. 12 La nds:H 7 sa te:lli l~ image capmred using Ihe: Enhanced Thema t ic Mapp~r Pl us Sensor that shows the Los AlamoslCe':rro Grande fire': in May 2000. This simulalcd natural colour compositc image was crealcd through a combination of Ihr« ~nsor bandwidths (3, 2, I) operaling in the visiblC' speCIrUIll . Image councsy ofWa)'llC' A. MillC'r. USGS/EROS Data Ontt"r.

49


Digital elevation models Digital elevation models (OEMs) are databases that conmin informacion abol![ the [Opography of a landscape. The grid cells in these databases cama in measurements of

elevation across a landscape (Figu re 2.13). It is possible (0

derive rerrai n models from OEMs char represent aspect,

ground slope classes, and shaded relief maps. It is also possible co perform a wide va riery of rerrain-based analyses, such as landscape visualizarion o r watershed analysis.

Elevation data can be coHeered by a variery of means,

including sensors located on satellite or aerial platforms. or phorogrammercic techn iques chat use aerial phorography in conjunction with CPS dara. Elevation data may

also be collected from the bonom of water bodies such as oceans, lakes. or screams, through rhe use of sonar and

acoustical sensors operated fro m bOa£s o r submersible

watercraft. This data can be used w create cross secrion

profiles or w support engineering projects that involve

bridges or other infrastrucrure.

Digital ortbophotographs Digital orthophotographs are essentially digital ae rial photographs (or ae rial photographs that have been

Figur~ 2. 13 Digi tal ~ l~valion model (OEM ).

scanned) that have been registered to a coordinate system.

The displacement common w aerial phowgraphs is usu

ally corrected through the use of precise posi tional data, OEMs, and information about the platform sensor (e.g. camera system used). The majori ty of the US has been represented by d igital orrhopitotography. created through a mapping program sponsored by the US Geological urvey (USGS). Digital orrhophotographs are generally made avai lable in porrions that match [he extent of USGS 7.5 Minute Series Quadrangle maps, and are often reFerred to as digital orrhophoro quadrangles (OOQs). Since the USGS Quadrangle maps cover large ground areas (7.5 minutes of longitude and latitude). digital orrhophorographs have been developed w cover portions o f Quad rangle maps as

well. Many count ies in the US have also commissioned

more derailed digi tal o rthophorographs, as have private

co mpan ies. Digita l orrhophotographs provide a data source fo r rhose interesred in ob£a ining a relatively fine

scale image of landscape o r in obtai ning a base data layer

fo r digitizing landscape features (Figure 2.14). One of the strengths of digital orrhophotographs is

that each image is geo referenced wa coordinate and pro

jection system; therefore. GIS users can use a deskwp GIS

software programs w digitize and crea te GIS databases

Figun 2. 14 Digital onhopholO quadrang1~ (DOQ). 50


using 'heads-up' digirizing. The rerm 'heads-up' digitizing

indica res that the person doing the digitizing is looking ar a co mpurer screen (i.e., (heir head is up) rather than a

digitizing table (which requires rhem [Q look down) when

digirizing landscape fearures. Through heads-up digirizing, you can quickly creare a GIS darabase from a digiral

orrhophorograph image on a compurer screen .

Digital raster graphics

Digital raster graphics (DRGs) are digirally scanned represemarions of [he USGS topographic maps (Figure 2. I 5).

These maps cover the emire United Scares, are published

Figure: 2. 15 Digi[al raslc: r graphic (ORG) image:.

at several different scales. comai n a wealth of information, and, very importantly for GIS projec[5, are avai lable as digital databases that can be lIsed by most GIS sofrware pro

grams that have raster display capabilit ies. Within Canada. [he Nat ional Topographic Dara Base (NTDB) is main

tained under the adm inistration of Natu ral Resources

Canada. The NTDB contains vecror databases (hat are

similar to rhe USGS ropographic maps and are available in

severa l digital formars ar scales of I :50000 and 1:250000.

The most detailed of [he maps produced by [he USGS are rhe 7.5 Minute (7.5') Quadrangle maps, which have a published map scale of I :24,000. The 7.5' refers [0 [he total

amounr of laritude and longirude, in degrees,

on rhe Earth 's surface rim each Q uadrangle

.. ' ....... -- :..:.-=--~...:-

represenrs. In some cases, fearu res from the

Quadrangle maps are available in a vecror for

mar as a digi,al line graphic (DLG) . Si nce [he

7.5 M inute Series maps typically illustrare cul[Ural resources, such as roads, large buildings.

elevation conrours, and narural features such

as water bodies, similarly ro DOQs, georefer

ellced Quadrangle raster databases can be used

as a base layer for digitizing orher landscape

features ofinreresL Quadrangles also provide a

great resource for rhose who wam ro learn

more about a landscape.

A closer look at USGS 7.5 Minute Quadrangle map Given [he broad avai lability of [he 7.5 ' maps, rheir cartographic

detail. and their populariry as a GIS darabase

template for foresrry and natural reso urce

applic3rions, we will closely examine one of

rhese maps. and describe some of the more

norewonhy components. Alrhough this

closer look is focused on an example using

[he Corvallis Quadrangle from Oregon (also rhe subject of rhe previous figures demon

SHaring a OEM and DOQ), the reatures

described below should be avai lable on most

orher 7.5' maps. In panicular, you should

look closely at rhe information (hat appears

<I, rhe bortom of rhe map (F igure 2. I G) . Quadrangle maps also provide information

primed along rhe rop margin or the map, bur

rhis is generally much more limited and a subser of what YOli find along {he bonom margi n of [he map. A[ [he time this book was being developed a digi[al copy of ,his

51


Figure 2. 16 Corval lis Quad rangle with neatJines around map areas 10

be described in detail.

~

tot<'O"'~ ' .. 'L ..... ' :10 ,tU "" .D .... <~ "'1',_,,,, .. , " ""'" "".~ .... __ "" """oc.o.. 00,· • • ,_

.". ~ .... "" .. .... It ... , .. . ~" ....... _ ,,"U"';' " ... 1>0._ _000 5.0 .... u..L&cOlOGC.OI.~" Dl.....,. 0I:Il0lA00 aont.,OIIIUU ... _ .. _ .- ............ ---.-~.--- .... -~.

Figure 2.1 7 Lower right corner of the Corvall is Quadrangle.

Quadrangle could be downloaded from page 8 of the list

of quadrangles avai lable at Imp:llwww.reo.gov/gis/data/ drg.,files/indexes/orequadindex.asp.

Lower right corner

The lower right corner of the Corvallis Quadrangle (Figure

2.17) contains a representative scale (1 :24,000) and several

scale bars with units expressed in miles. feet. and meters. lnfo rmarion is also provided for (Wo comour inrervals: the main contour inrerval 0(20 feet (shown by solid lines on the map's su rface) and a secondary contour inrervaJ of 5 (eet

that is represented by dashed lines. The reason for dual conlOur intervals is that the Corvallis Quadrangle includes a mixture of moderately-sloped, forested areas and relatively

fl ar areas where urban and agricultural development has occurred; the dashed five-foor contours are lISed for [he flar

rer areas. Below the scale bar is a sraremenr about compliance with the National Map Accuracy Standards, a subjecr rhar will be examined in more derail shoerly. Below the con

tour inrerval descriptions. there is informat ion abom where to purchase hard-copies of [he Corvallis Quadrangle and the availabil ity of topographic map and symbol descriptions.

Moving to rhe right . a graphic ca n be seen that indicates rhe location of rhe Quadrangle relative (Q rhe

Oregon State border. Below [his graphic, a note clarifies (hat [he purple areas of (he map were updated with aerial

photography captured 111 1982 {and hence edited in

, l • I

!, ~ .. , •

~ < ~

~ • :; 1 ' ~

I .. j.

r ,. J : •

~ ' <

. \ r

i .... ... ,,/

""-'J"-'._ l4M .... _._ """_.co _ ____ _ -..,~ .. ..... """ ..--- ..... ., --- ----r-, ' __ I "'~ --

L I CORVALUS.OREO --- __ .... 'r~ - _ .... _--_ .. " 'l) U-JJ-OU __"",".a; .. ____ ... --......... Y,,-- ,-1'toO'<lIIf"o~n,_ - ,,, .. _-

~

1_ .

52


1986). Moving ro JUSt below the bottom right corner of

the map, you will fin d a legend for road ma p symbols.

The name of the map is liSted (CO RVALLIS, OREG.) and

the map is descr ibed as ,he bottom righr corner (SE/4) of rhe 15' Corvallis Quad rangle. The Ohio code description, sometimes referred (Q as rhe USGS Map Reference Code, is liSted as 44 I 23-E3. This means rim the map is

locared in a block of geograph ic lar irude and longitude that begins at rhe intersection of 44° larirude and 1230

longirude (USDI US Geological Survey. 1995).

Each block oflarirude and longirude can comain up ro sixry-fou r 7.5' Quadrangle maps rhar comprise an eigh r by eight matrix . Leners are used from A- H [0 denoce

rows starr ing from rhe corner of rhe latitude and longitude inrersecdon and moving upward. umbers are lIsed from 1- 8 movi ng westwa rd [Q signify columns. Thus, rhe Corvallis Quadra ngle is located in [he matrix at the inrersecrion of rhe 5rh row and the 3rd col umn (Figure 2.18).

Many map disrriburo rs use th is Ohio code system to

idenriry rhe relative location of quadrangles. T he yea r of rhe o riginal aerial ph orography (1969) used to create rhe map is liSted, as is the laSt revision yea r (1986). The bot

rom corner of the mapped area has a set of geographic

, , ~ M ~ ~

45'

H

G

F

E3 E

D

C

B

A 44'

8 7 6 5 4 3 2 I

Figu.rc 2. 18 Ohio codc loca ti on of the Corva.l lis Quadrangle.

coo rdinates (44°30' and 123°15') rhat desc ribe itS loca

rion. JUSt ro the east and slighdy north of (his corner, a cross appears. These crosses are loeared in simila r loca

tions relative [0 all four mapped area corners and demonstrate how the mapped su rface corners can be adjusted [0

switch rhe datum of rhe map's projection from AD27 ro

NAD83 ( AD is short fo r North American Datum). A full Universa l Transverse Merca to r (UTM) casti ng

(479000 m) is found JUSt weSt of this corner and a full

UTM northing (4928000 m) to Ihe north. Fu ll UTM

coordinates are also given along the opposite map corner.

Hash marks [hat extend o utward from {he mapped area

indicate the locat ion on rhese coordinates relative ro rhe

rest of the map. T hese hash ma rks continue around rhe emire peri meter of rhe map's su rface. but the last three

digits of rhe easri ngs and notthings that accompany the marks are nOt primed in order [0 conserve map space.

LOlUty Itft comty The lower lefr corner of the map (Figure 2. t 9) States thar

[he map was produced by (he USGS with control o r reter-

"--":'

1'oIoo<""".,00K1_ 1927 .... "' ........ U"Ie.'~~ 10,()()O<_ .. ..t "" ... a~ 0. ..... <_4,." •• ,.,_. " .. 1 ~ .....

1000 .... .. ' U"..WI • •• • ..... w "' .... tot I~C Uk>. _10_,,101_ T. pIocI __ 1"tId"' .. """", ""-"-' DoIurot 1M.), __ -u. ..... U __ " ._--- .... - .. _-___ ... _--.._ .... --.01 t1-.frUoI_ .. SIt1Oo __ ..... .....

, ( , ,

,.,. ..... __ .. _......,--...... -

,

w- .. .... _ , __ "" _ ... --' .... """ ...... "",.

Figure 2. 19 Lower Itfl corner of Ihe Corvallis Quadrangle. 53

Chapter 2 GIS Oatabases: Map Projections, Structures, and Scale 43

ence points eStablished by [he USGS, US CoaSt and Geode[ic Survey, and [he S,ate of Oregon. Mapped sur

Faces were taken from 1967 ae rial photography and were field checked in 1969. Projection info rmacion is rhen listed and the base su rface is described as a polyconic projection, NAD27, using [he Oregon S,a,e Plane Nonh

Coordinate System. A line follows (Q describe rhe coloring of rhe UTM coordinates that are listed around rhe perime[er of ,he map, and [he UTM zone (10) [hal was used.

Instructions for converting rhe mapped su rface from NAD27 to NAD83 are also given and provide a quanti[a

rive assessmenr of how these twO darums differ in the quadrangle area. This information is useful for porenrial coordinate conversions in a GIS. In addition. the text states

(hat only la ndmark buildings are shown in map areas [hat 3re tinted red . Landmark buildings are those that serve rhe public. have cuimrai or historical significance. or are unusually large in relation to surrou nding buildings.

To [he righ[ of rhese S[3(ements, a graph ic at rhe map's orientation [0 seve raj definirions at norch is shown and text below the graphic explains that the orientation is trom the map's center. The longest nonh line is topped by a Sta r symbol and refers to asrronomic norch. The line to [he left of as tronomic nonh refers to grid nonh (GN) and is oriented 0°13' (0.22°) wesr ot astronomic nor[h. Grid norch is rhe direcrion in which rhe Oregon Stare Plane Coordinare Sysrem is reterenced. The line ro rhe right at asrronomi c north shows rhe magneric declinarion (19° easr), relarive [Q astronomic norrh, thar exiSled in 1986. Since magnetic nonh can Auctuate trom yea r [Q

year (even small daily shifts are also possible), ,he dale of (he measuremenr is imporranr tor those who wish (0 COI1-

ven [heir data to match the map's projection. Geographic coo rdinates appear at rhe sourhwesr cor

ner at rhe mapped su rtace, an d rhe UTM coo rdinate abbrev iations are li sted. A full listing of Stare Plane Coordina[es (NAD27) are liSted and also marked by harch lines to [he nonh (320,000 feet) and eaSt (1,260,000 feet) ot this corner. Full srare plane coordinares are also listed along rhe upper righr corner. Unmarked hash marks around the rest of {he map's perimeter denore gradations of the stare plane coordin ates. Periodically, range and township divisions appear as longer dashed. lines on {he map's surface. Along [he la[i[udinal axis of Figure 2. 19, you can see R. 6 W. and R. 5 W. T his signifies [he d ivi sion berween Range 6 and 5. west of (he reference meridia n (Willamene) rhar was lIsed for crearing rhe PLSS tor Oregon. Larger numbers appea r on the mapped surface and describe the boundaries between secrions and donation land claims (DLCs). As menrioned earlier in this chap-

rer, most of the wesrern US was originally divided according to [he PLSS. The PLSS spli[ regions of [he US in a grid

of townships (approximately six by six mile blocks created by rhe inrersecrions of township and range lines) (hat were created from a reference meridian with townships furrher divided into secrions measuring approximarely one square mile. Section numbers range fro m 1-36 in almost all PLSS

srates rhough you might find an occasional secrion numbered 37 where measurement irregularit ies warranted adjustments to rhe PLSS. Some stares, including Oregon , Florida, and New Mexico, had adopted less rigorous land measu rement systems [hat superseded the PLSS. Through

rhese sysrems, serders could srake cla ims (Q lands and rhese systems were generally referred to as DLCs. DLC bound

aries are numbered. srarring with 37. In general, green shading (gray in the black and while

image of Figure 2.19) is used to represenr foresred or narlIral areas, and no shad ing is used to represent developed areas. Roads. screams, and other cultural and "arural landscape featu res are also visible rhroughour the map.

National Map Accuracy Standards

Accord ing (Q informacion illusrrared in Figure 2. 17. rhe Corvallis Quadrangle map complies with [he Na[ional Map Accuracy Standards ( MAS). The US Bureau of [he Budge[ originally developed these standards in 194 1 so ,hal guidelines wou ld be available for [he establishment of

horizontal and vertical map accuracy a[ multiple scales. The guideli nes were also intended (Q help protect and inform consumers aOOm rhe qualiry of map products rhey acquired. The guidelines assume that organizations claiming adherence ro NMAS guidelines are responsible fo r ensu ring compliance. The NMAS was lasr revised in 1947 (Thompson , 1979) .

The guidel ines (Figure 2.20) provide horizontal accuracy standards for map scales larger [han I :20,000, and for

scales a[ I :20,000 or smaller. For [he larger map scales, no more than 10 per cem ot rhe points verified shall be in error by 1/30th of an inch, as measured on the map su rface . For smaller scale maps, this tolerance is 1/50th of an inch. The Corvallis Quadrangle Falls in the laner category. To resr tor NMAS compliance, locations or elevations from map poims are compared to their acrual measuremems, where locadons or e1evarions have been derived by highly accurare ground surveys. Within these comparisons, only 10 per cem of (he points ca n be in error by more than me tolerance. Table 2.1 describes the tolerances in relation ro some of rhe more common map scales. For [he Corvallis Quadra ngle, [his [hreshold wou ld be 40 feet,

indicating rhar nor more r.han 10 per cem of rhe rested 54


ence points established by rhe u. G ,U CoaS! a nd Geodetic Survey, and the Srare of Oregon. Mapped surF.rces were raken from 1967 ae rial phorography and were fiel d checked in 1969. Projeclio n info rmarion is then lis red and ci,e base s" rflee is described as a polyconic pro

jection, NAD27, usi ng the Oregon Srare Plane Nonh Coordinal< ystem. A line follows to describe the coloring of {he UTM coordinmes th iU are lisred around [he perime~

rer of the map, and the UTM zone (10) rim was used .

InSHlIcrions fo r convening rhe mapped surface from

NAD27 to NAD83 are also given and provide a quanti tative assessmelH of how these twO datums differ in th t

quadrangle area. This information is usefu l for pOlcnrial coordinate conversions in a GI . In addition. [he [ot( srates Ihat only landmark buildings arc shown in map areas [hat are rimed red. Landmark buildings are [hose dlat serve the public. have cu ltu ral or historical sign iftc3 l1ce. or are unusually large in relarion ra surrounding buildings.

To lhe righl of these Slatemen lS, a graph ic of rhe map's orienrarion m severaJ definidons of nonh is shown and (exl bc:low the graphic explains that the orientation is fro m the map's center. The longest nonh line is lopped by a Sta r symbol and refers to astronom ic nord,. The line [Q the lefr of astronomic norrh refers to grid norrh (GN) and is o riented 0° 13' (0.22°) west of astronomic north.

Grid north is (he dire rion in which rhe Oregon State Plane Coordinate ysrem is referenced. The line to (he right ofast'ronomic north shows the magneric declination (190 east), relative [0 astronomic nonh. [hat existed in 1986. ince magnetic north can AucUlate from ye-A r (0

year (even small daily shifts are also possible), the date of the measurement is important for those who wish [0 convert their data to march rhe map' projection.

Geographic coo rdinates appear ar rhe sourhwest corner of rhe mapped surface. and rhe UTM coo rdinate abbreviations are lisred . A full lisring of Stare Plane Coordinates (NAD27) are listed and also marked by harch lines to lhe norrh (320,000 feer) and eaSt (1,260.000 leer) of this corner. FilII stare plane coordinates are also listed along the upper right co rner. Unmarked hash marks around the resr of the map's perimerer denote gradations of rh e sta re plane coordin ares. Periodicall y. range and township divisions appear as longer dashed lines on the map's surflee. Along the latitudinal axis of Figure 2. I 9, you em see R. 6 W. and R. 5 W. This signifies rhe d ivision between Ra nge 6 and 5. wesr of the reference merid~

ia n (Willamcne) [hat was used for cre-dring the PLSS for Oregon. Larger numbers appear on the mapped surF.rce and describe the boundaries between sections and donarion land claims (OLCs). As menrioned earl ier in this chap-

[(r, mOSt of the western U was originally divided according [0 rhe PIS. The PLS splir regions of lhe U in a grid of rownships (approximately six by six mile blocks created by rhe intersecrions of mwnsh ip and range lines) thar were creared from a reference meridian with townships furrher divided into ections measuring approximately one square mile. ection numbers range fro m 1-36 in almoSt all PI_SS

srares though you might I1nd an occasional section numbered 37 where measurement irregularities warr3IHed adjus[mems to the PI..5S. orne Slares, including Oregon . Florida, and ew Mexico, had adopred less rigorous land measurement sysrems rhat superseded lhe PLSS. Through

these systems, sealers could stake cla ims [Q lands and rhese systems were generally re!erred to as DL . DLe bound

aries are numbered srart ing with 37. In gener. l, green shading (gray in the black and white

image of Figure 2. 19) is used to represen t fo rested o r nar

ural areas. and no shading is used to represeO( developed areas. Roads, streams, and other cultural and natu ral landscape featu res arc also visible throughout [he map.

National Map Accuracy Standards

According to information illustmed in Figure 2. I 7. the orvallis Quadrangle map complies wirh lhe National

Map Accuracy Standa rds (NMAS). The U Bureau of rhe Budger o riginally developed [hese standards in 194 I so that guidelines would be available for [he establishment of

horizontal and verrical map accu racy at multiple scales. The guidel ines were also intended [0 help protect and inform consumers abom the qualiry of map product they acqui red. The guidelines assume thar orga nizations claiming ad herence to NMAS guidelines are responsible for

ensuring compl iance. The MAS was last revised in 1947 (Thompson, 1979).

The guidel ines (Figure 2.20) provide horiwnral accuracy standards for map scales larger [han I :20,000, and for

scales at I :20.000 o r smaller. For lhe larger map scales, no more than 10 per cem of rhe points verified shall be in error by I130r h of an inch. as measured on rhe map surF.ce. For smal ler scale maps, this rolerance is I 150rh of an inch. The Corvallis Quadrangle F.rlls in Lhe latter Cl tegory. To (esr ror NMAS compliance. locations or elevations from map points are compared to their actual measuremems, where locations or elevations have been derived by high ly accurate ground surveys. Within these comparisons, only 10 per cent of the points ca n be in error by more lhan Lhe tolerance. Table 2. I describes rhe tolerances in relation ro some of the more common map scaJes . For the Co rvallis Quadrangle, this threshold would be 40 feer, indicating rhat not more {han 10 per cenr of rhe lested


Umlnl Stllt~ NfltlOlUU Map /\((1IMy SIIIIIdllfds

\'oOI. __ "'doo ___ .... pNI .. "''''~'''''I''' .. hIrto'''!,-ill ......... , .... 1>ro.od

..... 100 .u...wcl '" I"'ftCIf"I _"". I>uf .......... "" ...... "'" porunol>r -.. oI ..... idouI .. ...-our40,c!' «j .. ,,,,";v!IlI .... blbhaI .... po..., ~ .. ~.

I . ..... -.1 ~....,. . . ............ OII.....-- ..... 1Mpt I ..... 1-3I.IlUD...,. ........ tlw .. }O ptm'N 01 1hr~_.wloor .. _~ __ l "lC1_1>.-..-..d ....... ~~""""-r> .. ~~...,.,.,. <It 1.lO,cro. -a.... l -,o!nth. ".".. __ .. ---,- .twII""Y"" 00 ..... .. ~.,. ....,..6ffitw,J J"IItIl.. 00II) w" dtm.:.i 1""_ .... ,"'-!Nt ........ '" """""' ...... "",,, •• bIIoOllIN pound. ouch o. """ klIIow...,; _"""",t ... .... 11 ..... """" Of to.·""h .... , .... I""'I"'fn· 10. ... ......., . .-......mIl; _o«tl<>nJ "' ....... ,.oi~ •• "'" --" 011.0 .. buIldlrop ......... ,.. ..... ( .. _.-.ol.....n~ ttc.1n ............ k,otlo .. .,.dftirW'd __ Ioo~_~

"!wl"~"" Ihr,.,. '" Lht .... ...u.. 1100.,.h no ..... hAI rtIr...--- "' ...... 0\l00J Of

~ ...... -... .. ~~ .. .-IJ_ .. Ilhon.omoilIkl~.~~ ..... "'1hoo __ ....... '" -tI rw-. _ .... AI ..... __ ............ Id ob>->ouo.lv _ boo p' ....... bIrt wi,,,," "'-0

nIL ....... ;,,;....,..... _ u,.,.a.f..w.. "f'M .... 5""'tw;! .. iIhon cIo+r 1orIoiI. ~ ""' ... bot ~'II o. _ I"""" ... "'" rho- -'..,.-l "" ... th."'I!h ,,..., ._ .... \ bot __ . 1«.1 dootIj wpon Ihr ... plftlh;.<il..i""-..w«l'llf ............... .... ~ ...

2. V ............ "')' .... "rP'1od •• ru .. _ ""'1" "" .011 poaI.u.-...... """';.,., .li.oii bot.....,. th.I. !WI _

dldnW,..,..,.., .... , .... ...,., ............. ..,>h.oIIbt ................ ltt.tn ' ..... ...uIhe""'lOoninw ... ·.oI tr. ~ .... ..-.- .. ...". 1ft ... ,Iw..,. "'" oPf>M"" wttIC4II ...... _ .... -..",.,.,;1.,.. ~. ~~- .... ,..,..........~ .......... • ... r~~.".,"'"""

). n.. MftI."'Y ., .., ..., .or ... -.II t... """f'U"'I "'" ,.- '" "' ....... ",,,,- Ioc.o_ .. ...... _ ..., ....... n ap. ....... ~II _~ r-- .......... "'"""-'d by ............. ..r 4 hr~

• ", ........ rht. """~ to. nwde l') , .... pn>d ........ ~ .~~ .... "' .. whkh .ho.lI ...... Jo1<'rml ...... hl<h,~ 11> .... 1" _.,l't~ • ...,Uwn_ol...ch1dC!ft,t

L p,.........r .. ,.~ ... u-._..,.""'I-...- oh.oII_dwf..a_ ....... ~ .. "*""" -n.._,,"-rt- ""Ito ~ Moop _4C)'St.t~

, . hlolitoMd..."._ ........ ___ .f~ ohalI_ ""'" '''''''."'''''' _ ...... "'''' _..I""", .. )' •. "' ..... . ,.....;.a,.,., ....... -... ..... ~"'._pJr ... 'Injr (-...-. .... p'l"''''.f'''W'"''td INJ' '''''' *" ...... t. .... ,0<1 '" "'" IfrJ "" ~""""" ·ill .. ""'I' It ... ~.!I"_ 01. I~.tm ••• 01 ....... Ju~, ", .. -0. .... " ~~"...,tup_ ~ . r.!4.00J..c.0Io,.....IIN .... p

7. T. roc\u,OI, nooly I .... rdt.at>s • ....r ... of It.Ml< h.~1oot "" ... ,. c-Irudlooto _ .. 011 r...kt.r "..~ ~ ..... 1>IIiCrif'I .... "" o.nol pu/>Ikht<l-r-. w""'"' ..... ~ ........ __ <mi' _ ......... • ......... _""_rlO ... Io.:F"". _ ......... .......-_~~ ....... ~IS_ ... '"*"-..-J~."'~,-. ... }..J. ~_ ... _

•• _,,..... ,01 , , .. . UIl .. u Of THlluocrr

_'''':'''1'1< _,. ... 1· ,... ·

Figur~ 2.20 US alional Map Accur:.lIcy Slandards.

map points should diffe r from rhei r acmal locadons by

more {han 40 feer. Vertical accuracy srandards are applied by using half of rhe comour inrerval as a benchmark. For

verr ical accu racy of rhe primary COntour lines (20' inrer·

vals) of the Corvallis Quadrangle, the standa rd indicates char 90 per cenr of [he elevation points checked along [he contour lines should nor be in erro r by more [han 10 feer.

Typically on USGS 7.5' Quadrangles, 28 points are examined fo r accuracy verificatio n, representing a very

small portion of rhe population of map poims. h is also worth noti ng thar rhese poims are not randomly selected,

TABLE 2, \

1,1.200

102,400

1,4.800

UO.OOO

1,12,000

1024,000

1,63,360

uoo.OOO

Map scales and associated National Map Accuracy Standards for horizontal accuracy

Standard

:l 3.33 f~~l

:l 6.67 fcel

:l 13.33 feel

.t. 27.78 fcci

.t. 33.33 f~el

J: 40.00 feel

:l 105.60 fC~1

± 166.67 fee l

bur represent locations that are read ily visible on the pho

tography from which the map was created, such as road i nrersections, bridges, and other noreworthy srructures . Th us, you wo uld expect that the po ims represem [he mapped areas where the phorogramme[ric methods used ro crea te the map were [he most reliable for mapping pur

poses. I t is also worth noring rhat 10 per cem of those po ilUS could be off by any distance, and [he resulting map would still be MAS compliant. Even though you are

assured compliance with a published standard, potenrial fo r errors related to the accuracy of locarion of landscape featu res can st ill be signif\canr .

Our discussion of raster data s[rucrures and CIS databases above provided examples of so me of the more common rypes of raster data and a few porenrial applications . Ras[er data provide a powerful means ofinvenrorying and analyzi ng Earrh 's featu res and no doubt new raster dara

fo rmats and applica rions will arise in future years. The usefulness of raste r CIS databases will depend on the needs and capabil iries of each narural resource organization. bur if is increasingly likely that raster data will be parr of every o rganization 's data holdings. parr icularly as technological adva nces conrinue ro b ring economies to remmely sensed landscape data. We now {Urn our atten

tion to the other primary data strucru re fo r sparial databases: the vector data structure.

Vector data structure

Vector dara. as compared to raster data , is generally considered ' irregular ' in irs construction and appearance. T his descrip tio n is nor a co mmenr o n the quality or usefulness of vector data srrucrures. bur JUSt a characreriza

rion of rhe rype of dara it represems. Vecro r data are generally grou ped inro three catego ries: points, lin es, o r polygons. This categorization is sometimes referred (0 as

rhe fearure model of GIS. Almost any landscape feature on rhe Earth ca n be described usin g one of these three shapes. or a combination thereof (F igure 2.21). Points a re

Poin t line Polygon

Figur~ 2.21 Poine . line, and polygon v«to r shapes.

55


rhe mOSt basic of rhe shapes btl[ define the essence of all

three fo rms. A line is a set o f connecred paines. A polygon

is a co llection of lines thal form a dosed loop.

Poim. line, and polygon vecror fearures can be refer

enced by almos[ any coordinare system. To represent a poinr, a si ngle measure from each X (east-west) and Y (norch- sourh) axis is needed ro describe rhe location of

rhe poinr wi th in a coo rdina te system. Wi th lines and

polygons. each coordinare pair is referred [0 as either a node o r as a venex. The coo rdinates of poinr, line, and

polygon fearures allow calculations of distances berween

features. and in rhe case o f lines and polygo ns, dimen

sions of featu res. Point features have no dimension, or size, beca use a single pair of coordinares represenrs rhem.

A coord inare pair does nO{ allow for length, area, or vol

um e calc ulatio ns. Line feawres a re singl e dim ensio n

shapes in which coordina[e pairs can be used ro cakulare

a length. Polygon features are rwo dimensional in naru re. wirh coo rdinate pairs being used ro nor only cakulare a

perimeter d isrance a round a polygon. but also being used

ro calculate an area within rhe polygon.

Topology

Most GIS software programs Sto re loca tion informacion

(X, Y coordinates that describe the position of landscape

feam res) in separate GIS dacabases fo r each ropic of inrer

est. For example. the coordi nates that are used ro describe

a roads GIS database are separate from the coordinales char

describe a ",earns GIS database. Although moS( of the

location information thar defi nes vec[Qr feamres will be

rransparent to users of desktop GIS software programs, it is

vital in eS(abl ishing and mai nrai ning topology. Topology

describes the spatial rela tionships between (or among)

poinrs. li nes. and polygons. and is a very important con

siderarion when conducling spatial analyses. Topology

allows you [Q delermine such thin gs as the distance

between points. whether lines intersect, or whether a poi nr

(or line) is located within the boundary of a polygon.

Topology can be defined in a number of ways but the

most common defi nitions involve aspects of adjacency,

connectivity, and conrainmenr (Figure 2.22). Adjacency is

used ro describe a landscape featll re's neighbo ring features.

You might use adjacency relationships to describe poly

gons rhat share common borders (e.g., in support of green

lip requi remenrs in a forest managemenr conrexr). or to identify lhe lines rhal make up a polygo n (a rea).

Connectivity is rypically used (0 describe: linear ner\vorks, such as a network of culverts thar m ight be connected by

drainage d itches or a sr ream network. Connect ivity would

co Adjacent polygons Connected stream One polygon contained

network inside another polygon

Figure 2.22 Exam ples of adjacency, conn~ctivity, and containment.

allow you ro (race rhe fl ow of water (hrough [he stream

system. You can also incorporare direction in [heir descrip

tio n of connectiviry. Based on the topography of a la nd

scape in which a culven system is siruared , you cou ld

determine the overland fl ow paths o f warer rhrough the system, given tha[ water flows downhill. Conrainmenr

allows you (0 describe which landscape features a re

located with in . o r inrersect, the boundary of polygons.

Conrai nmenr in fo rmat ion can be used ro describe (he well

locations (points) or the power lines (lines) that are located

within a pro posed urban growth boundary, for example.

In order for ropology (Q exist. a system of coding

topology thar can be undersrood and manipulated by a

computer must also exist. With GIS darabases co mai ning

point features, there is li[tie need for anythi ng more than

a file of coordi nare pairs (X, Y coordinates) since all points

are idea lly separa te from one another, and thus there a re

no issues of adjacency. co nnectivi ty. and comainmenr ro

resolve. H owever, you must be ca reful in describing fea

ture locations and linkages when using GIS databases con

taining line and polygo n fearures. The spatial integrity of

lines and polygons is mainrained by managing rhe nodes.

ve rtices. and links of each feature. A node is rhe starring

and ending point of a line. and also represents rhe inter

section of twO or more lines (Figure 2.23). A vertex is any

poim rhar is nor a node but specifies a location or creares

¥ Vertex

.........Node ----

Figure 2.23 Examples of nod~$, links. and vertices.

56


a. Network of nodes, links, and polygons b. node coordinate file

C 5 Node X Y 4

2 0 1 0.5 2.4

Y 6 2 2 2.1 3.1

5 3 3 3.2 1.7

7 E

B 6 A 4 4.7 3.3

5 5.4 5.0 X

6 3.6 0.5

c. topological relationship file

link Begin End Left Righi node node polygon polygon

1 1 5 A C

2 5 6 A E

3 1 2 C B

4 2 4 C 0

5 4 3 E 0

6 3 2 B 0

7 3 6 E B

Figure! 2.24 V~Clor topological data. (a) Network of nodes, links. and polygons. (b) node coordin:lIc fi le, and (el topological rdationship file.

a directional change in a line. A link, sometimes called an

arc, is a line [har connects points as defined by nodes and

verr ices. Nodes, ve rt ices. and lin ks are usually numbered

and mainrained in a C IS database fi le [Q mainrain wpol

ogy. In a network of lines and polygons (Figure 2.24), this

would involve us ing numeric codes fo r network pieces

(nodes and links) to identilY the node locat ions, the nodes

cha r afC attached CO each link, and the polygons that may

fo rm on either side of each link.

Topology a lso allows yo u [Q inspecr rh e spa cial

inregr iry of lines and polygons. For instance, you can li se

topology informarion ro determine wherher any breaks or

gaps occu r in lines that are meant to represent streams.

F rom a polygon perspecrive, topology would allow you [0

dere rmine wherher a polygon forms a closed boundary, o r

whether an exrraneous polygon exisrs inside, or alo ng rhe

outside border, of another polygon (Figure 2.25).

One of the primary differences between full-featured

GIS softwa re programs and desktop GIS software progra ms is whether they can identify and co rrect topology

problems in v<owr GIS database features. Many deskwp

GIS software programs, such as ArcView 3.2, GeoMedia,

and M aplnfo, may use vecto r clara form ars thar are not

topologically-based (Chang, 2002). Thus landscape fea

tures, such as adjacent polygons, may nor be represented

as sharing a common boundary line wirh orher polygons.

Most full-featured GIS software programs such as ArcGIS

may also allow lise of vecro r fo rmars char are nor rypolog~

ica lly based. but will usually have tools or options to draw

and manipulate rypology. I n so me cases, use rs may be

a. An un-closed polygoo b. An extraneous polygon

Figure 2.25 Examplu or lopological urou. In (a). an undershoot has occurred and instead of a closed figure crcat ing a 1>olygon, a line has been crated. In (b), a small loop has been rormed extraneously adjacent to a pol)·gon . This might reprCUllt a digitiz.ing error or the result or a nawed o\'crlay process.

57


What is topology? Topology, o r topological coding, provides the intelligence in (he data structure relat ive

to the spat ial relationships among landscape feacuces (Lillesand & Kiefer, 2000). For example, in a vector

GIS database containing polygons, topological coding keeps "ack of each line [hat forms each polygon, and

able [0 define their own copological rules. or whether cer

tain topological relationships can be ignored in a specific database. The danger for users of desktop GIS software programs is that some programs will allow users co pro

ceed, without warning, with spatial processes and analyses even though ropological problems exist. Th is condition may lead [Q errors in linear and area measuremenr calcu

lations, or an inabilicy to complete certain spatial operations (hat rely on these dimensions for processing. Users

of desktop GIS sofrware programs may only become aware of (apological problems after careful examination

of GIS databases and associated analyses. or may not notice potential problems altogether.

The point. line. and polygon vecmr data structure provides a method to represent irregularly-shaped Earth features. More often than not. vector GIS databases do nO[

completely cover a landscape of interest (e.g .• a vegetation GIS database may only contain the vegetation located within the ownership boundary of a natu ral resource

management organization. and nOt the vegetation outside of the ownership boundary-<juite different from satellite

imagery or digital orthophotographs), and represent landscape features that are quite diverse (e.g .• polygons of dif

ferent sizes and shapes. rather than a regu lar size and arrangement of pixels). Examples of diverse vecmr databases include road and st ream representations. Both of

these rypes of databases tend to have unique geographic shapes that do not completely occupy a landscape (unless, of course, the ' landscape' is the size of a pothole. or some

pool in a stream). Some poim databases. such as those

that describe timber inventory cruise plots. come close to

a regular arrangement across a landscape. yet they usually deviate from regularity as a result of the sampling method selected for each stand. Point locations of wild life sightings are usually very irregularly distributed across a landscape. Polygons. whether they represent stands of similar trees. soils. or recreation areas. tend to be very irregular in shape. although in areas where the Public Land Survey

the common nodes each line shares with each other

line. In addition, the polygons that are formed on

either side of each line (since polygons may share a boundary defined by a line) are known. Thus with

topology you can understand which forest stands, for

example. are next to which other forest stands.

System or other culturally-based land delineations have

been implemented. some edges of forest stands now seem

to have an aspect of regulariry built into them.

Comparing raster and vector data structures

There are a number of ways in which the differences between vector and raste r GIS data can be described (Table 2.2). Since GIS users will ultimately use databases

representing both structures. and since users sometimes

convert raster to vecCQr data. and vice versa. an illustration of the differences is needed. Therefore. an examination of

a generic dara structure conversion process might be helpfu l to illustrate how the three main rypes of vector data (points, lines, and polygons) might be represented in a

raste r GIS database. The right side of Figure 2.26 also illus trates these three features but demonstrates how they

might be represented in a raste r database structure. The vector representation of points can yield fairly precise

locations, depending on the rype of coo rdinate system used to reference the points. This means that you could

use a GIS to determine with some degree of precision where these points could be located on a map that

TABLE 2.2

Structure complexiry

Location specificiry

Computational efficiency

Data volume

Spatial rcsolu[ion

Comparison of raster and vector data structures

Raster Vector

Simple Complex

Limited Not limited

High Low

High Low

Limited NO(lim iu!"d

Representation of topology Difficult NO( djfficult among feamrcs

58


• • •

• •

Figure 2.26 Point, line, and polygon features represen ted in vector and ras ter data StruCtures.

includes coordinate reference marks on its axes. While the

raSter represemation also shows these point locat io ns,

there is less precision (specificity) in their map location, which is dependent upo n the raster grid cell size assumed. You would know that the point is iocared somewhere in

the grid cell . bur the precise locat io n is elusive.

You can also see some similar relationships be[Ween the

data S[rucrures when describing line and polygon features.

Both the line and polygon vector features have very discrete

shapes that are somecimes lost when converted to the raster

data structure. This faCt is perhaps most noticeable when

you examine the junctions. or nodes. of the line featu re (A), or those places where polygon features intersecr (B).

The loss of specificity when converting a vector feature imo a raster can, at least in pan, be overcome by selecting a smaller raSter grid cell size to represent the rasterized vecto r features. This choice comes with a price, however: an increasing storage size requiremem fo r the resuhing raSter GIS database, and greater strain on computer processing resources. The loss of specificity when converting

from vector to raster data structures may be acceptable if your goal was to simply represent the rdative locations of landscape features. However, if you needed to know (he

precise loca tion of a well, road junction, or propeny boundary, representing these landscape features with a

vector da ta structure may be more appropriate .

In general. raster data struCtures may be more appropriate for representing continuous surfaces than the vector data structure. For example, if you were inrerested in describing precipitation, temperature, or species diversity across a landscape, raster data structures may do this more

efficiently because rhe dara may be more appropriately

swred and illustrated with grid cells. Because of the regularity of features (i.e., each grid cell is the same size and

shape) the computer processing requ iremems are lower when using raste r data structures. When performing G IS

processes wi th raste r data, generally no calculation of the intersection of landscape features (lines or polygons) is

needed , given rhe regular shape of the cells. In contrast, analysis processes that involve vector G IS data usually must deal with the potential intersection of landscape fea

tures (e.g., overlapping polygons) .

Unfortunately, GIS databases stored in the raster data structure can become very large, especially when fine reso

lution cells are assumed . One of the hindrances to using raSter data is that every cell must have a value associated with it; even cells where no landscape featu res of interest

are present (e.g., vegetation outside of an ownership). From

a computer storage perspective, this means that all raster grid cells have a value (e ither a valid value or a null value) that must be s[Qred and maintained. In conrrast, vector GIS

data need only have points, lines, o r polygon featu res in locations where landscape information is present.

Alternative data structures

Although points, lines, and polygons represent the most common forms of vector G IS data, several other forms of

vector GIS data that may be useful in representing land

scape features. These other data structures include triangu

lar irregular networks (TINs), dynamically segmenred networks, and regions. What follows is a brief discussion of each of these mysterious-sounding dara structures, and a potential application that might be useful in understanding the potential uses of these ahernarive data structures.

Triangular Irregular Network A Triangular Irregular Network (TIN), like a ras rer data structure, is useful for representing a continuous surface

59


(an entire landscape). A TIN, however, addresses some of [he problems (hat raster data structures have in accurately

representing landscape feamres , especially those problems that result when you use regularly sized raster grid cells to describe landscape feamres. If the raster grid cells are small. in comparison (Q the size of other landscape fea

tures , you will probably have success in accurately repre

senti ng mose features. If the raster grid cells are large, in comparison co orner landscape features , you might lose

some of {he integrity of [he landscape features in [he resulcing raster GIS database. A TIN attempts to avoid (his

problem by us ing. as the name implies, a set of trian

gles-rather than a set of squares-co represent landscape

features (DeMers, 2000). Each of the th ree sides of each triangle can, in fact, have a different length, making the rriangles irregular in nature. Thus a TIN is composed of

irregularly shaped objects. yet covers an entire landscape.

I n most applications. TINs are used to represent elevation

models. An elevation is associated with each triangle cor

ner, as illustrated in Figure 2.27. In landscapes that are highly irregular in terms of elevation (as are many forested

landscapes, for example), the TIN may berrer represent topography than a raster-based data structure. Working

with TINs, however, is beyond the abili ty of many standard desktop GIS sofrware programs because of the complexity involved in storing and processing irregularly sized

triangles and represent ing three-dimensional surfaces .

Some software developers ofTer modules associated with

desktop GIS sonware programs (at additional cost) that will allow GIS users ro util ize TINs.

Dynamic segmentation of linear networks A data structure that uses dynamic segmentation is based

on a netvlork of lines. and thus is a variation of the vector

Figure 2.27 TIN representation of an elevation surface.

data structure. The dynamic segmentation data structure

is designed to represent linear features. and traditional uses

of this strucrure include moddling efforts related co river

systems. utility distributions, and road networks.

Dynamic segmentat ion allows GIS users co create rOutes

that represent the movement or presence of an endty

along a linear network. The routes are actually stored as

information within a vector GIS database. Dynamic seg

mentation eliminates the need to create a separate GIS

database fo r each route and fucil itates advanced data handling and manipulation of GIS databases.

Underlying the route structure are sections and event

cables. Sections are the li near components or segments

that. when added together. form a route. Event themes

are (he data sources or attribute tables (hat are connected

ro the routes. The dynamic segmentation data model has the capability to associate information with any portion

or segment of a linear feature. Event themes can be asso

cia ted w ith each line or a single point on a line. This

information can then be stored, queried. analyzed. and

displayed without affecting the Structure of the original vecror GIS database.

Dynamic segmentation anempts to link a network of

lines based on a common attribute so that the lines are

grouped into categories of interest. An example of this

approach might relate ro a screams GIS database. A typical Streams GIS database uses a series of lines to represent

a stream netvlork. Each of the lines would have a set of

nodes, or beginning and ending points, and the nodes

wou ld be placed at all tributary junctions along the Stream netvlork. Depending on the size of the stream net

work, hundreds Ot thousands of lines might exist. A stream ecologist interested in analyzing the stream sys

tem. for example, could associate all lines that are used to

represent the main channel of a river. Any anributes that

are used to describe the main channel, such as length,

depth. or temperature, can then be summarized. The

stream ecologist might use this dynamic segmentation

approach for the entire stream netvlork to create a new

GIS database that groups all lines based on some attribute (such as the stream name) . The scream ecologist may also

be interested in maintaining specific point locations along

the stream database that contain features of interest. such

as a smolt trap o r culvert location. Point evenr tables of

these features could be stored within the dynamically segmented stream, and show not only (he locations of these features. but also distances and directions from other fea[Ures within the database.

Dynamic segmentation allows GIS users to organize a

60


GIS da[3base so that analysis and storage can be easier and more efficient. Dynam ic segmemation can also be used to

ass ist in schedul ing management operations that involve

transportation or movement with in a resource area, or in

planning or tracking almost any phenomenon that is associated w ith a linear network.

Regions

Another alrernarive vectOr data srrucmre is called [he region . This data srrucmce is based on polygo ns or approximat ions of areas, slich as stand boundaries or

ownership parcels. One of the featu res of a ' topologically correct' polygon srruc(Ure is that polygon features can nor be represented as overlapping areas in a topologically

enforced polygon database. When two polygons meet, a new polygon is created. to represent the overlapping area. As mentioned earl ier , some deskrop GIS software pro

grams do not allow you ro determine whether polygons

are topologically correct. A region data structure will

allow (he existence of overlapping polygons while also maimain ing (Opology. A forest scientist interested in cap

turing the locations of Fallen (fees within a steam channel

would find regions to be useful (Figure 2.28) . Polygons could be used co represent the lengths and widths of the trees, bur any trees that are s[3cked on (Op of each other,

like you might expec( co find in a log jam, will not be accu rately represented in a ropologically correct polygon

structure. Two fal len trees that overlap each other might

result in multiple tOpologically-correcc polygons: one or

Figure 2 .28 Example of me region data structure used to capture the placement of downed woody debris in a stream channel. Typical polygon topology would create II polygons (Q represent the: five woody debris pieces . Regions allow for polygons ro overlap. crearing a fiveshape database. with one complete shape for each piece.

more polygons co represent the non-overlapping areas of

trees, and other polygons for each of the overlapping areas of logs. For the five F...llen trees displayed in Figure 2.28, enforcing co rrect ropology for these landscape features

would create a total of II polygons. This would result in both a loss of information and the creation of a larger set

of database records than might be appropriate to describe the trees. With the use of the region data structure, you

can retain individual (fee data records. while also associat

ing the overlapping trees with one another.

Metadata

Metadata is 'data about data'; this is a relatively recent

phenomenon in working with spat ial databases.

Typically, metadara is a digital document that accompa

nies a GIS database that describes the content and quality

of the data. With recent advancements in some of the

full-featured GIS software programs it is now possible to have a metadata file digitally linked to a GIS database. New sofrware programs also make it easier ro create and

populate meradata fields with information about the data.

Metadata is an excellent place to store and retrieve infor

macion about the characteristics of a database. including

the map projection and coordinate systems used. Other

useful information in a metadata file might include a

description of the original data source, any editing that

has been done, a list of the attributes, the intended use of the darabase. and info rmacion related ro the database

developer. The descriptions should allow users [Q trace

the evolution of the GIS database. In the US, most federal and state agencies are required to make mecadaca available

for any GIS dacabase that is offered for public use. The US Federal Government has developed standards for producing and reporting meradata. Requirements fo r producing

metadata are highly variable among private natural

resource management organizations because no governing

body enforces meradata compliance. GIS users should

always ask for metadata whenever acqu ir ing a GIS

database.

Obtaining Spatial Data

The USGS has developed the most comprehensive collection of spatial data in the world. This collection includes OEMs, OOQs, ORGs, digital line graphs (OLGs), and many sources of raster and vector data fo r [he US. The

majority of this co llect ion is available via the Interner,

along with associated meradata. Several US and Canadian

61


federal agencies, as well as state and provincial groups,

produce and maintain spacial databases for [he lands that

they manage, and this information is also available to the

public online. A more detailed discussion of data acquisi

tion processes is provided in chapter 3.

Scale and Resolution of Spatial Databases

GIS dacabases are often characterized in terms of their

scale or resolution . Scale or resolU[ion refers [Q [he size of

landscape features represenred in GIS databases. Issues of

scale are usually associated with vector GIS databases.

while issues of resolution are associated with raster GIS databases. Typically, the scale or resolution of a GIS database relates to {he source material from which the GIS

database was created. Source material. as described in

chapter 1, can include aerial phocographs. existing maps.

sate ll ite data. o r information gatheted from survey

instruments such as total stations or GPS receivers. Many

sources of veccor data are derived from remote sensing

techniques, particularly from aerial photographs. The scale that is associated with vector GIS databases typically relates to photographic scale (a function of camera

height, lens length , and photo size) . Scale is often expressed as a ratio, or representative fraction, such as

I :24,000 or 1:100,000 (Muehrcke & Muehrcke, 1998). The ratio expression is unidess, and implies that 1 unit of

measurement on a map or phoco represenrs 24.000 or

100,00D unirs on the ground . Sometimes, confusion exists as to the correct use of the terms 'large scale' o r

'small scale'. The ratio I :24,00D is a larger ratio than 1:100,000 (I is a larger portion of 24,000 than of 100,000) and thus, 1 :24,000 is a larger scale than I: 1 00,000. If you examine both 1 :24,000 and I: I 00,000 scale maps printed on the same size paper, the I :24,ODO map would show less area bur greater detail than the

1: 1 00,000 map (Figure 2.29). Scale can also be referred

Summary

This chapter discussed one of the fundamemal consider

ations of any successful GIS program: spatial data. One of the main issues when working with GIS databases is

knowing what projection system the database is set within, and how this coincides with other databases being used within an organization. or perhaps by other

organizations with which data is shared. This chapter was

1:100,000 map 1 :24,000 map

Figure 2 .29 Map of stream network displayed at scales of 1:100,000 and ' ,24,000.

co in terms of relative units, such as 1 cm = 1 km, or

through the use of a scale [hat graphically illustrates approximate ground distances.

Wi[h imagery derived from satellite or aerial pla<forms, rhe abi lity of the electromagnetic sensor on rhe

platform to delinea<e landscape features on the ground determines the resolution. A I m resolution image implies

that the sensors used [Q colleer the imagery captured a

value for each square meter of the landscape. For raster

GIS databases that were developed by scanning from maps or photographs, such as a DRG or DOQ, the size of the raster grid cell in representing landscape features deter

mines the resolution. If each raster grid cell spans a 30 m ground distance, the raSter GIS database is said co have a

' 30 m resolution '. This means that each raster grid cell represents 900 m' (30 m X 30 m) of ground area.

Although scales or resolutions are associated with spa

rial databases . some users mistakenly believe that they can

improve [he detai l of GIS databases by focusing on small land areas . Users need co be cognizant that the scale or

resolution of a GIS database remains static, regardless of

how closely you view an area of the landscape.

developed to provide readers with a brief description of data projections and data structures-both raster and

veccor-as well as a few alternatives. In some cases, exist

ing databases such as DOQs or DRGs may provide an excellent source of base data from wh ich to verify other spatia l databases or CO create new spatial databases alto

gether. How GIS databases are stored and managed is a

62

52 Part 1 IntroductiOIl to Geographic Information Systems, Spatial Databases, and Map Design

function of the decisions natural resource managers make

regarding rhe purpose and inrenr of use for each GIS database. In many cases, however, YOli have no choice regarding rhe strucmre of GIS darabases. For example, satellite imagery uses a faSter dara Structure whereas veg

erarion and soils darabases acquired from rhe US Foresr Service generally use rhe vec[Qr data structure. In addi

rion, most natural resource organizations use venor data

srruc[U res [0 rep resem management units, roads . and

ocher landscape feamres . Meradara are useful in helping

Applications

2.1. Projection parame te rs . Your supervisor. Steve

Smith, has JUSt learned (hat another natural resource

organization that you intend ro share spatial data with

srores their GIS databases in a projection format that is

different from yours. Steve is unfamiliar with projections,

and asks YOll co provide some background for him. a) Whar is a projecrion? b) Why are projecrions necessary' c) Whar is an ellipsoid? d) What is a geoid' e) What are the major projection rypes, what are their

assumptions, and how have they been used?

2.2 . C hoosing a projection. You've been asked ro rec

ommend either a Lambert or Mercaror projection that

best fits the dimensions of the State or province mat you

live in. a) What is the largest north-south dimension of your

state or province in miles and kilometers?

b) What is the largest west-easr dimension of your

state or province in miles and kilometers?

c) Which of rhe cwo projecrions would YOll choose? d) Defend your projecrion choice.

2.3. Public land survey system. Wirh rhe exceprion of rhe original 13 colonies, several other states, and other

des ignated ownerships, much of the US has been sur

veyed (measured) using rhe PLSS. Significanr areas in Canada have also been surveyed using a similar approach.

Given the broad use of this system and its counterparts in

Nonh America, it is imponant that you understand sev

eral key components.

a) How many square miles (or square kilometers)

would you expect to find in a township?

you determine rhe characteristics of a GIS database.

including rhe projection system used. Finally, under

standing the scale or resolution of GIS databases and thei r

associated landscape fearures is important, as it relates co

the usefulness of a GIS database in assisting with analyses

related ro management decisions. Perhaps an interesting

ropic of conversation over a cup of coffee might be how

well 30 m grid cells obtained from sarellire images porreay forest vegetadon and help you develop management

recommendations.

b) How many acres (or square meters) would you

expect to find in a section?

2.4. GIS data structures. You have been hired as a land management forester for a timber company in the south

ern United States. As a recent college graduate you are

expected to have the most cu rrent knowledge of forest

measu rement and data acquisition techn iques. Your

supervisor, John Delaney, an older forester, is interested

in GIS and is cu rious about database structures. Describe

to him the difference between a raster and vector data

structure. and give an example of a GIS database that

might be designed with each structure.

2.5 Quadrangle challenge. Locare rhe USGS 7.5 m inute Quadrangle that contains your GIS classroom or

work location.

a) Whar is rhe name of rhe quadrangle? b) When was me map originally compiled? c) If rhe map has been updared, when was ir updared? d) Whar is rhe Ohio code descriprion of rhe map? e) When was rhe copography developed? f) How much magnet ic declination existS in the map?

2.6. Resolution and scale. A consulranr has proposed using sarellire imagery co quickly updare rhe foresr resources thar your natu ral resource management organi

zation manages. Some people in your organizarion are

arguing for a complete and fresh phow interpretation of

rhe land base to accomplish (his goal, resulring in a vector

GIS database of the vegetation condition of the landscape.

The differences in resolution and scale are two of me hot

copies when comparing these alternatives . Explain the dif

ference belVv'een resolution and scale. and how they relate

[Q raster and vector data structures.

63


2.7. Designing GIS databases. You have been hired by a natural resou rce consulting agency [0 develop and maimain a small GIS operation . While your expertise is in

natural resource management, the owners of [he consulrp ing agency were incrigued by your GIS expertise, and have been interested in providing chese services (0 their clients.

You, of course, (Oak the job because it is an opponuniry

to pur your GIS and namral resource management skills

to usc. As a stan, what data Structure might you use to describe the landscape features in the following databases?

a) timber stands g) precipitation

b) screams h) land ownership c) roads i) scream buffers d) inventory plots j) owl locations e) culverts k) owl habitat f) logs in scream

2.8. Map scale and ground distances. You are employed as a field forester for the Ministry of Natural Resou rces , and arc stationed in northern Maniroba. You

References

Cadle, F.W. (1991 ). Georgia land surveying history and law. Athens, GA: The University of Georgia Press.

Chang, K. (2002) . Introduction to geographic information systnm. New York: McGraw-Hili.

Clarke, K.c. (2001). Getting starud with geographic infor

mation systems. Englewood Cliffs, NJ: Prentice Hall, Inc. DeMers, M.N. (2000) . Fundammtals of geographic infor

mation systems. New York: John Wiley and Sons, Inc. Dent, B.D. (1999). Cartography thematic map design .

New York: McGraw·Hili. Ladell, J.L. (1993). They kft their mark: Surveyors and

th~ir role in the settlement of Ontario. Toromo, ON: Oundurn Press.

Lillesand, T.M., & Kiefer, R.W. (2000). Remote smsing

and image interpretation (4 th ed.). New York: John Wiley & Sons, Inc.

Muehrcke, P.c., & Muehrcke, J.O. (1998). Map use:

Reading, analysis, and interpretation (4th ed.). Madison, WI: JP Publications.

have been inventorying timber for the past three hours,

and now it is time for lunch. Your lunch. of course, is located in your truck. The distance you measure on your

map from your current posicion to [he Huck is 12 cm. If the map scale was 1: 12000, how far are you fro m your

£ruck?

2.9. Spatial resolution. The natural resource management agency you work for in [he imermoumain wes[ is

considering [he purchase of 30 m resolmion sa[elli [e

imagery for assiscing in [he managemem of [heir natural resources. How much area, in acres, does a single 30 m grid cell cover? How much area would be covered by a single 100 m resolution grid cell?

2.10. Spatial scale. Your supervisor has asked that you bring a map at the largest map scale possible to your next planning meeting. You have your choice of the following map scales: 1 :24000 or 1: 1 00000. Which map do you bring with you?

Robinson, A.H, Morrison , J.L. , Muehrcke, P.c. , Kimeriing, A.J., & Guptill, S.c. (1995) . Ekmmts of cartography. New York: John Wiley & Sons, Inc.

Snyder, J.P. (1987). Map projectiom-a working manual. Washington, DC: Uni[ed 5ca[es Governmem Priming Office.

Stewan, L.a. (1979). Public land surveys. New York: Arno Press.

Thompson, M.M. (1979) . Mapsfor America (3rd ed. ). Washington, DC: US Government Printing Office.

USDI US Geological Survey. (1995) . Geographic names information system, data users guide 6. RestOn, VA: US Geological Survey. Retrieved January 15,2003, from from: http://mapping.usgs.gov/www/ti/GNIS/gnis_ users-suide_toc.hcrnl .

Wolf, P.R., & Ghilani, C.O. (2002) . Ekmentary survey

ing: An introduction to geomatics (10th ed.). Englewood Cliffs, NJ: Prentice Hall, Inc.

64

Chapter 3

Acquiring, Creating, and Editing GIS Databases

Objectives

This chapter discusses a number of topics related co

acquiring, creating, and edidng GIS databases. Readers should gain an understanding of the opportunities and

challenges associated with the need [Q obtain GIS data from a variety of sou rces. At the conclusion of this chapter, readers should be able to understand and discuss issues related [0:

I . {he acqu isition of GIS databases, panicularly via the Imerner,

2. [he various methods for creari ng new GIS databases,

3. the processes for editing existing GIS databases, and

4. the error types and sources [hat are poremialiy associated with GIS databases.

Acquiring, creating, and editing GIS databases to address rhe needs of natural resource management decisionmaking processes is a continual process. Ideally, narural

resource professionals would have a complete and robust set of GIS databases at their disposal prior to performing an analysis. However. as new and inreresting opportunities to incorporate GIS analysis in decision-making processes arise, the G IS database needs change as well. Four general cases are common in natural resource o rganizations. as rhey relate to the availabi li ty of GIS databases:

1. GIS darabases required for a specific analysis do nor

eXIst.

2. GIS databases exist. bue they were created for other general uses and may not be quite appropriate to

address [he issues related to a specific analysis.

3. GIS databases exist, bue rhey were created for other

specific analyses and are not quire appropriate to

address the issues related to another specific analysis. 4. GIS databases exist, and they are adequate and appro

priate to address the issues of a specific analysis.

Ideally, you would hope that your organization is continually positioned near the fourth case noted above.

However many GIS users find. evenrually, that the first three cases are real , and that time mUSt be spenr acquiring or developing a GIS database. Several options are available

to GIS users faced with having to acquire, create, or edit GIS databases. These options include having someone else create a GIS database (e.g., a GIS contractor) based on maps and other input provided to them, using C PS or some other field-based data collection method to fuc ilitate

the development of a new GIS database, modifYing or

editing an existing C IS database. creat ing a new GIS database by digitizing maps, acquiring a GIS database from the Internet (for example, the National Werlands Invento ry from the US Fish and Wildlife Service [http://weriands.lWs.govl]), o r acquiring a GIS database from mher organizations. The decision [Q pursue one of these strategies will depend on several factors inherent to a person's job. such as the budgetary resources, time constraints. and the skills and computing resources that are available within the natural resource organization.

65

Acquiring GIS Databases

One of the main concerns of natu ral resource managers is locating and acquiring GIS data. Physically receiving the

GIS databases is relatively easy; they can be sen< and received as an email attachment via the Internet or by way of media such as portable USB drives, compact discs (CDs). or DVD disks. GIS databases can be acquired from a variety of clearinghouses. many of which are maintained

and supported by federal, provincial, or state organizations. The US federal government, in fact, is perhaps the larges< source of GIS dara in the world. A variety of federal agencies in the US provide data , and the Manual of Federal Geographic Data Products (US Geological Survey. Federal Geographic Data Committee. 2002) provides a wealth of informacion concern ing rhe agencies from which GIS databases can be acquired. Natural

Resources Canada (Natural Resources Canada. 2007) and the Geography Network Canada (Geography Network Canada. 2007) provide access to many types of geographic data within Canada. Provincial and state agencies. such as rhe Washingcon Depanment of Natural Resources. also d isrribuce a large number of GIS databases

related to the resources of each state. Acquiring GIS databases over the Internet has become

a widely used practice over the past few years. The disad

vantage, however, is that the fo rmat of the data is generally limited to that of the most popularly used data. and may not be directly compatible with some GIS software programs. Agencies that require GIS users {Q make requests for GIS databases may provide a wider variety of products to be acquired, as well as a wider variety of for

mats, depending on the provider. In the case of data

requests, however, the agency may require mat a payment accompany each request. The payment covers the Cost of the media and staff time required to package and deliver the GIS databases; the rates are usually reasonable because public organizations provide the data. People requesting

GIS databases are asked to provide a va riety of information specific to their request (Table 3. I). so that the final product will meet (as closely as possible) their needs without the staff investing extra effort in formatting, re

projecting. or adjusting the GIS databases. As an example of acquiring GIS databases via an

Internet site, rhe Gifford Pinchot N ational Forest (Washington State) maintains a website where a number of GIS databases can be acquired (Gifford Pinchot National Forest. 2007). The Internet site is located at http:// www.fs.fed .us/gpnflforest-research/gis/. By accessing this

Chapter 3 Acquiring. Creating. and Editing GIS Oatabases

TABLE 3.1 Typical information associated with a GIS database request

• G IS database requested

55

• Locarion (Township/Range/Section. Topographic quad indO[ Ohio

code [or nameD

• File format (e.g., Spatial Data Transfer Standard [SOTS] . Ardnfo export format, ESRI shaptfile. Tagged Image File Format [TIF1. Imagine format [IMG], ESRI geodat1lbase)

• Map projection. coordinate system. horizontal and vertical measurement units, and related parameters

• Metadara

• Contact person for questions related to the GIS database

• Delivery method (USB drin. DVD. CD. e·maiI. FTP, ere.)

Database compression format (Zipped. MrSID. TAR, ere.)

Billing information

Product license agreements

website. you will find that all of the vector GIS databases are

available in Arclnfo coverage (ESRI. 2005) expOrt format. Databases can either be downloaded directly from this Interner site, or obtained on a CD-ROM or 8 mm tape.

The COst of obtaining GIS databases on media is $40 (US currency). In addition. some meradara related to each GIS

database can be accessed through the Gifford Pinchot

Nati<;>nal Forest website. The data related to the forest trail system, for example. indicates that the source scale for the GIS database was I :24.000. that it was last updated in 1999, that the projection system is the T ransverse Mercator using the Clarke 1866 spheroid. that the coordinate system

is the UTM system. and that the datum is NAD27. In addition, the me[adata identifies the primary contact person and alerts GIS users [hat some gaps in the trai l system may

exist so that users know to examine the data closely before using i[ to make management decisions.

As you may have no ticed from the Gifford Pinchot example. a variety of GIS databases are available in addi

tion to [he forest trails system, including hydrology, elevation, forest stands, roads, streams, and others. While these GIS database are commonly used to support resource

management on rhe National Forest, they are mainly vector GIS databases. Raster GIS databases. such as digital orrhophotographs, related to a particular landscape are perhaps more difficult to obtain . As mentioned in chap<er I. digital orthophotographs look like aerial photographs but {hey are srored in digital form and are regis

tered to a coordinate and projection system . Raster GIS

66


,. ~ l'_ t ~'In"Depth ' ~1:..1~~<.. '.~ . ..

Reference is made in this book to a number ofInrernet

sites that you can visit for additional informatio n and

resources. The Internet is in a constant state of Aux,

however, and organizations often change website

addresses, or URLs. Since URLs change periodically, you might find that some examples provided in this book have become obsolete. Should you have a problem accessing the URLs we have provided, twO strate

gies can be employed to reach the approptiate address. First, verify that the website address being used

<xactly matches the URL listed in the book. Even the smallest of differences (one incorrect letter, or an extra

space) can resuh in not reaching the appropriate web

site. Second, with a liule ingen uity you can locate the

In ternet sites listed in this book by using an onl ine

search engine. For example, the URL to the Washington Department of Natural Resources

Information Techno logy Division order form has

changed several times since the first edition of th is

book. To locate the most cu rrent version of the order

form, you could anempr an online search similar to

the one oudine below:

databases can also be acqui red via the Internet. The

Minnesota Planning, Land Management Information

Center (2007), for example, maintains a clearinghouse ( http://www.lmic.state.mn.us!chouse!metalong.htmll) where GIS users can access the 'Imagery and Photographs'

How do YOli acqu ire data over the Internet? Usually.

clicking on an Internet link will starr the download

process by presenting a dialog box that asks where the downloaded file should be stored. Another way is to download GIS database files to a computer is to use a

FIP. FTP stands for File Transfer Protocol, and it is a

widely used method of transferring data over the Internet. This method allows you to transfer computer

files to other remote computers. or ro download com

puter fi les from a remote computer. One advantage of

• Search the Inrernet for 'Washington Departmenr of Natural Resources'

- Result: http://www.dnLwa.gov!

• Select 'Publications & Data' from [he list of items on [he DNR main page.

- Result: http://www.dnr.wa.gov/baselpublications. html

• Select 'GIS Data' from the lise of items under

Publications and Data sub-heading - Result: http://www.dnr.wa.gov/dataandmaps!

index.html

• Select the 'Available GIS Data' choice from the available links on the Data page.

- Result: http://www3.wadnr.gov!dnrapp6/data web!dmmatrix.html

At this point, you should be presented with a list of available databases that can be downloaded in an ESRI shapefile format (ESRl, 2005). Thus in four steps, you can reach the currenr website from wh ich spacial da[a

can be accessed from the Washington State DNR.

data category and download county-level digital aerial imagery, including orthophotographs, varying from 1 m to 10m spatial resolution. In add ition, the clearinghouse provides CIS users with some meradar3 related to the digital imagery. You can ascertain from viewi ng meradar3

this process is that PCs are able to communicate w ith

UNIX-based machines, and GIS database file formats are automatically convened into a Windows~-compat

ible format during the transfer process. FTP was once

only ava ilable on a PC th rough a DOS interface, but several low-cost Windows@ applications are available

that simpli fy this process and make additional file transfer or sharing options available. To locate such a utility, try an Internet search using the keywords 'FfP'

and 'Windows' together.

67

that much of the imagery uses coordinates referenced within the UTM system, with an GRS80 ellipsoid and NAD83 datum. The clearinghouse also provides information on the accuracy, consistency. and completeness for

some of the imagery. With advances in technology, many private firms have

emerged in recent years that develop and sell GIS databases. Most GIS and land surveying trade magazines will fearure advenisements from these firms . The breadth of

GIS databases and daca creation services cont inues (0

improve and all indicacions are that growth in [his area

will continue. The current possibilities range from small

land surveying firms who are capable of collecting highly precise and accurate vector data from relatively small land

areas using either digital tOtal stations or ground-based

LiOAR, to larger organizations that offer raster imagery

captured from aerial or satellite platforms that are capable of imaging large land areas (regions. nations, continents.

the globe). Many of these organizations also advertise their services via the Internet.

Creating GIS Databases

If GIS databases required for an analysis do not exist in digital form in your organization, and cannot be obtai ned

through other means, such as via the Internet or by

request from a state. provincial, or federal agency, you

may consider creating a new GIS database. Several factors

mUSt be considered when creating a new GIS database.

including the type of information needed to adequately develop the database, the intended format of the data, the projection and coordinate system required. and (he accu

racy desired of the resulting GIS database. Creating new


GIS databases can be a time-consuming and costly endeavour. The most common methods used to create new vec

tor-based GIS databases include traditional digitizing,

heads-up digitizing, and scanning. The process of creating GIS databases, either by digitizing maps, using a GPS

to capture spatial coordinates that describe landscape fea

tures, or by other means, usually amounts to 70-75 per

cent of the total time invested in GIS in support of a spa

tial analysis (DeMers, 2000) . As you will find in subsequent chapters. new GIS databases can be created as a

result of spatial analysis processes such as buffering, dipping, and overlay analysis. When creating new GIS databases with spatial analysis processes, concerns about the

projection system, the coordinate system. me datum, and

map units are lessened because the resulting GIS database:

is usually represented by the characteristics of the other GIS databases involved in the spatial analysis process.

If GIS darabases do not exist, but maps of the land

scape featu res of interest exist , these maps can be digi

tized using a manual digitizer. A series of measurement

reference points (sometimes called control points or 'tics')

mUSt be available to allow you to register the map to a

digitizing table. Reference points can include easily located landscape features, such as road intersections and

building corners, or less easily located landscape features, such as property corners, section corners, or a systematic

grid of points (Figure 3.1). Each of these reference points must be in a dearly definable location on the map, and the ground coordinates (coordinate system units) must be

known . A common source for ground coordinates is

USGS 7.5' Quadrangle Maps since they usually feature coordinates along the map border in geographic, UTM,

and State Plane Coordinate systems. It should be noted

490000 500435 510436: +----,-1----c---="--,,,--t 510436

490000, +--=:=...l.-JL....1-L-_c..._+ 500435 500000 5~

- Roads

D Stands

+ Reference points (with associated XandY cOOfdinates)

Figure 3.1 Measurement reference poinu for the Daniel Pickett forest to enable digitizing additional landscape features for the creation of new GIS databases.

68


that coordinates derived from paper Quadrangle maps would nor have high accuracy since the coord inates are

listed at broad unit imervals, and you must incerpolare

the location of landscape features. At least four registra~ cion points or rics are required to facilitate the digitizing of a map. Additional reference points, if available, wi ll likely increase the accuracy of the spatial position oflandscape features in the resulting GIS database.

At beSt, digitizing is an imperfect practice. and the quality of results can be dependent upon many factors. The accuracy of digitized GIS databases can be affecred by the experience of the person doing the digitizing, by errors in either the location of reference marks or their

associated geographic coordinates, and by imperfections

in digitizing equipment and software (Keefer et aI., 1988; Prisley et aI., 1989). One of the often-misunderstood accuracy issues relates co the digirizarion of the map itSelf. If the map is old, or if it had been exposed to moisture (or even humidity), it may be subject to shrinkage or expansion. The shrinkage or expansion could vary across the

map's su rEace, and thus the location (as well as the shape) of any landscape features that are digitized from it may be distorted. I n addition, the methods used to delineate landscape features for digitizing could cause inaccuracies

in the resulting GIS database. Regardless of how experienced a digitizing technician may be, if the map being

digirized includes poorly delineared landscape features (Figure 3.2), then perhaps the 'garbage in, garbage out'

Figure 3.2 A landslide drawn on a map with a r~gular sharpen~d p~ncil (upp~r left), a mark~r (upper right), a sharpened pencil, y~t in a sloppy manner- me landslide are:& is not closed (lower left), :& m2rk~r. yet in a sloppy manner-me landslide ar~a is barely closed (Iow~r right).

principle applies. In this example, digitizing technicians

may have rather precisely drawn landscape features to reference (upper left image) when creating a landslide GIS database, or some rather imprecisely drawn landscape fea

tures. When digitizing the upper right image of Figure 3.2, for example, would the landslide be defined by the outer edge of the thick line used to describe the landslide area, the inner edge of the thick line, or the center of the

thick line? When using the lower left image, the landslide area is not represented by a closed polygon, thus the technician would need to use judgment or intuition in digitiz

ing a closed polygon from the model provided. Within some GIS software programs, landscape fea

tures can be digitized directly from a registered image that

is displayed on a computer screen without having {O

establish registration points. This process is known as

'heads-up' digitizing and has increased the usefulness and popularity of goo-referenced raster products (such as the digital raster graphics and digital orthophotographs discussed in chapter 2). By not having {O establish registra

tion poims, the digitizing process does not require a dig

itizing tablet, is faster, and offers less opportunicy for

error. Two advantages of heads-up digitizing are the abil

ity {O use digital imagery as an on-screen backdrop during

the digitizing process, and the ability to change scales at which the digitizing takes place (by zooming in or Out).

Heads-up digitizing is explored in more detail in chapter

8, when the processes of updating GIS databases are described .

Scanning. as discussed in chapter 1, can also be used [0

conven a hardcopy map to a GIS database. Scanners sense

the differences in reflection of objects o n a map and

encode these differences numerically in a digital file. For instance, lines and points (if identified by dark ink) would generally be distinguished from background areas (if [he lines and points were drawn on white paper) , and raster

grid cells would be created to describe rhese features (Figure 3.3). The size of the grid cell (a rasrer data structure) will obviously be important when scann ing land

scape features . With increased resolution, or a greater

number of grid cells per unit area, the greater the ability

of the scanni ng process [0 return discrete shapes from a

scanned surface will be. With larger gr id cell sizes, less computer storage space is be required. bur some landscape featu res may not be as accurately captured as desired. If

needed, raster grid ce lls can then be converted to points. lines, or polygons (vector data structu res) using ras ter-tovecror conversion algorithms that are common to most

GIS software.

69

(al

Figure 3.3 A limber stand (a) in vector format. from tbe Brown T ract, scanned (b) or converted to a raster format wing 25 m grid cells, then convcru~d back to vector format (e) by con necting lines to the center of each grid cell .

Editing GIS Databases

There are numerous reasons why you would edit a GIS database, such as re-projecting a GIS database to a common proj ect ion system used by a particular natural resource organization, edge-matching GIS databases describing landscape features in adjacem areas (e.g .• (Qwnships. quadrangles. ece.) so tha[ they fie [oge[her seamlessly, and other generalization and transformation processes necessary to convert a GIS database to a standard format or resolmion. In additio n, some GIS databases

need co be continually updated and maintained, as landscape features and their attributes change over time. Processes rela[ed co upda[i ng G IS da[abases will be explored further in chapter 8, bue as an example, most forest industry organizations update their timber stands G IS da[abase annually co accoune for [he changes [hac have occurred in the forest land base over [he previous 12

months as a result of harvesting and Q[her management activities, growth of the forest reso urces, and disturbance evenes (e.g .• floods or wildfires). The da", collec[ion and repo rting associated with updating processes can be laborimcnsive and error-prone, indicating a need for standard

ized verification and editing processes.

Chapter 3 Acquiring. Creating. and Editing GIS Databases 59

The processes tha[ you would use co find landscape

features or 3nribuces requiring editing can be class ified as 'verification processes'. Wirh a verificadon process, [he

goal is to verify [hat a parricuiar set of values within a database is appropriate (or reasonable, or within some s[andard). Verifica[ion processes should be devised so [hac bo[h [he landscape fea[ures and [heir amibu[e da", can be assessed for completeness and consistency. These processes are probably bes[ accomplished by involving mulciple personnel so [hac G IS da[abases ace checked independently. and [hac a[ lease twO da", qualicy assessmenes are performed. The cypes of error [hac can easily be recognized include [he improper loca[ion of landscape feamres, an improper projection system, and miss ing or

inappropriate attribute data (data o utside a reasonable range of values). These data errors could arise at any stage in the da[abase upda[e process. and regardless of [heir origin. should probably be correc[ed. If errors are loca[ed. or ocher changes need co be made. [hen [he G IS da[abases

need CO be edi[ed. The following represents a shorr example illustrating a

framework for verifying data and locating errors in GIS

databases:

Assume you work for an organizat ion that manages a large area of forestland. over 200.000 ha. Wi[hin {his organization there are a variety of people who have responsibili [ies for upda[ing and managi ng [he GIS da",bases tha[ desc[ibe [he forestland. from inventory foresters who collect (he data. to infor

mation systems analysts who incorporate the data into standard database formats for use within the organization. A general process of updating the

inventory databases (both tabular inventory and spa[ial landscape fea[ures) might S[a[[ wi[h the inventory forester compiling new inventory data

(cimber cruise repons) and maps showing changes to {he forestland due to management accivities over a period of [ime. perhaps [he las[ year (Figure 3.4). This information is passed to the info rmation syscems analyses. who verilY tha[ [he data (boch maps and inventory) contain the appropriate type and forma[ of da[a needed ro successfully comple[e [he upda[ing process (verifica[ion process # 1). If errors are located, some of this information is passed back

to the inventory forester for clarification and editing. If the information is complete. and formatted correctly. the maps are digitized and the invemory da", files are encoded (e.g .• da[a is keypunched ineo

70


Inventory forester

maps, data files

Information systems analysts

r - ------- -- - - ---- - -- - -, ~ . I

Delineate changes maps, data files to be made to

Check data f(l( Verification process #1 inventory

, maps, data files I , , , , ,

GIS , databases

, , , , , , , , , , , , , , , , , GIS

,. , , , , , , ,

mistakes and omissions

L maps, data files

Digitize changes ~ to spatial data,

encode inventory

! Check data f(l( mistakes and -

omissions

! maps, data f

Integrate into ~ GIS

database

J

-; I I I I I I

J Verification process #2

iles

I I

Verification process #4

Check data for mistakes and databases

Check data for mistakes and J Verification

process #3 -omissions omissions

Figure 3.4 A gc ncraliud process for updating a forest inventory GIS databa.sc.

a compurer file format), and a subsequem verification process (#2) is used to check whether these processes were performed successfully. The inven[Ory data is then integrated into a standard GIS database format, and a third verification process is

used to ensure that all of the changes have been incorporated into the updared GIS database. One way to do so would be to check the resulting GIS database against the information supplied by the inventory forester. Finally, the GIS database(s) are distributed back to the field office, where the inventory forester has the opportunity [0 verify (process #4) whether the proposed changes have been incorporated into the GIS databases.

It becomes obvious that editing processes, not JUSt for annual inventory updates bur also for periodic changes that sho uld be made when discrepancies are found. should be considered in time and budget estimates to develop and distribute GIS databases to field offices. Some of the more common methods used [0 edit GIS databases include:

I. Add new landscape features (points, lines, polygons) [0 an exist ing GIS database.

2. Change the shape or position of existing landscape fe-atures.

3. Add new fields (columns) to the tabular portion of the G IS database.

4. Edit data in fields (columns and rows) in the tabular

portion of a GIS database.

Editing attributes

Attributes, as described in chapter 2, are values used to

characterize or describe landscape fearures and, thus, the qualities of the landscape. Through verification processes

you can determine whether the attributes of landscape features are appropriate by assessing whether they are out

side of the range of appropriate values. or missing entirely. In addition , as GIS databases are updated. you might assume that some attributes change over time. For example, as trees grow the characteristics of a forest will change. In the Daniel Pickett forest stands GIS database (Table 3.2), each stand is represented by a vegetation

71

TABLE 3.2 Attributes of stands in the Daniel Pickell s tand. GIS database

Re·

Vegetation BaW O riginal inventoried

Stand "'P' u ... • Age MBF" MBP

A 200 50 21.2 23.2

2 C 175 40 12.9 15.3

3 A 210 55 25.8 26.8

4 A 250 65 34.2 37.0

5 C 90 20 3.1 5.6

6 A 220 55 25.7 28.2

7 C 150 35 8.7 10.5

30 C 190 45 17.3 20.3

3 1 C 110 25 4.1 7.7

• square (ttl ~r acre

h thousand board feet ~r acre at [he dme of original inventory

• thousand board fect per acre afrer compiling new inventory

type, basal area, age, and volume (thousand board feet per acre, or MBF) at some panicuiar time in [he history of management of [he forest. If you had re-inventoried me

forest a few years after the stands GIS database was cre~ ated, you might have found that the trees within the forest have grown, and tha< the stands GIS database may be in need of edicing (as the age, basal area, and volume have likely changed).

Another example might include aquacic habir3r invencories that attempt ro monitor change in habitat conditions. such as fallen large woody debr is concenrcarions or pool densities within a suearn system. Aquatic habitat

variables are consrantly in Aux within river systems as flow characteristics vaty both by season and annually. Large

woody debris is continually introduced into some stream systems as the result of natural processes or management activities, and is cransponed through a river system as flow regimes allow. An inventory of such streams would

likely determine changing patterns of woody debris and pool concemrarions on at least an annual basis. If natural resource managers wanted to track changes, new data

columns could be added for each of the habitat characteristics chat are of imerest. and the updated invemory data could be entered into che new columns. If the position of


the stream segments was also of imerest. [he positions of

lines or areas that capture stream locations may also need

to be adjusted.

Editing spatial position

Attributes of landscape features can also include [he spatiaJ coordinates of the feacures, and as these change, or are

found to be inaccurate, chey require editing. For example,

as owls disperse, the spatial location of their nests may change, and the X, Y coordinates that describe owl

nesting sites require editing. Or as vegetation panerns

change within a research plot, the polygon representing the plot may require splining so that all patterns are recognized. Or as GPS data are collected and incorporated

into a GIS database, multi-pam error may be present, and should perhaps be either eliminated or corrected. GPS collection of data can also improve the accuracy of the loca

tion of landscape features that were previously defined and delineated with less accurate methods. For example,

GPS data collection methods can improve the accuracy of a roads GIS database [hat was originally created from

measurements collected from aerial photographs or with a staff compass and surveying tape.

Spatial position edicing techniques vary widely across GIS software programs due, in part, to the different file formats that the different programs use. In general. how

ever, editing the spatial position of a landscape feature requires chat GIS users make a spatial layer 'editable' . Once a GIS database is editable, edi ting tools are used to

move, copy. create, or delece points. lines, and polygons. While GIS databases containing point landscape features

are typically easy ro edit because a single coordinate pair represents them, databases that comain line and polygon landscape features usuaJly require more care because the topological integrity oflines and polygons must be main

tained. As discussed in the last chapter, topology describes (he spacial structure oflandscape features and is essentiaJ when comparing the positions of features to other features. While some GIS software programs have fearures to auromatically examine and correct topological problems. others offer no rools for topological considerations and will consequently produce flawed analysis results when correct topology is not in place.

Depending on the amoum of editing that is necessary. ahering the spatial position of landscape features can be a demanding chore. and one that requires great attention to detail. Regardless of the GIS software program used, a

72


good first step fo r any spacial editing process would be to

ensure that a back-up copy of the database is made and securely srored. Once in the editing process, it may be

imponant to remember to save the changes to GIS databases often (to avoid losing work due [Q power shortages

or computet failure). and to periodically document the editing progress (to avoid forgetting what work has been done. and what work needs to be done next) . Documentation may be particularly important if multiple people are editing (at various times) a single GIS database.

Checking for missing data

One issue that affects the accuracy of GIS databases is the omission of cenain landscape feacuces. How would you

know if one, or many, landscape feacures have been omit

ted from a GIS database? Comparing GIS databases to reference maps or photographs is perhaps [he simplest process. Feacures could be omined because of improper

map creation procedures or other blunders (for example.

changes in the landscape that were not accou nted for in

an update process). Omissions may also occur in the 3nribure data assoc iated w ith landscape features. For

example. wh ile a streams GIS database may contain all of the streams associated with a watershed, it may lack cer

tain characteristics of some streams, such as the width or

depth. Performing queries of landscape features to learn where attributes have been omitted will allow you to

locate the landscape features that need editing. In the

example illustrated in Figure 3.4, an information systems

analyse could query the updated GIS database (as part of verification process #3) for stands that were altered during

the update process, then examine the attributes of those

stands for missing data. In doing so. the analysts could verifY that all of the stands needing to be updated were. in fact. updated correctly (by comparing the updated GIS database against the maps and inventory data provided

by the inventory forester). Some GIS software programs are unable to explicitly

handle missing data values for numeric attribute variables

and will assume that a default value of 0 if an attribute value has not been specified. As you might imagine, this

shortcoming can result in sign ificant problems if these

values are included in analysis resul ts, as most statistical summaries or testS take sample size imo account, and

samples of '0 ' are included in the computations. One

strategy for handling this problem is [Q assign a large negative val ue. such as -999 o r -9999, to missing attribme

values. Regardless of what value is used to signify missing

data. the value should be a number that is far outside the range of acceptable data values and large enough to have a very noticeable effect in results if inadvertendy used for analysis.

Checking for inconsistent data

You should expect some level of map error in each GIS

database simply because all hard-copy paper maps contain errors and these errors are carried along inco any digital

form of the map that results from digitizing or scanning

processes. With app ropriate co ntrol in map creation

processes, this error should be kept within desired toler

ances. Map error can also result from inconsistencies in

how landscape features are defined. For example some

timber stands might be very finely delineated. whereas others are more coarsely delineated (Figure 3.5). In other cases, error arises because rwo (or more) GIS databases

were created independendy, using different encoding

processes. This may result in features within a GIS data

base being represented with different precision. For exam

ple, one database may have used one process (e.g .. digitizing) to create a fo rest stand GIS database. and anmher

process (e.g .• GPS) might have been used to independently create a roads GIS database (Figure 3.6). Upon close inspection. GIS users may find some public roads conta ined w ithin timber sta nds, when they should more

accu rately be represemed as being located outside of tim-

Figure 3.5 A tim~r stand drawn more precisely (tOp) and less precisely (bottom). Note that the lines on the south and ea5[ern portion of the figures are different.

73

Inconsistency _ Roads

o Timber stands

Figure 3.6 Spatial inconsistency betwttn a timber stand CIS databau: created through digitizing and a roads database created through GPS meuurtments.

ber stands, and under the jurisdiction of some state, county. or municipality. Depending on the quality of the GPS receiver that was used. the likelihood is that the GPS

database would more accurately represent the location of the road. Performing attribute queries of landscape fea{Ures with values that are unusual, or outside of some logical range of data. and observing the results either through tabular or graphical means. should help identify whether problems exist and where editing processes are needed. eressie (1991) provides some gu idance with identifying spatial data outliers, and suggestS verification processes such as creating histograms and distribution modelling.

Sources of Error in GIS Databases

One of the fundamental facts about computers is that they follow the instructions provided by computer users (unless they are suffering from an internal hardware problem or a computer virus). Computers have no sense of right or wrong. Therefore, assuming that the hardware is functioning correcdy and you have been diligent in scanning for viruses. any errors that are found in GIS databases should be assumed to be a result of either encoding (database creadon) or editing processes. Granted. some computer software programs are not perfect, and go through a number of versions to correct processing problems inherent in their computer code. However, when an error is located in a GIS database. it likely arose from a data creation or editing process. There are three sources of error commonly found in GIS databases: systematic errors, gross errors, and random errors.

Systematic errors, sometimes called instrumental errors, are propagated by problems in the processes and


tools used to measure spatial locations or other attribute data. Systematic errors are sometimes called cumulative errors, since they tend to accumulate during data collection. They can be corrected if you can understand how each measurement is systematically affected. For example. suppose in digitizing a map that the reference points that were used to establish the initial location of the map

were all erroneously shifted the same amount slightly ofT to the east. The landscape features that are digitized after this error is introduced will also be systematically positioned the same distance to the east of their true location. You might correct fo r this displacement by adding the corresponding distance, in coordinate units, in a westerly direction to the coordinates of all of the landscape fea[Ures in a GIS database. Systematic errors in the collecting and processing of attribute data can also exist. For example, if you were [Q compute the area of a series of watersheds using acres, and then convert the area measurements to SI units (hectares) using an inappropriate conversion factor (e.g., 2.4 hectares per acre rather than 2.471). the metric areas would all be systematically incor

rect. Corrections in this case can be made by a recalculation of the SI units by using the appropriate conversion factor.

Gross errors, sometimes called human errors, are blunders or other mistakes made somewhere in the data collection, map creation, or editing processes. For example, when digitizing a set of landscape features. such as land

slides. a landslide that was delineated on the map being digitized might be omitted from the resulting GIS data

base. Or, when collecting forest stand information, an incorrect species code may be used to describe cerrain tree species. There may be no pattern in the occurrence of these errors, thus they may only be identified and corrected through verification processes. A thorough verification process will involve database checks by someone who did not participate in data collection or recording. Undoubtedly, human errors have the potencial to exist in almost every GIS database.

Random errors are a by-product of how humans measure and describe landscape features. In contrast to machines, most humans are incapable of repeating a task over and over without any difference between repetitions. In the context of landscape measurements, ic is unlikely that a person will be able to use a measurement instrument and return measurements that are consistently accurate and precise. No maner how carefully data such as tree heights from a forest are collected, there wi ll be error in the representation oflandscape features and their asso-

74


cjared attributes. Chances are that recorded measure

ments will be ar leasr slightly off from rhe [Cue mark due

to (he limitations of vision, musculature, and instrument

se[pup and application. As long as consistent procedures

are followed and no blunders occur, measurements will

tend to be grouped closely around the actual measure

ment, with differences occurring slighdy in all directions.

These types of small errors are called random errors; rhey

remain after all sys tematic errors and blunders are

removed. Random error occurrence does tend to follow

rhe laws of probability, and rhus should be normally dis

tributed in a statisrical sense. The statistical distribution

of repeated measuremems can be estimated in order to

obtain an idea of the variation expected in either the spa

tiallocarion of a landscape feacuee or the associated attrib

ure dara. Through the process of least sq uares adjusr

ments, you can attempt to remove random error from GIS

databases when concern about accu racy issues is high

(Ghilani & Wolf, 2006). More ofren however, in narural

resource management it is assumed that data collection efforts were carried Out diligently with respect to [he accu

racy and precision of measurements, and {hat random

errors rend to cancel each other out. For this reason, random errOfS are sometimes termed compensadng errors.

Two (erms are important when assessing the useful

ness of a G IS database: logical consistency and completeness. Logical consistency is (he term most GIS afi

cionados use co describe how well the relationships of di fferenr types of dara fir rogetlher within a system. In

some cases (his refers to the consistency of the ropological

relationships among GIS databases. For example, when streams are displayed in conjunction with the contour lines of an elevation GIS database. all streams shou ld

What is error? Error can be defined as something produced by misrake or as the difference between rhe

true va lue of a feature and its observed value

(Merriam-Websrer, 2007). How would you know

there was an error in a GIS database? Perhaps by comparing rhe value (o r shape of a landscape fearure)

in a GIS database to what is known as the correct value (or shape of a fearure) from a field survey, pho

cograph, etc. When you consider GIS database erro rs, the goal is to understand three issues: the type of

appear fl ow downhill (as opposed ro uphill) . In orher

cases. logical consistency refers to the anribme dara of a particular GIS database. For example, the dominant tree

species in o ne polygon may be labeled 'Loblolly', whi le in

anorher polygon ir may be labeled 'Loblolly Pine', and in

a rhird, 'Pinus taeda'. Completeness is a term used to

describe rhe types and exrent oflandseape features rhat are

included in a GIS database, and conversely, those that are omitted. For example, in some cases not all types of streams (e.g .• ephemeral streams that only contain flowing water during rain events) are included in a streams GIS database. In add ition, smaHer streams may be omined

because they weren' t apparent in the remotely-sensed da ta that were used co create the database. If only a portion of total number of stream types were included in a streams

GIS darabase, you might conclude that rhe database is not complete.

Types of Error in GIS Databases

Since creating, editing. and acquiring GIS databases may involve many different processes, a variety of errors can

obviously crop up. Some of the more commo n types include those relared to the locacional position of a land

scape fearure, those rela ted co the tabular att ribmes of a landscape feature, and those resulting from compmational problems.

Positional errors simply imply that a landscape feature is locared in the wrong place in a GIS darabase. These errors can arise during GIS database creation processes, such as digitizing or scann ing maps. As mentioned earlier, the digitizing of landscape features requires that a map be registered (Q a set of ground coordinates. How well the

error that exists, the so urce of the erro r, and the extent of the error. All of these relate CO the uncertainty associated with the landscape features contained in a sparial database. Hopefully you have a

high level of confidence in the dara (i.e., uncertainty

regarding the location oflandseape fearures and their

attri butes is minim ized), so that analysis efforts can be used with confidence to positively facilitate decisions made regarding the management of natural resources.

75

registration is performed and how accu rate the coordinates are represented on a map are both factOrs that contribute to errors in a resulting GIS database. Those who

digitize maps also make errors-sometimes systematic errors (nor using a digitizing puck correctly throughout an enrire digitizing session), and sometimes gross errors (missing or displacing some objects entirely) . Estimates of positional error usually indicate thar some percentage of landscape features should be located within some disrance either horizontally or verticaIly from their true position. A statement of positional accuracy, for example, might indicate that 90 per cent of the landscape features are within 150 meters of their true, or on the ground,

position . The ultimate use of a GIS database is also considered

when describing the positional accuracy of the landscape features rhey contain. If the risk of a GIS user making a catastrophic mistake (e .g., an event that impacts human safety) when using the GIS database is high. developers of GIS databases may broaden the accuracy statement to safeguard themselves against lawsuits. An example of a sit

uation when human safety could be affected by the accuracy of a G IS database is in databases that are used as navigational maps for maritime applications. Using an

appropriate set of comrol standa rds may minimize the amount of error in a CIS database. Control standards can

Root mean square error (RMSE) is a common term used in GIS. RMSE measures the error between a mapped point and its associated true ground position. Commonly used when digitizing a map, RMSE measures the positional error inherent in the registration

points on the hardcopy map. RMSE calculates the squared differences between each reference point and its known or estimated position, sums these differ

ences, than uses the square root of the sum to compute a measure of the positional accuracy. The formula for RMSE when location coordinates are of interest is:

RMSE= n

Where: Xciata.i X (longitude) coordinate of each

collected point i


include using a minimum root mean square error

(RMSE) when registering maps for digitizing processes or for establishing a set of verification processes for elimina

tion of gross and systematic errors. In addition, when measurements ace taken of a featu re, be it point coordi

nates collected by a GPS receiver, linear features such as

scream or trail lengths, or area features sllch as watershed boundaries. a RMSE can be used to assess and repof( differences between collected measurements and the true or

estimated positions of featu res (FGDC. 1998). While the positional accuracy oflandscape features in

a CIS database can be estimated, the uncertainty about

what are termed 'local shapes' is more elusive (Schneider, 2001) . For example. assume that a road has been digitized (Figure 3.7) . and that one segment of the road has been represented by four vertices. The actual location of the road between the vertices is unknown, and can be described in a number ways other than by a direct line

between each vertices. The positional accuracy of these local shapes is therefore a function of the quality of the digital encoding process associated with the landscape feature (whether manual digitizing, heads-up digitizing, or capture of data through GPS field techniques was applied).

Errors in the attributes of landscape features arise when incorrect values are assigned to features. either

Xch«k.i x (longitude) coordinate of true or estimated location i

Ydm .; Y (latitude) coordinate of each collected point i

y (latitude) coordinate of true or estimated location i

n = number of observations (number of collected coordinate pairs)

A perfect RMSE is 0.00 where the reference points are located in a GIS database in exacdy the same relative

position as on the ground or when GPS collected data are exacdy the same as control points that are lIsed to

test accuracy. This is rarely obtained when using realworld data. A sample RMSE calculat ion is shown in Table 3.3.

76


TABLE 3.3 Example of Root Mean Square Error (RMSE) calculation for GPS coordinates

Point

2

3

4

5

GPS-X (m)

477395.3

477399.7

477405.5

477407.5

477406.9

Known-X (m) GPS-Y (m)

477397.2 4934669.5

477398.7 4934677.2

477405.3 4934670.8

477406.9 4934673.9

477405.7 4934670.4

Squared Known-Y (m) Error (m)'

4934671.8 8.90

4934675.2 5.00

4934675.3 20.29

4934671.7 5.20

4934668.5 5.05

Sum of squared crror (ml )!> 44.44

Average squared error sum {m l)e 8.89

Root Mean Squared Error (m)d 2.98

• Squared straight-line differences between CPS coordinates actual known positions: (X-direction error 2 + V-direction error !)

I> Su m of [he squared errors

C Average of the sum of the squared errors: (Sum of squared errors I number of poinu)

d RMSE: Square root of the average of squared errors: (Avt.l'Olge squared error) tI~

through editing processes or through spatial joins (which are discussed in chapter 9), or arise because the attribute data is outdated. Keyboard entry of attribute data can result in attribute data errors, particularly if (he people performing the anribmion processes are nO[ paying close

Digitized road segment

Real-world representation #1



Figure 3.7 Uncertainty of thc local shape of a road scgment (after Schneider, 200 1).

anentian to the quality of their work. As we mentioned earlier, verification processes can range from an examination of primed maps and tabular databases related to the GIS database of interest, to an independent third-party examination of the landscape features a nd associated anribures comained in the resulting G IS database.

Compurational processes can lead to another source of error that can be rather transparent to GIS users. Processes such as general ization. vector-to-raster conversion (or vice-versa), and interpolation cause alterations in the characterization of landscapes. I n vector-faster conversio n. fo r example. vector fearures are converted to raster grid cells. Obviously the size of the resulting grid cells, as discussed in chapter 2, will influence the quality of the landscape features contained in the converted GIS

database. With large grid cells (e.g., 30 m or larger), the curvature of roads and streams could be lost. as well as small (and perhaps important) extensions of polygons. Users of the converted GIS databases would also need to determine whether the spatial resolut ion of the grid cells tha t result after vector-faster conversion is appropriate for the landscape fearures being represented. For example. are 30 m (or larger) grid cells appropriate for representing all roads and streams? Is a 10m resolut ion more appropr iate? The drawback of using a 10 m grid cell resolution is [he relatively large amount of data required-nine times the amount of data contained in a 30 m resolucion database (nine 10m cel ls are contained withi n a single 30 m grid cel l).

77

Ideally, in lieu of starements of error, the processes used to develop GIS databases. such as any transforma

tions and conversions, should be documemoo and made

known co allow users [Q understand the potential direc

tion and magnitude of error associated with subsequenc

GIS analyses. In addition. meradara. such as projection

and coordinate systems, should be made available to allow

Summary

Ultimately, most narural resource managemenc organiza

rions will develop needs rhar will ourgrow rhe capabiliries of their current collection of GIS databases. Acquiring, creating. and editing GIS databases are common processes

encountered by natural resource professionals when they

are in need of data or databases to assist in making man

agement decisions or evaluadng alternacive management

policies. When rhe GIS darabases rhar are available to a natural resource management organizacion are not suitable

Applications

3.1. Acquiring GIS data about Arizona National Forests. Assume you are interested in obtaining informa

tion about the streams related to the Prescott National

Forest in Arizona. The website related to the GIS data is

h rrp:llwww.fs.fed .us/r31 prescorrl gisl i ndex.shrml (USDA Foresr Service, 2007). Based on rhe informarion rhat rhe GIS data s ite provides, what are the ca tego ries that

describe rhe available GIS darabases? In reviewing rhe meradara for rhe 'Fire History' GIS darabase:

a) Whar is rhe purpose of rhe Fire History darabase? b) How were the data creared? c) What datum, projection, and spheroid are used to

represent fires?

d) What data structure is used to represent the fires?

e) What is rhe spa rial extent of rhe fire hisrory darabase in longitude and latitude?

f) Who is rhe primary contact should you have further questions?

Note: Please do not contact the Prescott Forest GIS coor

dinator for answers to these questions.

3.2. Acquiring digital orthophotographs about Massachusetts. Your position as a natural resource con

sultant has allowed you to become involved in a project


users to consider how a GIS darabase was developed. This is, of course, rhe ideal case. For example, rhe GIS darabases used extensively in (his book are void of S[3[emems

of error and of meradara. They were developed as hyporhericallandscapes for students in rhe aurhors' GIS appli

cations courses.

to address a particular cask. acquisition. creation. and edit

ing processes must be considered. Those who simply view

GIS as a sysrem to make maps will likely underesrimare the amount of work required to acquire. create, or edit the

GIS darabases necessary to make rhose maps. In addirion, regardless of how GIS darabases are generared, rhe presence and elimination of errors must be considered, and

will likely require extensive verification processes to ensure

mat the pmential errors are minimized.

situated in Massachusetts. As such. you are interested in

obtaining digital orthophorographs about an area in

Massachusetts . One source of this data might be the

Massachuserrs GIS websire (http: //www.srare.ma. u.1 mgisl) (Commonwealrh of Massachuserrs, Office of Energy and Environmental Affairs, 2007) . Based on whar you can gather from the website:

a) Whar rypes of black and whire digiral orrhophorographic imagery are available?

b) Whar spatial resolurions are available for 1:5000 color orrhophorographic imagery '

c) Whar sparial resol urions are available for 1 :5000 black and whire imagery?

d) What datum and coordinate system are used to

represent these images?

3.3. Data acquisition (1). You have been hired by a private consultant in Washington State to develop a GIS

program . The consultant has a small office, comprised of

only five employees, and is interesred in developing a GIS program thar will urilize desktop GIS software (ArcGIS ArcView, MapInfo, or GeoMedia) and GIS darabases created by other organizations. To get started, you decide

that acquiring base maps describing the counties, towns,

secrion lines, and ownership of rhe Stare is important.

78


a) Is each of these da<abases ava ilable? liS[ any da<abases that are not ava ilable.

b) Wha< S[eps would you have [Q go through to be able <0 download the data?

c) What is the spatial reference of available data?

d) Who would you contact if you had problems downloading the data'

Hint: See Washington Departmenc of Natural Resources 2007a fo r additional info rmation.

3.4. Data acquisition (2) . Assuming you were inter· ested in acqu iring soils GIS databases for 10 rownships in

Washington State from the Washing<on Department of Natural Resources (see Wash ingtOn Deparrmenc of

Namral Resources. 2007b): a) What is the format of the database? b) What are the coordinate units? c) Who is [he contact for more information about the

soils database? d) Where did the soils information come from?

3.5. Data acquisition (3). The Washi ng<on Namral Heritage Program (Washington Department of Namral Resources, 2007c) maintains a GIS database of the highquality terrestrial and wedand ecosystems of the State, as well as locations of rare planes of concern.

a) What groups of professionals are the intended users of the Washing<on Natural Heritage GIS data set?

b) How would you gain access <0 the Washing<on Narural Heritage GIS data set?

c) What data formars are available for the GIS database?

d) Are you allowed <0 display GIS maps of the dataset on {he I ncerner?

3.6. Development of base map for a digitizing contractor. Assume char the standard process within your natural resource managemem organization for updating

G IS databases is to develop hand-drawn maps of the changes des ired, and then to deliver these po tentia l changes to a contractor for digitizing work. In the cemer of the Brown Tract is an open area of land. Assume that the owners of the Brown Tract recently acquired this land. Develop a base map. showing the currenc stands and the current roads. Create a GIS database of fou r tics (reference ma rks) and place them on the map as well, along with thei r assoc iated Xand Y coo rdinates. Print the

map, and then hand-draw the additional timber stands to

be digitized by the contractor. Use the digital orthophotograph associated with [he Brown Tract to hand-draw the new stands.

3.7. Owl locations on the Daniel Pickett forest. The wildlife biologist associated with the Daniel Pickett forest, Kim Dennis, is concerned about the quali ty of the owl nest location GIS database.

a) How could you determine whether the owl nest locations on the Daniel Picken forest are represented as being in the correCt place?

b) What are the coordinates associated with each owl nest location?

3.8. Errors in landscape feature and attribute data. As a user of the Brown Tract GIS databases, you are very imerested in the quali ty of data that each provides. What

might you say about the possible errors in the stands GIS

database features that are located in the fo llowing places? a) X = 1263950; Y = 371726 b) X = 1273647; Y = 363943

3.9. Verification processes. As technology progresses and the capabil ities of field personnel increase (wi th new college courses in GIS, continuin g educa tion co urses, etc.), the co llection and transfer of data can occur with

processes that would seemingly save effort and COSt to an organization. For example, in Figure 3.4 the inventory forester would transfer cruise reports (hard copies) and

maps (hand-drawn) to informatio n system analysts in an annual update process. Several verification processes were noted in an efforr to maintain the consistency and quality of data moving from the field to the information systems deparrment and vice versa.

a) H ow mighr these processes in the flow chart

change if the inventory fo rester were to provide spatial data that was digitized in the field office, and cruise data that was collected with a hand-held data collector?

b) Could the verifica tion responsibilities shift under these circumstances?

3.10. Sources of error. Your supervisor, Steve Smith, is interested in understanding the types of error that may be inherent in GIS databases. Describe fo r Steve the differences between the foHowing three types of error: systematic, random, and gross error.

79


a) Is each of mese databases available? List any databases that are not avai lable.

b) What steps would you have to go mrough [0 be able to download [he data?

c) What is the spatial reference of available data?

d) Who would you contact if you had problems downloading me data?

Hint: See Washington Department ofNarural Resources 2007a for additional info rmation.

3.4. Data acquisition (2) . Assuming you were interested in acquiring soils GIS databases for 10 townships in Washington State from the Washington Department of Natural Resources (see Washington Department of Natural Resources. 2007b) :

a) What is the formar of the database? b) What are (he coordinate units?

c) Who is me comact for more information about the soils database?

d) Where did me soils information come from?

3.5. Data acquisition (3). The Washington Natural Heritage Program (Washington Department of Natural Resources. 2007c) maintains a GIS database of the highqualiry rerresuial and werland ecosystems of the Stare. as

well as locations of r-are plams of concern.

a) What groups of professionals are the intended users of lhe Washington Natural Heritage GIS data set?

b) How would you gain access to the Washington Narural Heritage GIS dara sec?

c) What data formars are available for me GIS database?

d) Are you allowed to display GIS maps of the dataset on (he Incerner?

3.6. Development of base map for a digitizing contractor. Assume that the standard process within your natural resource management organizarion for updating

GIS databases is ro develop hand-drawn maps of the changes desi red, and then [0 deliver these po tential changes [0 a COlHractor for digitizing work. In (he cemer

of the Brown Tract is an open area of land. Assume thar

rhe owners of the: Brown T racr recently acquired this

land. Develop a base: map. showing [he currenc s tands

and the current roads. Create a GIS database of fou r [ics

(reference marks) and place them on the map as well. along wirh their assoc ia[ed X and Y coordinates. Prine [he

map. and then hand-draw the additional [imber srands (Q

be digitized by the contractor. Use the digital orthophocogl"dph associated wirh me Brown Tracr to hand-draw

the new srands.

3.7. Owl locations on tbe Daniel Pickett forest. The wildlife biologist associated with the Daniel Pickett forest. Kim Dennis, is concerned about rhe quality of the owl

nest location GIS database. a) How could you determine whether me owl nest

locarions on rhe Daniel Pickert forese are repre

semed as being in the correcr place?

b) What are [he coordinates associated with each owl

nest locarion ?

3.8. Errors in landscape feature and attribute data. As a user of the Brown Tract GIS databases. you are very inrerested in the quality of data rhat each provides. What

might you say about the possible errors in the srands GIS database features that are located in the following places?

a) X = 1263950; Y = 371726 b) X = 1273647; Y = 363943

3.9. Verification processes. As technology progresses and me capabili ties of field personnel increase (with new college courses in GIS, conrinuing educadon courses)

Ctc.), the collection and transfer of dara can occur with

processes that would seemingly save effort and COSt co an

organizadon. For example. in Figure 3.4 the invencory

foreSter would transfer cruise reports (hard copies) and maps (hand-drawn) to informarion system <1nalysrs in an

annual update process. Several verifkation processes were

nmed in an effort (Q maintain rhe consisrency and quality of data moving from me field [ 0 the information sysrems department and vice versa.

a) How might these processes in the flow chart

c hange if me invemory fo resrer were [Q provide

spatial data that was digitized in the field office. and cruise data that was coUeeted with a hand-held dam coiJector?

b) Could me verification responsibilities shift under these c ircumstances?

3.10. Sources of error. Your supervisor, S teve Smidt , is

interested in undersranding the types of error [hat may be

inherenr in GIS darabases. Describe for Sreve the differ

ences between the following mree rypes of error: systematic, random, and gross error.

3.11. Types of error. Given the inventory updating process oared in Figure 3.4, describe [he sources of error

(posi tional. a[[cibure. comput3rional) that could result at

each step in the process.

3.12. Calculating Root Mean Squared Error: Woodpecker nest GIS database. Assume you are digitizing a map of red-cockaded woodpecker nest cree locations of a

Nacional Forest in Florida. You have four reference marks

that you can use [0 reference the map [Q known coordi

nates. When registering the map you find that the Xdirecrion and V-direction difference berween the reference marks and the acwal known locations are as follows:

Registration X-direction Y.direction Point error (ft) error (ft)

3.526 0.963

2 -2.890 -2.452

3 0.985 -1.987

4 - 1.598 -2.850

Whar is the RMSE, or positional error, associarcd with the

resulting woodpecker nest GIS database?

3.13. Calculating Root Mean Squared Error: Trails GIS database. Assume you are employed by a National Park in Alberta, and are in the process of digirizing a map of trails [har were drawn by the recreadon specialist asso

ciared with che park. On the recreadon specialise's map

chere are six reference marks chat you can use co regisrer

che map [Q known coordinaces. When regiscering che map

you find that the X-direction and Y-direction difference becween the reference marks and (he accual known loca

dons are as follows:

Registration X-direction Y -direction Poine ~rror (m) ~rror (m)

3. 125 - 1.588

2 2.564 -1.992

3 2.548 -2.987

4 1.998 - 2.856

5 1.268 2.857

6 1.489 3.897

Whar is che RMSE, or posidonal error, associ aced. wich [he resulcing cra ils GIS darabase?


3.14 Calculating Root Mean Squared Error: Tree location database. Assume you work as a nacu ral

resource manager in Oregon. and your supervisor is con

cerned about the qualiry of data that you are colleCting wich a GPS receiver. You supervisor has asked you co cal

culate the RMSE between GPS-collected coordinates and coordinates that had been collected by a digital total stacion. Boch secs of coordinates are lisred below.

Total Total

Point GPS-X Station-X GPS-Y Station-Y

4934688.3 4934691.9 477311.7 477309.0

2 4934693.6 4934690.8 477310.5 47731 1.9

3 4934686.9 4934687.0 4773 16.1 4773 13.8

4 4934686.1 4934683.9 4773 12.7 4773 12.7

5 4934678.8 4934682. 1 4773 10.2 477309.0

6 4934680.3 4934683.2 477307.6 477306.0

3.15 Calculating Root Mean Squared Error: Stream gauging stations. Assume chac you are collecdng daca

from a stream survey. A stream ecologisc has given you a

set of GPS coordinates that had been collected from gauging stations locared nexc co a scream. She has also pro

vided a sec of coordinaces from the same gauging scadons

that were collected from LiDAR data. What is the RMSE of che differences between chese secs of coordinaces?

Point GPS-X GPS-Y UDAR-X LiDAR-Y

934681.0 477392.8 934676.6 477402.2

2 934670.8 477405.5 934675.3 477405.3

3 934673.9 477407.5 934671.7 477406.9

4 934670.4 477406.9 934668.5 477405.7

5 934665.8 477399.2 934666.9 477402.1

6 934668. 1 477399.5 934668.6 477398.3

80


References

Commonweal th of Massachusetts, Office of Energy and Environmental Affairs . (2007). Massachusetts g'ographic information sysum. Retrieved April 19,2007, from http://www.state.ma.us/mgisl.

Cressie, N. (1991). Statistics for spatial data. New York: John Wiley & Sons.

DeMers , M.N. (2000). Fundamentals of g,ographic

information systems. New York: John Wiley and Sons, Inc.

Envi ronmental Systems Research Institute. (2005). GIS

topology. Retrieved April 19, 2007, from http://www. es ri .comllibrary/whitepapers/pdfs/gis_topology.pdf.

Federal Geographic Data Commitree (FGDC) . (1998). GeospatiaL positioning accuracy standards. Part 3: National standard fo r spatial data accuracy. Reston , VA: US Geological Survey.

Geography Network Canada. (2007). Data. Retrieved April 27, 2007, from http://www.geographynetwork. ca/datalindex.html.

Ghilani , CD., & Wolf, P.R. (2006). Adjustment computations: Spatial data analysis (4th ed.). New York: John Wiley and Sons, Inc.

G ifford Pi nchot National Forest. (2007). Gifford Pinchot

National Forest geographic infonnation sysums, available data uts. Retrieved April 19, 2007, from http: // www.fs.fed. us/gpn fifo rest -researchl gisl .

Keefer, B.J. , Smith, J.L. , & G regoire, T.G. (1988). Simulating manual digitizing error with sradsricai models. Proceedings, GIS/LiS '88. Falls Church, VA: American Society of Photogrammerric Engineering

and Remote Sensing, American Congress on Surveying and Mapping.

Merriam-Webster. (2007). M"rian- W,bsm onlin, search. Retrieved April 27, 2007, from http://www.m-w.com.

Minnesota Planning. Land Management In fo rmation Center. (2007). LMIC's clearinghouse data catalog. Retrieved April 19, 2007, from http ://www.lmic. Sta te. mn. us! chouse/.

Natural Resources Canada. (2007). Mapping. Retrieved April 19, 2007, from http: //www. nrcan-rncan.gc. cal com/subsu jl mapcar-eng. ph p.

Prisley, S.P., Gregoire, T.G., & Smith , J.L. (1989). The mean and variance of area estimates in an arc-node

geograph ic information sys[em. Photogramm~tric

Enginuring and Remou S,nsing, 55, 1601-12. Schneider, B. (2001) . On the uncertainty of local shape

of lines and surfaces. Cartography and Geographic Information Sciences, 28, 237-47.

USDA Forest Service. (2007). Pmcott National Form: GIS-geographic information sysums. Presco[[, AZ:

USDA Forest Service. Retrieved April 20, 2007, from http://www.fs.fed.us/r3/ prescottl gisl index.sh tml.

US Geological Survey, Federal Geograph ic Data Committee. (2002). Manual offtdual geographic data products. Reston, VA: US Geological Survey.

WashingtOn Department of Natural Resources. (2007a). GIS data. Olympia, WA: Washington Department of Natural Resources. Retrieved April 20, 2007, from http://www.dnr.wa.gov/dataandmaps/index.html.

Washington Department of Natural Resources. (2007b). Avai"'ble GIS data . Olympia, WA: Washington Department of Natural Resources. Retrieved April 20, 2007, from htrp://www3.wadnr.gov/dnrapp6/dara webl dmma[rix.h[ml.

Washington Department of Natural Resoutces. (2007c). Reference desk. Olymipia, WA: Washington Department of Natural Resources. Retrieved April 20, 2007, from http://www.dnr.wa.gov/nhp/refdesk/gis/index.html .

81

Chapter 4

Map Design

Objectives

The common features of maps are described in this chapter. and emphasis is placed on developing those that field professionals can use to present results of GIS analyses or (0 illustrate themes of interest including forest management areas, tree species maps, harvest plans. wildlife habi

tat, and mher narural resource management actions. At the conclusion of this chapter. readers should have acquired a firm understanding of:

I. the main components. or building blocks. of a map; 2. rhe qualities of a map that are imporram in communi

cating information co map users; and 3. the types of maps that can be developed to visually

and quickly communicate information to an audience.

Within the various fields associated with natural resource management, we expect chat maps will be avail

able to illustrate resources and areas that we manage because of the prevalence of GIS use. Maps are amazing tOols that, if constructed properly, have the abili ty to

quickly and clearly communicate a message [Q an audience. Maps are an effective method of communicating spatial relationships among landscape features. Maps are

also engaging-people are drawn to maps. Most GIS software programs provide users the capability to produce sophisticated maps and maps often represent the output

produced by GIS analyses. thus most people mainly tend to associate GIS with map-making activities. Although this association may ignore many of the other analytical capabilities of GIS. the ability to geographically portray the results of an analysis is one of the primary distinguish-

ing characteristics that sets GIS apart from other software

programs. Maps have been part of human civilization for millen

nia and have been used for many purposes, including data storage, navigation, and visualization. Maps have been used to create and sway opinion in many disciplines. including those related to the managemem of natural

resources. In a manner similar to the use of statistics. maps can hold tremendous power over the message that is delivered to an audience and. when created skillfully.

maps can be used to influence people's opinions (Monmonier. 1995. 1996). One of the great dangers pre

sented by maps is that people assume the landscape features represented on maps are accurate ponrayals of the natural resources that they claim to manage. However. maps are. at best, abstractions of the real world, and will usually possess some measure of non-conformity, be it directional or proportional or both, from a set of landscape features . A skilled map-maker will be able to choose

a map projection that best preserves feature qualities (e.g .• area, shape) and that best suits a map's objective. This skill might also include applying strategies for representing data characteristics or qualities through different shapes, colors, or sizes. Understanding that maps must be created and imerpreted with a discerning eye is one of the first steps necessary to becoming a successful mapmaker

or user. Maps usually are two-dimensional representations of

the landscape. although th ree-dimensional maps can be used to show volume o r perspective. Symbols. colors, and text are combined to communicate information and, as with graphs, flow charts, and other diagrams. maps are graphical representations of information. Mapmakers

82


attempt to transmit ideas in a different manner than (hat used by ocher forms of communica(ion. The goal of (he map-making process is co produce a visual display (ha( communicates spatial information to pQ[encial map users. The human brain, with its limited capacity to s(Qre informarion, may be able (Q understand ideas more effectively when supporting concepts are presented graphically on a map (Phillips. 1989). The design of a map can aifec( (he abi lity to communicate spatial information, thus a well

designed map will likely communicate ideas co an audience (e.g .. co-workers and supervisors) more effectively

(han a poorly designed map. Poorly designed maps can lead to misinterpretations and cosdy or inappropriate decisions.

Cartography is the science and art of making maps, and for the bence part of the twentieth century it was a skill developed (hrough ex(ensive experience in making maps by hand. Since (he la(e 1980s when G IS began co be pervasive in namral resource organizations. the vast

majority of maps have been made by non-professional carrographers. This shift is simply because of the access ibiliry and ease of use of GIS sohware. Some people may argue that several of the prescriptive aspects of making maps are no longer necessary (Wood. 2003). However. we would consider our effort to educate readers abom the capabili(ies of GIS less than successful if we failed (0

describe the important aspects of maps, and suggest ways

(0 make maps aesrhecically appealing. When developing maps for others to use, mapmakers

should keep in mind tha( nor all map users will be operating on the same level of competence. In fact, map users can be categorized as experienced, inexperienced, o r reluc(ane (F ranklin. 2001). The de(ail and c1ariry of mapped feacures will likely afTecc how well a map coneribures (0

natural resource management. Within natural resource management, maps should be clear enough for users ro undersrand ewo main (hings: (I) (he land area (ha( me map represents, and (2) the message the map intends to

communicate aboU( the land area. In order to meet these two requirements, a mapmaker needs ro understand:

• (he objec(ive(s) of the map «he message). • (he people who may use ,he map «he audience). • (he da(a (ha( will be displayed in (he map «he in for

mation available), • the use of graphics sohware for displaying map infor

mation, and • the final format of the printed or digital version of the

map «he produc().

The size. shape. and symbology of each componene of a map should reAec( (he likely responses (0 (hese promprs. A variety of common [Ools can be employed (0 make a map both useful for natural resource management purposes and aesrhecically pleasing. The landscape feacures illusuared on a map should include enough landmarks co allow the users to reference themselves to the mapped area. and help navigare (hrough a landscape. Mapmakers should keep ewo importane aspecrs of maps in mind: (I) not everything known about a landscape needs to be displayed on a single map. and (2) co communica(e eifec(ively. maps should focus on displaying a limi(ed number of landscape features. These concepts emphasize that carrographers shou ld focus on characteristics that directly

relate to the map's intended message, and thus they should ensure that other map components do not mask or cloud (he message. The landscape fea(u res primarily emphasized on a map should be (hose associared wi(h (he main intent of the map. For example, on a map devel

oped ro illustrate stream classes. other landscape features. such as roads, timber stands, and soils, should be second

arily emphasized, or omitted from the map ent irely.

Map Components

When developing a map for a natural resource management purpose, several basic components should be con

sidered. These componenrs include (he symbols being used to describe landscape features, a north arrow, the scale. (he legend. and (he quali(ies (fone. size. ere.) of the (ex( (labels and anno(a(ion). In addi(ion. you may find i( necessary to include other components in a map, such as

a description of the mapmaker, the filenames and file loca(ions of (he GIS da(abases used. and map quali(y caveats. Each of these components is briefly discussed below, and it is important ro understand each elemem

before we discuss the common types of maps that can be developed using GIS sofeware programs.

Symbology

Symbology can be (hough( of as (he art of expression. based on symbols (Merriam-Websrer. 2007). A large suire of map symbols has been developed (0 ideneifY and illustrate significant landscape features on maps. Some of (hese symbols (Figure 4. I) were developed as na( ional standards for illustrating landscape features (e.g., conrour lines. hydrologic symbols) found on cerrain widely used maps. such as (he US Geological Survey (opographic

83


attempt ro transmit ideas in a differenr manner than that used by other forms of communication. The goal of the map-making process is to produce a visual display that communicates spa rial information co potential map llsers. The human brain, with irs limited capacity ro Stofe information, may be able to understand ideas more effectively when supporting concepts are presented graphically on a map (Phillips, 1989). The design of a map can affect the ability to communicate spadal information. thus a welldesigned map wi ll likely communicate ideas to an audience (e.g. , co-workers and supervisors) more effectively rhan a poorly designed map, Poorly designed maps can lead to misinterpretations and costly or inappropriate decisions.

Cartography is rhe science and arr of making maps, and for the bc(rer parr of me [wemicrh century it was a skill developed through extensive experience in making maps by hand. Since the late 1980s when GIS began to be pervasive in natural resource organizations. tbe vast majority of maps have been made by non-professional cartographers. This shift is simply because of the accessibility and ease of use of GIS software. Some people may argue that several of the prescriprive aspects of making maps are no longer necessaty (Wood, 2003). However, we would consider our dfoIT to educa.te reade.rs 3bom the capabilities of GIS less ,han successful if we failed to

describe the important aspec[S of maps, and suggest ways

to make maps aesthetically appealing. When developing maps for Q[hers to use, mapmakers

should keep in mind that not all map users will be operating on the same level of competence. In fact, map users can be categorized as experienced. inexperienced. or reluctallt (F ranklin, 2001). The detail and clarity of mapped featu res will likely affeer how well a map conuibutes to

namral resource management. Within natural resource management, maps should be clear enough for users [Q

understand twO main things: (I) the land area thar the map represents, and (2) the message the map intends to communicate abom [he land area. In order to meet these two requ_iremems. a map maker needs to understand:

• the objective(s) of the map (the message), • the people who may use the map (the audience), • the data that will be displayed in the map (the infor

mation available),

• rhe use of graphics software for displaying map information, and

• me final format of the printed or digital version of the map (the product).

The size, shape, and symbology of each component of a map should reAect the likely responses to these promp". A variety of common [ools can be employed to make a map both useful for namral resou rce management purposes and aesthetically pleasing. The landscape features illustrated on a map should include enough landmarks to

allow the users to rderence themselves to [he mapped area, and help navigare through a landscape. Mapmakers should keep two important aspects of maps in mind: (I) not evetything known aboUt a landscape needs to be displayed on a single map, and (2) to communicare effectively, maps should focus on displaying a limired number of landscape features. These concepts emphasiz.e that ca rrographers should focus on characreristics rhar direcdy rel ate ro the map's intended message, and rhus they should ensure that other map components do not mask or cloud the message. The landscape features primarily emphasized on a map should be rhose associared with the main intent of the map. For example, on a map developed to illustrate stream classes) other landscape fearures. such as roads) timber stands, and soi ls. should be second

arily emphasized. or omirred from the map entirely.

Map Components

When developing a map for a natura] resource managemem purpose, several basic components shou ld be con

sidered. These co mponentS include [he symbols being used to describe landscape features, a north arrow, [he scale, the legend, and the qualities (font, size, etc.) of the rext (labels and annmation). In addition, you may find it necessary [Q include other components in a map, such as

a description of the mapmaker, rhe menames and me locations of the GIS databases used, and map qual iry caveats. Each of these components is briefly discussed below, and it is imponant ro understand each e1emem

before we djscuss the common types of maps that can be developed using GIS software programs.

Symbology

Symbology can be thought of as the art of expression, based on symbols (Merriam-Websrer, 2007). A large suire of map symbols has been developed to identifY and iUuscrate significant landscape features on maps. Some of rhese symbols (Figure 4.1) were developed as narional srandards fo r illustrating landscape features (e.g., contOur lines, hydrologic symbols) found on cenain widely used maps, such as the US Geological Survey topographic

Campground ......... .. ... ..... . .... ..... lc.nqlounI!

Gravel. sand. clay. Of borrow pit ...... .... Grll~t!I?iI

Mine shaft ... .. . ..... ...... ....... ... ..... I!l

Seawall. ... ....... ................... ... .. S£AW" L~

ShoaL ......... ... ........ ........ . ... ...... Shoal

Spot elevation........ .. ............. ..... . mts

State or territory.... ........ ...... ..... -- - ---Tunnel: road...... .... .... .. .... .... ... ~_ £ ... _

Figure 4. 1 A subset of USGS topographic map symbols (USD I US Geological Survey, 2003).

maps (USDI US Geological Survey. 2003) or the National Topographic System maps of Canada (Natural Resources Canada. 2006). Symbols have also been developed to represent organizational standatds for identifying landscape features. For example. the US National Park Service has created a standard set of symbols for use on National Park Service maps (USDI National Park Service. 2003). which

include typical symbols for roads and other landscape features as well as the highly recognizable pictographs (Figure 4.2). The International Orienteering Federation (2000) has also developed a set of standard map symbols

fo r orienteering events. This provides a common approach to the interpretation of o rienteering maps, and therefore promotes a fair competition amo ng people involved in the sporL

Most GIS software programs provide a standard set of map symbols for map users. However. developers of maps

0 Airport

II Amphitheater

e Boat launch

I!J Boat tour ,. Bicycle tra il

El Bus stop/Shuttle stop

g Campfire

~ Campground

!!! Canoe access

Figure: 4.2 A subset of the slanda rd National Park Service pictographs for maps (USDI National Park Service, 2003).

Chapter 4 Map Design 73

can easily misuse them, since documentarion is usually

limited within the dialog boxes provided by the GIS software. Nevertheless, a variety of symbols are available in

GIS software programs that allow the mapmaker to describe landscape features. It is also possible within many

GIS software programs [Q create a customized symbol set. I n so me GIS software programs symbols are merely

bitmap graphic files that can be edited o r created th rough graph ic software programs. And. if the existing symbol

ogy within a GIS sofrware program is nO[ adequate, some GIS software programs may allow the use of customized

tools or products developed by third-party software developers. A variety of free symbols sees can be obtained over

the Internet (e.g .• Sheahan. 2004). and you can purchase special symbols from companies such as Digital Wisdom. Inc. (2006).

Direction

Mapmakers typically use cardinal di rections (north. south, east, west) (Q indicate map orientation. The use of

a north arrow thus provides map users with a systematic

method to not only help locate places on the ground. but also [Q understand where those places are in relation to

ocher landscape features. While most maps are usually oriented with north at rhe tOP of the page, and south at [he bo[{om of the page. it is appropriate to remove the

uncertainty associated with orientation of a map by explicitly indicating the direc(ion through the use of a north arrow. Omitting a north arrow from a map is con

sidered poor cartographic practice. A wide variety of north arrows has been developed to

help map users understand direction and location, and the choice of which to use is usually determined by the mapmaker. Many of these forms of north arrows are available within GIS software programs, and a number of o(hers can be developed by hand us ing lines. arrows. and

text (F igure 4.3). Some organizations. such as the US National Park Service, require the use of a standard north arrow on their official maps (USDI National Park Service, 2003). Prospective cartographers should also realize (har single-sided north arrows are used by some organizations to represent magnetic north . The Earth 's magnetic fields are in constant flux and cause compass needles [Q point in alignment. W ith in much ofNorrh America, the magnetic variation ranges berween 20° East and 20° West declinat ion , thus creating a large angular difference berween what many consider [rue north (which is astronomically derived) and magnetic north.

84


Figure 4.3 A variety of north arrow d~igns.

Scale

Maps are models of real landscapes. and a scale is used to indicate the ratio of (he map features [Q the actual landscape (i.e., map distance compared to actual ground disranee) [0 the map users. For this reason, scales are essencia! beca use they permit map users to reference map

features to their actual size. A sca le is almost always required on a map. and can be displayed using graphical, equivalent (verbal). or proportional scales (Figure 4.4). Graphical scales generally do not indicate the exact scale of a map (as do proponionai or equivalent scales) , but serve to visually associate the length of a map feature to

actual ground distances. Many people relate to this form of scale more effectively than they do to the proportional or equivalem scales. Equivalent scales are those where one

unit of d istance on a map (usually the left-hand side of

Graphical scales:

500m o 500m 1000m - -- -1 mile

Equivalent scales:

1 inch = 1 mile 1 inch = 500 feet 1 inch = 10 chains

o

1 em = 1,000 meters 1 em = 5 kilometers

Proportional scales:

1: 12.000 1 : 24.000 1 : 250,000

1 mile 2 miles

Figure 4.4 Graphical. equivalent. and proportional scales.

the scale) is equal to (using the equal sign) one unit of distance on the actual landscape. Each side of the scale can use a different unit of measure (e.g., 1 inch = 10 chains),

which distinguishes this type of scale from the proportional scale, where each side of the scale is unirless.

Proportional scales are generaIly presented using a representative fraction. such as 1 :24.000. With this type of scale, users should interpret 1 unit on the map as represent ing 24.000 of the same units on the ground (e.g .• 1 map inch represents 24.000 ground inches, or 1 map centimeter represents 24,000 ground centimeters). Proportional and equivalent scales are also interchangeable. For example. an equivalent scale that reads 1 inch =

1 mile is the same as a proportional scale of 1 :63.360 (I inch on a map represents 63.360 inches. or 1 mile, on the ground).

Whether graphic, equivalent , or proportional scales are used, the appropriate metries (English versus metric or

SI system. feet versus miles [meters vs. kilometers)) and appropriate font sizes should be employed to avoid distracring users from the map's main message. Puc another way, the scale is supplementary informacion on a map. and therefore it should not he so large that it auracts attention away from the map itself. Finally, the units dis

played in a scale muSt make sense to the user of the map. For example. a proportional scale of 1 :23.987. an equivalent scale of 1 cm = 2.3 km. and a graphical scale which is divided inro 700-meter sections represents relativdy uneven divisions. While the units displayed in scales may he automaticaHy created in this manner in GIS, the map

maker usually has comrol over them and can adjust them accordingly to provide a mo re logical representation of scale. In general, map users will be able to relate more easily to rounded scale figures. such as 1 :24.000 or 1: 1 00. rather than to more precise representations .

Legend

All of the features displayed in a map should be described in the map legend in order fo r users to fully interpret the map's message. Therefore. the symbology that is used to

display features in a map should be replicated in the legend and associated with some text that defines the symbols (Figure 4.5) . Of course. if you wanted to intentionally add myStery to a map . the legend may omit the description of certain landscape features. Some maps, such as the US Geological Survey topographic maps. may contain numerous features (and corresponding symbols). The legend that would be required for these maps would

85

Legend

Streams

Roads

--- Stand boundaries

Property boondary

~ Harvest area

EEl Log decl<s I Landings

........ Gates

o Houses

Figure 4.5 A map legend containing symbology and definitions.

overwhelm the map itself. In these cases, only a few land

scape features are noted in the map's legend. and users must refer to the published standa rds (e.g., USOI US

Geological Survey, 2003) for a full explanation of the

remaining map symbols. Legends can rake many different forms and can use

symbols. points. lines. polygons. colors. pa[(crns. and [ext

co clarify what users may see. Some legends should milize

a font size and font rype (hat is appropriate for the map.

Symbols sizes for features may also be varied to show the

differences in quamiries. The choices are nOt always obvi

ous, but as in the case of the map's scale, [he legend should nor distract users from the message of a map. In

addition , the appropriate descripro[s for each symbol

should be used. Abbreviations should be avoided if inter

pretation of symbols might be unclear, or if a broad audi

ence is targeted. When data are presenred and indicate

quantities (such as length or area). the measurement units

should be presented.

Most GIS sofrware programs now offer [Ools {hat allow

{he automatic creation of map legends. These processes

simply reference the GIS da<abases that are being used and

their corresponding symbology. Typically, [Ools are also

available in GIS software to allow you to modifY aU[Qmat

ica lly-created legends [Q suit your part icular needs.

Locational inset

The approximate location of {he mapped a rea within {he

conrext of a larger, more recognizable landscape feature

(e.g., a basin, forest, counry, o r State or provincial bound

ary) can he indicated on a map using a locational inset.

The locational inset may be exrremely helpful fo r the

map's audience if they are not familiar with the landscape

being illustrated. The locational inset might show the

location of a watershed within a drainage basin, or a prop

erty within the boundary of a county. The locational inset, however, should be a minor component of a map

and it must not compete with the main feature(s) of a map for the attention of an audience. Figures 4.6 and 4.7

Chapter 4 Map Oesign 75

Brown Tract Roads and Trails

............

Legend

Trails

.. Roads

~"' __ ""~, e Figure 4.6 A map of the Brown Tract roads and trails containing a neatline, locational inset, ti tle, legend, scale, and north arrow.

Brown Tract Roads and Streams

,"-nd - .... .. _ .. - ,

+ Benton County

Lr _c-.,.. ..... __ .. JfI07

Figure 4.7 A map of the Brown Tract roads and 5lreams conuining a ncacline. locational insct, title, legcnd. scale, and north arrow.

86


were developed as reference maps by s[Udenrs in one of

our G1S courses, and ahhough not perfect in every sense,

each example provides a loeational inset in [he lower left

corner. In one casc, the loeational inset contains the Out

line of the Brown Traer. In the other case, the location was approximated by the student using a symbol (a triangle), with the orientation and size of the symbol implying less accurate information about the locarion.

Th ree other types of insets can also be used, an enlargement inset, a related area inset , and a special

subject inscr. An enlargement inset can be used on a map co show more detail of a specific area located

within the primary map region . A related area inset can be used on a map [0 illustrate features thac are non

contiguous with the main map figure but are still impor

(3m (Q display. For example. related area insets are often

included in maps thar illumate the 50 Uniced Scaces. Here, Alaska and Hawaii are positioned in [he insets.

rather than in their actual geographic locarion {which

would rhen include pordons of Canada and vast

expanses of the Pacific Ocean}. A special subject inset can be used on a map to show different themadc representa

tions of rhe main map area (e.g. precipitation, canopy

cover). This is a popular mapping approach used in many adases.

Neatline

A neadine is a border that surrounds all of the landscape features on a map hue lies within the ourside edge of rhe

mapping medium (paper). Adding a neadine to a map is considered good canographic practice bur its presence is

generally less crirical than thac of a scale bar or a legend, Nearl ines can also be placed around other map elements

to help disringuish them and keep them separare from other objects (e.g., [0 separate [he loeational inset from

the legend). Usually a neadine is composed of ac least one line, but multiple lines can be used co provide a more dramarie effect. Regardless of the style, nearlines can be use

ful (Oots that bring organization and distinction to

mapped landscape features. Some GIS sofeware programs include mapping tools that will not only create a neadine,

bur that will allow you to speciFy how the area contained within the nearline will be filled. For example, the background area behind [he map tide, landscape features, and

legend, could be shaded. An amomacic neadine creadon tool in GIS sofcware will allow the creation of a presentation-quality map or poster, but neadines can also be cre

ated manually wichouc much difficulty.

Annotation

Map annotation, or tex( applied directly to the map, is

important in further describing landscape features beyond

what can be described with a legend. In some cases, it

may noc be practical (or possible) to indicace all of me intended characterist ics oflandscape features through the

use of a legend or symbology because of space limitations or because of the shape and size of the features. Therefore

mapmakers may find annotation helpful in communicat

ing additional messages to the end-users. Listed below are

several examples of map annotation in natural resource

management.

• Ownership: The owners of individual parcels may be displayed on a map wich words, such as 'GeorgiaPacific', 'Srace of Alabama', or 'Province of Alberta'.

• Road numbers or names: These may be applied as labels to maps to further describe the road system.

• Surveying or other locational information: Township

and range numbers (if using the Public Land Survey System or the Dominion Land System), or distances

and directions from a metes and bounds survey may

be illustrated on maps with annotation, as could the

type of markings (e.g., pink flagging, orange paint) used to delineate treatment area boundaries.

• Areas of concern : The names of the homeowners near

treatment areas may be placed on a map to allow land

managers to understand who they must contact in case

of a problem. • Stand auribures: While stand attributes can be used [Q

shade a thematic map (as we will see soon) several

attributes of forested stands are commonly displayed

with annotation inside individual stands. These attrib

utes could include the stand or vegetation eype, age, or

area (Figure 4.8).

Typography

The content and form of text used to describe map fea

tures is an important aspect of the communicative ability

of a map. and is often used informally to differentiate

professional-looking maps from maps made by GIS novices. Since colors and patterns alone might not be able

co fully explain the message of a map, the texc used in annotation, labels, titles. and legends plays a role in the

appearance and aestherics of a map. The abili ty of GIS users to interpret the written information rhey find on

maps is a function of many variables that can be described

87


were developed as reference maps by Students in one of our GIS cou rses. and ahhough not perfect in every sense.

each example provides a locational inset in [he lower left corner. In one case, the loeational inset contains the our

line of the Brown Tract. In the other case, the location was approximated by the student using a symbol (a triangle). with the orientation and size of the symbol implying less accurate information about rhe lacarion.

Th ree other rypes of insets can also be used. an enlargement inset, a rel ated area inset , and a special

subject inser. An enlargement inset can be used on a

map to show more detail of a speciFic area located within the primary map region. A related area inset can be used on a map [Q illustrate fearu res [hat are nOll p

conriguous wim rhe main map figure but are still impor

tant to display. For example. related area insets are ofren

included in maps rhat illustrate rhe 50 Un ited States. Here. Alaska and Hawaii are posirioned in me insets .

tather than in their actual geographic location (which would then include ponions of Canada and vast expanses of the Pacific Ocean). A special subject inset can be used on a map to show different themadc representa

tions of [he main map area (e.g. precipitation , canopy cover). This is a popular mapping approach used in many

adases.

Neatline

A neadi ne is a border tbat surrounds all of the landscape features on a map but lies within the oU[side edge of me

mapping medium (paper) . Adding a neatline ro a map is considered good cartographic praccice btl[ its presence is

generally less critical than that of a scale bar or a legend. Neatlines can also be placed around other map e1emenrs to help distinguish them and keep them separate from other objects (e.g. , to separare the locadonal inser from

the legend). Usually a nearline is composed of at leasr one line. but multiple lines can be used ro provide a more dramatic effect. RegardJess of the style, nearlines can be use

ful too ls rhat bring organization and disrincrion (0

mapped landscape features. Some GIS software programs include mappi ng [0015 thac will not only create a neadine,

btl( thar will allow you co specifY how the area contained within [he neacline will be filled. For example. the background area behind the map tirle. landscape fearures. and legend, could be shaded. An amomaric nearline crearion tool in GIS sofcware will allow the creation of a presenra[ion-quaJiry map or poster, bur nearlines can aJso be cre

ated manually without much difficulty.

Annotation

Map annoracion, or (ex( applied directly (0 me map, is

important in further describing landscape features beyond whar can be described wich a legend. In some cases, it may not be practical (or possible) to indicate all of the intended characteristics oflandscape features through the use of a legend or symbology beeause of space limitations

or because of the shape and size of the features. Therefore map makers may find annotation helpflll in communicat

ing addi tional messages to [he end-users. Listed below are several examples of map annorarion in natural resource

management.

• Ownership: The owners of individual parcels may be displayed on a map with words. such as 'GeorgiaPacific', 'Scare of Alabama'. or 'Province of Alberta'.

• Road numbers or names: These may be appl ied as labels to maps to further describe the road system.

• Surveying o r other loeational informacion: Township

and range numbers (if using the Public Land Survey System or the Dominion Land System), or distances

and direcdons from a metes and bounds survey may

be illustrated on maps with annotation, as could the

type of markings (e.g .. pink Aagging. orange paint) used to delineate treatment area boundaries.

• Areas of concern: The names of the homeowners near

treatment areas may be placed on a map to allow land managers to understand who they must conract in case

of a problem. • Stand attributes: While stand atrr ibures can be used {Q

shade a thematic map (as we will see soon) several

attributes of forested stands are commonly displayed with annotation inside individual stands. These atuib

Utes could include the stand or vegetar ion eype, age. or

area (Figure 4.8).

Typography

The co ntent and for m of [ext used to describe map features is an imponam aspect of [he communicative abiliry

of a map. and is often used informally to differentiate professional-looking maps from maps made by GIS novices. Since colors and patterns alone might not be able

to fully explain the message of a map. the text used in annotation, labels, tities, and legends plays a role in [he

appearance and aesthetics of a map. The abil ity of GIS users to imerprcr the written information they find on maps is a function of many variables that can be described

51 70.86

51 46 5.49 27.94

54 25.07

Figure 4.8 Map annotation: age (top, yurs) and area (bottom, hectares) of a ponion of the Brown Tract stands (vegetation) GIS database.

50 3.85

39 58.39

under the broad heading of typography. Some of the most important rypographical elements are typeface (font), weight (bold/normal), size (point size), and case (use of capitals) of text contained on the map itself. The font chosen will undoubtedly inAuence the ability of users to interpret maps, rhus a no rmal font (Times Roman,

Aria], etc.). and a normal and consistent font size is usu

ally appropriate for most maps. Some important thoughts on map typography include the following:

• Some font types may be easier co read than orhers.

Mixing font rypes on a map may create (he impression that parts of a map arc nor clearly or logically connected.

• Use larger font sizes for map fearures mat are relatively morc importam than others. However, if you were (0

differentiate landscape features by differem sizes of labels or annotation, you must remember that only a

small number of classes are discernable by most people. In addition, small font sizes may be difficult for some people to see.

• The tirle of a map should be displayed with a larger font size (han the rest of the components of the map.

Use font size judiciously to display the legend, scale, and Other material not contained in the mapped area. The font size of these items should not overwhelm the information contained in the mapped area.

Chapter 4 Map Design n

• In one study, Phillips et al. (1977) noted that the capability of people to search and find infotmation on a map was enhanced when the text was displayed in a

normal weight (not bold), with letters all in lower case, except an initial capital. However, capitals should be used for all letters in text when names are difficult to

pronounce or need to be copied accurately.

• Text set entirely in lower case has been shown to be

harder to locate on a map than text set entirely in

upper case. However when the initial letter of a piece

of text was slightly larger than the othet letters, locating the text was quicket (Phillips, 1979).

Color and contrast

It may be difficult to believe at first but people tend to

associate colors oflandscape features on maps with events,

emotions, and socio-economic status. Men and women

respond to color with similar emotional reactions (Valdez & Mehrabian, 1994); however, people's emotional reaction [0 colors may vary across cultures . Listed below are

general emotional react ions to various colors by south

eastern US college students, as suggested by Kaya and Epps (2004) .

• Green: they felt relaxed, calm, and comforted, and

associated me color with nature

• Blue: they felt relaxed , calm, and comforted, yet associated the color with sadness or loneliness

• Yellow: they felt lively and energetic, and associated the color with summertime

• Red: they associated the color with love o r romance,

yet also associated the color with anger

• Purple: they felt relaxed and calm, and associated the color with childhood or power

• White: they associated the color with innocence,

peace, purity, or emptiness. and also associated the color with snowfall or conon

• Black: they associated the color with sadness, depression, fear, and darkness, yet also with richness, power,

and wealth • Gray: they associated the color with negative emo

tions, bad weathet, and foggy days

Studies of people's responses to color have indicated

comp lex emotional relationships. For example. in the study by Kaya and Epps (2004), the color green evoked the most positive response among college students

because it reminded them of nacu re. Yellow was a close

88


second. Yet, students associated [he color green-yellow with feelings of sickness and disguSt. In an earlier Study of the effects of color on people's emotions, Valdez and Mehrabian (1994) also found green-yellow to be one of the leaS[ pleasant colors, YCt one of the most arousing.

In designing a map where fea tures will be colored, rwo general rules should be followed:

1. Color certain landscape features with their mOSt obvious associated color (e.g., roads-black; screams-blue) .

2. When coloring polygons in a thematic map, use gradarions of one or two colors to represem classes rather

than a set of conrrasting principal colors. The lance may confuse the ordering of importance of classes in the map user's mind.

Visual contrast is o ne of the most important facmrs in

creating a map (Robinson et a1 ., 1995). T he extent of contraSt employed by a mapmaker affects how well one set of in fo rmation is promoted above (he other informa

rion avai lable in the map (Figure 4.9). Feature size, shape, texture, and color can all be altered to introduce contrast

into a map. Contrast will help focus the attemion of (he

audience, and wi ll playa signi ficant role in the abi li ty of the audience co determine the clari ty of a map.

Mapmakers must also balance figure-ground relarion-

(a) limited contrast among groupings

Land allocations

Uneven-aged stands o Even-aged stands o Rock pits, research areas, meadows, oak

woodlands, shelterwood stands

ships so [hat the important objects of a map are separated

from those that are considered ancillary. In human per

ception, figures are the objects that are most strongly per

ceived and remembered by a map user, whereas data (text) are less distinc[ive and memorable (Dene, 1999). Techniques such as figure closure, comrast with other objeccs, and object grouping can be used to escablish distinctive ground-figure relationships.

Ancillary information

In some large namcai resource management organizations

where multiple people share data developmenc [asks, placing the names of people who contribu ted to [he developmene of a map on a map is typical ly discouraged. However, in field offices of narurai resource management

organizations. where field personnel are generally respon

s ible for developi ng maps to ass isc wich on-che-ground decisions (and hence not specifically [he development of GIS databases), it may be desirable to know both who creaced a map and when ic was creaced. Since GIS dacabases

may be modified frequently, knowing the date tha[ a map was creaced mighc be as imponanc as knowing che map's

developer. This sou rce informacion allows map users co

place che coneent of a map in perspeccive w ich che version

of the GIS database(s) used to creace the map.

l and allocations

(b) More extensive contrast among groupings

_ Uneven-aged stands

o Even-aged stands D Rock pits, research areas, meadows, oak

woodlands, shelterwood stands

Figwe 4 .9 An cxaInple of visual contrast. The limited contra.Sf among the groupings in the first map (a) d~s not promote the differences in stand types as stron gly as the more cxtens ive contra$l of the groupings in the second map (b).

89

For example. suppose it is currently September 2008

and you were examining a map developed in June 2007

that represented wildlife habirar across several thousand

acres of a landscape. Assume that the wildlife habitat being

mapped was a funcdon of forest stand conditions, and that

the GIS database describing forest stands had been updated

in December 2007. If the date were displayed on the map

Qune 2007). you would be able to understand that the

qualiry of wildlife habitat illustrated in the map was esti

mated using an earlier version (i .e., not the current version) of the forest inventory data. Withom such informa

tion. you could very likely assume (incorrectly) that the

estimates of wildlife habitat are current.

It is relatively uncommon for mapmakers to provide

the names of the files, projects. or compurcr code (e.g .. macro) used in making the map on the map itself.

However, providing [his informacion would allow you co

readily go back to the GIS darabases or the computer

code. modify some aspect related co the composition of the map. and generate a new version of the map relat ively

quickly. Without such guidance. you may find ir difficult [0 remember how a map was originally constructed, The

map projection might also be provided if the map per

spective is affected by the projecrion system used. This

rype of ancillary information is usually placed in a subor

dinate position on a map. in relation to the other aspects

of a map. and displayed with a relatively small font size.

Caveats and disclaimers

Increasingly. natural resource management organizations

are adding more information to the maps that they pro

duce both to clarifY the accuracy of mapped landscape

features and to clarifY the intended uses of the maps. This

information is important in helping users understand the

appropriate applications of mapped information and in

helping avoid damages and injuries that might result from

improper map use. For example, maps have long served as

vital navigation aids to mariners and pilots. The ability to

safely pilot passengers depends on the quality o f the

mapped information used as a navigational guide. Should

a landscape landmark be misplaced or unidentified. the

consequences to people and veh icles that are navigating with the erroneous data could be disastrous. As you might

imagine. there are other reasons why maps should contain

information related to the quality of data. However. pro

viding this information or deciding not to provide this information is, at least indirectly. a function of [he liti

gious nature of today's society.


A map disclaimer is a statement that embodies the

legal position of the mapmaker with respect to map users.

In many cases, the map maker uses a disclaimer to dis

rance himself or herself from any legal responsibiliry for

damages that could result from use of his or her map.

Caveats. similarly. warn others of certain facts in order to

prevent misinterpretat ion of maps. In general. caveats are

less sweeping than disclaimers, and may on ly address cer

tain portions or aspects of a map. Warranties . on the

other hand, are usually written guarantees of the integrity

of a map. and of the mapmaker's responsibiliry for the

repair or replacement of incorrect maps. In practice. dis

claimers and caveats are regularly used. and warranties are

rarely (if ever) used in associarion with maps and GIS databases. Quite often organizations add disclaimers or

caveats to their maps in an attempt to warn users of the

limitations of the map content. In some cases. disclaimers

are noted directly on a map. and in other cases disclaimers

are provided on websites devoted to the distribution of

maps. Pima Couney, Arizona, for exam ple. provides a very thorough disclaimer about its products on a website

(Pima Counry [Arizona] Department of Transportation.

2003). The mai n ideas found in caveats and disclaimers

include:

• rights reserved via copyriglu and permission require-

ments for modificat ion co mapSj

• degree of error found on the mapSj

• suitability for usej

• liability. or responsibility. for errors or omissions (e.g ..

organizations usually assume no responsibility for mis

use of their maps and subsequent losses); and

• contact information (e.g., addresses, phone numbers, e-mail addresses).

Caveats and disclaimers vary in form and content from

organization to organization. Listed below are four

examples.

• Indiana Geological Survey (2007) : The maps on this

web site were compiled by Indiana University, Indiana

Geological Survey. using data believed to be accurate;

however. a degree of error is inherent in all maps. The maps are distributed "AS-IS" without warranties of

any kind. eicher expressed or implied. including bur not limited to warranties of suitability to a particular

purpose or use. No attempt has been made in either the design or production of the maps co define the

limits or jurisdiction of any federal , state, or local gov-

90


ernment. The maps are intended for use only at the published scale. Detailed on-the-ground surveys and hismcical analyses of sites may differ from the maps.'

• USDI Bureau of Land Management (2001), Glennallen, AK: ' In fo rmation displayed on our maps

was derived from multiple sources. OUf maps are only

for graphic display and general planning purposes. Inquiries concerning informacion displayed on our maps, their sources, and intended uses should be directed (0: .. .'

• Orange County (Florida) Property Appraiser (2002) : The Orange County Property Appraiser makes every

effort to produce and publish the most current and accurate informacion possibl e. No warran ties, expressed or implied, are provided for the data herein, its use, or its interpretation. The assessed values are

NOT certified values and therefore are subject to

change before being finalized for ad valorem tax purposes. OCPA's on-line (cadastral) maps are produced for property appraisal purposes, and are NOT surveys. No warranties. expressed or implied, are provided for

[he data therein. its use, o r its interpretation.' • Town of Blacksbu rg (Virginia) (2007), on the

Blacksburg WebGIS si te: 'DISCLAIMER: T he infor

marion contained on this page is NOT [Q be construed or used as a "legal description". Map information is believed to be accu rate but accuracy is nor guaranteed. Any errors or omissions should be

reported to the Town of Blacksburg Geographic Informat ion Systems Office. In no event will the

Town of Blacksburg be liable for any damages, including loss of data, losr profits, business inrerruption. loss of business information or orner pecuniary loss that might arise from the use of this map or the information ir contains.'

Map Types

The type of map thar you develop should be a function

of: (1) the type of data (i.e., point, line, polygon, raster) that is contained in GIS databases, and (2) the message(s) [har you wish to communicate to an audience. For example, if the main GIS database used to create a map contains point features. and you wish to illustrate differences berween the point values. you may want to show the points as dots or graduated symbols (di fferent sizes of points based on the po int att ribute values) . If the main GIS database used ro creare a map contai ns line featu res, you may wam to illustrate the differences in the lines with

di fferent line types. If fearures of interest represent areas, GIS databases containing polygons can be displayed as thematic maps, qua litative area maps. and others. Volumetric databases. such as digital elevation models, can be displayed as gridded fishnet maps, as shaded relief maps. or simply as images with different shades or colors assigned to individual pixels.

If you were interested in illustrating features of a landscape across time, you can develop maps with multiple panels. where each panel would contain a view of the landscape during a differenr time period. In some cases, maps can be animated, as in a short movie. They can also

contain a ' fly through ' ability when viewed. on a computer screen. The next few sections of this chapter describe (he

most common types of maps developed for natural resource managemem purposes in more derail.

Reference maps

Reference maps are those that ill ust rate a number of different landscape features, and mat provide users with a

broad perspective of the landscape. Road maps are one example of reference maps, and may contain not only road types, but also the locations of towns, major rivers. and political boundaries (provinces, states, counties, etc.) that are set among the road system. Maps that display st ream systems or watersheds are another example of reference maps. H ere, you would be able to place a watershed or stream system within the context of a larger geo

graphic area, and thus these maps may also comain the locations of (Owns and political boundaries. Land ownership maps are a third example, and (hese may contain roads, streams, towns, and other features necessary to

place tile land you manages within a larger landscape context. Reference maps are commonly made when you develop management-related activity maps, such as those

for tree planting or locations for new features such as trails o r roads.

The characteristics of reference maps will vary depending on the audience. For example, some reference maps

might display unique landscape features that are essential for high quality recreational experiences. Edwards (200 I)

describes the desirable co ntent and features of fishing maps developed for anglers. It is suggested that these types of reference maps include the complete road system surrounding a fishing area, water depths, access points (Q

water. names of local features, locations of off-limit fishing areas, locations of fishing lodges and places to buy fishing permits, places (Q park vehicles, and, interes tingly,

91


ernmenr. T he maps are intended for use only at the published scale. Detailed on-rhe-ground surveys and hismcica.l analyses of sites may dHfcr from the maps.'

• USDI Bureau of Land Managemenr (2001), Glennallen, AK: 'Informarion displayed on o ur maps

was derived from mulriplc sources. Our maps are only

for graphic disp lay and general plan ning purposes. Inqui ries concerning information d isplayed on our maps, their sources, and intended uses should be directed to: .. .-

• Orange Counry (Florida) Properry Appraiser (2002): The Orange Counry Pro perry Appraiser makes every

effon ro produce and publish the most currem and accurate information possible. No warranties. expressed or implied. are provided lor the dara herein. irs use, or its interpretation. The assessed values are NOT cerrified values and therefore are subject to change before being finalized for ad valorem rax purposes. OCPA's on-line (cadasrral) maps are produced for properry appraisal purposes, and are NOT surveys. No warranties, expressed or implied, are provided for

{he data therein, its use, or its interpretarion.'

• Town of Blacksburg (Virginia) (2007), on rhe Blacksburg WebGIS site: ' DISClAIMER: The informarion comained on rh is page is NOT co be consrrued or used as a "legal description". Map information is believed to be accurate bur accuracy is not guaranteed. Any errors or omissions should be reponed to rhe Town of Blacksburg Geographic

Info rmat ion SyStems Office. In no event will the T own of Blacksburg be liable for any damages, including loss of data, lost profits. business imerrupcion. loss of business information or ocher pecuniary loss [hat might arise from the use of this map or rhe information it comains.'

Map Types

The rype of map thar you develop should be a function of: (I) the rype of dara (i.e., poim. line. polygon. raster) rhar is contained in GIS databases, and (2) the mess.ge(s) rhar you wish to communicate [Q an audience. For exam p

pie, if rhe main GIS database used to create a map contains poinr feat ures, and you wish to illustrate cfjfferences between the point values. you may wanr to show rhe points as dots or graduated symbols (different sizes of poinrs based on rhe paine anribute values). If [he main GIS database used [0 create a map conwns line feacures, you may wane ro illusuare rhe differences in the lines with

differem line cypes. If features of inreresr represent areas, GIS databases conta ining polygons can be displayed as thematic maps, qualitative area maps. and others. Volumetric databases, such as digital elevarion models, can be displayed as gridded fishnet maps, as shaded relief maps, or simply as images with different shades or colors assigned to individual pixels.

If you were inrerested in illust.rating features ofa land

scape across time. you can develop maps with mulriple panels. where each panel would contain a view of the

landscape during a different rime period. In some cases, maps can be animated, as in a short movie. They can also

contain a ' Ay through ' ability when viewed on a compurer screen. The next few sections of this chapler describe the most common rypes of maps developed for natural resource management purposes in more derail.

Reference maps

Reference maps are (hose that il1ustrate a number of different landscape feaOlres, and mat provide users with a

broad perspecrive of the landscape. Road maps are one example of reference maps, and may comain nor only road rypes, bm also the locations of [Owns, major rivers, and poljtical boundaries (provinces, states. counries, etc.) that are set among the road sysrem. Maps that display stream systems or watersheds are another example of reference maps. Here, you would be able to place a watershed or stream sysrem within the concext of a larger geo

graphic area. and thus these maps may also conrain the locations of [Owns and polhical boundaries. Land ownership maps are a third example, and these may comain roads, streams, [Owns, and other featu res necessary to place the land you manages within a larger landscape con[ext, Reference maps are commonly made when you develop managemenr-relared acrivicy maps. such as those

for tree planting or locations for new features such as trails or roads.

The characteristics of reference maps will vary depending on [he audience. For example, some reference maps

might display unique landscape features that are essential for high qualiry recreational experiences. Edwards (2001) descr ibes rhe desirable content and features of fishing maps developed for anglers. Ir is suggested that rhes. rypes of reference maps include the complete road system surrounding a fishing area, water depths, access points to Water, names of local features, locations of off-limit fishing areas. loca tions of fishing lodges and places to buy fish ing permits, places to park vehicles, and, in te restingly.

local pubs. In [he case of fishing maps, Edwards (2001) suggests that they be developed in such a way that the imporr3m information is easily accessible to the eye, and that they be represented with easily understandable

carrography.

Thematic maps

Thematic maps use colors or symbols [Q describe the spatial variadon of one or more landscape features. Map features displayed with a combination of color and texture have been shown (0 be easier to find on maps than features displayed with variations on texture alone (Phillips & Noyes, 1982). Several types of thematic maps are common. Perhaps (he most common is the choropleth map on which a range of appropriate values (Figure 4.10) or gradations of a color illustrate [he relative magnicucle of attributes of landscape features . Color schemes generally range from an empty shaded fill (for lowest valued attributes) to a full shaded fill (for highest valued attributes), with various shades of color for intermediate classes.

The legend is crit ical when developing choropleth maps because the colors related (0 the va lues muse be explicitly described. in order for map users (0 interpret [he values effeccively. Several design aspec[s muse be

Trees per hectare

D 0-1 ,000 D 1.001-2.000 _ 2,001+

(a) Three classes

Figure 4 .1 0 A range of classes of trees per hectare on the Brown Tract illustrated in a choroplerh map.


Trees per hectare

D 0-500 D 50H.000 D 1,OOH .500 c::J 1,501-2,000 _ 2,001.

Trees per hectare

D 0-20 D 2H20 D 12H60 D 16H90 D 191-220 _ 221-250 _ 251.

(b) Five classes

(c) Seven classes

92


addressed when creating a legend, The number of legend classes, or categories. is imponanr, Too few classes may

not contain enough informacion, while [00 many classes

may presenr [00 much detail or result in an overly 'busy' map. Sometimes it may be necessary (Q experiment with

several choices co determine which might work beSt (see Figure 4, 10) . Dent (1999) provides some guidelines related to this topic. Humans have difficulty differentiating more than 11 gray [Ones, so as a general rule. a mini

mum or four, and not more (han six, classifications should be used on a map.

The size or ranges of the intervals for each class also has a significant impact on a map's message. MoSt GIS

software programs offer processes to help create legends, and (hey may cake inca account (he distribution of [he data that are being mapped. An equal interval legend, for example. would take into account the range of data val ues

and create intervals (classes) that share an equal disrribution of the range (Figure 4.11) . A quantile diStribution would put an equal number of observations (e.g., polygo ns) into each interval (class). Intervals might also be creared based on how many standard deviations an observar ion is from the mean, or be created using natural break

points in the disrribmion of observations. While these 3momarcd processes can save rime when compared to

Trees per hectare

00-249 o 250-499 0500-749 _ 750-999 _ 1,000+

(a) Equal interval classes

manual methods of creating classification ranges, I( IS

advisable for map makers to visually examine the distribution of the data (hey are mapping to bettcr understand its character, and then to decide what legend type might be useful. Again , an important concept when utilizing GIS

software is that the software will usually do what you ask.

The responsibility !ails on the mapmaker to determine the appropriate classification for a legend.

The diStribution of data, such as the range of basal area within the stands of a property yo u manages, can take on many different shapes. but (he most common (for map-making purposes) are norma1. random. and even distributions (Madej, 2001) . Normal distributions follow a stat iscically-based representacion of va lues thar you

would expect to see from most populations or population

samples. As a result, a standard deviation legend classifica

tion, with its emphasis based on statistical variation from

an average. works well. Random disrribmions are present in data where you cannot discern a regular panern in the

occurrence of data vales. Natural breakpoints can be

established by locating sub-groupings of random data, thus you would manually create divisions between sub

groups. Even disrribmions include data where the va lues

do not appear to change very much . For example. if you

managed 1,000 hectares ofland, you might expect to find

Trees per hectare

00-277 0278-593 o 594-873

874-1 ,236 _ 1,237+

(b) Quantile interval of classes

Figure 4 . 11 An interval cla.uification and a quantile interval classification of trees per hectare on the Brown Tract.

•

93

an equal number of hectares in each 10 m' ha- I basal area class. and thus you would expect to find a relatively small standard deviation in the data values here. A quantile classification works well for even or uniform distributions because an equal number of observations are placed in

each category. Regardless of how thematic classes are created. map

makers need to ensure that the intervals are consistent and that they make sense from an interpretation paine of view. This also includes verifYing that legend classifications do not overlap and do not inadvertendy omit data ranges (Figute 4.12). If a characteristic of a landscape feature (e.g .• basal area per hectare) can be placed inro more than one class, then the classes overlap. Alternatively, if a characteristic of a landscape feature cannot be placed inco

any class. a data range has been omitted and the feature Falls into a classification level 'gap'. Either way. the potential problems with the classification must be addressed.

With the increasing capabilities and affordability of color printers and plotters. the use of color to graphically ponray different class ifications in thematic maps has become standard. In general, tonal progressions of a s ingle color are useful for illustrating magnitudes of change, with the lighter (Ones of colo rs indicating a lesser quanc:iry (or quality) of an attribute value than the darker tone col-

Compare this

W"h~e~m.~~~~~~~-r __ ~ polygoo in ' 17"O'...J-' 4.7(b)

Trees per hectare

D 0-500 D 501-1 ,000 D 1,001-1 ,500

(a) Overlapping classes

D 1,501-2.200 } 2001-2200 I _ 2,001 + " over ap


ors. A similar effect can be generated with gray tones. as demonstrated with some of the figu res in this chapter.

Single color progressions are particularly helpful for continuous numeric variables . For nominal data classifications. such as ownership or land use, or numeric data

with only a few categories. distinctly different colors or patterns can be used to make certain categories stand out

on a map. Contour maps (Figure 4.13) are also a type of the

made map. and are sometimes called isoline or isarithmic

maps. Here. lines or collections of similar features are used to emphasize gradients or distributions, such as ele

vations or precipitacion levels across a landscape (Star & Estes. 1990). The contour interval is the distance between adjacent contour lines. The choice of an interval is important when creating concour maps: tight intervals may result in a cluttered map while wide intervals might mis

represent landscape variation. To reduce clutter on maps. not every contour interval is described with a data value.

only those representing significant changes in elevationusually denoted themselves by the elevation interval. For example. while the comour interval between adjacent comour lines may be 10 meters, the only comour lines

represented with data values may be those that represent every 50-meter change in elevation.

(b) Omilted classes

Compare1tiese Trees per hectare wtth the same D 0-500 polygons in D 501-1,000 4.7(a)

B ~ :~~~=~',: } 1501-1600omilted _ 2,001+

Figure 4.12 A range of criteria used for a choropleth map, with (a) overlapping classes. and (b) omitted classes.

94


Figure 4.13 A contour map of the Brown Tract (elevation in meters above sea level) .

Until now, the discussion has focused on maps created using vector GIS databases, but raster-based maps are also

important for displaying data stored in the raster GIS data structure. These maps are very similar to the choropierh maps noted above: mapmakers must classify the values held by the rasrer pixels and then disp lay them on a map

using a legend. Raster-based maps, unless created by raste rizing a vector GIS database, usually have more of a hJzzy appearance than vector-based maps. and therefore

generally represenr more hererogeneiry across a landscape (Figure 4 .14).

Other types of maps

Dot density maps (F igure 4.15) were once commonly used in namral resource management, although less so now. Each dor in a dot density map represems a given value of an attribute. Thus areas with a greater density of dots are meam to represent areas with greater values of some particular anribme. Graduated circle maps place ci rcles on top of landscape fearures and scale their diameter proportionate to one of the feature's attr ibute values (e.g.> population of towns) to demonstrate differences among the landscape features. Carrograms (Figure 4 .16) are another type of maps in wh ich more than one an ribme of landscape fearures can be viewed. These types of maps are also fairly uncommon because of the large amount of clutter that can be generated in a map with a

Tree Seedling Measurements + N Randle, Washington II o 25 50

MeIers

Prepared by: Michael Wing last Updated: November 17. 2007

Seedling Diameter (cm) <B

8-10 _ 11- 12 _ 13-15 _

Skid Roads --

Figure 4.14 A raster map of uee seedling measu~ment$ .

large number of landscape areas, or in maps with a high density of landscape features within a given area (e.g .• a

multitude of small polygons).

Figure 4 .15 A dot density map of basal area on the Brown Tract.

95

110 .. ~ 110

Stand Attribute

D Trees per acre _ Basal area per acre

Figure 4.16 A cartogram map illustrating two measures offorc$[ density for each stand-trees per acre and basal area per acre on the Brown T net.

The Design Loop

Novice mapmakers, especially students, assume that their first attempt at a new map will be sufficient [0 effectively communicate informacion (Q their audience (or to cam

ple[e an assignmenc). However, maps usually should go through more (han one version before they are delivered to a customer, whether changes are needed based on the mapmaker's visual assessment of (he map. or based on a customer's suggestions (Figure 4.1 7), Each iteration in

the development of a map could be considered one irera[ion in [he design loop. Feedback from supervisors and co-workers will allow you to fine-cune maps that are made

for reports or management activity plans. Besides the aestheric concerns presented in this chapter (map type. number of classes shown, etc.), mapmakers should strive to

achieve visual balance within their map products. This concern is one of {he reasons why maps may need co be edited numerous rimes. Visual balance is affected by {he size of [ex[ (t ide, legend, and ancillary information) and the location of map components (north arrow, scale, and

location inset) in relat ion to the visual center of the map. One key to identifying an unbalanced map is the presence of a large. empty space in some portion of the map.

Develop Map

Get Feedback

Yes

Map Completed


No EdH Map

Fig-urc 4. 17 A basic design loop for making maps.

The number of iterations of a design loop will be a function of your abi li ty to address a range of visual concerns (visual contrasts. visual balance. legend. etc.), your abi lity to address a range of illustrative concerns (showing the correct information), and your time constraints

(the time remaining before a deadline) . I[ might be advisable co scan the map-making process by first developing some hand-writren notes that contain the main ideas

about the intended map message(s) or purpose and the rype of audience [hac is likely to view [he map. An outline format wi[h a primary objeccive and sub-headings that address other intended map purposes might be worth considering. Once these concepts have been identified. it might also be worthwhile [Q create a hand

drawn skecch of the basic map componenes and how they fit together. At this poine, the developmene of [he map wi[hin GIS can begin. A well-developed, visuallycentered map will be a reRection of one's professionalism as a natural resource manager.

Common Map Problems

Miscakes (rypographieal errors), oversighcs (wrong color or symbology used), and omissions (missing information) can impair a map's ability to deliver {he imended message to an audience. Probably the most important aspect of

96


creating a map is co focus on [he audience [hat will be viewing the map. If your co-workers (ocher foreseers, biologists, erc.) will be rhe primary audience, rhen perhaps

the basic map elements (e.g., a north arrow, bar scale. or

locational inset) may not be as importam ro include; [his

audience may expect to see instead more of [he technical

info rmacion, such as annotat ion or Q[her descriptive

informadon, that is related to the landscape feacuces illus

trated. They may also expect [Q see a morc extensive vari

acion of feature classes across the landscape. However, if rhe aud ience is composed of rhe general public-rhose

nor familiar with the landscape resources displayed on a

map- then it would seem imporram {Q include [he basic

map e1emems (e.g .• tide. credits. north arrow, scale bar,

and inset) that help the audience orient themselves to the

map. Less variation (fewer mapped classes of information) would reduce the confusion associated with the map. An

audience composed of academics o r scientists may want to see more detail in the data values.

Map problems are usually rhe resulr of leaving a key

element, like a scale or another detail important to the

audience. off of a map. Sometimes, these problems result

from misjudging the audience, and other times it is simply

a matter of oversight on the part of the mapmaker. For

example, since automatic spell-checking processes are

available in mOSt word processing software, many map

makers somerimes forger thar spell checking is usually a

manual operation within most GIS sofrware. In addition,

some place names (names associated with cities, regions,

Summary

Maps are used to convey information about the spatial

location and cha racteristics of natural and man-made

resources. When they are well developed. maps a re an

effective way of communicating ideas [Q an audience.

This chapter described a variety of map components that

can be used to help develop effecrive maps. These included legends, scales, annotatio n, symbology, and

typography. The representation of rhese components on a

map may be important to inevitable customers of the

map. For example, there are cerrain componentS of maps that are required when developing maps for co-workers or

others internal [Q a natural resource management organization, certain components thar are required when devel

oping maps for external clients (e.g., when submitt ing a

provinces, erc.) have unusual spellings and, if spelled

incorrecdy. may nor be detected even if a spell-checker is used . This requires rhar mapmakers carefully read all map

text before presenting the final product to their customers.

Excessive detail , or clutter, can also detract from a

map's message and intent. Most GIS software programs now feature impressive arrays of map-making symbols

and rools rhar are capable of producing any number of

cartographic symbols and orher aids. While many of rhese

tools can be quite helpful-not to mention interesting to

experiment with-too many objects on a map produce clutter and hinder a map's intended message. Excessive

detail, especially in maps that are intended general infor

mation displays. can also result when mapmakers insert

toO much text (annotation or labels) OntO a map.

It is also common that output devices (e.g., printer or plorrer) produce maps wirh colors rhar appear slightly dif

ferent from what is viewed on a computer screen . These

problems can be very frustrating, especially after you have

painsrakingly pur rogerher a colored legend scheme rhat differentiates between different colors or values. The sub

sequent adjustments can be frustrating. Sometimes the

easiest solution to a color mismatch problem is to simply

choose colors from a palette file that is compliant with the

output device. Other sol utions include using only the

addirive primary (red, blue, green) or subtracrive primary

colors (magenta, cyan, yellow), or creating color schemes

that take plotter translations of monitor colors closely

into account.

harvest plan to a stare or provincial agency), and certain

components are required when developing a map for per

sonal use. In addition, some organizations require that all

maps produced by their employees contain an organiza

tional logo. use a standard layout format, and use other features that reflect the organization and the map's

intended purpose. There is no singular, correct format

rhar firs all organizarions, and you should balance your

creativity with the advice provided in this chaprer. You

should, however, have an understanding of the map rype

options, and develop the appropriate map for the intended audience. Keep in mind that eve ry map is

potentially a reflection of your reputation as a narural resource professional.

97

Applications

4.1. Age class distribution map for the Brown T ract. The manager of rhe Brown T racr, Becky Blaylock, would like you co produce a map char illustrates the age class discribucion of the forest. To complete this exercise, develop

a rhemaric map showing I I age classes: 0-10 years old, 11-20, .... ,91-100, and 100+ years old.

4.2. Tree densiry map for the Brown Tract. Afrer reviewing your previous work, Becky Blaylock would like a map thac illustrates the trees per hectare for vegetation stands contained within the Brown T ract; she needs the

map for an annual reporr chat she is developing. Develop

a thematic map chat classifies the stands by trees per hectare. using five logical classes.

4.3. Owl locations on the Darnel Picketr forest. The wildlife biologisr associared wirh rhe Daniel Picke[[ foresr needs a map chat illustrates the historical spotted owl (StTix occithntalis) nest locations that were known to have

been used in the Forese. Develop a reference map illustrat

ing rhe two nest locations, and annotate rhe map with the

dare of rhe laS[ known sigh[ing of the owls.

4.4. Stream rypes of the Daniel Picketr forest. The hydrologiS[ associared wirh rhe Daniel Picke[[ foreS[ needs a map illustrating the different stream rypes, in o rder CO direct a summer crew co the locat ions he would

like to survey for fish species and habitat conditions. Develop a reference map that iIlusrrarcs the different

scream rypes associated with the Daniel Pickett fo rest.

4.5. Potential harvest unit. The land manager of the Daniel Picken forest is considering a timber sale in unit number 13 on rhe Daniel Picken forese He would like you (0 produce a management map indicating that unit 13 is a proposed harvesr area, and co display rhe road and stream systems in rdarion (0 the uniL

4.6. Brown Tract hiking map . Becky Blaylock, man· ager of rhe Brown Tracr, would like you ro develop a map illumaring rhe crail sysrem, highlighring borh rhe authorized and unauthorized trai ls. Recrearionists who visir rhe fo reS[ wi ll likely use [his map. Develop a refer· enee map that incl udes the road system and the contour lines (wi th associated elevations) as supplementary information.


4.7. Culvert installation dates. The road engineer associared wirh the Daniel Picke[[ foresr, Bob Packard, is in the process of developing a culvert replacement plan. He would like you co develop a map illusrraring rhe road system, the culverts, and the culvert installacion dates for

rhe Daniel Pickerr properey.

4 .8. D isclaimers) caveats, and warranties. Imagine

that you work for an agency chat has developed a statewide

streams GIS database. At one of your regular staff meet

ings, rhe discussion shifrs co rhis GIS darabase and the need co add or associate some SOrt of disclaimer, caveat, or war

ranty with the GIS database. During this conversation you

conclude rhar rhe group is very confused abour rhe use of the terms 'disclaimer', 'caveat', and 'warranty'. What guid

ance can you provide co help rhe sraff undersrand rhe differences between the terms, and how the terms might be used in relation to the streams GIS database?

4.9. Map scales. One of your colleagues, Mike Marshall , does nor like rhe eype of scale [har you common ly incorpo rate into your management maps. He

prefers to use another type of scale, and insists that you

use ir as weU . IdenrifY, define, and describe rhe possible advanrages and disadvanrages of rhree eypes of approaches for creating map scales.

4.10. Map development. A sma ll consul ring firm In

Brirish Columbia has recendy hired you, and one of your first assignments is to make management maps for various

planned act ivities on the land managed by your firm.

IdenrifY five irems or objecrs rhar should be placed on almost every map.

4.11. Map legends. The manager of rhe Brown Tracr desires a map illustrating the trees per hecta re for each

stand on the property. There are different approaches for

organizing and displaying sparial dara inro a map legend. a) Whar is a general guideline for choosing rhe num

ber of categories in a legend thar uses gray cones? b) Whar is an equal inrerval legend, and how does ir

display numeric data?

c) What is a quantile distribution legend, and how

does if display numeric data? d) Whar is a srandard devia<ion legend, and how does

it display numeric data?

98


References

Dent. B.D. (1999) . Cartography thematic map design . New York: McGraw-HilI.

Digital W isdom. Inc. (2006). Cartographic symbols & map symbols library. Tappahannock. VA: Digital Wisdom. Inc. Recrieved March 28. 2007. from hnp:llwww.map-symbol.com/sym_lib.htm.

Edwards. D. (2001). Maps for anglers. The Cartographic journal. 38(1).103-6.

Franklin. P. (2001) . Maps for the reluctan t. The Cartographicjourna~ 38(1). 87-90.

Indiana Geological Survey. (2007) . Copyright. map disclaimer. and limitation of warranti~s and liabilities. Bloomington. IN : Indiana Geological Survey. Retrieved March 3. 2007. from hnp:// igs.indiana.edul disclaimer.cfm.

International Orienteering Federation. (2000). Intanational specification for orienuering maps. Radio-katu, Finland: Incernational O rienteering Federation.

Kaya. N .• & Epps. H .H. (2004). Relationship between color and emotion: A study of college students. Co/l'ge Student journal, 38. 396-405.

Madej. J. (2001) . Cartographic dlSign using Arc View GIS. Albany. NY: OnWord Press.

Mecriam-Webster. (2007). Mariam-Webster online search. Retrieved March 28. 2007. from hnp://www. m-w .coml cgi-binl dictionary.

Monmonier. M.S. (1995) . Drawing the line: Tales of maps and cartocontroversy. New York: Henry Holt and Company.

Monmonier. M.S. (1996). How to Lie with maps (2nd ed.). Ch icago. IL: University of Chicago Press.

Natural Resources Canada. (2006). Topographic map symbols-introduction. Ottawa, ON: Nacucal Resources Canada, Earth Sciences Seccoc, Mapping Services Branch. Recrieved March 23. 2007. from hnp:llmaps.nrcan.gc.caltOpo 1 0 I/symbols_e. php.

Orange County (Florida) Propetry Appraiser. (2002) . OCPA

map record inquiry system. Retrieved March 29. 2007. from http://www.ocpafl.orgidocs/disclaimer_map.html.

Phillips. R.J . (1979) . Why is lower case bener? Some data from a search task. Applied Ergonomics. 10.211-14.

Phill ips. R.J. (1989). Ase maps different from other kinds of graphic information? Cartographic journal, 26. 24-5.

Phillips. R.J .• & Noyes. L. (1982). An investigation of visual c1uner in the topographic base of a geological map. Cartographic journal. 19. 122-32.

Phillips. R.J .• Noyes. L.. & Audley. R.J. (1977). The legibility of type on maps. Ergonomics. 20. 671-82.

Pima County (Arizona) Department of Transponacion. (2003). Department disclaimer and me restrictions. T llcson, AZ: Pima COlincy Department of T ransporration, Geographic Information Services Division.

Retrieved March 28. 2007. from http://www.dot. co.pima,az,lls!mapdis,hrm ,

Robinson. A.H. Morrison. J.L. . Muehrcke. P.c. . Kimerling. A.J .• & Gupti ll . S.c. (1995). Elements of cartography. New York: John Wiley & Sons. Inc.

Sheahan. B.T. (2004). The unofficiaL Arc/Info and Arc View symbol page. Victoria. BC: Spatial Solutions. Inc. Retrieved March 28. 2007. from http://www. mapsymbols.com/.

Star. J .• & Estes. J. {I 990) . Geographic information systems: An introduction. Englewood Cliffs. NJ: PrenticeHall. Inc.

Town of Blacksburg (Virginia). (2007). Blacksbu rg WebGIS site. Retrieved March 28 . 2007. from http:// arcims2 . webgis. net/blacksburgl defa ul t.asp.

US DI Bureau of Land Management. (2001). Map disc/aimer. Glennallen. AK: Bureau of Land Management. Retrieved March 28. 2007. from http://www. blm.gov/aklgdo/documents/map_disclaimer.doc.

USDI US Geological Survey. (2003). Part 6 publication symbols. Standards for 1:24.000- and 1:25.000-scale quadrangle maps. Reston. VA: US Geological Survey. Retrieved March 23. 2007. from http://rockyweb. cr. usgs. gov In m ps tdsl ac rodocsl q rna ps/6psym 4 03. pdf.

USDI National Park Service. (2003) . NPS map symbols: Updated March 4. 2003. Retrieved March 27. 2007. from http://www.nps.gov/carto/PDF/symbolsmapl . pdf.

Valdez. P .• & Mehrabian. A. (1994). Effects of color on emotions. journal of ExperimentaL Psychology: Genera~ 123. 394-409.

Wood. D. (2003). Cartography is dead (thank God!). Cartographic Pmpectives. 45.4-7.

99

Part 2

Applying GIS to Natural Resource Management

Pact 2 of rhis book focuses on the GIS applications common to namra) resou rce man

agement organ izatio ns. The majority of data support ing daily GIS use in namral resource management is stored in vectOr GIS databases. although rhe in tegration of vector

and raster GIS databases is becoming increasingly com mon. For example. the development of resource managemem plans may require rhe use of GIS databases representing fo res t stands (polygons), roads and srreams (lines), water sources and wildlife observat ions (points). and topography (raster). T he chapters included in this part of the book present a number of GIS processes such as querying, buffering. clipping. raste r analysis. and simultaneous integratio n of vector and raster GIS databases in an analysis. The ropics increase in complexity wi th each passing chapter. and rhe applications associated with each wpic integrate processes presented in previous chapters. Some advanced wpies are also presented. such as delineating the land classifications of a landscape and delineating the recreation opponunity spectrum classes of a landscape. In panicular, [he raster-oriented chapeers provide examples of advanced analys is techn iques and possibilities.

A variety of processing pathways can be used within GIS co address a si ngle management concern and it is imponant [Q note that there may be more than one processing solut ion for a part icular challenge. While we provide direct ion for the mOSt straightfor

ward processes. students should feel free to be crea tive in their approaches co addressing each application. Finally, some of the applicat ions require students CO think more broadly (beyond GIS) abom how the results of an analysis may affect natural resource management opportunities within a given landscape.

100

Chapter 5

Selecting Landscape Features

Objectives

A variety of methods can be used [Q locate and select

landscape features based on either their attribures or their

proximiry to other feawces. Spatial and attribute reference queries ca n be used [Q select features based on the

informacion scored in attribute rabies (layer database) or

(he spatial location of landscape feacuces. At the conclu

sion of this chapter readers should be /am iliar with, and have a working understanding of:

1. the variety of methods that can be used ro select landscape feawces fro m a GIS database;

2. the meaning of the term 'query', when applied spatially o r referentially; and

3. the methods you can use to develop a description of the resources located on a landscape.

In a reeem annual report to their stockholders. one of

the largesr timber companies in the United States said the

foll owi ng about the use of GIS: 'Our foresters use advanced Geographic Information System (GIS) models to get a picture of timber species. sizes, and age c1assesalong with a multitude of environmental detai ls. from

streams and fis h to wildlife populat ions and habitat' (Plum C reek Timber Co., 2001). This statement recognizes the extent to which GIS is viewed as a valuable man

agement tool and acknowledges the power of GIS to assist in the management of resources. Other natural resource management organizations also rely on GIS to store location information about the natural resources that they

manage (as well as those that they might not manage but

that are important in the management of their property). These o rganizations include public agencies such as the

US Forest Service. the Canadian Forest Service, the US

Park Service, the US Bureau of Land Management. State

and provincial orga nizatio ns, private industriallandown

ers, and those organizations that manage smaller tracts,

such as forestry consu ltants, and university research

forests. Each organization has a different mission, yet each

relies on similar methods fo r storing and organ izing the ir

geographic databases . The methods used to extract infor

mation from these databases are also very similar, and

help provide a picture of the namral resources that they

manage. The chapter emphas izes one of the most com

mon methods of data extraction from a database: the use

of queries . Besides asking questions like 'what is here?' or 'what

type of feature is that?'-the rypes of questions wh ich hold our auenrion in the first part of th is chapter-there

are at least four types of information acquisi tion processes

you can use when asking questions of a GIS database. The

four processes are outlined briefly below, and when we

arrive at the section entitled 'Selecting features based on

some database criteria', we will become engaged in one or

more of these processes. The applications provided at the end of the chapter will further reinfo rce the need for these

acquis ition processes in natural resource management.

1. Obtaining sp"ific focts Is there spotted owl habi tat w ithin the property that is

being managed? Are there any steep hiking trails within the property

being managed?

101

Is ther< any old-growth forest within the property

being managed?

2. Obtaining extended information Besides spotted owl hab itat, what other types of important wildlife habitat are found within the prop

erty bei ng managed? Besides steep hiking trails, what other types of trails are contained within the property being managed?

Besides old-growth forests. what other dassificacions of forest are conta ined within the property being

managed?

3. Obtaining broader information blUed on complrx queries Are there any areas that meet all of the habitat require

ments for all of the species of interest within the prop

erey being managed? Are there any trails that, when combined. provide a relacively easy hiking experience? Are there any forest stands that can be managed co provide both wildlife habitat and timber resources?

4. Obtaining information on resources in limited sup;" Are [here any areas of habitat [hat appear in small quantities across the area being managed?

Are there any trails that provide experiences that occur rarely across the area being managed? Are there any forest stands that represent unique forest types within the area being managed?

Selecting Landscape Features from a GIS Database

As we noted in the introduction to chapter I, natural

resource managers are consistently called upon to describe the condition of a landscape using GIS software programs and GIS databases. Generally speaking, natural resource managers are concerned with understanding where landscape features are located and what characteristic(s) they might have now, have had in the past, or will have in the future. There are at least eight processes you can use to select landscape features from GIS databases:

I . Select one feature {manually}. 2. Select many features {manually}. 3. Select all features (manually or automatically) . 4. Select no features {manually or automatically} .


5. Select features based on some criteria . 6. Select features from a previously selected set of features.

7. Switching (inverting) a set of selected features so that

all unselected items become selected. 8. Select features within some proximity of other

features.

Selecting one feature manually

The abi lity to select landscape features using a computer

mouse or digitizing puck is an essential tool for examining or editing individual landscape features in GIS, and for editing tabular data contained in an attribute table. As we

have JUS[ implied, within GIS software programs landscape features can be selected either from the window that presents the spatial display of landscape features, or from the window that presents the associated a[[ ribute table. GIS software programs are either designed to allow users

to select landscape features by default {e.g., Maplnfo} or to provide a well-positioned, easily accessible function (i.e., the 'select features ' tool in ArcMap or the 'select features ' button in ArcYiew 3.3) [hat allows users to manu

ally select individual landscape features. A careful positioning of the computer mouse's cursor over a landscape feature {either in the display of spatial landscape features or in the tabular database} and a simple click of the mouse

will usually do the job. Selected landscape features will normally be colored or shaded differently {in both the spatial display of landscape features and tabular database} from other landscape features in the GIS database of interest, allowing users (0 visually verify what has been

selected. GIS software programs use standard characteristics for displaying selected features, such as the light blue color used by ArcMap or the yellow color used by ArcYiew 3.3 for displaying a selected spatial feature or its attribute record. Some GIS software programs allow users to define the characteristics (i .e., color) of selected spatial features or a[[ributes. and most GIS software will allow users to specify which GIS layers can be 'selectable'. As a resulr, either those layers that are visible in a window are selectable. or those that have been chosen from a list are selectable. Th is ability can make feature selections more efficient and also prevent analysis errors.

Selecting many features manually

There are times when selecting many landscape:: features manually will also be of value in ass isting the develop-

102

92 Part 2 Applying GIS to Natural Resource Management

mem of information (map or data) co facilitate a natural

resoll rce management analysis. The process for selecting multiple landscape features is similar ( 0 the process for

selecting single landscape feamres manually, with the computer mouse playing {he cenrral role. The selection of multiple landscape features can generally be accomplished in one of twO ways:

1. Using the select tool associated with a GIS softwa re program, draw a rectangular box in the window that displays the spatial fearures. Here, you would specifY the location (by clicking and holding down a mouse

button) for one corner of the box. Then, drag the mouse [0 specify the diagonal corner, and release the mouse button.

2. Using [he seiect [001 associated with a GIS software program, select features individually while depressing a key on the computer keyboard (e.g. , the 'shift' key

when using ArcMap). This process can be used in the window that displays the spatial fearuces or in the attribute table.

Each GIS software program may involve a variation (or two) on these techniques. More than likely an alternative

exists that allows users (Q more efficiently select multiple landscape features manually. Fo r example, assume you were interested in selecting vegetation polygons that contained (rees that were, on average, over 100 years of age. Within an attribute table you could sort the vegetation polygon records by their age, either using an ascending (youngest to oldest) or descending (oldest to youngest) sort. This wi ll re-position the vegetation polygon records such that the ones that comain the oldest trees are grouped together. thus facilitating a more efficient manual selec[ion of multiple features. Had you not sorted the records, you would have needed to scroll through [he attribute table to locate records wi th average tree age vallies over 100; this likely would have lead to errors of omission (missed records) and it would not have been an efficient use of your time.

Selecting all of the features in a GIS database

The ability to seleer all landscape features from a G IS

database is a standard process among GIS software programs. In addition, most GIS software programs generally have specific functions to allow users to select all land-

scape features with JUSt one (or a few) c1ick(s) of a mouse ra ther than having to select all the landscape features manually. This ability is useful when summarizing data about all of the landscape featu res in a dambase or when you are considering spatial analysis processes. Some users of G IS prefer to select the landscape features to which the

GIS processes would be applied (all landscape features in this case). rarher than rely 0 11 the common default (if no landscape features are selected, an analysis applies to all landscape features). Another opportunity to select all the features of a GIS database is if you are calculating values for fields in an attribute table. Calculations are usually performed on selected records in a tabular database, so, to

perform a calculacion on all of the records, such as the calculation of some portion of a habitat suitability score for vegetation polygons, you may need to select all of the records as a preliminary step in the process.

Selecting none of the features in a GIS database

T he abil ity to select none of the landscape features in a GIS database (or to ' un-select' o r 'clear' the selection) is also a standard process associated with GIS software programs. Most GIS software programs have specific functions-either menu items or buttons-that allow users to

un-select all landscape features with JUSt one click of a computer mouse (rather than having to un-select all landscape features manually). There are many reasons for wanring to perform this ac tion; one of the most common reasons is to clear previously selected landscape features from a GIS database before performing a spatial process such as buffering. In a buffering process, when no spatial fearures are selected, generally all of the features are used to develop buffers. If one or more features are selected, only the selected features are buffered. It is usually after

viewing the results of a spatial process that you realize you have forgonen to un-select landscape feacures before performing (he operation.

Selecting features based on some database criteria

By now it may be evident that there must be a faster way to select a subset of the enrire set of landscape features mher than having to selec[ [hem manually. Within all GIS

software programs, users have the abi lity CO ask questions, that is, CO develop queries, about the landscape feacures

103


menr of inform arion (map or dara) to fac il irate a natural

resource managemeor analysis. The process fo r selecdng mulriple landscape feamces is sim ilar (0 the process for selecting single landscape feamres m an ually, wi th the

compucer mouse playing the central role. The selection of

multiple landscape feamres can generally be accompl ished

in one of rwo ways:

1. Using the seJect tool associated wi th a GIS softwa re

program, draw a reccangular box in rhe window tilar displays the spatial features. Here, you would specilY

the location (by clicking and holding down a mouse

button) for one corner of the box. Then , drag rhe

mouse to specify the diagonal corner, and release the mouse burton.

2. Using the sdect tool associated wirh a GIS soltware

program, sdccr features individually while depressing a

key on the computer keyboard (e.g. , the 'shilt' key

when using ArcMap). This process can be used in rhe

window that displays [he spatial fearu ces or in rhe

arrribur. table.

Each GIS software program may involve a va riation (or

[Wo) o n rhese rechniques. M ore than li kdy an alternat ive

exisrs that allows users to more efficiendy select multiple

la ndscape fearu res manually. For example, assume you

were interested in select ing vegecarion polygons tha t con

tained (rees that were, on average, over 100 years of age.

Within an attribute table you could sore the vegetarian

polygon records by their age, eicher using an ascending

(youngest to oldest) or descending (oldest to youngest)

sorr. This will re·posi rion [he vege tation polygon records

such that the ones that contain the oldest trees are

grouped rogether, thus rncilita ting a more efficient man

ual selection of mulriple features. H ad you not sorted the

records, you would have needed ro scroll th rough the

attribute [able to loca te records wi th average tree age val

ues over 100; [h is likely would have lead {Q errors of om is

sion (missed records) and it would nO[ have been an efficient use of you r time.

Selecting all of the features in a GIS database

The abiliry to sdecl all landscape features from a GIS database is a standard process among GIS software pro

grams. In addition, most GIS software programs generally

have specific funct io ns to allow users to select all land-

seape features with jUst one (or a few) click(s) of a mouse

rather chan having ro selecl all th. landscape featu res

manually. This abiliry is useful when summa rizing data

abour all of the landscape features in a database or when

you are considering spacial analysis processes. Some users

of GIS prefer ro select (he landscape fearures to which rhe

GIS processes would be appl ied (all landscape fearures in

this case), rather than rely on rhe commo n default (if no

landscape fearures are selec<ed, an analysis applies to all

landscape features) . Another opportuniry to selecr all the

fearures of a GIS darabase is if you are calculati ng values

for fi elds in an attribute tab le. Calculations are usually

performed on selected records in a tabular darabase. so, {Q

perform a calculation on all of me records. such as the cal

culation of some portion of a habita t suitabili ty score for

vegetation po lygons, you may need to selecr all of the

records as a prelimina_ry step in the process.

Selecting none of the features in a GIS database

T he abili ty to select none of the landscape features in a

GIS database (or to 'un-sdec[ or 'clear' rhe selection) is

also a standa td process associa ted with GtS software pro

grams. Most GIS soltware programs have specific func

tions-either menu items or buttons-that allow users co un-sdecr all landscape features wirh jUst one click of a

computer mouse (rather rhan having co un-selecr all land

scape featu res manually) . There are many reasons for

wanring (Q perform this anion; one of the most commo n

reasons is [Q clear previollsly selecred landscape featu res

from a GIS darabase before performing a sparial process

such as bu ffering. In a buffering process, when no spa rial

features are selected) genera lly all of the features are used

to develop buffers. If one o r mo re features are selected,

o nly the selected feamres are buffered. h is usually alter

viewing the results ofa spatial process that you real ize you

have forgonen to un-select landscape features before per

forming the operation.

Selecting features based on some database criteria

By now it may be evident that there musr be a faster way

to select a subset of [he ent ire se t of landscape fea tures

rather than having to sdeer rhem manually. Within all GIS software programs. users have the ability to ask questions.

[har is, {Q develop q ueries. ahom the landscape features

What is an attribute? It is defined as a characteristic or

quality of an object, or in our case, a characreriscic or

quality of some namral resource. Within GIS. you are

usually imeresred in a characteristic or quality of some

feature found on the landscape. such as the vegecarion . the so il , the water, or the land. More specifically, a

contained in a GIS database. A query is simply a question. or set of questions. used co request in fo rmation abom

some resource contained (or described) within a database.

Imagine a co-worker asking. 'Please help me find all of the fo resred areas that might require a pre-commercial thin

ning rrearmem.' To locate the potencial pre-commercial

th in ning areas , the request should be refined, then re

directed by asking questions abour the informacion con

tained in one or more GIS databases, in this case, perhaps

a forest stand GIS database. You might ask the GIS database 'Where are all of the young. overstocked. conifer stands?' This would seem to be a good start. yet a GIS software program wou ld need more specific. quancitative

instructions, detailing the anribuces co use in [he search for

the appropriate landscape feacu res, and detailing the

bounds of the values of [he amibutes. For example, to find the yo ung stands in a forest stands GIS database. you

might search an 'age' anribuce field for those forest stand

polygons [hat have values between 10 and 20 (IO and 20 years old). The more refined query then becomes 'find all of the forest stands where age ~ 10 and age S 20.'

You might ask. 'Why would we need to perform queries?' One reaso n was illuscrated with the need [Q

idencify the potemial pre-commercial th inning areas

there are times when natural resource managers need to

know where certain resources are located to help facili tate

making managemem decisions regarding the resources.

Queries can range from the rather simplistic (finding the

mOSt appropriate huming area) to the rather complex

(finding the most appropriate areas to commercially thin trees over {he next (WO years). Suppose a management

decision needed to be made regarding locat ing the most

appropriate area to develop a new hiking trail. You might

first locate and describe all of the cha racteristics of a land

scape (current hiking crails. timber stands of var ious characteristics, etc.) that would influence the placemem of a

new crail. A query of these GIS databases could facili tate


forester might be interested in the basal area, timber vol

ume. or habitat qua/iry of [he srands [hat are delineated on a property. An amibute of a landscape feature can be extended, however, co include its spatial position. size,

perimecer (or length). and even the cype of data it represents (e.g .• point. line. polygon. raster grid cell).

the selection of resources of imerest across a landscape,

and help a managemem team focus o n the more suitable

areas.

In developing GIS queries, you must build a set of criteria to enable a search of database, and subsequently to

enable the selection of appropriate landscape features . For

those more attuned to visualizing processes from a com

puter programming perspective, queries are similar to the

developmem of If stattments. As the auribures of each

landscape are examined. ifsome conditions (criteria) of the

landscape features are true (or conform to the criteria).

the landscape feature will be placed into a 'selected' set. The following examples of queries relate to the data presented in Table 5.1. Answers are provided to allow students to work through the queries o n their own, and to

understand how (and why) the results were obtained.

TABLE S,l A limber stand database

Stand Acra Hectares MBP Ag' TPA' TPW

100 40.5 12 25 200 494

2 70 28.3 20 45 150 371

3 250 101.2 13 26 200 494

4 80 32.4 6 18 300 74 1

5 60 24.3 2 12 575 1,421

6 120 48.6 10 23 200 494

7 40 16.2 7 20 400 988

8 60 24.3 14 28 150 37 1

9 75 30.4 3 15 550 1.359

10 95 38.4 10 600 1,483

• Thousand board ft et ptr acre

h Trees per acre

< T rets per hectare

104


Single criterion queries Single criterion queries examine a single attribute (also

called a variable. or field) of each landscape fearure. use a

single relational operator. and include a single threshold value.

a) How many timber stands are 20 years old? A[cciburc: age Relational operator: = (equals) Threshold value: 20

Query: age = 20 Answer: I stand (7)

b) How many timber stands are greater than or equal to 25 years old?

Attriburc: age Relational operator: ~ (greater than or equal to) Threshold value: 25

Query: age ~ 25 Answer: 4 stands (I.2.3.and 8)

c) How many timber stands are less than or equal [Q

20 years old? Attribute: age Relarional operator: S; (less than or equal to) Threshold value: 20

Query: age S; 20

Answer: 5 stands (4.5.7.9. and 10)

d) How many timber stands camain at least 15 thou-

sand board feet (MBF) per acre of timber volume' Amibute: MBF Relational operamc: ~ (greater than o r equal to)

Threshold value: 15 Query: MBF ~ 15 Answer: I stand (2)

e) How many timber stands have more than 700 trees

per hectare (TPH)? Amibute: TPH Relational operawc: > (greater than) Threshold value: 700 Query: TPH > 700 Answer: 5 stands (4.5.7.9. and 10)

f) How many timber stands have at least 950 trees per hectare?

Anribure: TPH

Relational operator: ~ (grearer than or equal to) Threshold value: 950 Query: TPH ~ 950

Answer: 4 stands (5.7.9. and 10)

g) How many timber stands are larger than 100 hectares in size?

Attribute: hectares

Relational operator: > (greater than) Threshold value: 100 Query: hectares > 100 Answer: I stand (3)

Multiple criteria queries Multiple criteria queries are combinations of single crite

rion queries, held together by logical operators (and. or,

not). They allow you to develop a complex query without havi ng to perform several single cr iterion queries in

sequence. Below are several mulriple criteria queries that

relate to the dara found in Table 5.1.

a) How many timber stands are less than or equal to

20 years of age, and contain more than 950 trees per hectare (TPH)?

Attributes: age. TPH Relational operators:

Age: S; (less than or equal to)

TPH: > (greater than) Threshold values:

Age: 20 TPH: 950

Logical operator: and

Query: (age S; 20) and (TPH > 950)

Answer: 4 stands (5.7.9. and 10)

b) How many ti mber stands are at least 25 years old. or contain at least 10 thousand board feet (10

MBF) per acre of timber volume?

Attributes: age. MBF Relational operators:

Age: ~ (greater than or equal to) MBF: ~ (greater than or equal to)

Threshold values: Age: 25 MBF: 10

Logical operator: or

Query: (age ~ 25) or (MBF ~ 10) Answer: 4 stands (I.2.3. and 8)

105


Single criterion queries Si ngle criterion queries exami ne a single attribute (also

called a variable, or field) of each landscape featu re, use a si ngle relational operaror, and include a single [hreshold value.

a) How many timber stands are 20 years old? Attribute: age Relational operaror: = (equals) Threshold value: 20 Query: age = 20 Answer: I srand (7)

b) How many timber s[ands are greater than or equal [0 25 years old?

Aucibme: age

Relational oper.ro r: <! (grearer rhan or equal to) Th reshold value: 25 Query: age ~ 25 Answer: 4 stands (I,2,3,and 8)

c) How many limber stands are less rhan or equal ro 20 years old?

Attribute: age

Rela[ional operator: S (less rhan or equal [0)

Threshold value: 20 Query: age S 20 Answer: 5 stands (4,5,7.9. and 10)

d) How many limber "ands conta in ar least 15 thou-sand board feel (MBF) per acre of timber volume?

Amibure: MBF Rela[ional operaro r: <! (greater than or equal to) T hreshold value: 15 Query: MBF ~ 15 Answer: 1 Sland (2)

e) How many timber stands have more than 700 trees

per hectare (TPH) ' Amibute: TPH Relational operator: > (greater than) Threshold value: 700 Query: TPH > 700 Answer: 5 stands (4.5.7.9, and 10)

f) How many dmber srands have at least 950 trees per hectare?

Anribule: TPH

Relational operator: ~ (gn:arer rhan or equal to) Threshold va lue: 950 Query: TPH ~ 950 Answer: 4 stands (5,7,9. and 10)

g) How many tim ber stands are larger than 100 hectares in size?

AttriblHe: hectares Relational operator: > (greater than) Threshold value: 100 Query: hectares > 100 Answer: I stand (3)

Multiple criteria queries Multiple criteria queries are combinations of single crite

rion queries. held rogether by logical operators (and. or, 1/ot). They allow you ro develop a complex query wi thout having [0 perform several single criterio n queries in

sequence. Below are several multiple criteria queries mat

relate to rhe data fou nd in Table 5. !.

a) How many c.imber stands are less than or equal to

20 yea rs of age, and contain more rhan 950 trees per hectare (TPH)?

Attribu(es: age. TPH

Relational operarors:

Age: S (less than or equal to) TPH: > (grea ter than)

Threshold values: Age: 20 TPH: 950

Logical operator: and Query: (age S 20) and (TPH > 950) Answer: 4 srands (5.7,9, and 10)

b) How many ti mber stands are ar least 25 years old, or co ntain at least 10 thousand board feer (10 MBF) per acre of rimber volume?

Attributes: age, MBF Relational operators:

Age: ~ (greater [han or equal to) MBF: ~ (greater than or equal to)

Threshold values: Age: 25 MBF: 10

Logical o peraror: or Query: (age <! 25) or (MBF ~ 10) Answer: 4 stands (I,2.3. and 8)

c) How many timber stands are at least 20 years old, and are no older than 30 years old, and contain

more than 500 trees per hectare? Amibutes: age, age, TPH Relational operators:

Age: ;" (greater than or equal to)

Age: S (less than or equal to) TPH: > (greater than)

T hreshold values: Age: 20

Age: 30 TPH: 500

Logical operators: and, and Query: (age;" 20) and (age S 30) and (TI'H > 500) Answer: I srand (7)

To illustrate the use of a complex query, we wi ll ask a few questions regarding the polygons contained in the Brown Tract stands GIS database. First, assume that the managers of the Brown Tract are interested in managing

the forest for timber production, and maximizing the growth potential of (he [fees in the forest. One way ro achieve this goal may be to use precommercial thinning. As a result, (hey need to understand whether any poten

cial commercial thinning opportunities exist. Assume that the criteria developed by the managers of the Brown

Tract [Q assist in (he analysis was based on four ideas:

l. Thinning should occur about 10 to 15 years prior to the fin al harvest age assumed by the organization (45-50 years).

2. Enough crop crees should remain un-cut in the

thinned stands so that they (the residual trees) sufficiently respond (within increased growth rates) to the increased availabili ty of light, water, and nutrients for

the remaining 10-15 years prior to final harvest. 3 . Commercial thinning will only be applied to even

aged forested stands. 4 . Commercial thinning operations should remove, at a

mInimUm, 10 MBF per hectare (abou, 4 MBF per acre).

Because the managers have specified a minimum residual volume level the dmber volume per unit area prior to

thinning should be substantially greater. The criteria for the query that the managers of the forest decide to use includes the age of the stands that could be thinned muSt

Chapter 5 Selecting landscape Features 95

be between 30 and 40 years old, the land allocation should include only the even-aged Stands, and the timber volume prior to thinning must be above 9 MBF per acre.

The criteria, placed within the structure of a query then

becomes:

(age;" 30) and (age S 40) and (MBF ;" 9) and (land allocation = 'even-aged')

The resulting eight stands (42 hectares) on the Brown Tract that conform to this query are illusuated in Figure

5.1. These areas can be considered. the poremial commercial thinning opportun ities for the fo rest in the near

future.

Selecting features from a previously selected set of features

Rather than develop a long, complex query containing multiple criteria. you can design a set of less complex queries that are hierarchical in nature and that reduce the

landscape features contained in the set of selected landscape features with each additional query. This process

may help you stay organized and prevent the occurrence of mistakes that may be difficult to understand when usi ng a long and complex query. To selec, landscape feacures from a previously selected set of landscape features,

a number of single criterion queries are assembled .

Figure 5.1 Stands on the Brown T net that meet the following criteria: age 2: 30 and age :5 40 and MBF 2: 9 and land allocation . 'even-aged' .

106


Example c, presemed ea rlier. involved the following multiple criteria query,

(age ~ 20) and (age s: 30) and (TPH > 500)

which could be subdivided intO three single criterion quenes:

age ~ 20 age s: 30 TPH > 500

Each of these can be performed in sequence; the first from the full set of stands GIS database landscape features,

age ~ 20 (6 stands [1,2,3,6,7 and 8])

the second from the set of 6 landscape features that were selected (stands 1,2,3,6,7 and 8),

age s: 30 (5 stands [1,3,6,7 and 8])

and the third from the remaining 5 landscape features (stands 1,3,6,7 and 8),

TPH > 500 (I stand [7])

resulting in the same landscape feature selected as when the multiple criteria query was used. The preference for a particular technique (selecting landscape features from a previously selected set or selecting landscape features using a multiple criteria query) wi ll vary from user [0 user,

depending on each user's confidence and experience. If you were co try this hierarchical process of selecting

landscape features on the Brown Tract thinning example from above, where rhe criteria was,

(age ~ 30) and (age s: 40) and (MBF ~ 9) and (land allocation = 'even-aged')

you could subdivide the querying process into four steps.

Step I: Select from rhe entire set of stands those stands where age ~ 30 (result: 212 stands shown in Figure 5.2).

Step 2: Select from the 212 previously selected stands, those stands where age s: 40 (result: 23 stands shown in Figure 5.3).

Step 3: Select from the 23 previously selected stands, those stands where MBF ~ 9 (result: 9 stands shown in Figure 5.4).

Step 4: Select from the 9 previously selected stands, those scands where the land allocation is even-aged (resu le: 8 stands shown in Figure 5.1).

Figure 5.2 Stands on {he Brown Tract that meet the following criterion: age' ~ 30.

Breaking down a complex query into smaller, single crite

rion queries may not work when the logical operawr involved is 'or'. In the following example, the complex query cannor be broken down into rhree single criterion

queries.

(age = 29) or (age = 30) and (TPH > 500)

The set of stands that might comprise TPH > 500 can be subdivided into those that are 29 years old. However, the resulring set cannot further be subdivided into sra nds (har

Figure 5.3 Stands from the previously sciecte'd set (age 2: 30) on the Brown Tract that meet the fo llowing criterion: age S 40.

107

Figure 5.4 Stands &om the previously ~Iected set (age O!: 30 and age :5 40) on the Brown Tract that meet the following criterion: MBF O!: 9]

are 30 years old (they are 29 years old) . Similarly, in the following example, the complex query cannot be broken down into three single criterion queries.

One of the most common mistakes made when asking questions of databases is that results are often accepted as 'truth' without considering whether results are reasonable. For example, the Brown Tract timber scands

GIS database contains a number of polygons that , when summed, describe a 2,123 hectare area. Within the Brown Tract a variety of ages of forests, ranging from recent c1earcuts (age = 0) to older stands. are present. To describe the current structure of the

Brown Tract, you could develop an age class distribution that indicates the area within. say. la-year age classes. After performing queries of the various forest age classes, the sum of the area queried should nor result in more or less than 2,123 hectares (the size of the properry). You should always ask yourself whether the results obtained seem reasonable, given the resources being queried. Whenever possible , if a method of verifying results is available, it is advisable to check your work or have a colleague check your work. If multiple queries are performed that are designed to completely describe the resources of the


(age = 29) or (soil_rype = 'PR') and (TPH > 500)

Again, we can locate the stands where TPH > 500, and from those we can locate the stands that have an age of29 years. However. there may be many other stands beyond those in the resulting set that have TPH > 500 and a so il rype of 'PR' (yee an age that is nOt 29 years).

However, the following multiple criteria query could be broken down into three single criterion queries:

(age> 28) or (age < 31) and (TPH > 500)

Here, the set of stands that might comprise TPH > 500 can be subdivided into those that are greater than 28 years old. The resulting Set can furrher be subdivided into stands that are less than 31 years old.

Inverting a selection

Occasionally, you may find yourself in a situation where you need to understand {wo aspects of the spatial features contained in a GIS database: what is the condition (state or characteristic) of one set of features, and what is the

forest, as in this case, the sum of area represented by the multiple queries should equal the sum of the resources in the original GIS database. If the sum of the area in the age classes is greater than the size of the Brown Tract, some areas were double-counted, per

haps using queries such as these,

Age class I: Age class 2:

(age ~ 0) and (age" (0) (age ~ 10) and (age" 20)

where the area of I O-year-old stands is included in both classes. If the sum of the area in the age classes is less than the size of the Brown Tract. some areas were

nor counted. perhaps using queries such as these.

Age class I: Age class 2:

(age> 0) and (age < 10) (age> 10) and (age < 20)

where the area of O-year-old stands (c1earcurs) is not included in age class I , and the area of IO-year-old srands is nor included in either age class.

108


condition of everyth ing else. Two sets of queries can be developed to identifY these two sets of features; however, if rhe second set contains 'everything else', simply inverting rhe selected fearures after rhe first query will produce rhe second set. For example, if you were interested in understanding how much land area was considered

'reserved' on the Brown Tract, and rhen how much land area remained 'un-reserved', you could first develop rhe query for rhe reserved areas,

(land allocarion = 'meadow') or (land allocation =

'research') or (land allocation = 'oak woodland') or (land allocation = 'rock pit')

and find that it contains 42 stands covering about 229 hectares. By then inverting the selection, you will find

that what remains is a ser of 241 stands covering abom 1,893 hectares. A second query for even-aged. unevenaged. and shelrerwood stands was not necessary.

The inven selection technique simply switches the GIS database selections, so that featu res previously selected are no longer selected. and vice versa. Some G IS software will make this capability available through a menu choice in the cabular database window while ocher programs may make this capability available through a menu or button in the spatial database viewing window. Some GIS software programs include borh capabi licies.

Example 1: Find the landscape features in one GIS database by using single and multiple criteria queries and by selecting features from a previously selected set of features A co mbination of query processes can be used if you believe that they are necessary to accurately arrive at the desired set of GIS database features. In this example, we use a GIS database created from the set of GIS databases

available for the Pheasant Hill planning area of the QU'Appdle River Valley in central Saskatchewan. Here, we have created a GIS database that contains so il s, topography, and land classification information . There are 168 polygons with in the GIS database. Assume that we, as natural resource managers, are interested in understanding the land areas that contain clayey soils, have steep or undulating topography, and that have been categorized as having no significant lim ita cion as they pertain to agricultural practices. Initially, we could develop a multiple criteria query to select from the larger set of features only those that have clayey soi l types. There are a number of soil types in the Pheasant Hill planning area, thus the query would be designed something like this:

(Soil_type = ' Indian Head Clay') or (Soil_type =

' Indian Head Clay Loam') or (Soil_type = ' Indian Head Heavy Clay') or (Soil_ type = 'Oxbow Clay Loam') or (Soil_type = 'Rocanville Clay Loam')

Given that a polygon is assigned only one soil type. we needed to use the relational operator 'or' in the query rather than 'and'. As a result of this multiple criteria query, we find that only 69 of the original 168 polygons have a clay component in their associated soil type. In order to locate those areas within this sub-set of the landscape features that are also located on undulating or steep slopes. we can perform a second multiple criteria query.

(Topography = 'STEEP') or (Topography = 'UN DULATING')

and here only select features from the previously selected sub-set oflandscape features (not from the larger, original set of landscape features) . In this case, we find that 28 of the polygons have both the soil characteristics and topO-

_ Areas that meet the query specifications

C=:J Other areas that do not meet the query specificatioos

Figure 5.5 The result of a query for areas with dayey soils, located on steep or undulating topography, and with no limitations for agriculrural practices using GIS databases developed for the Pheasant Hill planning area of the Qu'Appelle River Valley, Saskatchewan (1980).

109

graphic characteriscics of imerest (0 us. Finally. we perform a single criteria query to determine how many of the remain ing 28 polygons also have no sign ificant limitations for agricultural practices:

(Land Class = ' No Significam Limitations)

Again, this query is made by selecting from the previously selecred set of 28 polygons.

As a result of this final query, we find that 18 of the original 168 polygons have soil, topographic. and land classification characteristics suitable for our original natural resource management analysis (Figure 5.5). We could have arrived at the same answer by performing one long. mulciple criteria query. Alternatively. we could have arrived at the same answer by using single criteria queries to build up the selected soil rypes (adding to the selected set each rime additional polygons that have soil rype attributes of interest (Q us). then selecting from the previously selected set those that have the desired topographic and land classification attributes.

Selecting features within some proximity of other features

In addition to selecting landscape features based on the set of attributes available within the tabular portion of a GIS database, you can select landscape features based on their spatial relationship to other landscape features. This allows you to ask. for example. which landscape features are within a threshold distance of. adjacenr roo or in close proximity of other landscape features. For example. you may want to know which research plots are in older forest stands. what forest stands are next to research areas, or which water sources are within a cerrain distance of a road. The abil ity [Q ask questions in spatial terms is but one indication of the power of GIS. The following three examples provide a description of three common forms of spatial queries.

Example 1: Find the landscape features in one GIS database that are inside landscape features (polygons) contamed in another GIS database In this example, a natural resource manager may be ineerested in examining [wo GIS databases: one has landscape features that he or she is ineerested in knowing something about; the orner has landscape features that represent areas within which he or she is only concerned. Obviously the second GIS database suggests that it consists of polygons


fearures. since line or poine features do not describe areas. Alternatively. the first GIS database could comain point. line. or polygon features. In this example. assume that the first GIS database, the one containing features the manager wishes to know something about. contains points.

From a natural resource perspective. managers of the Brown Tract may be interested in understanding the habitat conditions within which certain wildlife species reside. In order CO collect habitat information ir may be necessary to locate and install forest inventory plots. and sample the characteristics of the forests. Permanent forest inventory plots. those that have already been instal led and are periodically re-measured. can also be used for this purpose. From a review of the natu ral history of the wildlife species of interest. you may decide thar only those research plots that are contained within older forest stands (those at least 100 years old) require measurement. Thus the problem becomes one of selecting the plots that reside within older stands. T he GIS database that contains the landscape features of interest is the research plot GIS database. and the GIS database that represents the older fo rest areas is the forest stands GIS database.

In order to complete the spatial query. you would first select the older stands in the forest stands GIS database of the Brown Tract, using a single criterion query:

age ~ 100.

The focus of the analysis is then sh ifted to the research plot GIS database. where the question is posed: how many plots are located within the selected landscape features of the stands GIS database (the older stands)? The entire sparial query process, in generic terms, can be described as this two-step process: (I) select the older stands from the forest stands GIS database using a single criterion query, and (2) develop a spatial query on the research plot GIS

database where the selection is performed using the spatial location of selected landscape features from within the fo rest stands GIS database. This spatial selection abiliry may be described as 'selection by location' or some other similarly named menu choice or burton, depending on the GIS software being used. Landscape featu res in the research plot GIS database are selected if they intersect the space covered by the selected landscape features in the stands GIS database.

The result of this spatial query process should yield 40 research plots that fu ll within the boundaries of older forest stands (Figure 5.6). Similarly. if you were interested in knowing how many research plots were located in young

110

graphic characreriscics of imerest to us. Finally, we per

form a single criteria query to determine how many of me remaining 28 polygons also have no significant limica w

dons for agricultural praccices:

(Land Class = 'No Significant Limitations)

Again, this query is made by selecting from the previously

selected set of28 polygons.

As a result of this final query. we lind thar 18 of the

o riginal 168 polygons have soil, topographic, and land

classification characteristics suitable for our original na(U

ral resource managemem analysis (Figu re 5.5). We could

have arrived at the same answer by performing one long,

multiple criteria query. Alternatively. we could have

arrived at the same answer by using single criteria queries

to build up the selected so il rypes (adding to the selecred

ser each time addirional polygons thar have soil rype

attributes of imeres[ (Q us) , then selecting from the previ

ously selected ser rhose thac have the desired ropographic

and land classification amibmes.

Selecting features within some proximity of other features

In addition to selecring landscape fearures based on the

set of attributes available within the tabular portion of a

GIS database, you can select landscape features based on

their spatial relationship to other landscape fearures. This

allows you to ask, for example, whidh landscape fearures

are within a threshold distance of. adjacenr (0, or in close

proximiry of other landscape feamres. For example. you

may wam {Q know which resea rch plots are in older for

est srands. what forest stands are next (0 research areas, or

which water sources are wirhin a certain distance of a

road. The abiliry (0 ask quesdons in spadaJ rerms is bur

one indication of the power of GIS. The following [hree

examples provide a description of three common fo rms of

spadal queries.

Example 1: Find the landscape features in one GIS database that are inside landscape features (polygons) contained in another GIS datahase In rhis example, 3 narural resource manager may be inter

ested in examining (wo GIS databases; one has landscape

features that he o r she is imerested in knowing something

about; the other has landscape features thar represent areas

within which he or she is only concerned. Obviously the

second GIS database sugges" that it consists of polygons


fe-drures, since line or point features do nO( describe areas.

A1ternarively. the firsr GIS database could conrain point,

line. or polygon feamres. In this example, assume that the

first G1S database, the one containing feamres the manager

wishes to know something about. contains points.

From a natural resource perspective. managers of the

Brown Tracr may be interested in understanding [he

habitat conditions within which cerrain wildlife species

reside. In order (Q coUee[ habitat information it may be

necessary to locate and install forest inventory plots. and

sample the characteristics of the forests. Pcrmanem forest

inventory plms. rhose that have already been instal led and

are periodically re-measured, can also be used for this pur

pose. From a review of the natural history of rhe wildlife

species of interest, you may decide that only [hose

resea rch plotS thar are contained wirhin older forest s rands

(those ac leaSt 100 years old) require measuremem. Thus

the problem becomes one of selecting the plots that reside

with in older Stands. The GIS database thai contains rhe

landscape fearures of interest is the research plot GIS data

base. and the GIS database that represents the older foreSt

areas is the forest stands GIS database.

In order ro complere the sparial query, you would firSt

select the o lder stands in the forest stands GIS database of

the Brown Tract, using a single criterion query:

age ~ 100.

The focus of the analysis is then shifred to the research

plot GIS darabase. where the question is posed: how many

plots are located within the selecred landscape features of

the srands GIS database (rhe older Stands)? The entire spa

rial query process, in generic terms. can be described as

this two-Step process: (I) select the older stands from the

forest stands G1S database using a si ngle crieerion query,

and (2) develop a spa rial query on rhe researdh plm GIS

daeabase where [he seleceion is performed using the spatial

location of selected landscape features from within the

fo reSt stands GIS database. This spatial selection abiliry

may be described as 'selection by location' or some other

similarly named menu choice or bu([on. depending on

the GIS softwa re being used. Landscape fearures in the

research plot GIS database are selected if they inrersect the

space covered by the selected landscape fearures in the

stands GIS darabase.

The result of this spatial query process should yield 40

research plotS that full within rhe boundaries of older for

est seands (Figure 5 .6) . Similarly, if you were imeresred in

knowing how many research plots were locared in young


Figure 5.6 Permanent plot point locations within older stands on the Brown Tract.

stands. you would first query the stands database for young srands (perhaps age!> 30). then perform the spatial query simila r [0 the process noted above. The result should yield 1 research plot.

Example 2: Find the landscape features in one GIS database that are close to the landscape features contained within another GIS database In this example. the imerest is again in examining two GIS databases: one has landscape feacures of interest; (he other contains landscape features [hat represent those

a reas around which (nor JUSt within which) there is concern. The second GIS dambase suggestS that it consists of polygon features. but here it could also consist of point o r line features. since [he area of concern is the area represented by a zone of proximity arou nd landscape features. The first GIS database could also contain point. line, or

polygon features. Assume that the GIS database of interest contains point features, and that the GIS database that will represenr the area of interest contains line features.

The managers of the Brown Tract may be inrerested in developing a fire management plan for the fores t, and thus would need to understand the types of water resources that are in close proximity to roads. Therefore,

the problem becomes one of selecting the water sou rces rhat are within some distance of a road. The GIS database thar contains the landscape features of interest is the water sources GIS database (because of the need to know where the appropriate water sources are located). The GIS database that represents landscape features around which one

can define an area of concern is the roads GIS database. In order to perform (his spatial query. you muse first

determine the distance from the roads that is cri tical for meering the needs of the fire management plan. Assume here that it is 30 merers, suggesting that water sources within 30 meters of a road may be of benefit to forest fire fighting efforts. This assumes that fi re-fighting vehicles can draw water from these sources and transport the water to the fire area. In the development of the fire manage

menr plan, you may have also assumed that only certain rypes of roads can support fire-fighting vehicles. although th is example will proceed under the assumption that aU roads on the Brown Tract can suppOrt these vehicles. A generic description of the spatial query process might then include the following two steps: (1) select all of the landscape features in the water sources GIS database, and

(2) develop a spatial query on the water sources GIS database where the selection is performed using the spatial location of landscape features con tained in the roads GIS database. Landscape features in the water sources GIS database are selected if they are located within 30 meters

of any road contained in the roads GIS database. The result of this spatial query process yields 5 water

sources that lie within 30 meters of a road (Figure 5.7). As you might imagine, this example, as well as the previous example. would also be helpful to those concerned with the proximity of certain resources (water sources, home sites) to potential management activities (herbicide or fertilization applications) , or even to potential sites for wildlife or fisheries studies.

o

Figure 5.7 Water source point locations within 30 meters of roads on the Brown T tact.

111

Example 3: Find landscape features from one GIS database tbat are adjacent to otber landscape features in tbe same GIS database In this example, the inrerest is in performing a spatial query that uses landscape features within a single GIS

database. Adjacency issues in natural resource manage

ment usually concern the placemenr of harvests or the location of habitat) and imply that activities may be pro

hibited from being implemented next to (or nearby) other

receody implemented activities. In the case of habitat

development. natural resource managers may desire co

develop habitat next to (or nearby) other good wildlife

habitat areas. Alternativdy. an invesrmem in research may

need co be protected by limiting activity in nearby or sur

rounding areas. Since this example concerns adjacency

issues, the GIS database used also suggests that it conrains

polygon features. This example will assume that a natural

resource manager is interested in understanding the

extenr and number of stands that are adjacent to research

areas. Since the Brown Tract is a working forest that con

tains some research areas, coordination of both research

and harvesting act ivities is paramount. particularly if the

harvesting activities affect a resource being smdied in the

research areas (for example, species of wildlife or hydro

logic conditions under canopy).

A generic description of the spatial query process might then include the following steps: (1) select the

stands in the stands GIS database that are designated a

'research' land allocation, and (2) develop a spatial query

on the stands GIS database where the selection is per

fo rmed based on how far away other stands are from the

research areas. In this case. you can assume that the stands

to be queried are 0 meters away from the research areas,

and essentially touch a research polygon. Depending on

the GIS software program used, the resul , of this spat ial

Structured Query Language, or SQL, is the most pop

ular computer language for querying and manipulat

ing data contained in relational databases. Sometimes

simply called 'sequel ', the language allows you to

develop quer ies similar to those presented here to

access data from large databases. Although IBM ,

Oracle. and Microsoft have led the recent developments of SQL. and many other organizations have


query process may yield 44 stands, including the research a reas . To remove the research areas from this set of

selected landscape featu res. you can perform a single cri

terion query from the previously selected set of landscape

feamres. such as,

Land allocation <> 'Research '

where

attribute: land allocation

relational operator: <> (not equal to)

threshold value: research areas

By performing this single criterion query on the previ

ously selected set oflandscape featu res. you can select and

identify JUSt those stands adjacent to the research areas.

Figure 5.8 illustrates the spatial location of the 37 stands

that are adjacent to research areas on the Brown Tract.

Figure 5.8 Stands adjacenllo research areas on the Brown Trace .

tailored the SQL language for various applications. the American Nationa l Standards Institute (ANSi)

and the International Organization for Standard

ization (ISO) have developed standard versions and

offer them for sale. Some GIS software programs sup

POrt the use of the SQL language, and extend it to

the management and manipulation of spatial data

features.

112


Advanced Query Applications

Advanced applications of GIS-related database queries have concentrated on limiting the focus of queries only to rhe features inside a spatial or temporal window

defined by the user. In addition to simply providing a summary of rhe resources contained within a specific area, as the user-defined window (location or time frame) sl ides is expanded. or is contracted, the query is updated (Q reflect those features that have left rhe window and those [hat have entered rhe window. Queries can be completely re-evaluated as a window slides (or

otherwise changes). or iteratively evaluated by updating the query by considering only the changes that have occurred (Edelsbrunner & Overmars. 1987; Ghanem et al.. 2007). Dynamic queries can be designed that allow users to adjust questions asked of GIS databases by incorporating slider objec[S in a window rather than ask

in g the user to redefine (by typing) the adj ustmentS needed. For example. a graphical user interface can be

designed to allow users to easily adjust the upper and lower bounds of a quantitative query along a scale, using

a computer mouse (Domingue et al.. 2003). and to provide those results quickly.

Syntax Errors

Syntax errors that occur when developing queries can

Summary

There are a variery of methods you can use (0 select landscape features from a GIS darabase. from using a mouse (0 manually pick the landscape features (0 selecting them with queries. The most common requests of natural resource managers that involve GIS queries include: (1) a map illustrating the landscape features that conform to

some criteria. (2) a GIS database containing only the landscape features that conform to some criteria, and (3) summary statistics of the landscape features that conform

to some criteria. Many of the applications described later in th is book require YOll to develop a map showing some landscape feat ures of interest. and to describe some characteristics of those landscape features. Queries will enable YOli to acco mplish these tasks quickly and efficiently. An important habit to develop is to ask yourself whether the

discourage some users of GIS. MoS[ of these problems occur because brackets or parentheses are missing from a

query; this results in an incomplete query. such as in the

two cases shown below from ArcMap queries.

[Height] >= 50} and ([Age] >= 25

( [Age] >= 25) and ( <= 30 )

(beginning and ending parentheses missing)

{the attribuce 'age' is

missing from the second part of the criteria}

Most software programs require [hat every bracket or

parentheses be balanced, so, if you begin with an opening

bracket or parentheses. you must end with one as well. These parentheses and brackets can also be used to control the order of operations in queries that include mathemarical operations. The dialog boxes that are generally used to help develop queries will assist with the placement of parentheses and brackets as long as the fields. operarors, and values are selected in a logical manner using the computer mouse. Syntax errors usually arise

when a user erases part of a query. then continues with its development (i.e .. through keyboard intervention). Queries with syntax errors usually need manual keyboard intervention to correct the resulting problems. One of the eas iest methods (0 alleviate syntax errors during

queries may be to simply close the query dialog box and begin anew.

results of an analysis (here. a query) are reasonable. It has become very easy in modern GIS software to create spatial and anribute reference queries. and to instantly

receive the results. This ease of use may lead you to

believe that the results should not be questioned. Most GIS programs are designed to perform complex queries provided that the query syntax is correct. H owever, rhey simply provide you with results based on your instructions. and do not have the abili ty to question whether a query was correctly designed. It is therefore important to

be critical of the ourpUt from a query. and to examine whether the results are within the bounds of reason. This will help you to detect errors either in the GIS databases being analyzed or in the methods being used to perform an analysis.

113

Applications

5.1. Daniel Pickett Forest Annual Report. For the AnnuaJ Repof[ ohhe Daniel Pickett forest. you have been asked by Hugh Davenport (DiStrict Forester) to provide some in formation related [Q rhe forest's resources. Me Davenport poses his request as a series of questions:

From the stands GIS database: a) How much area ofland contains forests:S; 20 years

of age? b) How much area of land contains forests > 20 years

of age and:> 40 years of age? c) How much area of land contains forests> 40 years

of age? d) How much area otland contains vegetation type A?

e) How much area ofland contains vegetation type B?

f) How much area of land contains vegetation type C?

g) How much area of land contains average timber

volumes ~ 49.4 MBF (thousand board feet) per hectare (20 MBF per acre)?

h) How much area of land contai ns average timber

volumes ~ 74.1 MBF per hectare (30 MBF per acre)? i) How much area of land contains average timber

volumes ~ 98.8 MBF per hectare (40 MBF per acre)?

From the soils GIS database: a) How much area ofland might have a high response

[0 fenilization?

b) How much area of land might have a medium response to fertilization?

c) How much area ofland might have a low response to ferti lization?

From rhe streams GIS database (map units are feet):

a) How many miles or kilometers of Class 1 streams are

in the database?

b) How many miles or kilometers of Class 2 streams are in the database?

c) How many miles or kilometers of Class 3 streams are

in the database?

d) How many miles or kilometers of C lass 4 streams are

in the database?

e) Why might these values be misleading, and what caveat might you provide to Mr Davenpon?

G raphics for the repon:

a) Develop a histogram of 10-year stand age classes, showi ng the amount ofland in each age class.


5.2. Information for a new supervisor of Brown Tract. A new supervisor (Sharon G illman) was recendy selected [Q manage the Brown T mct, and she wanes to get

familiar with the natural resources located mere. She has

asked you to provide some information about the T ract

and j[5 resources:

From the streams GIS database:

a) How many miles or kilometers of fish-bearing, large

streams are in the database?

b) How many miles or kilometers of fish-bearing,

medium streams are in the database?

c) How many miles or kilom<ters of fish-bearing, small streams are: in the database?

d) How many miles or kilom<tm of non-fish bearing, large streams are in the database?

e) How many miles or kilom<lm of non-fish bearing, medium streams are in the database?

J) How many milts or kilom<lm of non-fish bearing, small streams are in the database?

From the roads GIS database: a) How many miles or kilometers of road on (or near)

the Brown T mCt are rock roads?

b) How many milts or kilom<lm of road on (or near) the Brown Tract are dirt roads?

From the soils GIS database: a) How much area ofland of 'PR' soil type is there on

the Brown Tract?

b) How much area of land of 'DN' soil type is there on the Brown Tract?

c) How much area of land of 'WL' soil type is there on the Brown Tract?

From the water sources GIS database:

a) How many beaver pond water sources are on the Brown Tract?

b) How many hydrant sources are on (or near) the

Brown Tract?

c) How many water tank sources are on (or near) the Brown Tract?

From the trails GIS database: a) How many miles or kilometers of authorized trails

are there on the: Brown Tract?

b) How many miles or kilometers of unauthorized

tra ils are there on the Brown Tract?

114


c) How many mius or kilom.tm of proposed trails are there on rhe Brown Tract?

5.3. Brown Tract Annual Report. In addition to the requests by Sharon Gi llman to get her acquainted with

some of rhe resources located within and around rhe

Brown Tract. she asks you to supply the following information for the Annual Repon:

a) How much area of land is in even-aged forests? b) How much area of land is in uneven-aged forests?

c) How much area ofland is assigned to the 'research ' category?

d) How much area of land contains board foot volumes ~ 74.1 MBF per hecta re (30 MBF per acre)'

e) How much area ofland has a density of trees ~ 988 per hectare (400 per acre)?

5.4. Annual operating plan, Brown Tract. In order to

develop a budget for management activities next year) rhe

staff of rhe Brown T raet needs information regarding of the amount of land area that can be treated w ith various

silvicuicurai treatments. Through conversations with them. rhe criteria have been narrowed down to rhe

following:

Pre-commercial thinning cand idate stands: a) How much area ofland concains even-aged stands

that are s:; 20 years old and have ~ 988 trees per hectare (400 trees per acre)?

Commercial thinning candidate stands: a) How much area of land contains even-aged stands

that are ~ 30 and s:; 40 years old? b) How much area of land concains even-aged stands

that are ~ 30 and s:; 40 years old. with ~ 494 uees per hectare (200 uees per acre)?

Final harvest candidate stands: a) How much area ofland concains even-aged stands

that are ~ 45 years. s:; 100 years. and have a board foot volume ~ 74. 1 MBF per hectare (30 MBF per acre)?

5.5. Proposed recreation area. You received an e-mail

a few days ago from Erica Douglas, forester, which read:

'We are considering developing a campground or trail system on th e Brown Tract. Are there any sta nds or

groups of stands ~ 150 years old and ~ 25 hectares (6 1.8 acres) in size?' What will your response be?

5.6. Wildlife habitat. A note was placed on your computer's keyboard, from Will Edwards. the Brown Tract wildlife biologiSt, which read: 'Could you perform some queries of the Brown Tract stands database for me? I am

working on some wildlife habitat suitability models. and am incerested in the following:

a) Sharp-sh inned hawk habitat: area of land containing ~ 25 and s:; 50 year old Stands,

b) Cooper's hawk habi tat: area ofland containing ~ 30 and s:; 70 year old Stands.

c) Goshawk habitat: area of land containing ~ 150 year old Stands, and

d) Red tree vole habitat: area ofland containing ~ 195 year old Stands.

Also. could you make me a map of the sharp-shinned hawk habitat?'

5.7. Fire management plan. In preparation for the development of a fi re management plan the district manager of

the Brown Tract is interested in knowing the following:

a) How many water sou rces (all types) are within 30 meters (98.4 feet) of rocked or paved roads?

b) How many pond water sources are within 30 meters

(98.4 feet) of rocked or paved roads?

5.8. Research plots. One of the research sciencists asso

ciated w ith the Brown Tract is interested in measuring a

few of the research plots on the forest, to Study the growth of certain stand types. They want to understand the

following: a) How many research plots are located in research plots

on stands with ages ranging from 30 to 50 years?

b) If the quety were expanded to include Stands with ages ranging from 25 to 50 years. how many research plots would (his query contain?

5.9. Potential Fertilization Project. The managers of the Brown T ract are considering fertilizing all stands that

are aged berween 25 and 40 years old. The managers are not only concerned about the proximity of fertilization

operations to the stream system, bur also to water sources. How many water sources are within 60 meters (196.9

feet) of the stands that could potentially be fertilized'

115

104 Part 2 Applying GIS to Nalural Resource Management

c) How many miks or kilom, ,,n of proposed rra ils are there on the Brown T ract?

5.3. Brown T ract Annual Report. In addition to the requests by Sharon Gillman to get her acquain ted with

some of [he resources located within and around the Brown T racr, she asks you to supply the following informacion for rhe Annual Repon:

a) How much area of land is in even-aged forests? b) How much area of land is in uneven-aged forestS? c) How much area ofland is assigned to the 'research'

category? d) How much area of land contains board foot vol

umes ~ 74. 1 MBF per hectare (30 MBF per acre)? e) How much area ofland has a density of trees ~ 988

per hectare (400 per acre)?

5.4. Annual operating plan, Brown Tract. In order to

develop a budget fo r management activities next year, rhe

staff of the Brown Tract needs informacion regarding of the amount of land area that can be treated with va rious

s il viculrura.l treatments . Through co nve rsations with them. the criteria have been na.rrowed down to [he

followi ng:

Pre-commercial rhinning candidate stands:

a) How much area of land comains even-aged stands

that are S 20 years old and have ~ 988 trees per hectare (400 trees per acre)?

Commercial thinning candidate stands: a) How much area of land contains even-aged stands

that are ~ 30 and S 40 years old? b) How much area ofland contains even-aged stands

rhat are ~ 30 and S 40 years old, with ~ 494 trees per hectare (200 trees per acre)?

Final harvest candidate stands: a) How much area ofland contains even-aged stands

that are ~ 45 years, S 100 years, and have a board foot volume ~ 74.1 MBF per hectare (30 MBF per acre)?

5.5. Proposed recreation area. You received an e-mail

a few days ago from Erica Douglas, forester, which read : 'We are considering developing a campground o r trail

system o n the Brown T fa Ct. Are th ere any smnds o r

groups of stands ~ 150 years old and ~ 25 hecrares (61 .8 acres) in size?' What will your response be?

5.6. Wildlife habitat. A note was placed on your computerS keyboard, from Will Edwards, the Brown Tract wildlife biologist, which read: 'Could you perform some queries of the Brown Tract stands database for me? I am

working on some wildlife habitat suitability models, and am interested in (he following:

a) Sharp-sh inned hawk habitat: area ofland containing ~ 25 and S 50 year old stands,

b) Cooper's hawk habi tat: area ofland containing ~ 30 and S 70 year old srands,

c) Goshawk habitat: area of land containing ~ 150 year old stands, and

d) Red tree vole habitat: area ofland containi ng ~ 195 year old stands.

Also, could you make me a map of the sharp-shinned hawk habitat?'

5.7 . Fire management plan. In preparacion fo r the development of a fire managemem plan the district manager of

the Brown T ract is interested in knowing the fo llowi ng: a) How many water sou rces (a ll types) are within

30 meters (98.4 feet) of rocked or paved roads? b) How many pond water sources are within 30 merers

(98.4 feet) of rocked or paved roads?

5.8. Research plots. One of the research scientistS associa ted with (he Brown T racr is interested in measuring a

few of the research plotS on the forest, to study the growth of certain stand types. They wanr to unde rstand the

following: a) How many research plots are located in research plots

on srands with ages ranging from 30 to 50 years? b) If the query were expanded to include stands with

ages ra nging from 25 to 50 years, how many research plotS would rnis query contain?

5.9. Potential Fertilization Project. The managers of (he Brown Tract are cons idering fen il izing aU stands [hat

are aged berween 25 and 40 years old. The managers are nor only concerned about rhe proxim ity of fertilization

operatio ns to {he stream system, bur also (0 water sou rces . How many water sources are within 60 mecers (I96.9

feet) of the stands that could potentially be fertilized?

References

Domingue. j., Stun, A" Manins, M. , Tan, ] ., Pcrursson,

H .• & Morea. E. (2003). Supporting online shopping (hrough a combination of ontologies and interface metaphors. Intenzational Journal of Human-Computer Studirs. 59. 699-723.

Edelsbrunner. H .• & Overmars. M.H. (1987) . Zooming by repeated range detection. Information ProctIsing Lmers. 24. 413-1 7.


Ghanem. T.M .• Hammad. M.A .• Mokbel. M.F .• Aref. W.G .• & Elmagarmid. A.K. (2007). Incremental evaluation of sliding-window queries over dara Streams.

IEEE Transactions on Knowledge and Data Engineering, 19.57-72-

Plum Creek Timber Company. (2001). Plum Creek annual report 2000. Searde. WA: Plum Creek Timber Com pany.

116

Chapter 6

Obtaining Information about a

Specific Geographic Region

Objectives

This chapter is designed co provide readers with examples and applications from namral resource managemem (hat will allow you to analyze the resources contained within

specific geographic regions. Specific geographic regions can be defined in a number of ways, for example, (a) by using query processes (the subject of chapter 5), (b) by using buffer processes (the subject of chapter 7), or (c) through GIS overlay processes (the subject of chapter II). From a land managemen t perspective. there is a wide range of reasons why we would want to understand what is contained within these regions, and we will illustrate a few of these. At the conclusion of this chapter. readers should have acquired knowledge of:

1. how a clipping process works. and what products shou ld be expected when it is used;

2. how an erasing process works. and what products should be expected when it is used; and

3. how to use both clipping and erasing processes CO

obtain information about specific geographic regions. and obtain in format ion that is relevant to natural resou rce management planning.

As natural resource managers, we are often interested in understanding the characteristics of the land resources we manage within . or perhaps outside of, specific geographic areas. For example, if you were to manage riparian areas, where limited amountS of activity can be pre-

scribed. you might be interested in [he type and quantiry of resources within these areas, as well as the type and

quant ity of resources outside these areas-subject to a wider range of managemenr. Understanding the soil conditions within a property is another example. since many soils GIS databases are acquired from governmental organizations. where the coverage of data extends well beyond the boundary of the land you might manage. The twO main GIS processes that can be used to obtain information about a specific geographic region. and hence the focus of this chapter, are the clipping and erasing

processes. To some people. the term 'cl ipping' conjures up

thoughts of American football athletes using an illegal blocki ng maneuver on their opponents; to other people it

conjures up thoughts of snipping pieces of vegetation from a plant. It is also used invariably as both a noun and a verb, such as (a) a way [Q hold something in a tight grip, (b) a device to hold cartridges for a riAe, (c) a single instance or occasion. or (d) the act of cutting (Merriam

Webster, 2007). In a GIS context, a cli pp ing process implies something similar to cutting cookies from a sheet of dough, when baking holiday-related cookies, although nothing is actually baked here. Imagine a landscape rolled our fl at, like a map on a table. If you were to cut our a ponion of the landscape with a pair of scissors, you would have essentially 'clipped' it from the landscape. There are a number of reasons why you would do such a thing. and we will explore some of the more common applications in this chapter.

117

Chapter 6 Obtaining Information about a Specffic Geographic Region 107

The term 'erase' also has several meanings. and is

mainly used a verb in (he English language. in manners such as (a) (Q rub ou( or scrape away. (b) to remove writ

ten marks, (c) co remove recorded data from a magnetic

medium, and (d) to nullifY an effeC( (Merriam-Webster,

2007). When the term 'erasing' is used in a GIS context,

it is much more closely aligned with the notion most people have about rubbing or scraping rhings away: some fea

cures are being removed (erased) from the landscape.

Imagine two GIS databases, a fo resr stand GIS database

and a stream buffer GIS database. If you wanted to visu

alize all of (he forest stands areas oU[side of the stream

buffers, you could use the polygons that describe the stream buffer area co erase all of those areas from the timber stand GIS database. As you can see you can use clipping and erasing tools co obtain resource information

about specific geographic regions.

The Process of Clipping Landscape Features

One of the assumptions behind rhe use of a clipping process is that you are interested in creating a new GIS

database that comains only those features within a specific

geographic region. A clipping process involves the use of

twO GIS databases (Figure 6. I), and results in one new

ourpur GIS database. The process involves the location of

the intersection of lines (in the case of line and polygon

features being clipped) and the location of features wholly

contained within an area (in the case of all rypes of GIS

features), The location of line imersection points is an

essential part of GIS (Clarke, 1995). Cl ipping processes can be manually called upon wirhin GIS, and in some

cases are aurornatic and transparent to GIS users, as in the case of websites that are designed to allow users to specify an area within which data will be extracted.

When using vector GIS databases, one of the input GIS

databases needs [Q comain polygon features «(he cookie

cutter); the other (rhe GIS darabase to be clipped) can

camain either poin t, line, or polygon features. The cookie-cuHer GIS database is overla id on the GIS database to be clipped, and only those features within the bound

aries of the polygon(s) in the cookie-cutter GIS database

are retained in (he output GIS database. Thus, (he output

GIS database contains the same rype of spatial features as

the GIS database being clipped. In addi tion , rhe size of features (lines or polygons. but not points) may be spa

tially ahered in the output database. as they may have been cur a{ (he edges of the polygons contained in the

GIS database

Database to be clipped: stands

Spatial features

Input GIS databases

Database use~dO do the clipping: 150·foot (45.7 m) stream buffers

Resulting database: stands within the 150·foot (45.7 m) stream buffers

Output GIS database

Tabular attributes

Stand attributes: basal area, volume per acre, etc.

Buffer attributes: buffer distance, etc.


Figure 6.1 Clipping the $lands within the 150·foof (45.7 m) sueam buffers on the Daniel Pickett forest.

cookie-curter GIS database. This implies thar lines could

be shorrened and the shape of polygons altered. The out

put GIS database contains all of the attributes of the GIS

database that was clipped, but generally none of the

attributes of the GIS database that was used to do the clip

ping. The spatial extent of the output GIS database is lim

ited to the boundary of rhe polygons contained in rhe

cookie-curter GIS database. For example. in Figure 6 .2. a road and a fire area overlap the boundary of a land own

ership polygon. The land ownership polygon can be used as a cookie-cutter to clip the portions of the road and the

£ire area rhat actually res ide within the ownership bound

ary. In doing this, the road (initially described by line 2),

is clipped at the intersection with lines 11 and 104 of the properry boundary polygon (creating line 2\) , and the fire

area is cl ipped with lines I , and I , . Line 3\ of the fire area

is shortened , original line 3, discarded, and lines 3, and 3, creared based on the location of lines 13 and 1 ~ of rhe

properry ownership boundary. Clipping a GIS database with a very large extent (such

as a national soi ls database) to the boundary of a managed

properry is one common application of this type of 118


a. GIS databases prior to a clip process. +----- Road

<Of-- Property boundary

j __ '1 I

/ ( J'-' Fire area I , • I ..... .. - "

C. GIS databases after a clip process. (0 = node)

i--ll.:l---- Clipped road

Property boundary

Clipped fire area

b. GIS databases during a clip process. (0 = node) 1,

Property 1, boundary I ,

(cookie cutter)

I ,

1,

Properly 1, boundary I ,

(cookie cutter)

I ,

Road belore clipping process

2

31 ... ...... 1 " . Rre area

before , I

clipping,' I I I

process: ./ 3l .... 'tY-, ...

RoadJ after clipping 21 process

Fire area after 3!.'''3 clipping ~ __ ~ 4

process 33

Figure 6.2 Clipping a road and a fire area to tilt: pro~rty boundary of a land ownership.

A clipping process is essenrially the same as making cookies from a sheet of cookie dough. It is called 'eras

ing outside' by some GIS software programs, which is simply another way to describe how ir works: a map of a landscape is laid flat on a table, a solid object is placed on tOP of it {perhaps a book}, and everything that is visible outside of the boundary of the book {except what is under the book} is erased. The process of 'erasing outside' (or clipping) is the inverse of anorher process we

describe shordy, called the 'erase' process. We expect some level of confusion due to the similar terminology,

but are confident that with study and hands-on practice with GIS, readers will be able to grasp the differences.

In an erasing process, you seek to remove from one

GIS database everything that is spatially located under the features conrained in another GIS database (which

contains polygons). Using the example of the map and the book that lies upon it, everything on the map that is under the book would be removed in an erasing

process. Thus an erasing process is me inverse of a clip

ping {or erase outside} process. The example provided using the municipali ty boundaries and floodplain , wet

land. and treed areas GIS databases illustrates how a single GIS database {the municipalities} can be divided into [wo completely separate, non-overlapping databases using the clipping and erasing processes {Figure 6.3}.

MuniCipality boundaries in the Pheasant Hill planning area of the Qu'Appelle River Valley in central Saskatchewan

Floodplains, wetlands, and areas with trees in the Pheasant Hill planning area

Floodplains, wetlands, and areas with trees for each municipality (municipalities clipped using floodplains, wetlands, and treed areas)

Areas that are not floodplains, wetlands, nor areas with trees for each municipality (municipalities erased using floodplains, wetlands, and treed areas)

Figure 6.3 Application of clippi ng and erasing processes.

119

Chapter 6 Obtaining Information about a Spec~ic Geographic Region 109

process. There are a number of other reasons why you would want [0 clip a set of spatial features co the bound

ary of a property. One of them relates to the accuracy and consistency of an organization's GIS databases. GIS databases can be digitized in-house or by comracrocs, created through other spatial operations, developed with GPS

technology, obtained from organizations that sell dacabases, downloaded for free off of the Internet, or simply passed from one person to another. Given the wide va ri ety of ways o rganizacions can acquire GIS databases. it is not unreasonable to imagine that the extent of the coverage of the G IS data wi ll likely not perfectly fit the extent of an ownership's boundary. Some organizations require that the extent of each GIS database fit perfectly with the boundaries of their land ownership, and they ensure this

by clipping each GIS database to their ownership boundary GIS database. Granted, there are some G IS da tabases, such as roads and streams, that you may not want clipped [Q an ownership boundary (interest may center on where [he roads and screams come from and where they go beyond a property boundary), but there are GIS databases, such as soils and land cover, where the argument may hold (interest may not cemer on the soils of other land owners).

Other types of polygon features could be used in a dipp ing process. For example, you may be interested in the resources contained with in a riparian zone, or within a watershed. Further, when natural resource management organizations share GIS databases, rhey may decide to

limit what is shared. For example, in Washington Stare,

where watershed analysis has been an important aspect of foresr management, a coordinated effort among organizations leads to the idemification of the limits of appropriate management activities within watersheds. In many watershed analyses. a single organization will perform rhe G IS analysis tasks, and rhus acqu ire all of the G IS data

bases related to a particular watershed regardless of landowner. Usually private natural resource organizations are very hesitant to share their GIS databases with their competitors-some GIS databases are considered proprietary and may contain sensitive information. In addition, organizations may be hesitant ro release GIS databases that are dated or that may conta in unverified information . However, since watershed analysis may benefit all landowners in the long run, some GIS databases are usually shared among land management organizadons. In cases such as these, organizadons may decide ro share as little GIS data as possible to meet [he goals of the watershed analysis. and thus avoid revealing any other informa-

tion regarding the status of the natural resources thar they manage. Therefore, a clipping process is used ro limit the amount of information shared ro mat concerning specific

geographic regions, such as individual watersheds.

Obtaining information about vegetation resources within riparian zones

As a first example of using a clipping process, let's assume that we are interested in the vegetation resources contained entirely within some pre-defined riparian zones within a fo rest. In chapter 7, we will describe the process of taking spat ial features, such as streams, and creating physical zones (buffers) around them . For now, let's assume that a GIS database containing polygons that describe 50-meter riparian wnes (stream buffers) for the Brown Tract already exists (F igu re 6.4) . The type of information we may be interested in knowing includes the amount of land and the volume of timber within the riparian zones, which are generalJy limited-use management areas. A clipping process would allow us to develop a GIS database containing only those timber stand areas that are within the boundaries of the polygons that describe the riparian zones (Figure 6.4). The original timber stand polygons that intersect the stream buffer polygons would be redesigned such that their boundaries now coincide with the edges of the riparian zones. Those timber stand polygon boundaries that fall entirely with in the riparian zones would be left intact, those timber stand polygons that fall entirely outside of the riparian zones would be e1imina[ed .

What you should find in the tabular database (attrib

utes) of the clipped GIS database is the same set (number and type) of attributes that can be found in the original vegetarion GIS database. However, the values of rhe anributcs associated with 'area' (acres and hectares) may

need to be adjusted to reAect only the area of the polygons within the clipped GIS database. In addition, some polygon GIS databases may also contain a perimeter measuremem that describes the linear distance around each

polygon border. The perimeter distance may also need to

be recalculated in order to accurately represent perimeters that were affected by a clipping operation. Some GIS software programs perform these adjustments auromatically depending on the type of spatial database; other GIS software programs require users to recalculate the areas with a second (albeir automated) process. Arrribures that describe something other than an 'area' of land (e.g., tree

120

Chapter 6 Obtaining Intormation about a Specmc Geographic Region 109

process. There are a number of other reasons why you would wanr to clip a set of spatial fearures (Q the bound

ary of a properry. One of them relates to the accuracy and consistency of an o rganization's GIS databases. GIS databases can be digitized in-house or by contractors, created through other spatial operations, developed with GPS technology. obtained from organizations that seU databases, downloaded for free off of the Internet, or simply passed from one person [0 anomer. Given me wide variery of ways organizations can acquire GIS databases. it is not unreasonable (0 imagine [hat the extent of me coverage of the GIS data wil l likely not perfectly fit the exrenr of an ownership's boundary. Some o rganizations require that the extent of each GIS database lit perfectly with the boundaries of their land ownership, and tbey ensure this

by clipping each GIS database to their ownership boundary GIS database. Granted, there are some GIS da tabases, such as roads and screams, mat you may nO[ want dipped [0 an ownership boundary (interest may ceorer on where the roads and streams come from and where they go beyond a property boundary), but there are GIS databases,

such as soils and land cover, where the argument may hold (interest may not center on the soils of other land owners).

Other rypes of polygon features could be used in a dippi ng process. For example. you may he incercsted in the resources contajned with in a riparian zone. or within

a watershed. Further. when narural resource management organizations share GIS databases, they may decide to limit what is shared. For example, in Washington State,

where watershed analysis has been an important aspect of forese management, a coorciinared dfon among organizations leads to the identification of the limits of appropriate management activities wirhin watersheds. Ln many watershed analyses, a single organization will perform the GIS analysis tasks, and thus acquire all of the GIS data

bases related to a pan.icular watershed regardless of landowner. Usually private namral resource organizadons are very hesitant to share their G IS databases with their competitors-some GI S databases are considered proprietary and may contain sensitive information. In addicion. organizations may be hesitant to release GIS databases that are dated or chat may coma in unverified information . However, since watershed analysis may benefit all landowners in the long run, some GIS dacabases arc usually shared among land management organizations. In cases such as these, organizations may decide to share as lirrle GIS data as possible to meet the goals of the watershed anaJysis. and rhus avoid revealing any other informa-

tjon regarding the starus of the natural resources that they

manage. Therefore, a clipping process is used to limit the amount of information shared to that concerning specific

geographic regions, such as individual watersheds.

Obtaining information about vegetation resources within riparian zones

As a fi rst example of using a clipping process, let's assume that we are iD(~res(ed in [h~ vegetation resources con

tained enti rely within some pre-defined riparian zones within a forest. In chapter 7. we will describe rhe process of taking spat iaJ features, such as Streams, and creating physical zones (buffers) around them. For now, ler's

assume thar a GIS database containing polygons that describe 50-meter riparian wnes (stream buffers) for the Brown Tract already exists (Figure 6.4) . The type of informarion we may be interested in knowing includes the amount of land and the volume of timber within the riparian zones, which are generally limited-use management areas. A clipping process would allow us to develop a GIS database contajning only chose umber stand areas that are within the boundaries of the polygons that describe the riparian zones (Figure 6.4). The original tim

ber stand polygons that intersect the stream buffer polygons would be redesigned such that their boundaries now

coincide with the edges of rhe riparian zones. Those timber stand polygon boundaries that fall entirely within the riparian zones would be left intact . those timber stand

polygons that fitJI entirely outside of the riparian zones would be eliminated.

What you should find in the tabular database (attr ib

Utes) of the clipped GIS database is the same set (number and rype) of attributes thar can be found in the original vegetarion GIS database. However. the values of (he attributes associated with 'area' (acres and hectares) may

need to be adjusted to reAect only the area of the polygons within the clipped GIS database. [n addition, some

polygon GIS databases may also comain a perimeter measuremem that describes [he linear distanc~ around each

polygon border. The perimeter distance may also need to

be recalculated in order to accu rately represent perimerers that were affected by a clipping operation. Some GIS software programs perform these adjustmems automaricaIly

depending on the type of spatial database; other GIS software programs require users to recaJculate the areas with a second (albeit automared) process. Arcributes mar describe something other than an 'a rea ' ofland (e.g., tree


(a) so· meter stream buffers.

(b) Vegetation polygons.

age or stand volume). are not adjusted during the dipping

process. With the lise of a dipping process. you should be able ro understand how much area is comained within

the riparian zones, 446 ha in the case of the Brown Tract

(Table 6.1). The resulting dipped GIS database also pro

vides information necessary for subsequem analyses, such

as those that involve understandi ng the average age of the

riparian zone vegetation. or the timber volumes contained

within the riparian zones.

Obtaining information about soil resources within an ownership

Soil resources for North America have been mapped at various scales for each Canadian province and US St3te.

While special soils surveys have been conducted for indi

vidual landowners. the most widely used soils databases

were developed by governmental agencies. For example. in (he United Scates, the USDA Natural Resources

Conservation Service (2007) provides an online inrerac

tive process that allows you to acquire soils (and other)

GIS databases for a specific ponion of the country. One

TABLE 6.1 A subset of the tabular data contained in the GIS database that resulted from clipping Brown Tract stands within SO-meter stream buffers

Stand number Acres Hectares Age Volume'

0.63 0.2; ;2 12.7

(c) Vegetation polygons within 50-meter stream buffers. 3.06 1.24 ;2 12.7

Figure 6.4 Clipping the stands within 50-meter stream buffers on the Brown Tract.

2 12.37

2 2. 16

2 0.06

2 1.80

2 053

3 4.47

3 3.24

270 0.14

283 4.03

To,.] 1,101.39

• thousand board reet per acre

;.01

0.87

0.02

0.73

0.21

1.81

i.31

0.06

1.63

44552

46 13.3

46 13.3

46 13.3

46 13.3

46 13.3

;1 16.6

;1 16.6

2 0.0

43 I.;

121


product that can be obtained is the soil survey geogtaphic database (SSURGO) for most counties in the US. The majority of SSURGO data were mapped at a 1 :20,000 scale. and the minimum mapping area is 1-2 hectares. This level of detail in the mapping of soils was designed for use by Farmers. landowners. and other natural resource

organizacions. The STA TSGO soils database is another national-level

soils GIS database for the United States (USDA Natural Resources Conservation Service. 2006). However, the STATSGO data is a general soil map, and not as spatially refined as the SSURGO data. STA TSGO was mapped at a 1 :250,000 scale, and the minimum atea mapped is about 600 hectares. This level of detail in mapping soils was designed for broad natural resource planning and management uses.

The National Soil Database of Canada (Agriculture and Agri-Food Canada, 2006) contains databases on soils, landscape features, and climatic data for each Canadia n province. and is the national archive for land resource information collected by federal and provincial field surveys. The GIS databases contained in the National Soil Database range from the more general, mapped at a 1: 1,000,000 scale or smaller (like STATSGO), to the more detailed, mapped at a 1 :20,000 scale or larger (like SSURGO).

Figure 6.5 Soil polygons in me Brown Tract and surrounding at(~a.

Assume.you were to acquire the SSURGO data for the county within which the Brown Tract is located (Figure 6.5). If the managers of the Brown Tract were interested in the eype of soi ls that the Nacucal Resources Conservation Service has delineated for the land that they manage. a dipping process can be used CO obtain that information. In this case, you would use the boundary of the Brown Tract as the polygon theme to perform the clip, and the SSURGO soils database as the theme on which the clip would be performed. The resulting GIS

database provides an indication of (he major types of the SSURGO soils that are being managed on the Brown Tract (Figure 6.6).

Obtaining iruormation about roads within a forest

In the previous examples provided in this chapter, interest was placed on obtaining information aboU[ polygon features (timber stands and soils) that were located within some geographic region . This type of information is informative for land managers, yet it is a1so possible ro

~ Dixonville-Gellatly, steep slopes

t : : : : I Price-MacOunn-RItner complex, steep slopes

• Dbconville-Gellatly, moderats slopes

o Joey silty clay 1oam, Iow slopes

D Jory-Gelderman complex, moderate slopes

D Other soil types

Figw~ 6.6 Major soil types within the boundary of th~ Brown Tract.

122


obtain information abour other types of landscape feacures (lines or points) within certain geographic regions. Most forest road GIS databases, for example, contain a system of roads that extend well beyond the boundary of the property that is being managed by a nacural resource management organization. Th is allows these organizations and their managers to see how the road system they manage is integrated with the road systems managed by states, counties, or other nacural resource management organizations. In addition, it allows organizations [Q

weigh their options: If you were to harvest timber stand X, which route could be used to deliver the timber to the mill? If you were to fertilize timber stands Y and Z. how would you get the fertilizer to those stands? If you were to

perform an owl survey in watershed A, how could you get to (and get around) watershed A?

There are times, however, when an organization might need to understand only the characteristics of the road resources within the boundaries of the land that they manage. Each year. for example, a land manager may need to develop a budget for road maimenance expend icures, and over a longer period of time. a plan for the continued maintenance of the road system. When developing a long-cerm plan for maintaining rock surface roads (for example), you may first want to understand the extent of rock-surfaced roads within the land being managed.

As an example of a clipping process, the roads GIS database of the Brown Tract can be dipped to the property boundary. If you were to open the roads GIS database in a GIS software program, you would find that the database contains over 79 kilometers of paved. rock. and native surface roads (Table 6.2). These roads extend well

beyond the boundary of the Brown Tract in some cases. and allow the forest managers to view the landscape that they manage in a larger context.

However, after clipping the roads GIS database to the

ownership GIS database. and making sure that road lengths were updated in the output database. you would

TABLE 6.2

Road ty~

Paved

Rock

Native Surface

Total

Length and type of road within the roads GIS database developed for the Brown Tract

Mil .. Kilometers

5.7 9.3

41.3 66.4

2.3 3.6

49.3 79.3

TABLE 6.3 Length and type of road within the boundary of the Brown Tract

Roadrype Miles Kilometers

Paved 2.0 3.3

Rock 37.5 60.3

Native Surface 1.5 2.5

Total 41.0 66. 1

find that only about 66 kilometers of road (Table 6.3) actually reside within the boundary of the Brown Tract

(Figure 6.7). This exercise also produces an example of a potential

problem related to this type of information extraction process. If you were to look closely at the resulting clipped GIS database. the eastern edge of the forest (Figure 6.8) comains a discontinuous piece of a road. Upon inspection, you might find thac chis road is a rocked road and that one of four situarions has occurred:

I. The road was incorrectly digitized into the GIS database (which could be verified by viewing the digital orrhophotograph associated with the Brown Tract).

2. The road was incorrectly laid our in the field. and

_ Paved _ Gravel _ _ . Dirt

Figure 6.7 Roads within the boundary of the Brown Tract.

123

Chapter 6 Obtaining Information about a Specnic Geographic Region 113

A discontinuous piece of road

/ Figure 6.8 A potential error in the clipped roads GIS datab~.

actually does reside within the boundary of the Brown

tracr.

3. The boundary of the Brown Tract was incorrectly dig

itized into the boundary GIS database.

4. The road and boundary are correctly located and the road may be (he result of an use easemem, a remnanc from a previous road network. or a potencial ingress by an adjacent property owner.

In any event, developing a mainrena nce plan that

includes rhis. and ocher, small p ieces of road may not

make sense from an operational perspective.

Obtaining information about streams within a forest

Most sc reams GIS databases managed by natural resource

organizat ions are designed in a fash ion similar to roads

GIS databases: they contain a system oflines or links that

extend well beyond the property boundary of the organi

zat ion so that the managers can understand how their

activities are integrated within a larger watershed system.

For example, you may be interested in knowing where

water flows, in the event that the need to monitor forest operations (after logging, fertilization. or herbic ide oper

ations) is important. There are times. however, when you

may need to quan ti fy the stream characterist ics only

within the boundary of the land that your organization

manages. For example. in order to develop a budget for

stream surveys, you may need to know how many and what rype (along with their length) of streams are located

within the boundary of land that your organization man

age. This implies that you are interested in understanding

TABLE 6.4

Stream type

Fish-Ixaring 1 large

Fish-Ixaring I medium

Fish-Ixari ng I small

Non-fish-bC'aring 1 large

Length and type of streams within the streams GIS database used by the Brown Tract

Miles Kilometers

0.9 1.4

3. 1 5.0

4.9 7.8

0.0 0.0

Non-fish -bearing J medium 0.0 0.0

Non-fish-bearing I small 25.7 41.4

Total 34.6 55.6

the extent of certain landscape features within a specific

geographic region. and a clipping process can be used to

extract those resources from other GIS databases.

Using the Brown Tract as an example. prior to clip

ping the streams to the boundary of the fo rest. approxi

mately 56 kilometers (35 miles) of streams are represented

in the streams GIS database {Table 6.4}. The resulting GIS

database. after clipping the streams GIS database to the

forest boundary GIS database and updating the stream lengths (Figure 6 .9). contains only about 45 kilometers

(28 mi les) of streams that are actually located with in the

boundary of the forest (Table 6.5). You find with this

analysis that (here are no large, fish.bearing streams

Figure 6.9 Streams within the boundary of the Brown Tract .

124


TABLE 6.5

Stream typ<

Fish-bearing I large

Fish-bearing I medium

Fish-beari ng I small

Non-fish-bearingJ large:

Length and type of streams within the boundary of the Brown Tract

Mil" Kilometers

0.0 0 .0

2.1 3.4

2.8 4.5

0.0 0.0

Non-fish-lxaring I medium 0.0 0.0

Non-Fis h-bearing I small 23.3 37.5

Tmal 28.2 45.4

within the Brown Tract, although some are present in the more extensive streams GIS database. In addition, while only about one·quartet of the streams (by length) in the more extensive Streams GIS database are fish -bearing, almost half of the streams (again by length) removed as a

result of the clipping process are fish-bearing. Since the Brown Tract contains the headwaters of several stream systems. it is not unreasonable to assume that (he fishbearing portions of these systems are located in the lower reaches (i.e .• off of the Brown Tract).

One note of caution about clipping processes: prior to performing other rypes of spatial analyses. such as the buffering processes, you must consider

whether or nO[ a clipping process is appropriate. For example, if you wanted to understand the extem of riparian areas on the Brown Tract, you

might nor want to clip the st reams to the boundary of rhe Brown Tract as an initial step in the analysis. By doing this, streams outside of rhe forest bound

ary are igno red. to which you probably would reply '50 what?'. Well , those streams may have a riparian area about them that extends inside the boundary of rhe Brown Tract. Put another way. just because a panicular stream resides outside the boundary of the forest you manage does not necessarily imply it can be igno red: part of its area of inAuence (rhe riparian area). and an assessmem of activities mar YOll might be considering within this area. may need to be included in your management plan.

The Process of Erasing Landscape Features

Thus far. our imerest has been centered on understanding the extent of resources that are located within certain geographic regions. Now. our focus will sh ift to obtaining informatio n abour the resources located oZltsid~ of certain geographic regions. The erasing process is well suited to

this task. and is essentially the opposite of the clipping process. When using an erasing process. you are interested in creating a new GIS database that contains landscape features located outside of a speci fic geographic region. Just as with a clipping process, an erasing process involves using two GIS databases as input databases (Figure 6.10). and the process results in one output GIS

database. When using vector GIS databases. one of the input GIS databases (the eraser) needs to comain polygon features; the other GIS database (the database in which landscape fearures will be erased) can contain point. line,

or polygon features. The eraser GIS database is overlaid on the GIS database containing the fearures of interest. and only those landscape features located outside of the

GIS database

Database to be erased : stands

Spatial features

Input GIS databases

Dalabase used~O do the erasing (Ihe eraser): 150-loot (45.7 m) stream buffers

- - -- ------Output GIS database

Resulling

~ database: stands outside the 150-foot (45.7 m) stream buffers

Tabular Attributes


Buffer attributes: buffer distance, etc.

------ ,


Figure 6.10 Erasing the stands within I 50· foot (45 .7 m) stream buffers from tht Danid Picktn stands GIS databast.

125

Chapter 6 Obtaining Information about a Specffic Geograph ic Region 115

bou ndaries of the polygons contained within the eraser GIS database are rerained. Thus the output GIS database contains the same type of features as the database being

emsed. In addition, the size of featu res (lines o r polygons, but not points) may be spatially altered where they overlap with the edges of the polygons conta ined within the

ems« GIS database. Figure 6.1 1 illustrates a small example and utilizes a

fire (the eraser) and a timber stand (the polygon to be erased). After the erasing process has been performed, you can see that [WO of the o riginal lines that defined the boundary of a timber stand (l , and I ,) were sho rtened [Q the point of intersection with a fi re area, and a third line was created (J ,) to describe the edge of the fire area tha t is common wi th the timber stand. The resuh

ing erased timber stand GIS database has all of the attributes of the original timber stand GIS database, yee the spacial extent is equal [0 the original cimber stand database minus [he overlap with [he fi re database. As with

[he clipping process, feature measurements in the OU(-

3. GIS databases prior to an erase process.

0 - Timber stand

" , , /" I

, ~ Fire area I , , ' , ' ......... _ /

b. GIS databases during an erase process. (0 = node)

1, 1,

Erased 12 Timber stand 13

(to be erased) Fire area 31 .. ...... ~ 12 Timber stand 13

1,

1, (eraser) /' : , , , , , , , ,

, '3 '"..._" 2

c. GIS databases after an erase process. (0 = node)

,~ , Erased timber stand

:...- Fire area , , ' , ' ' "...- "

, ,

Figure 6.11 Erasing a fire area from a timber 5t:lnd.

1,

put darabase fro m an erasi ng process should be updated (Q reRect changes in area or perimeter of polygons. or length of li nks in line databases. If your GIS software does not make these updates automatically. you shou ld ensu re that you use software or other app roaches (Q

update the measurements. There are a number of reasons why you would con

sider using an erasing process. One reason involves the need to understand the characterist ics of a landscape Out

side of areas that are considered Ttstn·cttd. Alternatively. the goal would be to define the unrestricttd areas of a

landscape with regard to management act ivities. For example. earlier in this chapter a clipping process was used to develop a GIS database that allowed us to summarize the resources located within riparian zones. Management within riparian zones is generally restricted in some fo rm or fashion. Areas outside the riparian zones could then he considered unrestricted, assuming there are no other co nstrai nts on land management, such as those related to owl nest locations, research areas, and so on . Understanding the extent of the landscape where management is not restricted, may be important when considering decisions related [Q harvesring operations, the use of herbicides or fertilizer, or other types of management

practices.

Obtaining information about vegetation resources outside of riparian zones

To build upon the examples previously provided in this chapter, let's obtain information about some landscape features (vegetat ion polygons) loca ted outside of the 50-meter riparian zones that were developed for the Brown Tract. An erasing process allows us to develop a GIS database containing o nly those features (in this case vegetat ion polygo ns) located outs ide the boundaries of the polygons that describe the riparian zones. T he origi nal vegetation polygons are again redesigned such that their boundaries coincide with the edges of the polygons that describe the riparian wnes. Those vegetation polygons that were entirely located outside of the riparian zones are left intact, and those vegetation polygons (hat were ent irely contai ned within the boundaries of riparian zones are eliminated (Figure 6.12). The tabular database related to the resulting erased GIS database should contain the same set of attributes that were contained in the

original vegetat io n (stands) GIS database, with only the area (a nd perhaps the perimeter) values adjusted to

126


Figure 6.12 Vegetation polygons (stands) outside of 50· mcler buffers on the Brown T race.

Summary

Clipping processes are spatial operations that allow users of GIS to obmin information about landscape features within

cercai n geographic regions. When using vec[Qr GIS data

bases, polygon features are used to clip the feacures from a

second GIS database comaining poines, lines. or polygons.

The feacures that are comained in the OUCput GIS database

are those comained within the boundaries of the polygons

of [he clipping GIS database (the cookie cu[(er). Erasing processes can be viewed as the inverse of clipping processes. With an erasing process, you can obtain informadon about

spat ial features located outs ide of certain geographic regions. In fIct. you could use a single GIS database to clip landscape features (used as the cookie cu[(er). and subsequently to erase landscape features (used as the eraser). If

Applications

6.1. Obtaining information about features within a watershed. Suppose that [he hydrologist associated with the Daniel Picke[( forest. Michelle Rice. has been working for some time on a watershed analysis with a few

other natural resource managemem organizations. The watershed being analyzed is the Dogwood Creek Watershed. The warershed analys is team is now ar the

reAeer the sizes of the redesigned polygons. When performing a check of the data. you should find that the size of the resulting erased GIS database. about 1.677 hectares (4.143 acres). is-and shou ld be--less than the size of the original stands GIS database (which was about

2.123 hectares or 5.245 acres) . Erasing processes can be used for other purposes as

well. For example. if an area of the Brown Tract were designaH~d for sale, the managers of the Brown T Tact may be imerested in knowing what resources would remain after the pending sale. Alternatively, if you were co develop a land classification for the Brown Tract which involved buffering streams and classifying uplands from riparian zones, you may erase (he buffer woes from a stands database (for example) as a intermediate stage of classifying

the landscape and identifying upland areas. These types of analyses may prove useful in (he applications described in subsequent chapters of this book.

both processes were applied to [he same GIS database (e.g .• stands, roads, or streams), the twO output GIS databases, when combined, should cover the same landscape area as

the original GIS databases that were clipped or erased. For

example. a GIS database was created, using a clipping

process, to represem those vegetation polygons on the Brown Tract that were located within 50-meter riparian

zones. A second GIS database was also created, using an

erasing process. to represem those vegetation polygons

located outside of the 50-meter riparian zones. If com

bined. these twO GIS databases (the clipped and erased GIS databases) should equal [he land area and vegetation resources thar can be found within the original vegetation

(stands) GIS database--no more. no less.

point where they need to obtain as much GIS data as possible to describe the currenr condition of rhe water

shed. Michelle asks you to provide her with the following informacion:

a) a summary of the area of timber stands located

~ithin the Dogwood Creek watershed. by vege .. -(Ion type;

127


b) a summary of the length of roads located with in the Dogwood Creek watershed. by road type; and

c) a summary of the length of streams located within the Dogwood Creek watershed. by stream type. In addition, summarize these values in terms of

scream miles per square mile of land. by stream

type.

Develop a shon memo add ressed (Q Ms Rice that details

the resulrs of your analyses.

6.2. Summarizing resources within a management area. Jane Hayes is developing an annual reporr on [he

management of the Daniel Picken forest and has become

very interested in certain aspects of the forest resou rces.

Since she knows that you are becoming proficient with GIS,

she has asked you ro provide some information she feels is necessary for her report:

a) amount of land in each watershed.

b) length of rock road in each watershed.

c) length of dirt road in each watershed. and

d) length of stream classes 1- 3 in each watershed.

In addition, she is imerested in knowing something about the resources that are within 50 merers of the streams.

Using the 50-foot stream buffer provided by the GIS Departmenr, provide the following:

a) area of land within 50 meters of the streams,

b) area of older forest (age ~ 60) with in the 50-meter stream buffers,

c) length of paved road within the 50-meter stream buffers, and

d) length of rock road within the 50-meter stream buffers.

Provide a memo addressed (Q Ms Hayes that details the

results of your analyses. Keep in mind the appropriate

units (perhaps kilometers fo r length rather than meters). and the appropriate precision (to the nearesr 0.1 hectare

or 0.1 kilometer) for this type of report.

6.3. Fertilization possibilities. Within the so ils GIS darab~ of the Daniel Pickerr foresr is an attribute, fer

rresp, which was meant to ind icate the probability of tree

response to fertilization as a functio n of the underlying

soil type. Describe the types of vegetation found in the 'high response' areas using (he following format :

0-20

21-40

41-60

61- 80

80.

A

Vegetation type

8 c

6 .4. Potential sale of watershed for conservation reserve. The managers of the Daniel Pickett forest have been approached by a non-profi t o rganization specializing

in developing and managing conservation reserves. The

non-profit organization is specifically interested in acquir

ing a specific portion of the Daniel Pickett forest-all of the land located in the Trout Creek Watershed. It seems that the non-profit group has been acdve in developing a larger reserve system in the Trout C reek area, and the

Daniel Picken forest just happens to contain the headwa

ters of the watershed. The region manager of the Daniel

Picken forest, Becky Blaylock, is interested in under

standing the effect this land sale may have on the manage

ment plan for the area. She asks you to provide her a before- and after-sale description of the resources, in the

fo llowing format: a) Before-sale conditions (area) of the Daniel Pickett

forest.

Age class

0-20

21- 40

41-60

61 - 80

80.

A

Vegetation type

8 c

b) Alier-sale conditions (area) of the Daniel Pickett forest.

Age class

0-20

21-40

41-60

61- 80

80.

A

Vegetation type

8 c

128


In add ition, she has also requested rhe following: c) a map showing the after-sale arrangement of vege

rarion classes (rypes) on rhe Daniel Pickett forest, and

References

Agriculture and Agri-Food Canada. (2006). The national soil database (NSDB). Ottawa, ON: The National Land and Weather In formation Se rvice,

Agriculture and Agri-Food Canada. Retrieved February II , 2007, from http://sis.agr.gc.ca/cansis/ nsdb/intro.html.

Clarke, K.c. (I 995) . Analytical and computl!T cartography.

Upper Saddle River, NJ: Prentice-Hall. Merriam-Webster. (2007). Merriam- Webster online

search. Rerrieved February 4, 2007, from http://www. m-w.com/cgi-bin/diccionary.

d) a brief summary of your opinion of rhe effects of the sale o n the management of the forest,

USDA Natural Resou rces Conservar ion Service. (2006) . US general soil map (STATSGO). Washingwn, DC: Narional Cartography and Geosparial Cemer, USDA Natural Resou rces Conservation Service. Retrieved February II, 2007, from http://www.ncgc.nrcs.usda. gov I p rod uctsl da rasetsl sta tsgo/.

USDA Narural Resources Conservation Service. (2007). Geospatial data gateway. Washington, DC: USDA

Natural Resources Conservation Service. Retrieved February 9. 2007. from http: //datagateway.nrcs.usda. gov/NexrPage.aspx?HitTab= I.

129

Chapter 7

Buffering Landscape Features

Objectives

Chapter 7 is an introduction and examination of GIS

buffering processes. A number of examples and applications are presented in this chapter co provide readers with

experience in several of the common GIS-related tasks in

namral resource management that require the use of a buffering process. At the conclusion of this chapter. read

ers should understand. and be able co discuss, the peninent aspects of:

I. whar bujf<ring spatial landscape features implies; 2. how different buffering techniques can be applied to

point, line, or polygon features; and

3. how buffering can be applied (Q assess alrernarive management policies and to ass ist in making natural

resource managemenr decisions

To someone unfamiliar with [his GIS process. the term

buffering may lead to some confusion. Technically, the noun 'buffer' refers to (a) a device fo r reducing shock when contacted, (b) a means fo r cushioning fluccuations in busi ness activities. (c) a protective barrier, (d) a sub

stance capable of neutralizing both acids and bases. and

(e) a temporary sto rage unit on a computer (Merriam

Web"er, 2007). In GIS appl ications in natural resource management. we generally refer to the buffering process as a method for creating a buffer zone. which is defined

as a land area that delineates separate management activ

ities or emphases. Wirhin GIS, a buffer rone is a polygon that encloses an area within a specific distance from a

point. line. or polygon, and is useful in analyses that

have a proximity criteria (Association for Geographic

Informarion, 1999). The GIS process of buffering usually infers that a

boundary is about to be drawn around some selecred features. There are numerous reasons why you would want to

draw boundaries around selected landscape features in nat

ural resource management. For instance, one of the guid

ing management policies for an organization may suggest

that some management activities may be prohibited within a certa in distance of a stream, a road, a trail. or a

home. Therefore. as a namral resource manager. you may

be interested in the appropriate limits of allowable man

agement activity. As another example, managemenr activ

ities within a certain distance of nesting, roosting. or for

aging sires of a wildlife species of concern may be curtailed during certain times of the year. Therefore. delineating

these 'home ranges' or 'critical habitats' may be an impor

tant aspect of namral resource managemem. In these cases,

you might develop sparial buffers within GIS to identifY resources located within certain distances of important

landscape features (poinrs, lines, or polygons). You mighr also be incerested in examining the potential impacts of rhe policies that suggest the use of buffers. in order to

understand how the objectives of namral resource manage

ment be affected. As you may gather, buffering has been widely used in natural resource managemem to identify

riparian managemenr areas (streamside management zone)

and {Q define areas where management may be restricted

for one reason or another. Yet buffers have also been used

to track and assess the site impacts of logging equipment on soil resources and residual stand conditions (Beninger

er al., 1994; McDonald er .1., 2002) .

130


Fortunately, GIS software programs provide the ability co easily identify features that are within some proximity of other features (Star & Estes, 1990). Developi ng the boundaries of a region within a specific distance from a

landscape feature, or set of selected landscape features, is

often called a 'proximity analysis', or buffering process.

Therefore the subject of [his chapter involves the identificarion and delineation of natural resources w ithin cer

rain distances of other landscape features.

How a Buffer Process Works

Buffer processes work by using marhemacical algorithms

to delineate the space around selected landscape features.

When using vector data, one or more features of interest

are selected. the desi red buffer distance is specified, and a

line is drawn in all directions around the features until a

solid polygon has been formed . Point , line, and polygon

(a)

(b)

Buffer ---..I around

, , "

the point

Buffer around the line

Vertex 1

"" (Tangent 1) I , '

,

, .. , ,

, ,

\ , \ " , ,

, (Tangent 2)

\ , , , Vertex 2

"

, , , ,

features can all be buffered but the buffer creation process depends on {he feamre rype. To visualize how a buffer is

created around a point feature. imagine a point on a piece

of paper; with a pencil and compass set, a circle is drawn around that point (Figure l .la). GIS software programs can perform this type of operation on thousands of poims

in a few seco nds. Delineating buffers around lines and

polygons requires a similar process bur involves some

additional process ing. With lines. a buffer is created

around each venex (Figu re 7 .1 b)' then tangents are ere

ared between each of these buffers, and only the outside

edge is kept, forming a closed polygon. W ith polygons, you may have the choice of creating buffers that represent only the area outside of [he polygon being buffered, the area outside of the polygon pi us the entire area of the

polygon, the buffered area both inside and outside of the edge that define [he polygon, or the area buffered inside the polygon (Figure l.ic) . The type of proximity analysis

(e) C D Original polygon · . o Outside buffer · . · . · . · . Inside buffer · . ,., .. "':. . ........... ···r ",

[ = =: Tangents around vertices ' . · , ------

D Result 1: Buffered area represents only the area ootside of the polygon that was buffered.

Result 2: Buffered area represents the area outside of the polygon that was buffered, as welt as the area of the polygoo itself.

Result 3: Buffered area represents the area outSide of the polygon that was buffered, and the area inside of the polygon that might have been buffered.

~ Result 4: Buffered area represents just the area inside of the polygon that might have been buffered.

Figure 7 . 1 Developing a buffer around (a) a point lUing, (b) a line with three vertices, and (c) a polygon .

131

that is required will suggest the appropriate 'Ype of buffering process required when polygons are concerned. For example. if you were interested in the type of vegetation within 1.000 meters of a set of polygons chac define cru

cial owl habitat, you would create a buffer outside the sec of polygons. On the other hand, if you were interested in understanding rhe amount ofland chat is associated with a policy chat prevents management activity within 100 meters of the edge of a managed property (to avoid conAict with homes and o ther developed areas oU[side rhe propenyL you would create a buffer inside rhe boundary of the managed proper'Y.

Buffering processes performed on vector GIS data may require some rather complex geometrical calculations. with lines and tangents CO compute, and overlapping areas perhaps merged together. To remove overlapping areas, imersecrion and dissolving processes are used. Buffering processes performed on raster GIS data involve

couming the number of pixels away from selecred or specified pixels.

Buffers used in natural resource managemem can rake on many forms, bur rhe [wo most commonly employed are a conS[(lnt buffer widrh and a variable buffer width. Constant width buffers are the most commonly used

form in natural resource managemem (Bren, 2003), and assume a symmetrical distance around each buffered landscape feature (same distance buffered on each side of a srream, for example) . Variable width buffers assu me that fearures are buffered differently based on some inherem or assumed characteristic, such as stream size. Other rypes of buffers include [hose based on (a) environmentalloading values and (b) other outside influences. Stream buffers based on loading values, for example, might take imo accoum the amoum of area comriburing to an impact. For example. stream reaches that have larger water con

tributing areas might be buffered using a wider buffer width than stream reaches with smaller comribucing areas. These are differem than variable width buffers in thar each section of a stream may be buffered a differem distance based on the size of the watershed that contributes water (Q that secrion of the stream. Buffers based on other outside influences may include stream buffers that take inro accoum the amoum of sunlight that reaches the stream irself. In these cases, buffers on the southerly sides of streams may be wider (han buffers on the

northerly sides of streams (Bren, 2003). As we mentioned, common buffer distances can be

constam (fixed) distances. o r they can vary for each feature in a GIS database based on an att ribute of those


fearures . Most GIS sofrware programs can accommodate both buffering approaches. To illustrate these differences, suppose you have a GIS database that includes 10 streams, each of a different stream class (T able 7. I) . With this hyporhetical data, it is assumed that the lower the stream

class number, the la rger (wider) the stream. One task in planning natural resource management

activities may be to delineate riparian buffers around these streams. If a constant buffer distance of, for example. 30 meters is assumed. each stream would be buffered the same distance (30 m). However, many regulations

pertaining to the proximity of management activities around riparian areas require wider buffers around larger Streams, and narrower buffers arou nd smaller streams.

Therefore, distances specific to each landscape feature (each srream reach in rh is example) can be used in the buffer process, to allow the development of variable dis

tance buffers. For example, for each of the 10 streams you could have developed an attribute to describe the appropriate buffer distance (Table 7.2), and use that attribute to guide the buffering process. Readers will examine borh of these buffering assu mptions (constant distance and variable width) in the forthcoming examples as well as in the 'Applications ' section at the end of this chapter.

When buffering mult iple landscape features, a buffer is created around each feature independent of the other features. One option available with most GIS sofrware concerns the handling of the overlapping areas. The choices are [0 retain individual buffer polygons for all features to be buffered (caJled uncomiguous, or non-

TABLE 7.1

Su ....

2

3

4

5

6

7

8

9

10

Ten hypothetical streams and their stream claBs, length, and width

Su .... da>.

Stream length (m)

Streun width (m)

2

2

3

3

3

4

4

5

1000

750

500

450

375

450

400

300

250

275

50

45

10

10

3

3

2

o

132


TABLE 1.2 Ten hypothetical streams and their stream class, length, width, and buHer distance

Sueam Strum Stream Buff., S ....... d,,, Ie.ngth (m) width (m) distance: (m)

1000 50 30

2 750 45 30

3 2 500 10 20

4 2 450 10 20

5 3 375 3 10

6 3 450 3 10

7 3 400 2 10

8 4 300 10

9 4 250 10

10 5 275 0 0

contiguous polygons), or co eliminate the overlapping

areas, creating contiguous polygons. The advanrage of

retaining individual buffer polygons is tha t once (he

buffers are created, [he buffer perta ining (Q each individ

ual landscape feature can be accessed, which may allow

you [0 determine which fatuus are within what distanu of other fearures. This allows individual analys is for each

point. line, or polygon fearu re that was buffered. The dis

advantage is that there is a high likelihood thar some of

the retained individual buffers overlap; rhus the overlap

ping area can porentially be counted more than once in

any subsequent area summary calculations. The creation

of a single buffer from overlapping buffered areas avoids

this problem. However. the ability CO understand the

buffer required for each individual landscape feature is [hen obscured. The goals of each analysis should direct

users to the choice of one buffering method or the other.

Generally when buffers are delineated with a GIS

process. they are saved in a new GIS database that is

separate from rhe one containing the landscape features that were buffered. To further enhance the

power of buffer processes, most GIS software programs only buffer the features that are selected (manu

a lly or through a query) . If no landscape features are selected, generally all of the landscape features are

but users shou ld understand how the two methods differ in their approaches and results.

Buffering Streams and Creating Riparian Areas

Ripar ian a reas can be defined as land areas that are in

close proximity ro a stream. lake. swamp. or other water body, and those that are often are occupied by plants

that are dependent on their roOtS reaching the water rable

(Society of American Foresters. 1983). Alternatively,

they a re areas where vegetation and microclimate are

inAuenced by seasonal or year-round water. high water tables. and soils exhibir some wetness characrerisrics

(Oregon Department of Forestry, 1994a). The first def

inition includes administrative and ecological aspects.

wh ile the second is based mainly on ecological and physical aspects. When we work with riparian a reas in natu

ral resource management. they are more commonly

defined administratively rather than ecologica lly.

Generally. riparian management area widths are desig

nated by federal . stare. provincial. or organization policies, and are designed ro provide adequate areas ro retain

the physical components. to maintain the functions nec

essary to meet protection objectives and goals for fish.

water quality , and other wildlife (Oregon Department of Forestry, 1994b). While some policies suggest that ripar

ian areas should be protected from logging, grazing. and

other types of exploitation. ocher policies allow a set of

limited activities within certain distances from certain

rypes of streams. Thus it is important for land managers to know where riparian areas are on a landscape. and to

understand whar resources are affected by riparian area

designations.

In the following examples, streams will be buffered first

with fixed (constant) buffer widths. then with variable

buffered but an examination of buffer output can

confirm whether your GIS software follows this

assumption. One processing step commonly forgot

ten is either [Q remember to select the features that

need to be buffered (perhaps just all C lass 1 streams),

another is [Q clear all previously selected features (if some features were selected for a reason unrelated to

the buffer process) .

133

buffer widths (according to a set of stream buffer guide

lines) to del ineate the riparian managemenc areas.

Fixed-width buffers

In this first example of a buffering process, the streams

GIS database of the Brown T ract (Figure 7.2) wi ll be used to generace fiXed-width buffers, o r buffers that do not

vary based on some 3cuiburc of the landscape features being buffered. In this case, assume thar an organizational

policy exis(5 chat directs the managers of the Brown Tract to delineate a fixed )50-foot buffer around all of the

streams. During the buffer process, a buffer polygon will be created around all stream lines in the streams GIS data

base. As noted earl ier, mOSt GIS sofrware programs provide the options of either leaving the buffers as individual

polygons around all stream lines or el iminating the over

lapping areas. H ere, we will illustrate the overlap being eliminated, so that buffer area estimates will not be overstated. At the conclusion of the buffer process, areas 150 feer on either side of (he Brown Tract streams (Figure

7.3) are delineated; no other land areas are represemed by the buffer polygon(s) contained in the new buffer GIS

database.

Variable-width buffers

Ra[her [han a single, fixed-width buffer, some managemem objectives may require a buffer [hat varies based on an auribuce of a landscape feamre. For example, in some S[ates or provinces rules exist which indicate that the size of riparian areas should be a function of the type of

D

"

- Stream D Forest boundary

Figure 7.2 The stream system within and arollnd the Brown Tract.


D Stream buffer D Forest boundary

Figure 7.3 Fixed-width (I 50-foot} riparian management areas, generated by buffering the streams GIS database of the Brown Tract.

stream with which they correspond. In some cases, these riparian designarions allow no activity within a cerrain distance from the stream system; in other cases, limited acrivity. The general norian is that, with wider streams,

Streams with year-round warer flow, o r streams with known fish populations, wider buffers are required. Once [he set of buffers is based on the characteristics of the

streams, they are considered variable-width buffers because [hey vary according to the characteristics of each srream. In Oregon, for example, the riparian guidelines require a ) DO-foot buffer around ' large' fish-bearing or

domestic water use streams, and lesser buffer widths around smaller screams (Table 7.3) and streams that are

nO( curren tly fish-bearing or used as sources of domesric water (Oregon State Legislature, 2005) .

Since the Brown Tract is fict ional, the buffer widths are assumed for the various stream classes in rhe streams GIS database. Stream class 1 is the largest srream, inro which all of the other streams flow, so a larger buffer (100 feet) is

When you wanr co create riparian management areas by buffering a set of streams, you usual ly do so by ind icat ing how wide the buffer should be on

either side of a stream, rather than by indicaring the rotal w idth of the buffer (from o ne edge of the

buffer, across the stream, to the other side of rhe buffer).

134


TABLE 7.3

Medium"

Small<

State 01 Oregon riparian management area policy

Riparian management area width (feet)

Domestic water we or fish.bearing

100

70

50

Non·domestic water we and non-fish-bearing

70

50

20

• Average annual flow of 2: 10 cubic feet per second.

h Average annual flow of ~ 2 cubic feet per second and < 10 cubic feet

per second.

~ Average annual flow of < 2 cubic fec t per second, or drainage area

s: 200 acres.

Source: Oregon State Legislatu re. 2005

assumed around this st ream class, and smaller buffers are

assumed [Q be required around the other Stream classes according to the direction provided in Table 7.3. Fortunately. most GIS software programs allow users to

des ignate a field (also called column, attribute, or variable) in a GIS database to use as the refe rence for the desired buffer width fo r each landscape feacure. Since each row in the tabular porrion of the streams GIS database represents a Stream line or reach (Table 7.4), the values located in the 'buffer width ' field can be used to represent the appropriate buffer width for each stream. During the buffering

TABLE 7.4 Sample stream reaches represented in the Brown Tract streams GIS database, their characteristics, and resulting buller width-

Buffer

Stream Length Fish Stream width ,each (r.<I) bearing~ size (feet)

362 no small 20

2 176 no small 20

3 992 no small 20

4 384 no small 20

174 1953 no small 20

175 2143 Y" mcdiwn 70

176 3159 Y" small 50

• Stream reaches arc no t necessarily or:clusivcly located within the bou ndaries of rhe Brown T racr.

process, each stream line will then be buffered according to the appropriate buffer width, based on each stream's class. The buffering process will read the buffer distance

from an attribute table, one record (row) at a time. to create the buffer. Most GIS programs will do this very quickly, such that only a few seconds or less are required.

Following the creation of all buffers, subsequent processing will eliminate all overlapping buffer areas, even though [he size of the buffer may change in the overlapping area. A map of the variable buffer widths associated with the Brown Tract streams (Figure 7.4) shows that the amount of land area in the riparian areas will vary by stream class, and that no other land area (outside the variable width buffer) is represented in the buffers.

Buffering Owl Nest Locations

Up to this point our concentration has been placed on one of the more typical GIS buffering operations performed in supporr of natural resource managementbuffering streams. However, any type of landscape feature (owl nest location , road, wetland, etc.) can be buffered. For example, in the western United States it is important to protect an area around spotted owl (Strix

occidtntalis) nests. The area within these buffers may either totally prohibit management activities or may limit management activities by duration and extent. Thus. it

may be imporrant to understand the amounr of resources located within owl buffers. As an example, assume that an owl nest is located in the central portion of the Brown Tract, and assume that federal regulations requi re land

o Stream buffer o Forest boundary

Figure 7 .4 Variable.width riparian management areas, generated by buffering the streams GIS daubue of m e Brown Tract.

135

Users should bear in mind that the buffer distances

and map coordinate units of GIS data layers that are being buffered must be considered. A buffer opera

tion will typically assume that buffer distances spec

ified either through fixed-width or through vari

able-width processes are in the same units as the map coordina te system of the G IS layer. If the

buffer distances and map coordinate unics are in

differem measuremenc systems (51 versus USeS) or are in the same system bur at different scales (e .g. meters versus kilometers) the appropriate conversion should be applied to the buffer distances prior

to beginning the buffer process. Some GIS software

will prompt you for the buffer or G IS layer units,

and will even do any conversions necessary during

the buffer process. In th is case, as long as the correct

conversions were specified by the user, the buffer output should be coerceL In other cases, however, the user must verifY that all buffer-dependent meas

urement units are in agreement.

managers to manage the area within 1,000 feet of these

nests much differently than the areas beyond 1,000 feet of

an owl nest. A buffer process can be performed using the owl nest locarion as the selected landscape features, and a 1 ODD-foot radius as a fixed (constant) distance around the

nest locarions. The result of the buffer process is a new GIS database that delineates the areas within 1,000 feet of

the nest location (Figure 7.5). If more than one nest were located on the Brown Tract, and the resulting buffered

areas overlapped, you could elect to eliminate the overlap

ping area (uncontiguous result) or allow the overlapping areas to remain (cont iguous result). If your goal was to

determine the amount of area and land resources associ

ated with each individual nest then the unconriguous resulr would be best. Conversely, if your goal was to determine the toral area and land resources encompassed by all owl nest buffers then the contiguous result would

be preferred.

Buffering the Inside of Landscape Features

In addition to delineating buffers outside oflandseape features, you can develop buffers inside of landscape features


• Owl nest location

o Forest boundary o Owl nest buffer

Figure: 7.5 Owl nest location and associated 1,000 fOOl buffer on the Brown Tract.

as well. Of course, when using vector GIS databases this is

only possible with polygon features. To illustrate this

process. let's assume that the managers of the Brown Tract are concerned about the impact of management activities on nearby homeowners. In some cases, homes are very close to the edge of me forest. In orner cases, homeowners'

yards and personal belongings (sheds, etc.) are on the edge

of the property. To avoid any potential instance of damage

to adjacent homes or property, or any potential physical

harm CO nearby landowners. the managers have decided that they will allow only limited activity within 200 feet of

the boundary of the property. To understand how much

of the property will be allocated to a limited activity land

classification, the inside of the boundaty of the forest (a

single polygon) can be buffered (Figure 7 .6) , and the

resulting area (397 acres) can be compared with the total

area of the forest (5,245 acres) to determine the impact of

the policy (7.6 per cent of the forest shifted to limited use) .

As an alternative co this policy. the managers of me Brown Tract could delineate this limited activity zone by buffering

the homes a cenain distance. although locating these areas

on the ground would be much more difficult than using a policy such as 200 feet .from the boundary, since the buffer around each home is circular.

Buffering Concentric Rings around Landscape Features

If you needed to generate multiple buffers around the same landscape fearure(s) , you could perform each buffer-

136


o

o Buffer o Forest boundary

Figure 7.6 A 200-foot buffer inside the boundary of the Brown Tract.

ing operation independently using different fixed-width buffer distances. However, the resulting buffers would contain so me areas [hat overlap. Ahernariveiy. if you wanted to avoid the overlap that would ultimately OCCUf

with this method. most GIS sofcware programs contain a process that enables users to easily develop concentric, non-overlapping rings. One caveat for this process is that

the buffer interval for each ring needs to be constanL As an example of the use of concentric rings of buffers. [he

Maryland Deparrmenr of Narural Resources (2005) recently esrablished guidelines for rhe managemenr of land near bald eagle (Haliaeetus lrococephalus) nesr reees. The guidelines require managers [0 acknowledge th ree buffer zones around each nest tree.

• Zone I extends from a nest tree ourward to a radius of 330 feer.

o Zone 2 exrends from rhe edge of Zone 1 (330 feer) ourward to 660 feer in radius.

o Zone 3 exrends from rhe edge of Zone 2 (660 feer) ourward to 1,320 feet.

Zone 1 prohibirs land use changes, such as those relared (Q development or dmber harvesting. Zone 2 proh ibits development (clearing, grading, building, etc.) but allows selective timber harvesting. Zone 3 prohibits any activity during the eagle nesting season. If you were interested in quickly developing buffers to represent Zones 1 and 2, the buffer interval for the concentric rings would be 330 feet. Zone 3 is not 330 feet from the edge of Zone 2 and thus would need to be developed separately. Figure 7.7

D Lake

0 CJ D Land

"-I 0 X Eagle nest 1)\) D Zone'

[)]] Zone 2

\J

Figure 7.7 Concentric 330·foot buff~rs around two agle nests.

illustrates the development of the concentric ring buffers. where Zone 1 extends 330 feet outward from the nest tree.

and Zone 2 extends another 330 feet from rhe edge of Zone J. Upon closer inspection, you would find thar the buffers describing rhe rwo Zones do not overlap; therefore, (he area underneath is nor double-counted.

While these examples describe the use of concentric ri ng buffering around point features. the process can also

be used to develop rings of buffers around line or polygon features. if management objectives suggest they are necessary. For example. ripa rian buffers for forested areas on

Prince Edward Island need to be 20-30 merers wide, ye t a 15-meter undisturbed area must be maintained (Legislative Counsel Office, 2006) . If you were managing land on Prince Edward Island. and assumed that the maximum fo rested riparian buffer width would be 30 meters wide, you could develop I5-meter concentric ring buffers around the line features that describe [he streams. This would enable the mandacory 15-meter area to be represented on a map. as well as the larger 30-meter buffer

boundary.

Buffering Shorelines

The actual or planned management of areas near shorelines of lakes may be of interest to natural resource man

agers. 10caJ land use planners. and citizen stakeholders. 137

Buffer output can take different shapes around the linear features that are being buffered depending on your GIS software. Some GIS software will allow you to specifY which side (left or right) of a line to create a buffer, rather than buffering both sides. The trick in this case is [0 determine which side of your line is left and which is right , a co ndition which will often he determined by (he direccion in which your lines

As a result, information regarding the extent of actual or

planned land uses near lakes may help inform land planning processes. JUSt as an example. assume that the area of

interest near the shorelines of lakes in the Pheasam Hill planning area of the Qu'Appelle River Valley in cemral Saskatchewan is 300 meters from the edge of each lake. A buffer process can be performed to create polygons that represem areas 300 meters outside of the edge of each lake (avoiding buffering the inside of each lake feature). The result is a new GIS database (hat contains areas within 300 meters of the shorelines. A clipping process can men be employed to clip the zoni ng GIS database associated with the Pheasam Hill planning area of the Qu'Appelle River Valley, and obtain the land use classes that are designated within 300 meters of the shorel ine (Figure 7.B) . A quantitative assessment can then be made of the amount of

area of each actual or planned land use within a close

proximiry to the lake, as well as visual assessment of the

juxtaposition ofland uses near the shoreline.

r=;:] lake ~;, ;~~j Recreation

_ Agriculture priority 2 ~ Urban

[=:J Indian reserve [=:J National area

[=:J Other areas beyond 300 m of the shoreline

Figure 7 .8 Planned or actuaJ land uses within the Pheasant Hill planning aua of the Qu'Ap~l1e River VaJley. Saskatchewan (1980).


were originally digitized or created. Individual lines can also have their ends buffered through a circular

shape, which is the usual default, or through a flat shape. If you chose the flat shape the output result of buffering a straight line would be a rectangle. Lines that change direction and are buffered through this process will produce output polygons with flat (noncurved) ends.

Othe r Reasons for Using Buffering Processes

The examples provided in this chapter have focused on common natural resource management concerns. The

delineation of riparian management areas and zones

around wildlife nest locations are twO typical examples of using a buffering process in namral resource management planning. There are, of course, a number of Q[her reasons

why you would use bu ffe ring operations in natural

resource management. including:

1. Buffering stream systems [0 delineate me zone that herbicide operations must keep oU{ of. due co [he proximity to water systems. For herbicide operation planning, local buildings (particularly houses), roads, agricultural fields, and orchards may also require buffering.

2. Buffering research areas (plots or stands) CO prevent

the planning and implementation of logging operadons within them. Generally speaking, research plots

require extra protection because trees rhat are treared

(given a researchable application) should nOt also be considered edge trees (rrees adjacent to a cleared area).

therefore the plots need to be buffered fro m any nearby harvest activities.

3. Buffering trail systems or roads to delineate areas of

visual sensitivity within which logging operations may

be limited. In many forested areas, degradation of

recreacion opporrunides is a co ncern, whether the

recreational activities involve humans walking (hiking)

or driving. The ability to quickly delineate and visualize these areas of concern is a valuable asset in land

management planni ng. 4. Buffering property boundaries to recognize mat devel

opment codes, which may limit structures o r other

138


landscape alternations from occurring within a threshold dis,.nce from property lines. are observed. This

may also apply to easements or utility co rridors. which are often subject [0 municipal. county. or provincial

development regulations.

In addition. buffer processes may assist namra! resource managers in evaluating the potential impacts of local for

est regulations. Local forest regulations are generally con

cerned with protecting envi ronmental quality and aesthet

ics, and with safeguarding local government investments

in roads and other infrastructure (Martus et al.. 1995). They are mainly developed as a result of the conflicts that occur with the continuing shift of the human population

Summary

GIS buffering processes are powerful tools that allow you

ro investigate the nearness of landscape elements ro your

feacures ofinteresr. These features of interest can be repre

sented by points. lines. or polygons (as demonstrated here) or by raster grid cells . Buffering processes are spatial oper

ations that allow users of GIS software programs to identifY areas within some proximity of selected landscape feacures .

After landscape features are selected. a zone (a polygon) is delineated around them to represent the buffered area. By

default. if no landscape features are selected. all features are buffered. but you should confirm whether your GIS soft

ware uses this approach. Buffer OUtpUt approaches. such as

contiguous or uncontiguous results. can be customized to

meet analysis objectives. In addition, some GIS software

Applications

7.1. Current Riparian Policy for the Brown Tract. The currell[ organizational policy for the Brown Tract

indicates that the following riparian area buffer widths

should be used in conjunction with management activities:

Class I: 100 feet C lass 2: 75 feet Class 3: 50 feet C lass 4: 40 feet

Becky Blaylock. the Manager of the Brown Tract. wants to know (he 'current situation' with regard to riparian

areas:

from urban to rural settings (Cubbage & Raney. 1987). The types of controls include requiring the development

of forest plans. buffering specific landscape features. and placing restrictions on certain silvicultural practices. For

example. timber harvesting ordinances developed at the

local level are intended to limit site degradation and envi

ronmental qualiry in association wich logging activit ies.

Harvesting operations may be restricted within certain dis

tances of public roads or ocher public resources. therefore

requiring a buffer around these resources. Natural resource

managers may need to identify these buffers in a harvest

plan. and may also be concerned about the cumulative

impact of local regulations on the profitability and feasibility of their management operations.

enables users to specify whether buffers created around

lines have a round or flat shape at the beginning and ending of the line. The resulting buffer GIS databases allow users co visually understand the area that lies within a cer

tain distance from some landscape feacure(s) of interest,

and to quantify the resources contained within the

buffered area. Buffering streams and other resources of

importance (e.g .• owl and red-cockaded woodpecker nest locations) to create limited management zones is a com

mon management objective included in natural resource

management plans. Estimating the impact of these types

of restrictions on natural resource management is impor

tant, and helps landowners investigate the effect of current

and proposed policies.

a) How much area (acres) is located inside the riparian areas?

b) How much area (acres) of each vegetation type is

located inside the riparian areas?

c) How much area (acres) of each vegetation type is loca(ed outside the riparian areas?

d) How much timber volume is located in the ripar

ian areas?

Ms Blaylock also wants you to develop a map that illustrates the stream buffers and includes the roads. streams.

and timber stands. 139

7.2. Proposed Organizational Policy. Becky Blaylock recently 3([cnded a meedng of policy makers, where she

heard rhar rhe riparian rules mighr change. She now needs [Q undersrand rhe paremial impaer of a proposed 125-foot no~harves[ buffer around all streams on [he Brown

T racr. She requem rhe following: a) How much area (acres) is locared inside rhe ripar

ian areas? b) How much area (acres) of each vegerarion 'Ype is

located inside the riparian areas?

c) How much area (acres) of each vegerarion 'Ype is located oU[sicle the riparia n areas?

d) How much timber volume is located in the ripar

ian areas?

Again. Ms Blaylock also wams you [Q develop a map rhar illustrates these stream buffers and that includes the roads.

streams, and timber stands. In addition. she wants you co

develop a memo that describes the diffe rences becween

rhe policy nared in Applicarion 7. 1 and rhe paremial policy noted here.

7.3. Ballot Initiative. A proposed bailor iniriarive. developed by a local conservation group, suggestS that the

following riparian buffer widths may soon be required for the region within which the Brown Tract is situated:

Large. fish-bearing srreams ISO feer Medium. fish-bear ing srreams 100 feer Small. fish-bearing srreams 75 feer Large. non-fIsh-bearing srreams 125 feer Medium, non-fIsh-bearing streams 75 feet

Small. non-fish-bearing srreams 50 feer

As a resulr. Ms Blaylock wams to undersrand rhe following: a) If rhese rules were applied to the Brown Traer. how

much area (acres) would be locared inside rhe riparian areas?

b) If these rules were applied to rhe Brown Traer. how much area (acres) of each vegerarion 'Ype would be located ins ide the riparian areas?

c) If these rules were applied to the Brown Traer. how much area (acres) of each vegetation type would be

located outside the riparian areas?

d) If these rules were applied to rhe Brown Traer. how much rimber volume would be locared in rhe riparian areas?

As before. Ms Blaylock also wants you to develop a map that illustrates these stream buffers and that includes the


roads, streams, and timber stands. In addition, she wants

you to develop a memo (hat describes the differences

berween rhe policies nored in Appl icarions 7.1. 7.2. and rhe poremial policy nored here.

7.4. National Forest Riparian Policy. A local Narional Forest uses me following riparian area guidel ines in con

junction with harvesting activities:

Large srreams 250 feer Medium srreams ISO feer Small srreams 100 feer

As wirh rhe arher pol icies. Ms Blaylock wams to undersrand rhe following:

a) If rhese rules were applied to the Brown Traer. how much area (acres) would be locared inside rhe riparian areas?

b) If rhese rules were applied to rhe Brown Traer. how much area (acres) of each vegerarion 'Ype would be located ins ide (he riparian areas?

c) If rhese rules were applied to rhe Brown Traer. how much area (acres) of each vegetation type would be

located outside the riparian areas?

d) If these rules were applied to rhe Brown Traer. how much timber volume would be located in (he ripar

ian areas?

And again. Ms Blaylock also wams you to develop a map that illustrates these stream buffers and that includes the

roads, Streams, and timber stands. In addition, she wants

you to develop a memo that describes the differences

berween rhe policies nored in Applicarions 7.1 . 7.2. 7.3. and rhe porenrial policy nared here.

7.5. Current Owl Buffer Policy. Suppose rhar rhe current pol icy regarding owl nest locations is ro maintain a 1 ~O-acre no-harvest buffer around owl nest site locations.

Develop a memo and a map for Ms Blaylock rhar describes rhe amoum of land (acres) of rhe Brown Traer rhar would be covered by rhe single owl buffer pertain ing ro this property.

7.6. Protection of Research PlotsThousands of research plots have been established across North America ro facil

itate the estimation of the response of forests and wildlife

co a variety of silvicultural treatments. Unfortunately, har

vesting operations are usually implemented independ

ently of research programs. and tOO often the research plots are harvested because the loggers were unaware of

140

7 .2. Proposed Organizational Policy. Becky Blaylock recently attended a meering of policy makers, where she

heard char the riparian rules might change. She now needs to understand the potential impact of a proposed 125-foot no-harvest buffer around all screams on the Brown

Tract. She requestS rhe following: a) How much area (acres) is located inside the ripar

ian areas? b) How much area (acres) of each vegetation type is

located inside tbe riparian areas? c) How much area (acres) of each vegetation type is

located Qucside me riparian areas? d) How much amber volume is located in the ripar

ian areas?

Again, Ms Blaylock also wants you to develop a map that illustrates rhese stream buffers and rhat includes the roads. streams, and timber stands. In addition, she wants you to

develop ;:I memo that describes lhe differences berween me policy noted in Application 7.1 and the potential policy noted here.

7 .3. BaUot Initiative. A proposed ballot initiative, developed by a locaJ conservation group, suggests thar [he fo llowing riparian buffer widths may soon be required for the region within wh ich the Brown Tract is Si(U3red:

Large, fish-bearing streams 150 feet Medium. fish-bearing stteams 100 feet Small. fish-bearing streams 75 feet La rge, non-fIsh-bearing Streams 125 feet Medium, non-fIsh-bearing stteams 75 feet Small, non-fish-bearing streams 50 feet

As a result, Ms Blaylock wants to understand rhe following: a) Ifthese rules were applied to the Brown TraCt, how

much area (acres) would be located inside the riparian areas?

b) If rhese rules were applied to rhe Brown TraCt, how much area (acres) of each vegetation type would be located ins ide the riparian areas?

c) If rhese rules were applied to the Brown T raCI, how much area (acres) of each vegetation type would be located oU[side rhe riparian areas?

d) If these rules were applied to the Brown Tract, how much timber volume would be located in the riparian are-.lS?

As before, Ms Blaylock also wants you to develop a map that illus(rates these Stream buffers and that includes the


roads. streams, and timber stands. In addition. she wanrs you [0 develop a memo that describes rhe differenc.es

between the policies noted in Applications 7. 1, 7.2, and the potential pol icy noted here.

7.4. National Forest Riparian Policy. A local National Forest uses the following riparian area guidelines in conjunction with harvesting act ivi ties:

Large Streams 250 feet Med ium Streams 150 feer SmaU streams 100 feet

As wim the other policies, Ms Blaylock wants to understand the following:

a) I f these rules wefe applied (0 the Brown T ract, how much area (acres) would be located inside the riparian areas?

b) If mese rules were applied to the Brown Tract, how much area (acres) of each vegetation type would be located inside rhe riparian areas?

c) If these rules were applied to the Brown T ract. how much area (acres) of each vegetation type would be located outside [he riparian areas?

d) If ,.hese rules were applied to the Brown Tract, how much umber volume would be located in the riparian areas?

And again, Ms Blaylock also wants you to develop a map that illustrates these stream bu ffers and [hat inducles the roads, Streams, and timber stands. In addition, she \Yams you co develop a memo char describes [he differences between the policies noted in Applications 7.1, 7.2. 7.3, and the potential policy noted here.

7.5. Current Owl Buffer Policy. Suppose that the currem policy regarding owl nest locations is to maintain a IOO-acre no-harvest buffer around owl nesl site locations. Develop a memo an d a map for Ms Blaylock that describes rhe amount of land (acres) of me Brown Tract that would be covered by the single owl buffer pertaining to this property.

7.6. Protection of Research PlolS.Thousands of research plots have been established across North America to fac ilitate the estimation of the response of forests and wildlife [0 a variecy of silvicullUral uearmems. Unfortunately. harvesting operations are usually implemented independently of research programs. and tOO often the research plots are harvested because the loggers were unaware of


their locarion, and withom the researchers knowing that

the plots were in jeopardy of being destroyed. The resulting loss of the research plot investment (layout, tagging of trees, etc.) may be considerable, and the loss of the opportunity for one final measuremem may be equally as

important. In many organizations, a communication sys[em norifying researchers of potencial impacts ro research

plms has been developed. allowing researchers to either

measure (he plots onc last time, o r CO delineate the

research areas as off-limits to harvesting activities.

Assume that rhe permanenr inventory plots on the

Brown T faCt were designed [0 evaluate {he long-term

growth and yield of the forest properry. Assume further that these plms were designed {Q remain untouched, even

though rhe surrounding forest may be harvested (clearclH

or thinned) . Finally, assume that the plots need to be maintained with a sufficient buffer of trees around them

so that rhe effects (windthrow, increased sunlight, etc.) of

harvesting trees outside the plot (o n the plot's trees) are

min im ized . These plots are circular l/5-acre plots, and

References

Association for Geographic Information. (I999). GIS dic

tionary. Retrieved February 4, 2007, from http: //www.

agi.org.uklbfora /systems/xmlviewerldefau lt.asp 'a rg= DS_AGI_ TRAINART _701_firsttide.xsIl90.

Bettinger, P., Armlovich, D., & Kellogg, L.D. (1994). Evaluating area in logging trails with a geographic infor

mation system. Transactions of the A.s:.1E, 37(4) , 1327-30. Bren , L.J. (2003). A review of buffer strip design algo

rithms. In E.G. Mason and c.J. Perley (Eds .), Procudings of the 2003 ANZ/F Uoint Australia and

N ew Zealand Institute of Forestry) Conference (pp. 32~35) . Retrieved September 5, 2007, from http:// www.forestry.org.nz/articles/ conf2003/Bren. pdf.

Cubbage, F., & Raney, K. (1987). Counry logging and tree protection o rdinances in Georgi a. Sottthern Joumal of Applied Forestry, 11, 7~82.

Legislative Coun sel Office . (2006). Chapter £-9,

Environmental Protection Act. Charlottetown, PE:

Gove rnmem of Prince Edward Island . Retrieved

February 4, 2007, from http: //www.gov.pe.callaw/ statu tesl pd fl e-09 . pdf.

require an additional 100-foot buffer from the edge of the plot boundary to be considered 'protected'. Develop a memo for Becky Blaylock that describes how much land area would be off-limits from harvesting operations

as a resuh of protecting the research plots. Develop a

map of the permanent inventory plots and their buffers.

Include the roads. streams, and timber stands on the

map. In addition. for added protection of the investment in

research , a second 100-foot buffer (for a total of200 feet) could be delineated around the research plots. In this supplemental area we can also assume that the trees in the

buffer are designed to remain untouched, even though

the surro unding forest may be harvested (clearcut or

thinned) . Based on the timber volume contained in the

buffers associated with the first (I OO-foot buffer) and second (200-foot buffer) cases, and a price of$400 per MBF, what is the cost of each case, and therefore what is the

additional cost to require the added protection around

each plot'

Martus, CE., Haney, Jr. , H.L., & Siegel, W.C (1995) . Local forest regulatory ordinances: [rends in the east

ern United States. Journal of Forestry, 93(6), 27-3 1. Maryland Department of Natural Resources. (2005).

Sustainable forest management plan for Chesapeake

forests lands, Chapter 8, Wildlife habitat protection and

management. Annapolis, MD: Maryland D epartment

of Natural Resources, Forest Service. Retrieved

February 4, 2007, from http: //www.dnr.state.md.us/ forests/download/sCmgt_plan_chapters/chapter8cA. pdf.

McDonald, T .P., Carter, E.A., & Taylor, S.E. (2002). Using the global positioning system to map disturbance pa((erns of forest harvesting machinery. Canadian Journal of Forest Research, 32, 310-19.

Merriam-Webster. (2007). Merriam- Webster online

search. Retrieved February 4,2007, from http: //www. rn-w .coml cgi-bi nl dictionary.

Oregon Department of Forestry. (1994a). Oregon forest

practius rules and statutes. Salem. OR: Oregon

Department of Forestry.

141

Oregon Departmem of Forestry. (I994b.) Forest practi« water prouction ruks; Divisions 24 and 57. Salem, OR: Oregon Departmem of Foresrry.

Oregon State Legislatu re. (2005). Chapter 527-imect and disease control; form practices. Rerrieved February 5, 2007, from hrtp:llwww.leg.srare.or.us/ors/527 .hrml.

Chapter 7 Buffering landscape Features 131

Sociery of American Foresrers. (I983) . Terminology offorest scitnct technology practice and products. Be(hesda,

M D: Sociery of American Foresrers. Srar, J., & Esres, J. (1990). Geographical information sys

Urns: An introduction. Englewood Cliffs, NJ: Prenrice Hall.

142

Oregon Deparrmenr of Forestry. (l994 b.) Fomt practict water prouction roks; Divisions 24 and 57. Salem. OR: O regon Deparrmenr of Forestry.

Oregon State Legislarure. (2005). Chapur 527-iIlS«1 and diJ~as~ controL; forest practius. Retrieved February S, 2007. from htrp:llwww.leg.srate.or.us/ors/527.html.

Chapter 7 Buffering landscape Features 131

Sociery of American Foresters. (1983) . Terminology offorest scienct technology praChct and products. Berhesda, M D: Sociery of American Foresrers.

Star. J .. & Estes. J. (1990) . Geographical information systems: An introduction. Englewood Cl iffs. NJ: Prentice Hall.

Chapter 8

Combining and Splitting Landscape Features, and Merging GIS Databases

Objectives

The objecdves of this chapter are to provide readers with

an understanding of rhe opportunities related to, and

potential pitfalls associated with, using a GIS CO combine

and split landscape features . In addition. since merging

twO or more GIS databases rogcrher is similar to combin

ing landscape featu res. another objective is (Q describe rhe

pros and cons associated with this GIS process. After com

pletion of this chapter, readers should have the knowl

edge and ability to understand:

I . why. when . and how YOli might wane to combine

landscape feacures; 2. the reasons for splitting landscape feacures. and the sit

uations where th is process might be appropriate; and

3. why two or more GIS databases might be merged,

and what you would expect co find within a merged database.

In rhe previous chapte rs an emphasis was placed on

understanding how much of a resource (fo r example. the

length of road or an area of land) was located within a cer

tain geographic region. such as within a set of stream

buffers. Queries were used. along with clipping. erasing.

and buffering processes, to determine the size of the resources in question. In performing chese analyses, imer

est was placed only in the end result of the set of GIS

processes performed, a result that most likely included some very precise and accurate, yet perhaps unexpected,

landscape features . For example. as a result of performing

a clipping process, numerous spu rious polygons might

have been createdj polygons so small that it would seem

un reasonable (0 manage the land they represem in a COSt

effective manner (let alone find them on the ground).

In this chapter GIS processes a re imroduced to help

accomplish two goals: (1) clean up GIS databases and (2)

facilitate more efficiem spacial analysis processes. The GIS

processes emphasized relate to combining, spli rting, and

merging landscape features. In introducing these GIS

processes, examples ranging from facilitating wildlife

habitat analysis to estimating unrestricted (from a forest

managemem perspective) areas in a landscape are used to

help readers understand rhe usefulness of these procedures

within a natural resource managemem context.

Combining Landscape Features

Multiple landscape features within a single GIS database

can be combined to produce a single landscape feature.

The combine process generally begins by assessing what

the landscape features of interest have in common. To

make the management of GIS data more efficiem, similar

landscape features could be combined so chat a smaller

number of features are contained in a spatial database. These similarities could be associated with spatial posi

tion (fearures touch one another), or attribute values (they

have the same characterist ics-age, type, ere.). In Figure

8.1, for example, twO polygons are to be combined based on rheir current spatial position-they share an edge.

143


After the combine process is completed, we find that the edge that was shared was eliminated. because the line that

defined the shared edge was not needed to define the boundary of the new polygon. In a spatial database, the initial two polygons represented in Figure 8.1 would each be stored separately. In other words, twO polygons and twO polygon records would be represented. After combining the polygons, only one polygon record would be contained in the resulting database. Although the reducrion in database complexity is modest in this example, it may be substantial when hundreds or thousands of spatial features are involved. In addition, although this example featured twO adjoining polygons, the landscape feamres being combined may physically overlap, or may be physically separated by a gap. In the case of overlapping landscape feacuces, the overlap is eliminated when the polygons are combined through the creation of a single polygon represeming the overlap area in the oucpuc database. In the case of physically separated landscape features, the combined landscape feature is composed of two

or more distinct pieces. In some GIS software programs, a

combine process is known as a dissolve process.

During the management of GIS databases, or as a

result of an analytical need, it might be necessary to com-

a. Prior to combining the polygons

r Shared edge

Polygon 2

Polygon 1

b. AHer combining 100 polygons

I

,---------,I r Polygon 1

Figure 8.1 Combining twO polygons. by diminating a shared edge. to produce a single polygon.

bine landscape features. Landscape features should be of the same feature 'Ype to be combined. In general, polygons are combined with other polygons, and lines with other lines. The reasons for combining landscape features

ace numerous, but are generally based on [he fact mar it is

easier [Q manage a smaller database (one with fewer

records) than a larger one. In addition. spacial analysis and data storage considerations are typically more effi

cient with a smaller database. There are at least six reasons

why you might want (Q combine landscape feamces:

l. You may wish to eliminate unintended small land

scape features that were created through digitizing or some other GIS process (e.g., clipping, erasing) that affected the geometry of a line or polygon spatial database. Combining landscape features can then be effectively used to reduce [he number of feamces being managed and can correct unintended feam ces. For

example, after a GIS process, spurious polygons may be present. Spurious polygons are fractions of polygons broken or created as a result of a GIS process. and you

may wanr [0 combine them with other neighboring

polygons to reduce the number of polygons being managed.

2. Changes in organizational policies may suggest that

some spatial features need to be combined. For exam

ple. an organization may redefine the minimum map

ping unit (hat it manages. The minimum mapping

unit defines the smallest sized unit that should be present in certain types of GIS databases . A change in an

organizational policy, say to increase the size of the

minimum mapping unit, may require eliminating

some small polygons or lines. Making the minimum

mapping unit larger not only reduces the volume of data to be managed (a benefit), but also reduces the resolution of landscape features recognized (a COSt).

Making the minimum mapping unit smaller has the

inverse effecc: a higher spacial resolution is recognized,

and more data needs to be managed.

3 . The acquisition of GIS databases from mher sources

(other than developed internally within a natural resource management organization) may prompt the

use of a combine process. You may find within an

acquired GIS database, that the mapping unit stan

dards are inconsistent with the standards used by their

organizat io n. In addition, existing adminisrra d ve boundaries, be they socio-political (ownership) or natlIral (watershed areas), may nO[ be desired in a spatial

database. For example, a private company may acquire 144


a GIS database from the US Forest Service. The acquired database may include polygons smaller than what the private company typically manages, suggesr

ing that some landscape features need to be combined [Q adhere to the minimum mapping unie standard chat the company uses. A polygon layer (hat contains watershed boundaries at a sub-regional scale may provide unneeded detail if a regional watershed boundary wi ll suffice. Sub-regional watershed boundaries could be aggregated through a combine process.

4. Comb ining landscape fearures may be necessary because it simply makes sense from a management

perspeccive. For example. in a stands GIS database there may reside two polygons side-by-s ide that describe fo rested areas with trees of similar ages, similar structural conditions. similar sire classes. and similar growth rates. The field personnel responsible for managing these stands might also suggest (hat the two stands would be tceared with similar treatments, at about (he same time. and with similar equipmenc. Thus from a management perspective. combining the

two polygons into a single polygon within a GIS database might make sense.

5. Since landscape features can change in shape and characteristic over rime, managing these changes may sug-

Combining processes must be used in a thoughtful manner. Once landscape features are combined. the topology that describes the resulting landscape feature is altered in the Output database. Should a user decide ro delete the input databases (a common thought once a new database has been created) the orig inal descript ion of the landscape topology is lost. Given this risk, larger natural resource organizations have generally placed the decision to edit and manage GIS databases (where combining landscape features may be necessary) to the person who has been given 'ownership' of the GIS database. All decisions rega rding the development and maintenance of a given GIS database are then the responsibility of the database owner. Other individual users of the GIS database. however. can perfo rm GIS processes. such as combining features, on copies of the original GIS database. However, once performed by someone other than the owner of the

gesr using a combine process. For example. the condition of a road may change due co improvemen(S made (Q ir over rime. A rocked road may become a paved road, a native surface road may become a rocked road.

o r a road of any type may become a decommissioned (obliterated) road. If cond itions of landscape features change, it may seem reasonable to combine those landscape features with other adjacent landscape features of the same stature or condition. A similar example might involve stream network measurements such

as what occurs during a standard watershed analysis project. Field stream crews will visit selected streams

and segregate Stream networks into similar hydrologic or geomorphic categories. or categories related to the surrounding land cover. These groupings are stored in a spat ial database as separate line fearu res with associated dara records containing descriptive information. Some co mbi ning of streams in a GIS database might

be necessary to manage the stream network more efficien tly.

6. It may be appropriate to combine landscape features to facilita te a spatial analysis. For example. co delineate one category of the recreational opportunity spectrum,

we may need co identify the size (area) of contiguous timber stands where rhe average age of rrees is 50 years

database, the altered database is no longer considered the official database of the organization . For example,

John Goheen, a GIS analyst, may be the 'owner' of a roads GIS database within an o rganization. John may then give a copy of this database to Paul Chapman, a field forester. If Paul were to edit the database by combining or splitting roads, the copy of the GIS database that Paul uses will not be considered the official roads GIS database of the organization. even if the database

helps Paul make better management decisions. Can Paul simply provide the edited roads GIS database to John? Certainly. However, John will likely need to ensure that the changes Paul made conform to organizat ional standa rds related to data maintenance, and then subsequently verify that the database does not contain any errors. If John can do these things. the data edited by Paul can be incorporated into the original (and official) roads GIS database.

145

Chapter 8 Combining and Splitting landscape Features, and Merging GIS Databases 135

or greater. Using a query, we can identifY these stands,

but to determine how large the contiguous area might

be would requ ire (a) either combining the queried Stands, or (b) sum by hand the area of all adjacent polygo ns meeting the size requirement. The lance

technique may lead [Q error, rhus co mbining the queried polygons may be more appropriate. A combine or dissolve process (depending on the GIS software being used) would help faci litate this analysis.

Some balance must be struck between the appropriate number and size of landscape features being managed. and the amount of real-world general ization that occu rs when fewer features are used [0 represent a landscape. The decision to combine landscape fearu res should be made after a serious contemplation of these issues. The following three examples describe the end-result of combining landscape features.

Contiguous, similar landscape features

Suppose you queried the Brown Tract Stands GIS database for even-aged srands berween the ages of 40 and 45, using the fo llowing query:

(Land allocation = ' Even-Aged') and (Age ~ 40) and (Age S; 45)

From this query we find that several stands touch each other, or are contiguous (Figure 8.2). From a manage

mem perspective. you may decide to combine some of

c:::J Forest boondary c:::J Even-aged stands

between the ages r ___ ...::Of..:,4o:,;and 45

Figure 8.2 Stands on the Brown Tract that au even-aged. and between the ages of 40 and 45.

these stands together (such as those illustrated in Figure 8.3), co reduce the number of managemem units tracked

in a database. There is one imponam issue thar must be kept in mind before combining landscape features: you should make a note of the attributes of each landscape feature before combini ng them. The combine GIS process, depending on the GIS software program being used. will either (a) comain the a((ribute data related co one of the landscape featutes, (b) comain the attribute data related co the other landscape feature. (c) comain an average. o r some other stat istical summary. of numeric data associated with all combined features. or (d) not contain any anribure data of either landscape feature. Users

should take heed of the options available when running a combine process so that the intended output results. Additionally, in some GIS software programs there may be more than one process that effectively combines land

scape fearures, and the resulting combined landscape feature may contain either no attribute data, o r the attribute data of the firSt landscape feature selected for combining

Figure 8.3 Two similar-aged stands on the Brown T rac{ that share a common border. Both are even-aged. and between the ages of 40 and 45 .

146


TABLE 8.1 Results of combining two stands

Stand Acres Ag. Site

index Trees per

hectare Height

(m) Board feet per hectare

Both stands before using a 'combine features' process

First wmd ulut~d 75 7.5 3.0 44 100 250 23 15.325

&COlli stand u l«ud 88 9.9 4.0 45 11 7 492 28 39.388

Combined stand after using a 'combine features' process 0 0 0 0

Combined stand after using a 'union features' process 75 7.5 3.0 44

(Table 8. I). In either case. the attribute data of the combined landscape feature may need to be edited if the combined landscape feature will represent a weighted average of the conditions of (he original twO landscape featu res .

Multiple spatial representations within a single landscape feature or record

At first glance, you might expect that each landscape fearuee, such as a forest stand, would be considered a si ngle

record in a GIS database. However, this is not necessarily

true. Discontinuous landscape featu res can be combined

co produce a single landscape feature describing a portion of the landscape (Figure 8.4). Thus, another consideration in (he development and management of GIS data

bases is whether sparialJy disconrinuous landscape featu res should be represented with one database record or with multiple records. In the case of nand 283 on the Brown

Tract, for better or for worse, a single database record rep

resents two areas (regions) . Perhaps prior to the develop

ment of the rock pi t that now separates these two areas,

the stand was represented by a single contiguous polygon .

T he Brown Tract databases are, as mentioned earlier,

nOt perfect. However, they do allow an examination of

some rather typical problems users of GIS databases must consider, such as this one. There are three options related

to the GIS database management of stand 283:

1. Leave the stand as it is-represented by twO spatially

discontinuous regions, yet a single database record. This

may be consistent with the standards used by the managers of the Brown Tract. This option would require no additional effort co manage the GIS database.

0 0 0 0

100 250 23 15.625

2 . Split the stand into two separate parts, creating twO

separate stands. Separate database records wou ld then

represent each stand. The owner of the G IS database may decide that there is a sufficient amount of time

and budget to comb through the database, locate inconsiste ncies such as this, and correct them.

Spl itting stand 283 into twO separate stands might be considered a logical response; spli tting landscape fea

(Ures will be discussed in more detail shordy.

Figure 8.4 Two polygons (regions) represen le<l by a single da[abase record in the Brown Tract stands GIS database (stand 283).

147


3. Combine the small discontinuous piece of the stand into another adjacent stand. Combining the small portion of stand 283 to another scand thar is adjacent to

[he small pardon would require chat (he adjacent

stand have similar characteristics (age. volume. density, ere.) appropriate for the management of (he

potential combined area.

Overlapping polygons

Although it may not be your intent to create landscape fearures that overlap when developing or maintaining a GIS database, overlapping features may resulc as [he OU[

pm of a GIS process. There are numerous reasons why you may find overlapping features in a GIS databasei edit, buffer, and merge processes can all lead to the development of overlapping landscape feacures, especially with GIS software that does nOt enforce ropological rules. When editing polygon feacures within GIS databases, for instance. you can easily affect (he shape of polygons such that they either overlap or not touch at all (Figure 8.5). When editing GIS databases. it is wise co understand the process available within GIS sofrware programs to 'snap'

the verrices of one landscape feature to those of another. With the abiIicy [Q snap vertices together. you can edit the shape or position of polygons and allow a precise match of the boundary of one to that of another. The challenge for most GIS users is to remember to acriva.te the snapping ability. It also requires practice. once activated, to learn

Gap

Overlap

Figure 8.5 Ov~rlap and gap r~maining aft~r ~diting polygon boundarj~s.

how to use snapping tools correctly. Practicing on a test GIS layet before editing an actual database can help reduce snapping errors.

A discussion of buffering point, line, and polygon features was provided in chapter 7. The polygons that are created as a result of a buffer process can, perhaps. over

lap. Usually you have the choice, at the time of buffering, of maintaining the overlapping areas or directing the GIS software program to remove them (Figure 8.6) . In the case where the overlapping areas of the buffer remain,

these polygons can subsequently be combined, removing the overlap and reducing the number of polygons and database records that describe the buffer.

Merging polygon GIS databases, as will be described later in this chapter, can also result in overlapping polygons. When a merge process is used, the overlapping areas

among polygons and lines ate genetally not affected, meaning that no new nodes are created at the intersection of lines, and that overlapping polygon areas are not removed. Quite simply, when GIS databases are merged,

Overlapping areas of the buffer are removed

Overlapping areas of the txJffer remain

Figure 8.6 Th~ results of twO buff~ring operations, on~ wh~r~ th~ oY~rlapping ar~as of th~ buff~r around ~ach str~am ar~ r~moy~d, and th~ oth~r wh~r~ th~ ov~rlapping areas r~majn.

148


How would you know whether a polygon GIS database contains overlapping polygons' One method might be to examine very closely the boundaries of each polygon-a tedious process. Another method may be co compare the sum of the area of polygons in

the suspect GIS database with another polygon GIS database of the exact same spadal extent. For example.

if you were concerned about overlapping polygons in the Daniel Pickett forest stands GIS database, you could compare the sum of the area of the stands to [he

area of the boundary GIS database. The extent of the two databases should be exacdy the same: the stands

one set of landscape features is simply laid on top of another sec.

Splitting Landscape Features

The decision (0 use a splitting process general ly arises from some need {Q redefine the topology of spatial data. The word 'split' is defined as a process ro divide or sepa~ rate an item into pares or ponions (Merriam-Webster,

2007) . Within GIS, we use a splitting process to divide polygons and lines (but not points) into smaller pam or portions. Points do not describe areas; however, buffers around points can be spli t because they are polygons.

To illuscrate several reasons for using splining

processes. cons ider the sub-dividing of land ownersh ips

that regularly occurs in many rural and urban areas

throughout Nonh America. Many property owners sub

divide their lands in order to earn revenue or bequeath

property to land stewardship organizations or heirs . Land

ownership records and GIS database are kept by county. provincial. or metropolitan organizations and mUSt be

updated to accurately represent new parcels that result from subdivisions. Spliuing processes are used to separate

parcels from one another and lead to the creation of addi

tional polygons in a GIS property boundary database. As another example involving polygons. imagine a state or province where cleareut size limits are imposed. For

organizations that plan c1earcur activities in these regions. the forest management units represented in their stands

GIS database should probably be smaller in size than the maximum clearcut area allowed. Therefore. these poly-

GIS database should contain polygons that are a subdivision of the ownership defined by the ownership boundary. The stands GIS database has many polygo ns, and the boundary GIS database has only one polygon, yet the sum of the areas should match. You could also choose to combine all polygons within a copy of the stands layer. The area of the resulting combined stands database should also match. If the sum of the areas does not match. either one or more overlap

ping polygons exist in the stands GIS database, or some

gaps exISts between polygons in the stands GIS database.

gons should probably be broken down (split) into smaller management unitS to enable land managers ro plan har

vests mote appropriately (Figure 8.7). In terms of linear

3. l60-acre (64.8 ha) polygon and a stream that will be used to split it Into two smaller polygons

<r---------~- Node of line defining L the polygon

• ~ Stream line used to split ttle polygon / -

__ Polygon to be split

/

b. Resulting two smaller focest management untts after splitting ttle 160·acre (64.8 hal polygon

Polygon 1

Polygon 2

Figure 8.7 A 160-acre (64.8 ha) polygon split along a stream. forming two smaller forest mamlgement units.

149

Chapter 8 Combining and Splitting landscape Features. and Merging GIS Databases 139

fea[l1res, assume mar you are involved with [he planning and maimenance of a road system for a namral resource management organization . Over some period of time, the

scams of a porrion of a woods road may have changed. perhaps from a rocked surface [0 a paved surface (or alternatively, parr of the road was obliterated in a restoration

process). Within the roads GIS database. splitting the line that represents the road at the location of the stams change would seem appropriate, since each resulting piece of the road should be described by different attribute data. The same argument for a sp!iHing process can be used for stream data managemenr. In this case, assume a

recent stream survey identified some differences becween (he GIS data and the acruai stream system. Some stream reaches, fo r example. may have been found [Q be able [Q

support fish populations. yet the GIS data indicates otherwise. In these cases. the various lines that represent

Draw a polygon around one of the nl.lltiple

representations

Stands GIS database

Select the Ofiginal po/ygoo

Clip

Stands GIS database

Remove original polygon from

the stands GIS database

Copy into the stands GIS database the

two portions of the original stand

streams may need to be split to be[(et delineate the capac

ity to suppOrt (or not support) fish populations. The way you go abour spli tting polygons or lines (yet

nor points) varies according to the GIS software program being used. In some GIS software programs. splitting is as easy as drawing a line through a landscape fearure (Figure 8.7). This holds true for polygons or lines that are solid or continuous. However. where a landscape fearure is repre

sented by multiple regions (or objects). and a gap separates these regions (such as that illustrated in Figure 8.4). drawing a line through the gap. without touch ing either of the pieces of the landscape feature . may not result in (he fearure being spli t inro twO separate pieces. In these

cases. a more complex process using combining. clipping. editing. or pasting ptocesses may be more applicable (Figure 8.8). Alrernativeiy. some GIS software programs have processes for convening multipart features to single

Save the original polygon In a

GIS database

One portion of the original

polygon

Erase

portion of the original

polygon

Figure 8.8 A complex process that can be used to split a polygon, initially represented by twO

regions, into twO separate landscape features.

150


pieces. These types of automated processes make the task

of separadng non-adjacent pieces of spacial data more efficient.

Merging GIS Databases

Merging. for the purpose of managing GIS databases. is

defined as the process of combining multiple GIS data

bases into a single database. Therefore, when you use a

merge process, a new GIS darabase is created from a set

of two or more previously developed GIS databases. Point.

line. and polygon databases can be merged together. how

ever, (he database structure in the resulring GIS database

will be mixed. Generally. merged databases contain the same G IS database structure as [he inpU( databases. For

example. you might merge several polygon GIS databases

together. The resulting merged GIS database would then

include aU of the polygons from each of the original GIS databases. and could potentially contain polygons that

overlap. You must therefore be aware (hat a summary of

(he characteristics of the resulting merged GIS database

may result in an overestimate of areas or diS[ances (in (he

case of merged line databases) .

There are two main reasons why a merge process

might be used: (l) to create a template [0 faci litate a sub

sequent spacial analys is process, and (2) to facilitate map

ping processes. In the fi rst case, suppose that from a for

est management perspective yo u were interested in

defin ing those a reas that have no resrrict ions placed on

them either by regulatory or organizational policies. You might call these 'unrest ricted areas'. In rhese areas the full

range of silviculmral practices appropriate to the forest types and the soil and slope condicions can be considered .

T o define rhese unrestricted areas on a map, you might firsr anempt (Q define the restricted areas: those that are

within a cenain distance of the stream system (requiring

a buffer), near some endangered species habitat (requiring

a buffer), o r designated as resea rch areas (requir ing a

query o r buffer). These are the areas within which man

agement may be restricted to some extent. Sin ce the

intent is to delineate and summarize the characteristics of

the remaining areas within the landscape being managed,

the GIS databases that describe the stream buffers. endan

gered species habitat buffers. and the research areas (a ll

represented by polygon GIS databases) could be merged

into a single polygon GIS database. This merged GIS database can then be used in conjunction with an erasing process to delineate those areas on the landscape that are

outside of the land area that they cover. When we use the

merge process for this purpose, our concern is not placed

on the likely overlapping polygons in the resulring

merged GIS da tabase. bur on makin g the analyt ical

process more efficient. With a merged GIS database that

represents restricted areas, a single erasing process can be used to arrive at the unrestricted areas (full property -

restricted areas = unrestricted areas) . Without developing

the merged GIS database. three erasing processes would

have been needed: (I) fu ll property - stream buffers =

temporaty database I; (2) temporary database I - endan

gered species habitat = temporary database 2; (3) tem po

rary database 2 - research areas = unrestricted areas.

In the second case, where a merge process facilitates a

mapping process, ou r concern is not placed o n the likely

overlapping polygons in the resulting merged GIS data

base, but rather on the message that we communicate

with the primed map. Using the previa LIS example, the

message we want to communicate to the map customers

would be ' Here are the rest ricted and unrestricted areas.'

By merging several GIS databases together and creating a

single GIS d atabase of a common theme ('res tr icted

areas'), you could make the canograph ic process mo re efficient. For example, rather than needing to specify the

color scheme for the three GIS databases that represent

the restricted areas (stream buffers, endangered species

habitat buffers. and research areas), the color scheme of a

single GIS database (the merged database representing the

res tricted areas) needs o nly to be specified. In add ition,

many desktop GIS software programs now contain automated fu nctions to assist users in developing thei r maps.

W ithout merging the restr icted a reas inca a single GIS database, we may find several GIS da tabases listed in the

map's legend-each representing portions of the

restricted area. In this case, management of the map's leg

end may be required before we print it and present it to

o ur customers.

Determining how much land area is unrestricted

To demonstrate using a merge process, the following

exa mple will expand on the d iscussion of developing a

representation of unrestricted areas by applying the con

cept to the D aniel Pickett fo rest. Knowing what the operable 'decision space' is on the forest may be important

when decisions regarding harvest ing, herbicide opera

tions, etc., must be made. As with many of the tasks presented in this text, there are several pathways you can take

within GIS to complete an analysis of th is type. The focus 151


pieces. These rypes of automated processes make rhe [ask of separaring non-adjacent pieces of spada! data more efficient.

Merging GIS Databases

Merging. for rhe purpose of managing GIS databases. is defined as rhe process of combining multiple GIS databases inco a single database. Therefore, when you use a merge process, a new GIS database is created from a set of two or more previously developed GIS databases. Point. line. and polygon databases can be merged roge<her. how

ever, rhe database structure in rhe resulring GIS database will be mixed. Generally, merged databases contai n the same GIS database strucrure as rhe input databases. For example. you might merge several polygon GtS databases ,oge,her. The resulring me' ged GIS darabase wou ld then

include aU of the polygons from each of the origina l GIS databases. and could potentially contain polygons ,ha, overlap. You must therefore be aware thar a summary of rhe characreristics of the resul'ing merged GIS da",base may result in an overestimate of areas or distances (in rhe case of merged line da,abases).

There arc tWO main reasons why a merge process migh' be used: ( t ) to crea'e a ,empia,e to facili",re a subsequent spatial analys is process, and (2) to faci litate mapping processes. In the first case, suppose that from a for

est management perspective you were interested in defin ing those areas thar have no resrrictions placed on rhem either by regulalOry or organizalional policies. You might call these 'unrestricted areas', ]n these areas the full

range of silvicli lcural pract ices appropriate to the forest types and the soil and slope condirions can be considered. To define these unrestricted areas on a map, you might first attempt {Q define the restricted areas: those that are within a certain disrance of the stream system (requ iring a buffer) . near some endange,ed species habi,a, (,equiring a buffer), o r designated as research areas (requiring a query o r buffer). These are the areas within which manageme nl may be resrr icred to some extent. Since [he intent is (0 delineate and summarize [he characteristics of

the remaining areas within [he landscape being managed, rhe GIS darabases thar describe ,he srream buffers. endangered species habira, buffers. and ,he research areas (all represented by polygon GI databases) could be merged imo a single polygon GIS darabase. Th is merged GIS dalabase can [hen be used in conjunction with an erasing process [0 delineate those areas on rhe landscape that are ourside of the land area that rhey cover. When we use [he

merge process for u,is purpose, our concern is not placed on ,he likely overlapping polygons in lhe resul'ing merged GIS database. bur on making ,he analytical process more efficient. With a merged GIS database ,ha, represents restricted areas, a single erasing process can be lIsed to arrive at the unrestricted areas (full property -restricted areas ~ unrestricted areas) . Witham developing the merged GIS da",base. rh,ee erasing processes would have been needed: (1) full property - srream buffers =

,emporary da,abase I; (2) ,emporary da",base I - endangered species habirar = temporary da",base 2; (3) ,empe

rary dacabase 2 - research areas = unrestricred areas. In the second case, where a merge process faci litares a

mapping process, ou r concern is not placed o n the likely overlapping polygons in rhe resul'ing merged GIS darabase, but rather on rhe message that we communicate with the printed map. Using the previa liS example, the message we want to communicate to the map customers

wouJd be 'Here are the restricted and unrestricted areas.' By merging several GIS da,abases rogether and crea,ing a single GIS database of a common th eme (,restr icted areas'), you could make the carrographic process more efficient. For example, rather than need ing to specify rhe color scheme for rhe rhree GIS da,abases [hat ,epresent

the restricted areas (stream buffers, endangered species habi,a, buffers. and research areas). ,he color scheme of a si ngle GIS dalabase (rhe merged database representing ,he resrric,ed areas) needs only ro be specified. In addition.

many deskcop GIS software programs now contain automated fu ncrions co assist users in developing thei r maps.

Withour merging rhe restricted areas into a single GIS database, we may find severa) GIS databases listed in the map's legend-each representing portions of rhe

restricted area. In rhis case, management of rhe map's legend may be required before we prinr it and present il to our Cllstomers.

Determining how much land area is unrestricted

To demonstrate using a merge process, the following example will expand on the discussion of developing a representation of unrestricted areas by applying [he concep' ro ,he Daniel Pickert forest. Knowing wha, ,he operable 'decision space' is on the forest may be important when decisions regard in g harvesting, herbicide opera(ions, etc., must be made, As with many of the tasks presented in this text, there are severa) pathways you ca n rake wirhin GIS '0 comple,e an analysis of ,his type. The focus


Streams GIS

database

Roads GIS

database

Owl nest localion GIS

database

Buffer 100 feel

Buffer 100 feet

Buffer tOD~ feel

Merge

Buffered streams GIS

database

Buffered roads GIS database

Owl nest location GIS

database

Merged GIS dalabase

Stands GIS database

Erase Unrestricted

areas GIS database

Figur~ 8.9 A process that an ~ used to delineate unreuricted areas in a forested landscape.

here will be on developing several GIS databases that describe me types of restricted areas, then on merging them together as a single GIS database. The polygons contained in the restricted area GIS database can then be erased from (he boundary or stand GIS databases, leaving unrestricted areas as a resulr. The process can be described with a flow chart (Figure 8.9) to help visualize the steps necessary [0 complete the task.

In the delineation of unresrricccd areas of the Daniel Pickett forest, some criteria area needed to describe (he restricted areas:

• 30.48 m (J 00 feet) around all stroams, • 30.48 m (J 00 feet) around all paved roads, and • 304.79 m (J ,000 feet) around tho owl nest locations.

Using the process illustrated in Figure 8.9, you will find that 140.8 hectares (348 acres) of tho Danid Pickett are resrricted in some form or fashion (Figure 8.10), and

870.9 hectares (2, I 52 acres) are thus unrestricted. The unrestricted areas, given the crireria noced above, are open to (he full suite of management activities appropriate for

the forest types and landscape conditions of the Daniel Pickett forest.

D Unrestricted areas

D Restricted areas

Figure 8.10 A description of the restricted and unrestricted areas on the Daniel Pickett forest.

152


Summary

The need to combine and splir landscape fearu res and the need to merge GIS darabases together are influenced by the progression of GIS processes you choose in order ro address a management issue. If there is a need co eliminate small landscape features created by some o ther GIS process (e.g., clipping, erasing), combining rhese wirh

other landscape fearu ces would seem approp riate. If there is a need (Q combine multiple feacures within a single GIS darabase (regardless of rheir size) to fac ilirare fur

ther analysis. combining them would again seem prudent. If rhere is a need ro physically separare pieces of a

Applications

8.1. Characterizing unrestricted areas. The Region Manager associated with the Brown Tract, Becky Blaylock. is very interested in the managemenr oppOrtunities associated with this land, given its proximity ro an

area of suburban growth. Since she knows you know something about GIS, she has again come to you for some information. Specifically, she is interested in understanding the extent of the forest resources that are outside of

areas where fo rest management is restricted for one or more reasons (e ither by regulation or by an organizational policy). She defines rhe zones where fo rest management is

restricted as: • Areas wirhin 152.4 m (500 feer) of aurhor ized

trails;

• Areas wirh in 152.4 m (500 feer) of homes; • Areas wirhin 2.4 km (1 .5 miles) of owl nest loca

tions;

• Areas within the riparian zones: - 30.5 m (100 feer) around large fish-bear ing

streams, 2l.3 m (70 feer) around medium fish-bea ring streams, 15 .2 m (50 feer) around small fish-bearing streams,

- 21.3 m (70 feer) around large non-fIsh-bearing streams,

- 15.2 m (50 feer) aro und medium non-fishbearing streams, and 6.1 m (20 feer) around small non-fIsh-bearing streams; and

• Stands with the following land allocations: Meadow, Oak Woodland, Research, and Rock pir.

polygon or line, rhen a splirring process would be warranted. If there was a need ro combine features con

rained in separate GIS databases. a merge process would be appropriate. The decis ion to combine or spli t landscape feacuces. o r [0 merge GIS databases must not be made lighrly. While rhere may be a variery oflogical reasons for us ing these GIS processes, there may be anocher

reason (such as the contem of the resuhing anribuce table) that suggests an alternative process should be used. Documeming the workAow when using these processes is therefore important.

a) How much area ofland is umestricted? b) How much area of unrestricted land is included in

rhe following land allocarions'

a. Even-aged b. Shelrerwood c. Uneven-aged

c) Develop a map of rhe Brown T racr , illustraring rhe unrestricted areas. Include on the map the road

and stream systems.

8.2. GIS processing. How would you have add ressed

problem 8.1 if you incorporared a process rhar merged the GIS databases that represented unrestricted areas? How would you have addressed problem 8 .1 if you incor

porated a process that combined all landscape features representing unrestricted areas into a single polygon? Provide a Aow chart for each alternative process.

8.3. Combining landscape features . Your co-worker, Ka rl Douglas, has suggeSted during one of your

momhly inventory meetings that all management un its in the stands GIS database smaller than four hectares should be combined with another adjacenr managemenr unie. His argument is that [his will make the process of managing the forest more efficient. Besides the faCt thar

some small polygons may represent sign ifica nt landsca pe fearures (rock pits, wildlife habirar, erc.), and rhus should be disrincrly represenred in a GIS darabase, whar argument might you provide against this potential change in GIS database management policy. particularly from the GIS processing and inventory management perspectives?

153


References

Merriam-Websrer. (2007). Mtrriam-W,bsur on/in, search. Retrieved April 28, 2007, from http ://www. m-w .coml cgi-binl dic[ionary.

154

Chapter 9

Associating Spatial and Non-spatial Databases

Objectives

This chapter provides an inrroduction to techniques that

will allow you to associate fea mres in spatially- referenced

GIS databases with data from other sources (other types of

databases) which may not have an explicir spacial refer

ence. Once this chapter has been completed. readers

should have a firm underscanciing of:

l. how (Wo or more databases can be remporarily com

bined withom creating a new database. modifying a

database rabie, or modifying landscape feacuresi

2. what types of GIS processes are available when there is

a need to associate data from different sources;

3 . how non-spacial data can be associated with spatial databases. and how data from one spatial database can

be associated with data of another spatial database; and 4 . what it means to relate (link) two tables, and how this

process is different than joining darabases.

In the last few chapters a concentra rion was placed on taking (WO (or mo re) GIS databases and . with a

process such as erasi ng o r clipping, creating a third (new)

GIS database that featured differenr. o r modified, land

sca pe features. The GIS databases were, in essence, combined in a permanent fashio n in the new database. In th is

chaprer the databases will be combi ned in a way that is

temporary bur that st ill retains each database's o riginal structure (both the spatial structure and attribute table

structu re) . Si nce new landscape features are nOt being

created in th is process. the re is no need to create a new

GIS database; however, this is also possible and we will

discuss this process later in the chapter. To present addi

tional methods of combining databases, two processes of

associa tion will be introduced: the jo in and the relate

(link) processes. The first sect ion of this chapter examines joining data

in non-spacial databases with landscape features in spatial

d atabases. and desc ribes several of the common join

processes you might encounter. The relationship between

the non-spatial data and rhe landscape features is based on a common attribute value found in both da rabases. The

second section of the chapter examines joi ning landscape

features from [wo differen t GIS databases. and how the

relationship between the landscape features in each GIS

database is a fun ction of the location and type (poi nt.

line, or polygon) of the features on the landscape. The

third sectio n of the chapter discusses how you can make

the temporary joined associations among databases per

manenr. The final section of the chapter exam ines relar

ing {linking} features or data from one database to that of another. With a relate process. you can view one of the

related databases. selecr a landscape featu re. then view the

associated related data in the ocher database even rhough

the database will not appear to be physically associated.

Join processes do not follow this approach. but. instead, result in a single database that contains dara from borh

joined databases.

155

Joining Non-spatial Databases with GIS Databases When you wane [Q join twO databases together the objective is to associate both databases such chat a single data

base resul rs. Therefore, dara from one darabase (in rhis

case a non-spatial database) is transferred to rhe 3ncibucc

rable of rhe other darabase (rhe sparial darabase) . There

are several types of possible associarions when joining

non-spa rial darabases wirh spa rial GIS darabases. The [wo

most common join associations are one-co-one and one

to-many joins. A non-spatial database is one chat lacks associated landscape feacures and their geographic reference. Perhaps the simplest example is a text file chat you

can create in a word processor or text editor (Table 9. I). You mighr logically ask why, if a GIS darabase of land

scape feacures exists, would you store ocher data associated

with those features in a separate. non-spacial database? Perhaps there are instances where it is moce efficient for

analyses (or foresters. biologises. etc.) to develop data

(such as wildlife habirar su irabi liry scores) separare from

GIS, knowing mat a process exists [Q quickly associate the

dara developed back ro rhe appropriare landscape fearures.

Perhaps an analyst may wam a separate tabular da[3base

that they can then import into a statistical software pro

gram. In addition, there are other software packages. such

as hydrologic simularors, growrh and yield models , and

landscape analysis models. that can take GIS output.

process the output so that additional information is

added, and feed rhe new resulrs back imo a GIS. There are

other reasons as well, including the comfort some people

TABLE 9,1 A non.spatial database in AScn text file format illustrating comma-dellmited data

·S r.md ', 'HS12010', 'HSI 20 1;', 'HSI 2020'

1,0.2%, 0.312,0.325

2.0.4;8, 0.49; , 0.516

3,0.333, 0.36;, 0.372

4,0.87;, 0.88;, 0.889

;,0. 12;, 0.215,0.23;

6, 0.468, 0.476, 0.48;

7,0.906,0.908,0.9 11

8,0.648,0.74;,0.7;3

9, 0.378, 0.42;, 0.431

10,0.096,0.102,0.118


In performing join processes, three terms are impor

ram: rhe ,ollrce tabu, the targ't or tk,tination tabu, and rhe join it<m (or field) . The source rable comains

rhe dara rhar will be moved ro the rarger rable and

associated with some part icula r landscape fearu res

srored rhere. The rarger rable comains rhe landscape

fearures with which the source table's data will be

associated. After the join process is complete, the

source table's data will be transferred [Q the target

rable so rhar when you view rhe rarger rabie, all amib

ures from boch darabases should be presem. The join

item is the attribure or field that is common berween

the source and ta rget rabies , and is the irem thar

brings rhe [wo rabies rogether. If no common amib

ute ex.isrs berween the source and target tables. there

is no basis fo r a join. Some examples of common

attributes are stand numbers, road names, stream numbers, culvert numbers, and watershed names, yet

any amibure specified by a GIS analysr can be used.

have with performing calculations in a spreadsheer rather

than in GIS. As you will see with one-to-many joins. join

ing is an efficient way of associating (temporarily) non

spa rial dara wirh landscape feawres.

One-to-one join processes

A one-to-one join process assumes mat mere is exactly the same number of records in the source cable as there is in

the target table, and that each of the records in the source

table is associated with exactly one record in me target

rable. For example, suppose you have a G IS darabase of

permanent growth and yield measurement plots (Figure

9.1), and you want to join to this database to a file con

taining the installation dates of each plot. The join item in

this simple example is obviously 'Plot' in the source table

and the ' Plot' attribute in the target table. A5 you can see,

the re are exacdy six records in both the source and target

tables. and each installation date record from the source table is associated with only one unique record in the GIS

database containing the permanem plo[s.

The original assumption behind one-to-one join processes can be relaxed. and [he one- to-one join process can also be made with fewer records in the source [able

[han in the target [able. For example, assume thar record

156


Database

Comma-delimited text file containing plot number and Installation date

GIS Database representing permanent plots

Resulting database: The original permanent plot GIS database with the temporary field Mlnstallation date"

Table structure

Plot 1

2

3

4

5

6

Soon;e

r-Plot , Installation dale 1 1998

t--- 2. 1997 3, 1999 4, 1998 5,2000 6, 1999

Target (Destination)

Plol Vegetation Type L...-. 1 OF

2 WH 3 OF 4 OF

5 WH 6 OF

Joined database

Vegetation Type Installation date

OF 1998 WH 1997 OF 1999

OF 1998 WH 2000 OF 1999

Figure 9.1 Performing a one-to-one join usi ng a fi le of installation dates as the source table, and the Danid Pickett permanent piau GIS database as the target table.

6 (Plor 6, insrallarion year 1999) was missing from the source rable. If YOli were to perform a join process using the source and target rabies. a one-to-one join would still occur, yet Plot 6 in the GIS database would not be joined with any data from the sou tee table (Figure 9.2).

To work through a one~to~one join. assume you have an ASCII text file (HSl.rxt) containing habitat suitability ind ices (HSls) for salamanders related to every dmber stand on the Daniel Pickett forest. HSls range from 0.0 (poor habitat) to 1.0 (optimal habitat) and can be quite complex ro calculate; therefore. it is nO[ unreasonable ro assume that they were generared outside of GIS. perhaps in a sp readsheet or stacisdcal sofrware package. The challenge. once HSI values have been calculated. is [0 bring them back into a GIS environment [0 allow the crearion of a thematic map. To perform a join process of the nonspatial HSI database (HSl.rxt) and a GIS database (Daniel Pickett stands) the following general steps can be taken when using ArcG IS 9.x:

Database

Comma·delimited text file containing plot number and installation date

GIS Oatabase representing permanent plots

Resulting database: The original permanent plOt GIS database with the terTij)Orary field -Installation date-

Table structure

Plol 1

2

3

4

5

6

Soon;e

Plot, Installation date 998 .---- 1, 1

2,1997 3,1999 4, 1998 5, 2000


Plol Vegetation Type L..,. 1 OF

2 WH 3 OF

4 OF

5 WH 6 OF

Joined database

Vegetation Type Installation dale

OF 1998

WH 1997

OF 1999

OF 1998

WH 2000

OF

Figure 9.2 Performing a one-to-one join with one r«ord missing from the source table.

I. Open the Daniel Pickett stands GIS database (this is the target table).

2. In the table of contentS, right-click the target table (stands GIS database). seiect ' joins and relates' , then seiect [he join option.

3. IdentifY the join item from the target table in option 1. 4. Choose the source table (HSl.rxt) for option 2. 5. IdentifY the join item from the source table in option 3. G. Perform the join process (press OK).

In ArcYiew 3.x these general steps can be taken to per~ form the join:

I. Open the Daniel Pickett stands GIS database. 2. Open the Daniel Pickett stands attribute table (this is

the target table) . 3. Open the HSl.rxt database (th is is the source table) . 4. I n the source table. use the mouse to seieer the join

item (stand).

157

5. In the target table, use the mouse [Q select the join item (stand) .

6. Click the table join button.

Once the one-to-one join process is complete, the HSI values from the HSI.txt database should be temporarily

srored inside of the stands GIS database attribute table. To confirm this, you muSt open and visuaUy examine the stands GIS database attribute table. A thematic map can

be created (Figure 9.3) using the HSI values, to illustrate the spatial arrangement of habitat on the Daniel Pickett forest. Areas with in each habitat grouping can also be calculated to enable the development of a report concerning the amount of suitable habitat on the forest for a particular wi ldli fe species.

One-to-many joins

In COntrast to one-to-one join processes, the assumption behind one-to-many join processes is that there are more records in the target table than in me source table, and that each record in the source table may be associated with more than one record in the target table. Assume that you have a streams GIS database that contains 1,000 stream

reaches. Assume also that you desi re to buffer the Streams to create a map of diffe rent riparian management zone

policies, and that each stream needs to be buffered a disrance that is based on its size. If you were interested in developing variable-width buffers <as illustrated in chapter 7) for each policy, the appropriate buffer width for each stream must be associated with each stream reach. Manually updating the attributes of each of the 1,000 streams for each riparian policy would be time consuming

and would likely contain errors. W ith a one-to-many join

Habitat SUitability Index (HSI) value

CJ 0.000-0.200

CJ 0.201- 0.400

CJ 0.401-0.600

c:::::El!I 0.601- 0.800

_ 0.80H.000

Fi.gure 9.3 Habitat Suitability Index (HSI) valu~s for salamand~rs on the Daniel Pick~tt forest.

Chapter 9 AssoCiating Spatial and Non-spatial Databases t47

process, the likelihood of errors is minimized, and the

process of associaring different buffer width data with each stream is fitst and efficient. For example, Figure 9.4 illus

trates a one-to-many join process where the source table has four records and the targer table has seven records. The join item is 'Stream type' in the source table, and the

'Type' attribure in the target table. Two of the source table records (,Perennial - large' and ' lnterminenr') are associ

ated with more than one record in the ta rget table (hence 'many' records). The source table could have included four

(or more, or less) items and me target table could have included 1,000 or more items-it makes no difference to

the one-to-many join process. However. similar to me oneto-one join processes, should a record be omitted from the source table, the affecred records in the target table would

be represented by null or missing values in the joined field .

Many-to-one (or many-to-many) joins

In certain cases, the source table can contain errors and

lead to results that are different than what was originally

Database

Comma·delimited texl file containing stream type and buffer distance


Resulting database: The original streams GIS database with the temporary field "Buffer"

Table structure

Stream Type, Buffer · Perennlal-large", loo -Perennial-small". 75 "Intermittent", 50 "Ephemeral ",25


Stream Type 1 Perennial-large

2 Intermittent 3

4 Perennial-large 5 Intermittent

6 Ephemeral

7 Intermittent

Joined database

Stream Type

1 Perennial large

2 Intermittent 3 Perennial small

• Perennial large

5 Intermittent 6 Ephemeral

7 Intermittent

Buffer

100

50

75 100

50 25 50

Figure 9.4 P~rformjng a one-to-many join wing a fiI~ of buff~r distances as th~ source tabl~, and streams GIS databas~ as th~ targ~t tabl~.

158


intended. These are cases where twO o r more records in

the source table (and one or more instances of such) are associated with a single record in rhe target rable. These can result in a many-tO-one join. For example. the manyto-one join process illustrated in Figure 9.5 shows that rhe

fourth and fifth records of the source cable have exacuy the

same the join item value (,Stream type' = 'Ephemeral') . When rhe join process is performed only one 'Buffer' value from the cwo source table records can be associated

with the ephemeral stream in the target table (the first value in ArcGlS 9.x, the last value in ArcView 3.x). The example in Figure 9.5 shows that the first value (25) was present in the joined database, not the last value (35). Which value is presem in the target table (the first instance or subsequent instances in rhe source table) depends on

the GIS software program being used. Many-to-many join processes behave in a similar fashion (Figure 9.6).

The following two examples bring together some concepts that wefe introduced in (his and earl ier chapters. In each example. a non-spatial database is joined co a GIS

Database

Comma-delimited text file containing stream type and buller distance


Resulting database: The original streams GIS database with the temporary field "Buffer"

Table structure

Sou",.

Stream Type, Buller "Perennial-large", l00 "Perennial-small ", 75 "Intermittent", 50

"Ephemeral ",25 -====::::::::"Ephemeral ", 35 -


Stream Type 1 Perennial-large

2 Intermiltent

3 Perennial-small

4 Perennial-large

5 Intermittent

6 Ephemeral 7 Intermittent

Joined database

Stream Tyll" Buffer 1 Perennial large 100 2 Intermittent 50 3 Perennial .maD 75 4 Perennial large 100 5 Intermittent 50 6 Ephemeral 25 7 Intermittent 50

Figure 9.S Performing a ma ny-to-one join using a file of buffer distances as the source table, and a streams GIS database as the target table.

Oatabase

Comma-delimited text file containing stream type and buffer distance

GIS Database representing permanent plots

Resulting database: The original streams GIS database with the temporary field "Buller"

Table structure

Soun:.

Stream Type, Buller "Perennial-large", l00 "Pereooial-small ", 75 "Intermittent",50 "Ephemeral", 25 "Perennial- large", 125


Stream Type

Perennial-large

2 Intermittent 3 Perennial-small

4 Perennial-large

5 Intermittent

6 Ephemeral

7 Intermittent

Joined database

Stream Tyll" 1 Perennial large

2 Intennlttent

3 Perennial small

4 Perennial large

5 Intermittent

6 Ephemeral

7 Intermittent

Butler

125

50 75

125 50

35 50

Figure 9.6 Performing a many- to-many join using a Ale ofbufTer distances as me source table and a streams GIS database as the target table.

database. Then, a spatial query is performed to determine which of the point fearures is contained within an area

represented by a polygon feature. Ultimately, some informarion that was originally contained in [he non-spada I database is summa ri zed based o n its assoc iated spatial locat ion.

Example 1: Determining the number of hardwood sawmills in a state Assume (hat you currently work for Dunn and Herndon, Inc. in centra] Tennessee, and are considering building a new hardwood sawmill somewhere in (he state. Initia lly,

what you might find helpful is an est imate of the number of hardwood sawmills in the state of Tennessee. Another piece of information that would be valuable is an estimate of the number of people that they employ. To develop rhis information we will use a CIS database of the southeastern US states, a GIS database of mill locations, and a non-spa rial database of mill attributes. Mill locat ions and

their associated non-spatial attributes were acquired from 159

rhe USDA Foresr Service (2006). The process includes rhe fo llowing sreps:

1. Join the non-spatial mill attribute data to the GIS database of southeastern US mills. The non-sparial database comains a field called 'MILL_lD'. This is rhe join item from rhe sou rce rable. The mill GIS database contains a field called 'MILL-lD' . This is the join irem from rhe rarger table. Once the two tables are joined, the spatial features (poims) in the mills GIS database will have associated wirh them rhe attributes of mills from [he non-spatial database.

2. Query rhe southeasrern US srates GIS database for the scate of Tennessee. This is a query that simply uses a field (srate) of the southeastern US "ares GIS darabase to locate the appropriate actcibuce of the scares (State =

Tennessee). 3. Perform a spatial query of rhe mills GIS database to

determine (by location) which mills are completely contained within the Scate of Tennessee (the current

selected feature in the southeastern US states GIS dacabase).

Once these steps have been performed, we will have selected those mills mac are within the Scare of Tennessee. The question now is whether they mainly accept hard

wood [fee species, and whether they are considered a sawmill. A final query using the attribures of the mills GIS database is therefore needed [0 seiect from (he currently

selected features (all mills in Tennessee) those that are sawmills. and mainly accept hardwood reee species

. . . . . . , ' .

. . ' . . '


KY

..

(Figure 9.7) . As a result, you might find that there are 329 hardwood sawm ills in the State of Tennessee that employ 3,959 people, assuming that the data are current.

Example 2: Determining sawmill employment in a county As a second, similar example. assume mat you are a con

sultant based in Mississippi. working for Saunders

Geomacics, LLC. Assume also that you are doing some

CQncract work for the Nuxubee County Chamber of

Commerce. They want (0 know how many sawmills are

in the county, and chey want an estimate of how many

people rhese mills employ. To develop rhis information we will use a GIS database of me southeastern US counfies, a GIS database of mill locations, and a non-spacial

database of mill attributes. Once again. milliocacions and

thei r associated non-spacial attributes were acqui red from

the USDA Forest Service (2006). The process includes the following steps:

1. Join rhe non-spatial mill attribure data (0 the G IS database of southeastern US mills. as was described in

example 1 above. 2. Query the southeastern US counties GIS database for

Nuxubee County, Mississippi . This is a query that

simply uses a field (county) of the sourheastern US counties C IS database [0 locate me appropriate attrib

ute of rhe counties (County = Nuxubee). You need to be careful here if there are muldple counties in the

southeastern scares with me same name {e.g .. 'Floyd' is

(he name of a county in more than one s[a[e).

.: .. . .

". :

MS Al

Figun 9.7 Hardwood sawmills in Tennessee.

160


3. Perform a spatial query of the mills GIS database [Q

determine (by location) which mills are completely contained within Nuxubee County (the current

selected feature in me southeastern US coumies GIS database).

Once these steps have been performed, we will have selected those mills that are within (he county. The question now is whether they are sawmills. An examination of the mills table finds that there are three sawmills in the

county (although only two are seemingly visible on the map), and that they employ 23 I people, assum ing that the database is currene.

Joining Two Spatial GIS Databases

With a spatial join process. our intent is to learn abour

the qualities of some landscape features that are near other landscape features of imeresr. In joining non-spacial databases [Q spatial databases, a common field (the join item) was used [Q associate the data in the non-spatial database [Q the landscape features in the GIS database. The associ

ation itself was non-spatial-the location of landscape features in the GIS database (the target table) was not used to assist in making the association. The spatia!locarion of landscape fearures can, however, be used (Q associate land

scape fearures from one database with those in another database. This is a powerful feature that often goes unused by GIS users. Both databases, however, mUSt contain spatial data. For example, if you were interested in knowing the type of forest stands that sources of water (ponds, springs, etc.) fall within, you might join a GIS database containing a set of points (representing water sources) with a GIS database containing a set of polygons (t imber stands) to understand the type of forest that surrounds each water source. Thus the attribute data within the timber stand database can be associated to the arr ribute data within the water sources database based on the

spatial location of the landscape features within each GIS database.

In some softwa re packages. such as ArcelS 9.x, a spatial join results in the creation of a new layer that contains the joined info rmation . Check you r GIS software to determine whether new databases 3re created during spatial joins. You should also be aware that spatial joins will usually require that a map projection be associated w ith all involved layers. A key component of every map projection is the definition of a map unit that

describes the coordina te division intervals. typically expressed in feet, international feet, or meters. Spatial joins are based on the comparison of feature (point. line, or polygon) locations to other feature locations. There are many GIS processes that inherently recognize the coordinate system of a layer without needing to know the real-world definition of the map unit. Without the map unir definition, however, sparial joins will usually not be possible.

As with non-spatial join processes, a source database

and target database are required, yet the join item will be slightly different-it is the spatial position of each landscape feature in each GIS database. The [wo most common types of spatial join processes are those that are defined by which features are closest (sometimes called

the nearest neighbor) and those that are evaluated by whether a feature is intersecred by another feature or is completely inside another feature. A third type of spatial join involves linear features and is evaluated by determining whether one linear feature is located along or inside the extent of another linear feature . The outpur

properties and options of the spatial join process will depend upon the feature type (point, line, or polygon) of the source and target databases and are summarized

in Table 9.2 . The nearest neighbor spatial join works for almost all

feature type comparisons except for line on line. Other than this exception, the nearest neighbor join process allows the characteristics of features (points, lines. or polygons) in a source table to be associated with the closest feature in a destination table. Thus you can use spatial join processes to not only identify the nearest point (e.g. tree), line (e.g. road) or polygon (e.g. watershed) feature from all point (e.g., house) features in a GIS database, but you can also determine the distance to the neareSt point

feature . Typ ically. the distance [Q the nearest feature and the anribures of the nearest feature are included in the

Output database. A point-in-polygon or ' inside of join process is rather

straightforward: determine the polygon (from a source table) within which each point (in a destination table) is located, then join the attributes of each polygon with each associated point. For example, you may be interested in understanding the characteristics of t imber stands containing the owl nest locations that exist on the Daniel Pickert forest (Figure 9.8). In the Daniel Pickett owl GIS database there are twO owl nest location points. Associated wirh each point is information, based on owl surveys. regarding the number of adult and juvenile owls

161

150 Part 2 Applying GIS to Nalural Resource Management

3. Perform a spacial query of [he mills GIS dalabase to

de<ermine (by locarion) which mills are completely contained within Nuxubee County (the current selecred feature in the SQUmeasrern US counties GIS database).

Once these Steps have been performed, we will have selected those mills that are within rhe county. The question now is whether they are sawmills. An examination of the mills table finds that there are three sawmills in [he county (al[hough only two are seemingly visible on the map), and that they employ 231 people, assuming that the database is currenL

Joining Two Spatial GIS Databases

With a spatial join process. our imenr is to learn aboul

the qualities of some landscape features that are near other landscape featu res of interest. In joining non-spadaJ databases ro spat ial da[abases, a common field ([he join item) was used [Q associate the data in the non-spatial database to [he landscape features in rhe GIS database. The associalion itself was non-spatia I-the location of landscape fearures in [he GIS database (the target rable) was not used fa ass ist in making the association . The spadallocation of landscape features can, however. be used (Q associate landscape features from one database with those in another database. This is a powerful feam re that often goes unused by GIS users. Both databases, however. must concain spatial data. For example. if you were interested in knowing the rype of torest stands that sources of water (ponds, springs, etc.) fan within, you might join a GIS database containing a set of points (representing water sources) with a GIS database containing a set of polygons (dm ber stands) [0 understand the type of forest that surrounds each water source. Thus rhe anribute data within the timber stand database can be assoc iated to the arnibme dara wirhin the water sources database based on the

sparial locarion of the landscape fea rures within each GIS darabase.

In so me softwa re packages. such as ArcGIS 9.x, a

spatial join results in the creation of a new layer that contains the joined info rmation . C heck you r GIS softwa re CO determine whether new databases are created during spatia l joins. You should a150 be aware that spatial joins will usually requi re that a map projection be associated with all involved layers . A key component of every map projection is the definition of a map unit that

describes the coord inate division inrerva'!s, typically expressed in feer. international fec t, or meters. Spatia l joins are based on the comparison of feamre (poin t, line, or polygon) locadons to other feamre locarions. There are many GIS processes thar inherently recognize the coordinate system of a layer without needing to know [he real-world definition of the map un it. Without the map unit definitio n. however. spatial joins will usually nOI be possible.

As with non-spatial jo in processes. a source database

and rarget database are required. yet the join item wi ll be sligh tly differem-it is the spatial position of each landscape feature in each GIS database. The twO mosr com mon rypes of spat ial join processes are those fhar are defined by which features are closest (sometimes called the neareSt neighbor) and [hose thar are evaluated by whether a feature is intersected by another feature or is completely inside anolher feature . A th ird type of spatial join involves linear features and is evaluated by determining whether one linear feawre is located along or inside the extent of another linear feature. The outpur

properties and options of the spatial join process will depend upon Ihe fearure type (poim, line, or polygon) of the source and target databases and are summarized

in Table 9.2 . The nearesr neighbor spatial join works fo r almost all

feamre type comparisons excepr for line on line. Other

than this except ion. the nearest neighbor join process allows the characrerisrics of features (poinrs. lines, or polygons) in a source table to be associated with the closest feature in a destination table. Thus you can usc spatial join processes ro not only idemifY rhe ne-dtest point (e.g. uee), line (e.g. road) or polygon (e.g. warershed) fearure from all point (e.g., house) features in a GIS database, but you can also determ ine [he distance to the neareSt point

feature. Typ ical ly. the distance to the nearest feature and the anribmes of me nearest feacure are included in the

Output database. A point-in-polygon or 'inside or join process is rather

straightforward: determ ine the polygon (from a source rable) wirhin which each point (in a desrinarion table) is localed, then join the attributes of each polygon with each associated paine For example. you may be interested in understanding rhe characterist ics of (imber sta nds containing [he owl nest locations mat exist on [he Daniel Pickert forest (Figure 9.8). In the Daniel Pickerr owl GIS database [here are twO owl nest location points. Associated with each po int is information, based on owl surveys, regarding the number of adult and juvenile owls


TABLE 9_2 Spatial join options by target and source feature type (Italics indicate output products for each option)

Target

Points

Lilla

Polygons

Owl Point #1

•

•

Stand #25

Poinu

I. Nearest points

Artribuu summary H ow ma,,} a,~ IUllrrSI

2. Nearest point

Attributn &diJlllllU

1. Nearest points o r points

intersected

Attribu/~ summal] Attrihum &-dimmu

2. Nearest poi n t

A ttributn &distallu

I . Poin ts that rail inside

Attrihutr f ummfl,]

How mall) are imitk

How many imm«r

2. Nearest point AllTibutn d- distanu

Owl Poinl#2 • •

Stand #29

Figure 9.8 Associating owl nest locations with the: timber stands withi n which they are located.

1. Intersecting li nes Attrihuu summary How mall) are IU(lreSl

2. Nearest line AttribuUl &diltflllU

I . In tersecting lines

Attribute ru",mary

2. W ithin o ther lines

AltrihuiN

I. Imersecring lines Artribuu sum",ary

2. Nearest line

A ttribum ¢ diuau«

Polygons

I. Falls within AttrihuUl

2. Nearest polygon AttribuU! 6-diJtllfirt

I. In tersects polygons Aurihult' summary How N/ally II"

lIea"st

2. NarcS[ polygon or intersecting polygon Attribuus d- diSlI.m"

I. ImersectS polygons

Attn'buu summary H()w mall] imfl'S«t

2. Compk[dy inside Auribum

fou nd there (Figure 9.9), along with the first and last sighting of the owls.

What is not known by simply viewing the owl GIS dacabase are the characteristics of the forest surrounding each owl nest. With just two points. this can be determined by visual inspection: owl poim 1 is located within stand 25, a rather densely stocked l O-year old stand; owl point 2 is located within stand 29, a 5D-year old stand of trees. However, when a large number of points are presem in me destination table, or the source table comains a number of relatively small features (making visual inspection difficult)' an automated process may be preferred. such as the general process noted below chat can be used in ArcGIS 9.x:

1. Open the Daniel Pickett owl GIS database (target table) and stands GIS database (source table).

2. In the table of contents, right-click the target table (stands GIS database), and seleCt joins.

3. Make sure the first option in the dialog box that opens is set to 'Join data from another layer based on spatial location' (the default setting is 'Join attributes from a table)_

4. Select the stands GIS database as the layer to join to

this layer (use ArcCatalog to add spatial reference information if necessary)_ The Join Data dialog box

162

152 Part 2 Applying GIS 10 Natural Resource Management

Database Table structure

Sourr:e

GIS database representing timber stands

25

: 29 I

A I

A I

. . 260 I 70 I 37.7

: : 200 I 50 I 21.1 "


GIS Database representing owl locations

Point

1

2

Ad"1s

2

1

Fledglings Firstsight Lastslght

1 19950618 20070821

0 19980623 20070901 !-

Joined database

Point

1

2

Resulting database: the owl pOints database with the appropriate stand conditions that StXround eacfl point

Adults Fledglings Firstslght

2 1 19950618

1 0 19980623

laslsight SIan<1 Veo_type Basal_area Age Mbl

20070821 25 A 260 70 37.7

20070901 29 A 200 50 21.1

Figure 9.9 Spatially joining the Danid Pickett stanch GIS database with the owl GIS database.

will update to show you the types of feature classes that you are joining, in this case Polygons CO Points.

5. Use the radio or option burron and choose that you

want each point to have the 3([cibures of the polygons [hat 'it fulls inside' ramer (han 'is closest [0 it'.

6. Specify an outpUC location and name for the resulcing joined database.

7. Choose OK to initiate the spatial join.

In ArcView 3.x these general steps can be taken {Q perform the join:

1. Open the Dan iel Pickett owl GIS database (targer table) and stands GIS dat.abase (source table).

2. Open the amibute tables of both GIS databases. 3 . Clear all selecced landscape features in both amibute

tables (to ensure no landscape fearures are selected). 4. With the mouse, click on the join item in the source

table (e.g .• the 'shape' field in ArcYiew) . 5. With the mouse, click on the join item in the target

table (e.g .• the 'shape' field in ArcYiew) . 6. Click the join table bunon to initiate the spatial join

process.

While this example is relatively straightforward, imagine a case where you have several hundred poines. such as research plots. and the goal is to quickly and accurately determine what rype of stand each research plot is located within. A poine-in-polygon join process would seem to be a logical and efficient option to accomplish this goal, and would likely result in fewer errors than a manually driven process.

The spatial join process can also be used to identify the number of features within another point. line. or polygon database that are closest to features within a target database. For example. suppose you had four possible ttail· heads withi n a watershed where you co uld park YOUT

vehicle in order to visit six rain gauges from which YOli

need {Q collect precipitation measurements. Using the trailhead locations (poi nts) as rhe target database. you could spatially join the six rain gauge locations (points) and determine wh ich of the nailheads was closest to the largest number of the six gauges. This would help you at least (Q pick a trai lhead locacion in which to begin your sampling. Another example might include examining a number of watersheds (polygons) and wanting to deter-

163

mine which was the most densely forested according to

(he number of uees, assuming that you had coordinates (points) of all trees throughout your watersheds. Using the watersheds as your target layer, you could spatially join the trees layer and return the number of trees, complete with an aruibute summary (average tree height for

example) for each watershed.

Making Joined Data a Permanent Part of the Target (Destination) Table

After a join process has been completed, you might decide that the joined data should permanently reside within the target table. One strategy to accomplish this goal would be to add the appropriate number of empty fields to the target table that will ultimately contain the joined data, and declare that the data type of the empty fields be the same as the data type of the joined data. The va lues of the empty fields can be calculated to equal the values of the joined data, thus filling the empty (yet permanent) fields with the joined (yet temporary) data. Saving the target table at this point results in a permanent change to the database, with the data from the source table now a per

manent pan of the target table. In addirion, removing or changing the source table will not result in a corresponding change to data in the new fields in the target table.

Another suategy may be to perform a join process, and tI,en save (or export) the spatial GIS database that represents the target table to a new database. In some GIS soft

ware programs the newly copied and saved GIS database will contain all previously joined data as a permanent pan of the GIS database rather than as a temporary association. Finally, exporting the target table (the tabular portion of the GIS database) after a join process was performed wi ll generally create a new fil e that includes all data. Unfortunately. this process does not preserve the landscape features of the target table, only the underlying attribute data. An exported target table (after join processes have been performed) may rn.cilitate some natural resource management processes, such as report generation, metadata creation, or other subsequent non-spatial analyses.

Linking or Relating Tables

On occasion, you may want to simply link or relate twO

GIS databases together, allowing you to view both the source and target tables as separate ent ities, and to view landscape features that are associated with each other in

Chapter 9 Associating Spatial and Non-spatial Oatabases 153

both databases. With this process, the goal is to have the abi lity to display twO GIS databases, and to be able to select a landscape feature from one and view the associated landscape feature{s) in the other. For example, assume you have a source table represented by a GIS database that contains multiple records related to landscape featu res in another GIS database (the target table). In Figure 9.10, the tables of twO GIS database tables are illustrated; one represents a road system GIS database and the other represents a culvert GIS database. As you may notice, there are multiple culvertS associated with each road (e.g., culvens 4 and 5 both are associated with road 602) . Joining these twO databases together would result in a manY-CQ-one join process or o ne-to-many join process, depending on what is chosen as the source and target tables. With a manY-(Q-one join process (culverts as

source, roads as targetL some of the records in the source table will not be present in the target table after the join process has been completed . With a one-to-many join process, (roads as source, culvens as target) you can identify which road is associated with each culvert, however, you will not be able to seiect a culvert and automatically view the associated road .

Database

GIS database representing a road system with 1007 road segments

GIS Oatabase representing culvertlocatlons

I

I

Table structure

Unked Table 11

Road Type 1 -2 Rock 3 Rock

602 I Rock

• • 1006

I Rock

1007 Oirt

Linked Table 12

Culvert Type 1 Aluminum

2 SIee1

3 Cedar 4 Polyethylene

5 Polyethylene

6 Polyethylene

7 Aluminum

I

Road 544

544

544

602 -602 -714

714

Figure 9. 10 Linking a roads GIS database with a cuJvcru GIS database.

164


Which process-Joining or linking-is more dynamic? Perhaps linking. With linking. you can visualize both the source and target tables. select landscape feamces from either table. and view the

associated links from wither perspective. Further, if

To further illustrate the link or relate process, a general set of steps can be followed in ArcGIS 9.x to associate two GIS databases related (Q the Brown T fact: a culverts data

base (Culverts. txt) and a roads GIS database:

1. Open the Brown Tract roads GIS database and culvens text file.

2. In ,he ,able of contents. right-click the target table (roads GIS database). and select Joins and Relates. then select Relates.

3. Selec, the relate field in the ,arget table. 4. Identify the source table. 5. Select the relate field in the sou rce table. 6. After relating the two databases. perform an identify

process. selecting one of (he roads that are noted in the culvert database. You should be presented with informarion for both the road and the associated culverts along tha, road.

In ArcView 3.x, the following general set of instructions may work to create a link between the roads and the culven databases:

Summary

Join and link processes provide GIS users with a way to

temporary assoc ia te two or more databases. allowing an expansion of the mapping and analysis opportunities within GIS without making permanent changes to the databases. Before utilizing one of these processes you should consider the types of databases available. and the type of association desired (e.g .. one-(O-one, one-to

many. and so o n). In addition. the purpose of the process should be understood. which mighr provide guidance in the choice of a join or link process. For example. you may decide on different courses of action if the purpose is to:

some data are not linked (in either the source or target ,able) they are st ill visible and available for further ana lysis . When a join process is used, source table data not joined with target table data are unavailable

for funher spatial analysis.

1. Open the Brown Tract roads G IS database. 2. Open the roads GIS database attribute ,able (this is the

target table). 3. Open the Culverts. txt file (this is the source table) . 4. Select the link item in the source table (road). 5. Select the link item in the target table (road). 6. Choose Link from ,he Table menu.

At this paim a one-way link becween the roads and culverts databases has been created. The process may need to be repeated with ,he roles reversed (roads GIS database as the source. Culverts.cxt as the target) to create a cwo-way link.

By linking the (wo tables together. you ca n select records in one database (or features in either database), and the landscape features they are associated. with may be selected and highlighted in the other database. For example. after linking together the culvert database with the roads GIS database, you can select one or more culvens. and the roads associated with those culverts may be simul

taneously selected and highlighted in the roads GIS da,abase. In ArcGIS you can view the related data by using the identify tool.

A. Bring together spatial and non-spatial databases to

make thematic maps. B. Funher the abili ty to perform other spatial operations,

such as assoc iating non-spatial stream buffer data with a streams GIS database in order to facilitate a buffer

process. c. Understand the spatial relationship between a sec of

points and other landscape features (through a point

in-polygon process) . D . Understand the association among multiple landscape

features in one database and their counterparts in another database (through a link process).

165

Joining and relating processes often produce databases char are only 'viccually' combined. meaning that no new

databases have been created. but char exist ing informa

rion appears ro be associated either in one [able (with a jo in) or in twO tables (with a relate) . GIS users should recognize mat this temporary condition exiS(5 and that fur

ther acr ian is necessary in order fo r a permanent record of

rhe joined or linked info rmation to be created. The

Applications

9.1. Salamander habitat suitability index. Bob Evans, the Brown Tract's wildl ife biologist/hydrologist, developed some habitat suitabili ty index (HSi) values for a salamander. He suggests joining rhe data provided in 'SAL_HSl.txt ' with the Brown T ract stands GIS database. He then wants you ro answer several questions abour the extenr and spadal distribution of these areas, which are summarized below.

a) How much land area is contained in the 0 .8 [Q 1.0 HSI range?

b) How much land area is contained in the 0.6 to

0.79 HS I range? c) How much land area is contained in the 0.4 [Q

0.59 HSI range?

9.2. Newt habitat suitability index. Bob Evans has also developed some habitat suitabil ity index (HSi) values for a newt. He provided data in the file 'NEWf_HS l. rxr' . After joining the dara with the Brown Tract stands GIS database, please address his needs, which are noted below.

a) Determine how much land area of high quali ty newt habitat (HSI ~ 0.65) is contained with in 100 m of the road system.

b) Determine how much land area of high quality newt habitat (HSI ~ 0.65) is contained withi n 1,000 m of the owl nest site.

c) Determine how much land area of high quality newt habitat (HSI ~ 0.65) is contained in even-aged stands over 50 years of age.

9.3. Sharp-shinned hawk habitat suitability index. In addition to his previously discussed developments, Bob Evans has also developed some habitat suitability index (HSi) estimates for the sharp-shinned hawk covering the years 2010, 2020, 2030, and 2040. His rendeney to provide this data in a texr file continues, and you can fin d it in 'SSHAWK_HSl.txt'. After joining the data with the


exception to this vinuai [esuh is with a spatial join, in which results are usually stored in a new database that conrains all sparially jo ined info rmation. You should

check your software package to determine wherher it fol

lows these OutpUt results in the same way. Regardless of

which process (tabular-based or spatial join) is used to associate two databases. users should track thei r proce

dures and manage the ourpur acco rdingly.

Brown Tract stands GIS database, please address his needs noted below.

a) Determine how much land area of higher quality habitat (0.6-1.0) is contained within the Brown Tract.

b) Prod uce a single map of the sharp-shinned hawk habitar showing HSI values for the years 2010, 2020, 2030, and 2040.

9.4. Water sources and land allocations. John Frewer, a forester associated with the Brown Tract, is interested in knowi ng what types of land allocations the water sou rces were located w ithin. To accomplish this task, perform a

poine- in-polygon operation (or selection by locar ion query), using the water sources as the source table and the stands GIS databases as the target table.

a) How many water sources are located in 'Even-aged' stands on the Brown T ract?

b) What types of water sources are located in unevenaged stands (lisr the water source types)?

c) How many water sources are located in research areas?

9.5. Sawmills in a woodshed. You have recently been hired as a procu rement forester for Chupp and Daughters Sawmill in Floyd County, GA. You need to understand the competit ion for wood in the area. To perform rhis task, use the southeastern counties GIS database, the southeastern mills GIS database, and the mill data DBF fi le. Join the mi ll data DBF file to the southeastern mills GIS database and determine how many sawmills are within 100 miles of Floyd County, GA. The mill locations and non-spatial mill a[cribures can be acquired from the book's website or the USDA Forest Service (2006).

9.6. New mill location. You work for Walker, Avery, and Housron Lumber Company, a company that is

166


considering building a new hardwood sawmill mill in Coffee County, AL. They need to understand how much hardwood volume currently exists in (he area

around the county. To perform this task, use rhe southeastern counties GIS database and rhe county volume DBF file.

References

USDA Forest Service. (2006). US wood-using mill locat;0",-2005. Research Triangle Park, NC: USDA Forest Service, Sourhern Research Stadon. Retrieved

April 16, 2007, from http: //www.srs.fs.usda.gov/econ/ data/mills/miIl2005 .htm .

Join the county volume DBF file to the somheastern counties GIS database and determine how much hardwood (soft and hard) volume is conrai ned in the counties that surround, and include, Coffee County. The countylevel can be obtained from the book's website or the USDA Forest Service (2007) .

USDA Forest Service. (2007) . FIA data mart: Download files. St Paul , MN: USDA Fo rest Service, North Central Research Station. Retrieved April 16, 2007, from http ://www.ncrs2 .fs.fed.us/FIADatamart/ fiaclaramarc.aspx/fiadaraman.aspx.

167

Chapter 10

Updating GIS Databases

Objectives

This chapter is designed (Q provide readers with a discussion of GIS processes that should be considered when updacing GIS dacabases. Database updates are necessary when landscapes and associated characteristics. such as

ownership, change. There are a variety of methods you can use to update a GIS database, yet only a few are pre

semed here. The objective of this chapter. therefore. is to

provide an introduction to the poremiai applicarions in (his area. More specifically, at the conclusion of this chap

ter, readers should be able to understand:

I. why GIS databases need to be periodically updated and maintained,

2. what issues might be associated with an update process, and

3. whar GIS processes could be used to physically update a database.

To accomplish these objectives, a discussion of the reasons for updating GIS databases is firsr presented. Two rypes of update processes are then examined, one where new land

scape features are added to an existing GIS database, and

another where me landscape features and attributes in an

exisring GIS database are modified. These cwo examples likely address rhe [WO most common fo rms of GIS darabase updares. This chapter relies heavily on the GIS processes associated with edicing GIS databases. For a

review of these editing processes. please refer to chapter 3 . GIS databases are rarely considered stacic entities: vege

tation conditions change due to human manipulation and

namral disturbances. roads are constructed and obi iter-

ated, and some stream characteristics {pools. sediment, fish

abundance} may change as woody debris moves through the system. Although you may have developed or acquired GIS databases at one point in time. as management needs

or direction change. or as the resources you manage

change, GIS darabases used to describe landscapes must be updated. Table 10. 1 illustrates a number of events that

could occur and affect the landscape being managed, suggesting that the GIS databases used to describe rhe landscape being managed must be updared to reflect changes. Most natural resource management organizations (as well

as data development o rganizations) have created a set of

processes and protocols to guide the updating of GIS databases. Finding and illustrating a standard protocol is.

therefore. difficult because each organization generally will develop the steps they feel are necessary to integrate new

dara within their system of natural resource information.

For example. assume a tract ofland was recendy purchased

by a land management organization. Integrating the forest

stand component of this new tract into a forest stand GIS

database can be accomplished in a number of ways, such

as the three processes described in Figure 10. 1. As database protocols and organization strategies vary from one organ

ization to anomer. there is no one update approach thar will work for every organization .

The users of GIS databases are the ultimate customers

of groups (GIS departments. consultants, agencies) that produce the sparial data. As GIS databases become available, and users begin to explore the usefulness of rhe data for assisting in natural resource management processes. the limitations of [he databases will become evident. The

period of rime from initial GIS database availability to

serious consideration of updates tQ the databases may last 168


Process A

Digitize an area

1 Erase overlaps using corporate

database

1 Attribute the new spatial features

1 Update

COij)Orate database

TABLE 10.1 A sampling 01 reasons lor updating GIS databases

Events

Stochastic disturbances

T ransiriOlls of (orcslS

Management activi ties

T ransacrions

Regulations

Organizational policies

Improvemcnu in technology

Organizational initiatives

New data availabiliry

C hanging map projections

Collaborative projectS

Periodic maintenance

Process B

Digitize an area

1 Erase over131>S using corporale

database

1 Select and copy newly

digitized features

t Paste new features

Inlo corporate database

1 Attribute the new spatialfealures

Update Update spatial tabular

Examples data data

hurricanes. fires. insect outbreaks 01' 01'

growth and yield 01'

harvesting. road consuucrion, 01' 01' insrallalion I removal of culverts. creation of trails. thinnings. NC.

land acquisitions. donations. sales 01' 01'

riparian management areas, owl 01' 01'

habitat areas, woodpecker habitat areas

special areas. personal reservations 01' 01'

digital onhophotographs, GPS captu re: 01' 01' of road data, ownership boundaries, crc.

periodic I annual cruises. 01' 01' photo interpretation of harvested areas not normally recorded via normal processes

databases developed by other organizations 01' 01'

conform ing to new o rganizational standards 01'

watershed analyses. Ia.ndscape planning 01' 01' efforts

cleaning up databases after spatial operations. 01' 01' digitizing, or anributing processes

Process C

Collect GPS data lor an

area

1 Edit spatial

features (e.g., remove muttipath)

1 Select and

delete features to be updated (i l necessary)

1 AddGPS

features Into corporale database

1 Attribute the new spatial features

from a few hours to a few months. Users of GIS databases will ultimately suggest a variety of enhancements to the databases that would facilitate further analyses. For example, the Brown T ract vegetation GIS database could be modified to show more explicitly (he riparian areas, or could include more anributes that describe forest stand structure. A roads GIS database might also be enhanced to

show the type of road surfacing or all of the trails (unauthorized roads) that weave through the property. Updating these GIS databases ro include all of the informarion (hat is necessary to make natural resource management decisions may be, however, limited by the time and budget available ro make the changes, the quality of (he information available to make the changes. and other organizational data standards. The needs of namral resource managers. with regard to GIS databases. must eventually be considered along with (he costs of data development.

The Need for Keeping GIS Databases Updated

Figure 10.1 Three examples of update processes related to land acquisition.

Natural resource managers generally base managemem decisions on the best available data. The quality of data

169

can range from very precise and accurate (collected with a high qual ity GPS receiver) to somewhat imprecise and inaccurate {drawn by hand from memory}. Keeping the data used for making decisions accurate and updated is therefore imponant, and thus the interval between updates becomes important. For example, the update interval that a resource managemem organization uses to

refresh the spatial extent {history} of their management activities and the growth of their forest inventory is imponant, since subsequent management decis ions might be affected by previously implemented managemem decisions. The imerval chosen can range from six momhs. to a year, or even (WO years between updates, depending on the GIS database considered. The interval chosen depends on the organization's perception of the usefulness and cost-effectiveness of such an update on a GIS database. For example, if the goal of an organization (e.g., a southern US forest management organization) were to generate revenue for its stockholders, the need for updating the data related to its primary resource (pine forest stands) may be more important, and updated more frequently than data related to secondary resources (hiking trails). Other resources, such as roads, streams, culvens, water resources, and wildlife may be more or less imporram, depending on the goals of the organization, thus the frequency with which these GIS databases are updated may vary according to the organization's perceived need to do so. At the extreme end of the spectrum. every GIS database could be updated continuously; however, the cost of doing so may be qu ite high and the task would require employees {or consultants} dedicated to the task.

Two ofche more important questions an organization must address, beyond determining when a GIS database should be updated, are how the update process will be accomplished, and who will do the work. As mentioned earlier, the methods by which a GIS database could be updated vary considerably; the fo rm of input cou ld range from hand-drawn maps to LiDAR-derived measurements, and the GIS processes could involve scanning, digitizing. attributing, and other methods {Table 10.2} . As you may have gathered from chapter 3, when a GIS database is being updated, rhe database is being edited. In some form or fashion , the imem is to change something about a GIS database-either the landscape features or their underlying attributes, or both. Two examples of GIS update processes are now presemed, one related to a forest stands GIS database maimained by a forest industry organization in Florida, and the other related to a streams GIS database

TABLE 10.2

Input


Inputs and processes that can be used to a ssist a GIS database update

Hand-drawn maps GPS features T2bu lar datab2Ses Field nOles A person's memory GIS Features developed by field personnel Digital orthophotographs and subsequent interpretation

GIS processes Digitiring Scanning Joining Updating Linking Copying J p2Sting Importing Attributing Querying and verification

mai ntained and distributed by the Washington State Department of Natural Resources.

Example 1: Updating a forest stand GIS database managed by a forest management company

A typical forest management company in Florida might update their forest stand GIS database on an annual basis. Their field personnel collect information related {Q

changes in irs forest land ownership throughout a calendar year, and the forest stand GIS database is updated near the end of the calendar year. Why would they update the forest stand GIS database once a year? The forest stand GIS database is arguably the most important GIS database for assisting industrial forest management activiries, and field-level managers require high quality data (maps and inventory data) to make managemem decisions. In addition, most corporations require an annual estimate of the value and volume of resources, for planning and tax reporting purposes. A less frequent updating interval may not be appropriate given the short rotacions typical of southern industrial forestry operations. For example, waiting two years between updates of a timber stand GIS database may represent 8-10 per cent of the lengrh of a forest harvest rotation. A more frequent imerval, say six months, may provide field personnel with higher qualiry information with which to make management decisions, particularly in cases where a large amount of activity takes place over a six-month period. Some have argued (hat continuously updating GIS databases may be appropriate. but the: time and cost required to update a GIS database may make a nearly continuous update process impractical. Further, field personnel could easily become confused when faced with a cominuously changing set of GIS data-

170


bases. thus updating databases and leaving a window of time {a year. perhaps} between changes may be perceived as more des irable.

The changes to the forest stand GIS database that are

recommended by field foreSters and other natural resource managers may be indicated on hard-copy maps and timber cruise forms. or they may be comai ned in d igital databases created in GIS or with GPS. Field foresters. timber procurement managers. or other profess ionals

responsible for managing land will typically indicate {draw} on maps the changes {e.g .• harvest and regeneration activi ti es} that have occurred on [he forest land base

as these act ivit ies have been completed. This informacion is usually sent to a central office {Figure 10.2}. which takes ownership of the timber Stand GIS database. The cemral office checks rhe new data for mistakes and omissions according to a set of organizational standards, and may ask for clarification from the field staff. The information is rhen digitized, either in-house or by an external con n actor. The resuhing digitized GIS database is checked again for mistakes and omissions, and then ime

grated into the official {sometimes called 'corporate} GIS

Field office Central office

r------ ---- ------- ----, .. , Delineate changes Check data for

to be made to ~

mistakes and a database , omissions , ,

L , , , , , Digitize changes ~ , , , ,

~ Make management , , decisions using , the database

, Check data for , , mistakes and r-, , omissions , ,

L , , , , Integrate into , ~ , corporate ,

database , , ~

, , Check data for Check data for mistakes and mistakes and -

omissions omissions

Figurc 10.2 A gcnerali"lcd proces.s for updating a forest stand C IS database.

, , , , , , , , J

-,

, J

database. and again checked for mistakes and omissions. Finally. the forest stand GIS database is distr ibuted back to the field office. either as a GIS database. or as hard-copy maps and tables. The field office may then have itS own

verification procedures for checking the updated database (or maps) fo r m istakes and omissions that may have arisen during the update process. Processes such as these. with a systematic method for data collection. entry, and verification, are designed to ensure that high-qual icy data

will be developed and avai lable for use in natural resource decision-making contexts.

Example 2: Updating a streams GIS database managed by a state agency

In rhe State of Washington, all fo rest harvest plans must be subm itted to the Department of Natu ral Resources

{DNR} for review and approval. A map must accompany each plan, and illustrate the juxtaposition of proposed activities in relation to, among other landscape features, the stream system. To ensure a consistent definition of the 'stream system', the DNR provides (at a minimal COSt, as was illustrated in chapter 3) a streams GIS database for the entire state. This database is conti nuously updated by the DNR as new info rmat ion is co llected. However,

processes and protocols exist that are related [Q each potential change to the GIS database. For example, assume a private landowner surveyed a stream reach and

noted that the type of stream {and perhaps location of the stream} on the landscape is different than the type of stream illustrated in rhe DNR streams GIS database. The landowner has the option to submit certain documentation [Q the DNR in suppOrt of a request to change the DNR streams GIS database. The DNR d irects each request

through a review process, and based on the outcome of the reviews, decides [0 ei ther accept or reject the pro

posed changes suggested by the landowner. The amount of time required to make a change in the streams GIS database, from init ial submission by the landowner to official incorporation in the streams GIS database, may require several months. The process is considered a continuous one since approved changes to [he streams GIS database can be made at any time during a calendar year.

Therefore, landowners may need to continuously review the statuS of the DNR streams GIS database in the areas where they own or manage land, and acquire updated data as they deem necessary to reflect the latest stream

info rmation. 171

Updating an Existing GIS Database by Adding New Landscape Features

GIS databases can be updated with new landscape features (points. lines. or polygons) by either adding the new landscape features (Q an existing GIS database, or by editing the existing landscape features. or boch . Two examples are provided below to illuStrate updating a GIS database by adding new landscape features . The firSt example involves a land purchase and subsequenr addition of twO

forest stands co a stands GIS database. The second example involves the addition of new trails to a trails GIS database. In each case, assume that the new landscape features

were either digitized or collected with a GPS SYStem and are available in a GIS format. Prior to [he iniciarion of the update process, you should assume that the new data are comained in GIS databases that are separate from the GIS databases that need updating. Refer back to chapter 3 for a review of methods and tools for development of a new GIS database.

Updating a stands GIS database

Assume that (he owners of me Daniel Pickett forest have

pu rchased 80 acres (32.38 hectares) of land adjacent to the southwest corner of the original forest boundary (Figure 10.3). Following process B illuStrated in Figure 10.1. the Stand boundaries of this area have been digitized inco a new GIS database chat is separate from the o riginal stands GIS database. and these features have been attributed wim data fields similar to that in the original stands GIS database {Table 10.3}. The edge between the newly digitized stands and original stands is seamless,

implying that there is no gap between the polygons of the two GIS databases. and no overlap if the two sets of poly-

D Original stands

D Stands in land purchase area

Figure 1 0.3 Dani~1 Pick~[( for~$[ stands and land purchase area.


TABLE 10.3

Smnd Hectares

2

17.24

15.t4

• squar~ f«t per acr~

Attributes of stands in a 32.38 hectare (80 acre) land purchase adjacent to the Daniel Pickett forest

Vegetation Basal Acus type area" Age MBP

42.6

37.4

A

8

190

15

55

7

21.3

0.8

h thousand board fttt per acr~

gons were placed together. By simply copying the land

scape features from the land purchase GIS database into the stands GIS darabase. it is possible to bring the newly

digitized land purchase polygons into the stands GIS database, however. the attributes of the new stands may not

be present. depending on the GIS software program being used (Figure 1 0.4) . The new fo rest stand polygons would then need to be attributed a second dme, after they have been pasted intO (he original stands GIS database.

To avoid duplication of effort in update processes, three options are clear: (l) digitize the new landscape features directly into the original stands GIS database. (2) use a merge process to combine the newly digitized stands with the origi nal stands GIS database. or (3) if available in

Stand

1

2

30

31

o o

VegType

A

C

c c

Basal Area

200

175

190

110

o o

D Forest stands

Age MBF

50 21.2

40 12.9

45 17.3

25 4.1

o 0 o o

Figur~ 10.4 Daniel Pick~n forest stands and land purchas~ ar~a aft~r copying and pasting landscape fntures from th~ land purch3# GIS datah3# to th~ stands GIS database.

172


rhe GIS software program being used, use an 'updare' funcrion . ArcMap and ArcView 3.x, for example, both

have (he ability to use an update function made available

rhrough the XTools extension (Data Easr, 2007; Oregon Department of Foresrry, 2003). When using a merge process or an update funC[ion, the stands GIS database

will be updared with rhe new polygon dara contained in

the land purchase GIS darabase. If rhe land purchase GIS database includes fields named and formarred exactly as those in the forest stands GIS database, the 3ttrihme data wi[hin [he land purchase GIS da[abase will be moved

(along wi[h rhe associared purchased polygons) to rhe updared srands GIS darabase (Figure 10.5).

The assumprion was made rhac [he polygons in che

land purchase GIS darabase seamlessly marched [he edges of polygons in rhe Daniel Picke" foresr scands GIS da[abase. How is this possible? Matching rhe spacial juxtaposition of the new landscape features to the landscape feacures in [he GIS da[abase being updaced (the stands GIS da[abase) can be accomplished using one of at leasc cwo

mechods, depending on what rype of process is available within the GIS software program being used: (I) copy [he new polygons into the original stands GIS database and

use snapping tools [0 properly match rhe new polygons wi[h the original scands polygons, or (2) use a process as described in the fi rst cwo sceps of processes A and B of Figure 10.1. Here, you might firsc digi[ize the new land

Stand

1

2

30 31

o o

VegType

A

C

c c A

B

Basal Area

200

175

190

110 190

15

D Timber stands

Age MBF

50 21.2

40 12.9

45 17.3

25 4.1

55 21.3

7 0.8

Figu~ 10.5 Daniel Pickett forest stands and land purchase ana after updlUing the stands GIS database wing the land purchase GIS database.

purchase polygons beyond che excent needed in the new land purchase GIS database, creating an area of overlap

with the polygons in [he "ands GIS database (Figure 10.6). Then, erase from [he land purchase GIS da[abase the area of overlap with the stands GIS database, creating

a second (new) land purchase GIS database. In [his new

land purchase GIS database, [he edges of the new polygons seamlessly match the edges of [he associated polygons in the stands GIS database (Figu re 10.7).

Updating a trails GIS database

The existing trails system for the Brown Tract was digi

tized several years ago using hard-copy maps provided by the forest recreation planner (Figure 10.9). While suitable for recreation planning and the development of

recreation maps to guide visitors around the area, the trail syscem described in the [rails GIS database could very well be considered out-of-da[e. The [rail system, like ocher fea[Ures of a landscape, evolves as the managers of the forest

develop new trails, or as people find different hiking or mountain biking rouces through the landscape. The latter

Overlap

D Or~inal stands

Stands in land acquisition area

Figure 10.6 Overlap of new land purchase polygons with a GIS database that will be updated.

D Original stands

o Stands in land acquisition area

Figure 10.7 Land purchase GIS dat:lbasc after erasi ng the overlap with the stands GIS database.

173

The term digitizing, as described in chapter I. means to

cooven a hand-drawn (or other rype 00 map CO a digical image of a map. Normally. digitizing is performed using a digidzing table and a digitizing puck. A map is laid on the table, taped down to ensure that it doesn'[ move during [he digitizing process. and at least four control points on the map. for which on-the-ground coordinates are known, are emceed imo the compurcr system using the digitizing puck (similar co a computer mouse) . The puck is <hen used ro trace all of <he polygons or lines to be digitized, or (Q nme the poinrs that need ro be digjrited. These landscape features are then saved as a GIS database. The person using the digitizer can control [he number of vertices that describe lines or polygons. Digitizing points along 'salient features' (i .e .• placing more vertices at very distinct changes in stand boundaries. or road cUlVes) is a common method of digitizing. Other types of digitizing include allowing the creation of vertices along equal disranees moved by the digitizing puck {e.g., a venex created every millimeter mat the puck moves} or at equal time intervals during the digitizing process (e.g., a vertex created evety second during <he digitizing process).

The term heads-up digitizing probably arose because a person's head is up. facing a computer screen, when landscape features are being digitized directly on a computer monicor rather than on a digitizing tablet. When performing traditional digitizing using a digitizing table and puck. a person generally has <heir head down. since they need to look down upon their map. With heads-up digitizing, a reference GIS database (perhaps a digital or<hophotograph) is generally used as a guide for the creation of new points. lines, or polygons. A compucer mouse is used to draw the new landscape features. Depending on the ski ll of <he person performing the heads-up digitizing

function and factors associated with the supporting spatial databases (scale and resolution), the accuracy of this method may be just as good as when digitizing using a digitizing table and puck. However, heads-up digitizing is much easier and faster. In addition. the chance for error through heads-up digitizing is greatly reduced because registration coordinares need not be entered and verified. Of course, this assumes that the reference GIS database (e.g., <he digital orthophotograph) contains a limited amount of error. Given these trade-offs, a decision must be made with each digitizing project regarding the method of developing new landscape features. The decision is gener-


ally based on <he standard protocols within an organization, (he risks relared to potential errors in the resulting GIS database (e.g., errors mat may lead to making incorrect management decisions in the future), the quality of both hardcopy and digital products <hat would be used to

suppon manual or heads-up digitizin~ and the time and cost of the dara development effon. Thus a balance must be struck between <he organizational policies. <he level of

effon required to delineate landscape features, and the need to adequately and accurately describe those landscape

features. For example. Figure 10.8 illustrates twO artempts to digitize a young forest, with more effort (and rhus rime) being applied to one over the other. Which one of these is more accurate or useful ro those making management decisions? Unfortunately the answer is uncenain.

Figure 10.8 Two delineations of a young fores l, one using twice ;u many vertices when digitizing (above) than the other (below).

174


--- - Trails -- Roads [:=J Forest

boundary

Figure 10.9 Trail system of the Brown T r:lCL

case usually involves the development of unauthorized tra ils. which the fo rest managers mayor may not decide to more fully develop and mainta in (or they may decide to develop measures to hinder the use of those trails) . In addition, as resources become more popular and visitations increase (as is the case with many urban-proximate recreation destinations), there may be a need CO identify additional resources and trails with which to decrease the density of use. In some cases, closing trails either seasonally or permanently is necessary to prevem resource degra

dation such as soi l compaction and erosion. The forest recreation planner decided that a new

authorized (rail would be of value (0 the recreation program (Figure 10.10). Mor the new trail was developed, the spatial coordinates of the trail's location were collected using GPS, brought into a GIS software program as a new GIS database comaining line features, attributed, and saved as the 'proposed trail' GIS database. Since both the original trails GIS database and the proposed trails GIS database are composed of line features, and not polygons, they can be brought together without the worry of creating landscape featu res that overlap (and hence, in the case of polygons, leading (Q a double-counting of some areas in area calculations). However, careful anention must be paid to the

connectivity of the network of lines that describe the trails .

\. ./

.•..••.. I

'. <" .......

....•.

1

i .. ....... ~ .

\

---- . Trails

- - - Proposed trail

- Roads

CJ Forest boundary

Figure 10.10 Proposed new trail on the Brown Tract.

For example. the new trail may not end with a node that allows direct connection to a vertex of a trail in the origi

nal trail system (Figure 10.1 1). This creates a gap in the linear extent of the trail at the intersection point and will

require some post-merging editing to correct. To update the original trails GIS database, the original

trails GIS database could be merged with the proposed trails GIS database. When doing so, only the fields (attributes) with in the proposed trail GIS database that match exactly with the attributes within the original tra ils GIS database (in both attribute name and type) will be moved into the new merged GIS database. The spatial position of the proposed trails , as mentioned above and described in Figure 10.1 I. may then need to be edited. In addition, a verificat ion of the attribute data (Figure 10.12) may suggest some alterations as well (e.g .• the trail number of the proposed new trail is the same as another existing (rail in the original trails GIS database) . An understanding of the update process (Figure 10.13) will be of value in the planning of projects that involve alterations or updates to GIS

databases.

---- . Trails

-- Proposed

.... , .""',.,.,"

trail

",

....... "',

Figure 10.11 Proposed new trail and iu relation to another trail from the origina1 trails GIS d21abase.

175

.... < ..

c;. .,,0

---- Traits

c::::::J Fores. boondary

----'.'. ! ;

:~ ~:r, ...

><"'::~:;::"'~(' <./ .: .'

Trail Length Conation Source

410.5 AuthOfized Trails

2 1183.2 AuthOfized Trails

42 704.1 Unauthorized Trails

43 1261.5 Unauthorized Trails

Figure 10.12 Updated trail system of the Brown Tract.

Large GIS database updating projectS require careful con

sideration of the time and COst required to successfully complete the project. For example, a roads GIS database for a 100,000 hectare foresr may have been originally digitized from hard-copy maps (drawn by hand) and, therefore, might contain some spatial position errors. If you were to consider updating many of the roads in the GIS database by collecting new data with a GPS, the following should probably be considered:

I. The development of a sta ndard protocol for GPS data collection (e.g., maxi mum PDOP), to ensure an acceptable and consistent level of accuracy in the data coUected.

2. The need to drive (or walk) all roads that need

updating. 3. The need to differentially correct and manage the

GPS-collected databases. 4. The identification and elimination of error in the

GPS data (such as multi-path error).


Collect GPS data IOf an

area

~ Edit spatial

leatures (e.g., remove multipalh)

~ Attribute spatial

features

~ Merge new

features into original database

~ Edit spatial

position of new features

~ Verify I edit

allribtJte data In updated database

Figun 10.13 The process used to update the trails G IS database.

5. The removal of the old roads from the updated roads GIS database.

6. The connectivity of new roads [0 old roads that

were not updated. 7. The developmen( of a verification process to ensure

that (he attributes of (he new roads are correct.

Determining the number of person-days required to accomplish each step will depend on the people, equipment, and technology available. The alternative [0 a large, single process fo r updating a GIS database is to perform it in small phases. However, while the COS( of using

multiple smaller phases may be lower than a single large project (assuming it requires several years to complete all of the phases), the total cost of the update process will likely be lower if the entire project were completed as a single project due to the economies of scale (fewer stanup and clean-up operations) . In addition, fewer errors might arise since the same people will be working with a protocol that is clearly stated and understood.

176


Updating an Existing GIS Database by Modifying Existing Landscape Features and Attributes

An alternative to updating GIS databases with new landscape features contained in other GIS databases is [0 modify existing landscape features using [he editing functions described earlier in this book. While this alternative may seem morc logical than what was previously described in

this chapter. the ri sk of damaging the GIS database being updated is grearer. For example. the processes described earlier have a relatively low risk of damaging the original GIS features (the ones not requiring updating). These processes involved creating and modifying landscape fearures in a G IS database separare from the original GIS database, then moving the new features into the original GIS database only when it was appropriate ro do so. Here, editing the original GIS database may pose a higher risk of damaging landscape features thar did not requi re updat

ing due [0 human error. And it is possible that these errors can occur withom realizing a mistake had been made. In addi rio n. unless the steps taken in editing a GIS database were carefully documented, when errors are located it may prove difficult to understand wh ich landscape fearures had been verified as correct, and which may

require furthe r editing. Two processes are next briefly described to illustrate

rhe update of GIS databases by modifying existing landscape features. The first process requires editing the location of landscape features with the assistance of digital

orthophotographs. The second process illustrates updating attribute data in a GIS database with the assistance of

a JOIl1 process.

Editing the spatial position of landscape features using digital orthophotographs

As described earlier. heads-up digitizing may be used to assist in the GIS database update process. Digital orthophotogtaphs may be of benefit in updating the position oflandscape features if the orthophotogtaphs are registered appropriately [Q the correct landscape position, and if they have been stored in the coordinate and projection system consisrent w irh the GIS databases to be updated. Point. line. and polygon GIS databases can be displayed in a GIS software program on top of a digiral

orthophotograph to examine how well rhe landscape fearures are being represented. Using the boundary GIS database of the Brown Tract as an example, you will find that a particular line may either incorrectly ident ify the forest boundary, or that a management operation on an adjacent landowner's property may have been incorrectly located (Figure 10.1 4). If this is. in fact. an incorrect boundary line specification within the boundary GIS database, then editing the appropriate vertices that define the boundary while using the digital orthophotograph as a guide can easily modify the spatial position of the boundary. If. however. yo u needed to be very precise regarding the location of me forest boundary, informa-

Figure 10.14 Boundary line issue on the Brown T raCl.

177

tion from a land surveyor survey-grade GPS measuremems of properey corners would be more appropriate in updating the spatial position of the boundary.

When updating the spacial position of landscape featu res in a GIS database, you must also be aware of the potential issues that may arise in other associated GIS databases. Here, for example, the imem may be to simply update the position of the boundary of the Brown T race. However, by doing so, the spatial extem of the land ownership is no longer consisrem with other polygon databases used to represem the Brown T racti the stands and soils GIS databases being cwo good examples. Thus, afcer updating the boundary of the Brown Tract. a corresponding update of the affecced polygons in the stands and soils GIS databases may also be requi red.

Updating the tabular attributes using a join process

In some circumstances only the attribute data of a GIS database may require periodic maimenance. For example. if over a given year no activities have been implememed on part of an ownership then perhaps only the a[[ributes that describe the structural condition of the forest(s) need [0 be updated. In this process {Figure 1 O.IS}, the update might be accomplished by passing the stand-level forest inven[Ory data through a growth and yield model, summarizing the resulting forest structural conditions. saving this data in a non-spatial database. then joining the non-spatial database to the stands GIS database. A unique stand identifier. such as the stand number. could be used to connect records in the non-spatial database [0 the forest G IS layer.

Using the Daniel Picken forest as an example. a non

spatial database that represents the updated growth of stands {Update. txt} can be joined [0 the stands GIS database attribute table. using the stand identification number as the join item. Then. the anributes within the original stands G IS database can be replaced with the joined

Summary

There are a variery of methods you can employ [0 update a GIS database. Several of the approaches were described with the examples provided in this chapter. GIS databases are rarely static. and in only in a few situations, such as in

the delineation of national. provincial. state. or couney boundaries. can you be con fidem that changes to a GIS database will rarely occur. Landscape features can change


Stand-level forest

Forest growth and yield model

Summarize stand -level

data

Stands GIS database

Replace old summaries with new summaries

Remove the join

Figure 10.15 A process to update attribute data in the Daniel Pickett stands GIS d:lt.abase.

anributes that represem the updated basal area. age. and volume {MBF} . The joined non-spatial table can subsequently be removed from the original stands GIS database, and the updated stands GIS database can be saved.

quite often: watershed boundaries change as the processes used to define watershed change, public land survey {PLS} sect ion lines may change as corners are reestablished. stream locarions change as they are better mapped using GPS or digital orthophotographs, and of course, vegetation stand boundaries change wi[h management of natural resources or with nacural disturbances. Attribute data

178


associated with GIS databases can change just as often,

and may need periodic updati ng. In addition, given con

tinued improvemenrs in measurement technology, it is a

certainty that the effort and COSt requ ired for collecting update informacion for natural resources will conrinue CO

decrease in the future . The reduced resources required fo r

collecting update information will likely lead to an increase in update frequencies for many natural resource

organizations. At the very least, update COSts will play less

of a role in dererm ining how often to update a spatial or non-spacial database.

The processes used to perform an update of a GIS

Applications

10.1. Land purchase. In the middle of the Brown Tract you may have noticed a piece ofland that is managed or

owned by someone else. Let's assume that the owners of

the Brown T ract purchased this piece ofland, in an effort

to consolidate the ownership area. You have been asked to

develop stand boundaries for this area, and incorporate them into the stands GIS database. Specifically, you need to delineate the stands into logical age or structural cate

gories, although the only attribute you are asked to add to the GIS database is one for ' land allocations' (even-aged or uneven-aged). The forest staff will evemually develop an

inventory for these stands and produce age, timber vol

ume, and other statistics. To accompl ish [his updaring

task, you decide to use the digital orthophotograph associated w ith the Brown Tract GIS databases as a backdrop,

and to use heads-up digicizing techniques to delineate the new stands.

The forest planning staff needs the following information :

a) How much area ofland in even-aged srands will be added to <he Brown Tract?

b) How much area of land in uneven-aged stands will be added to the Brown Tract?

c) A map of the new stands that illumates the land allocation of each new stand, and includes the

Brown Tract roads, streams, and digital orthopho

tograph.

10.2. Pondering the update process. In one example in this chapter an update of (he Daniel Picken forest stands

GIS database was described, where a 32.4 hectare (80 acre) parcel of land was purchased. The edge between (he

database will likely be different from one o rganization [Q

the next, and from one 'Ype of GIS database to the next. Careful cons ideration of the components of a system (the

people involved. the databases cons idered. the data acqu i

sition options. the software and hardware technology

available, the budgetary constraintS), and the needs of the ultimate customers (field perso nnel) will help determine

the appropriate update process for each GIS database. In any update process, copies should be made of the databases that are subject to updates. These copies should be kept until such time that the updates are veri fied, and that

no furthe r need exists for the updates.

polygons representing the land putchase and the polygons in rhe original stands GIS database was assumed to be

seamless. a) What GIS processes could be used to ensure that

the edge would be seamless? b) Draw a Row chart of a process that could be used

to ensure a seamless edge.

10.3. Adding new roads to the Brown Tract roads GIS database. Assume locations of a new set of roads have

been captured with GPS equipment. The GPS-captured features are stored in the GIS database called 'New_roads' . Update the Brown Tract roads GIS database by incorporating these new road features . Use your best judgment to

attribute them in a consistent manner with the attributes

in the roads GIS database. a) How many kilometers (or miles) of dirt or native

surface roads were in the original roads GIS data

base?

b) How many kilometers (or miles) of dirt or native su rface roads are comained in the updated roads

GIS database? c) How many kilometers (or miles) of rocked roads

were in the original roads GIS database? d) How many kilometers (or miles) of rocked roads

are contained in (he updated roads GIS database?

10.4. Update process for a streams GIS database. A field forester working on the Brown Tract has compared

mapped locations of streams to actual stream locations.

and has discovered some differences. She has proposed that the mapped meams be updated and has asked for

179

your guidance in how this process might be accomplished. Describe three options for gathering the data necessary to fuci lirate an update of the streams GIS database. and the merits of each approach.

10.5. Update intervals and approaches. A recreation manager. who has learned of you r geospatial data skills. has asked your advice abou t updating the I O-year-old GIS (fails system. Two trails have been added [0 the system during (he past five years . The recreat ion manager is new

to the area and does not know much about GIS and

related technologies. The trails system is near an urban

cenrer and indications are that use demity is increasing.

Evidence of increasing use density includes a growing number of conflicts being repocted by horseback riders.

dog owners. and hikers throughout the forest. Additional evidence includes an unauthorized trail that is now clearly visible on the landscape. and which shows signs of signifieanr use at one of the primary trailheads.

a) What is an approp riate update interval for trail

information? Defe nd your choice.

b) How wou ld you recommend that any new spatial data be collected?

c) How does any new spat ial and attribute informa

tion collected be integrated into the exisdng GIS trails database?

d) What arrribures of trails would you encourage the recreation manager to collect during field visits co

the trails? Defend you recommendations.

References

Data Easr. (2007). XTools pro. Retrieved April 21, 2007. from http://www.xtoolspro.com.


10.6. Update tools and approaches. You've been asked to create a spacial data layer representing scructures

(buildings). streams. roads. and watershed boundaries within a relatively small 259-hectare (640 acres) experimental fores( (hat is home to studies on hydrological

observarion and testing. At present, a polygon identifies

the adm inistrative boundary of the experimental forest

and only primary roads and perennial screams are

included in exisring spatial databases. The exisring data was created from hard copy maps at a scale of 1:24.000. You have at your disposal a 1:24.000 digital topographic quadrangle. a 30 m digital elevation model. a I m' resolution digiral orrhophotoquad. a consumer grade GPS. and a tOtal station. To use the rotal station, cwo reference

benchmarks are located within 2.0 km (I.2 miles) of the forest boundaries.

a) What data source andlor inscrumenr would you

use to capture the new spatial information relared

(0 :

I. structures?

ll . secondary roads?

111. smaller streams, including ephemeral streams?

IV. watershed boundaries?

b) Which of the new spatial information sources

described in pan (a) would you incorporate into

existing GIS databases to update them?

c) For the data you identified to be updated into exisring GIS database (described in parr (b». how would you incorporate the new information into

existing GIS databases?

Oregon Deparrmenr of Forestry. (2003). Guide to Xtools extension. Salem, OR: Oregon Department of Foresny.

180

Chapter 11

Overlay Processes

Objectives

One of [he grea< strengths of GIS is the capability to integrate landscape features and attribute information from

more [han one GIS database into a single GIS database. This capability is useful because it allows you to actively investigate and determ ine relationships between land

scape features. Upo n co mpletion of this chapter. readers should have a suffic ient amount of cools in their GIS [001-

box to perform many of the common vector GIS overlay

analyses required of field personnel working in natural resource organizat ions. whether they are employed in

forestty, wildlife, soils, fisheries, or hydrology fields. The objectives of this chapter to provide readers with an intro

duction to three primary rypes of overlay analysis: the

intersect, identity, and union processes. In addition, the

ability of these overlay processes [Q accommodate differ

ent vector feature rypes (point. line, and polygon) is considered. When this chaprer is completed, readers should have obtained knowledge and understanding of:

1. the Outcomes from using an overlay process to accom

pli sh one or more analytical tasks w ith in GIS;

2. the circumstances that help you decide when each of the three overlay processes might be used (0 suppOrt an analysis or research objective; and

3. the differences among the th ree overlay processes, and

between them and other similar GIS processes.

The topics discussed in this chapter relate to spatial

overlay processes. Overlay processes are spatial analysis techniques that involve two or more GIS databases. More

specifically, overlay processes can be used to investigate

and identify spatial relationships becween twO or more

databases. Overlay processes are powerfu l GIS tools and they represent what many consider ro be the essence of

GIS: the ability to integrate and organize information

from multiple spatial data layers into a single database. Overlay processes can be thought of as modell ing techniques that allow us to consider what might occur from

(he inregration of info rmation from multiple sources.

Perhaps the most widely recognized example of pre-G IS (ma nual) overlay analysis is that demonstrared in Ian

McHarg's boo k Design with Nature (I 969). As mentioned earlier in chapter I , this book provided examples

of manual map overlays that were used ro idenrify areas

that were suitable for certa in activities. Design with Nature inspired many people to apply manual overlay techniques for their own analysis needs and to also con

sider developing digital tools (GIS) that might be used to make overlay analysis more precise and efficient.

The topics discussed in rhis chapter relate to spatial

analysis processes that are similar ro merging (chapter 8),

and to obtai ning information about specific geographic

regions (chapter 7) . There are, however, some very dis

tinct differences between the merge process and (he over

lay processes presented in this chapter. When twO (or

more) polygon GIS databases are merged, for example, the resulting GIS database may consist of overlapping poly

gons. The processes presented in this chapter-the inter

sect, identity, and union processes-all result in new out

put GIS databases where the landscape features do not overlap. Overlapping areas and attributes are combined.

in some form or fashion, rhus the potential exists for poly

gons to be splir and combined and their topology reassessed in the result ing GIS database. Calculating the area

181

covered by polygons in a merged GIS da",base may therefore be misleading, due to the presence of overlapping polygons. Funher. you may be imerested in the characteristics of rhe actual areas that overlap, such as the overlap

ping soils and forest stands. and how this information can

help you make berrer (or more informed) management decisions. To accomplish this with a merged GIS database is difficult, as the overlapping polygons are independent and share no informadon mher than that which the user

can obtain visually on a map or computer moniroc.

Intersect Processes

When performing an intersect process. you hope to

acquire information about rhe overlapping areas of [wo

GIS databases. The term 'inrersecc' implies (hat features meet or cross at cenain points. and (in the case of polygons) share common areas (Merriam-Webster, 2007). In using an imersect process. a third. new GIS database will be created [hat consists of only those areas where the two

original GIS databases ove rlap, no more, no less. For

example, Figure 11.1 shows rwo GIS databases , one that

represems vegetat ion (a stands C IS database), and the

other representing a burned area from a fire. If you used

an intersect process on the two GIS databases, the result

ing GIS database wou ld conta in only the geographically overlapping landscape features. Spatially, the extent of the output will be defined by the boundaries of the polygons in the original two GIS databases: stands outside

the burned area are excluded and the burned area out

side of the vegetarion stands is excluded. The extent of

the attribute data that will be contained in the resulcing

GIS database can include attribute data from one data

base or all arrribute data from both of the original GIS databases.

By comparison, a similar result can be obtained using

a clipping process. Here, you might clip the vegetation

GIS database using the fire GIS database. This alternative, while providing the same geographic representation of the

vegetat ion burned by the fi re. provides no attributes of

the fire in the resulting output GIS database. Although the example includes only one fire, suppose you had a fire

GIS database that contained the geographic boundaries of several fires that may have occurred over a summer. If

each fire polygon was attributed with the day, month, and year of its inception, this information would not be contained in rhe output GIS database if a clipping process

was used . To generate a GIS database that not only

included the stands within the burned areas, but also the

GIS database

Database to be intersected: stands

Database used to perform the intersectioo: fire


Spatial features

Input GIS databases

Tabular attributes

attributes: basal area, volume per acre, vegetation type, age

attributes: day, month, year

------------------Resulting database: stands, defined along original stand boundaries yet only within the boundary of the fire

Output GIS database

attributes: basal area, volume per acre, vegetation type, age, day, month, year

Figure ) ).) Intersecting the stands GIS database with the fire GIS database on the Daniel Pickett forest.

stand and fire attributes. you shou ld consider using an Intersect process.

The intersect process works by locating the arcs (links) that are present in one GIS database that overlap some

area in the complementary database. For example, GIS

database # 1 in Figure I 1.2 contains a single polygon defined by two arcs. GIS database #2 contains twO polygons and 7 arcs. The polygon contained in GIS database #1 does overlap the polygons in GIS database #2, as arc I , runs from the middle of arc 2, to the middle of arc 2" crossing over arc 2<\ in the process. Therefore, arc 11 over

laps an area of represented by the polygons in darabase #2, while arc 12 does not. Further, portions of arcs 24• 26•

and 2, overlap some area represented by the polygon in

database #1, yet arcs 2" 2" 2" and 2, do not. The resulting output GIS database will contain two polygons, with

one side bounded. by portions of arc I I (split at the inter

section with arc 2<\), and the other sides by portions of

arcs 2<\. 26 , and 27 ,

To further illustrate the power of the imersect process,

the next example illustrates how you can use the results [Q assist in developing information relevanr to natural

182


Input GIS Database #1




Output GIS Oatabase

2,

~ ~

2,

2.

. 2,

, , , : 22 , , , , , , , . 2,

2,

_~I.o'--- In#l and #2

~ Inl1 , outside 01112

+-- --1- In #2, outside of #1

AP~r,on ola~ olaret , .

A portion

A portion of arc 26

A portion of arc 2,

of arc 2 ~

Figure 11.2 An example of the processing of landscape features during an inH:rsect process.

resource management planning. In this exam ple. assume YO ll are interested in developing information for a poten

tial forest fertilization project, and an examination of the inrersecrion of [he Daniel Picke[[ stands and so ils GIS

databases would be helpful , since the decision to fertilize may be based on both forest srrucrural co nditions and soil conditions. Separately. each of these [\.'10 GIS databases comains a theme: the stands GIS database describes the forest structural conditions of the Daniel Pickett forest and the so ils GIS database describes the underlying soil

types and their characteris tics. The stand polygon boundaries are defined by tra nsitions in forest structural conditions (e.g., a change from young fores t to older forest defines a stand boundary), roads, and perhaps st reams. The soil polygon boundaries are defined by changes in soil character istics, although some would argue that these boundaries should be considered 'fuzzy' because soils, generally speaki ng, do not change as abrupdy as stand characteristics might , bm change gradually over a larger

area. In addition , the ability to accurately and precisely

measure and map changes in soil types is limited by the tremendous effort that is required to sample and del ineate soils. Nonetheless, by overlaying the twO GIS databases

using an intersect process (Figure II.3), a GIS database is created where polygon boundaries are now defined by

those lines that were present in both the stands and soils

GIS databases. The outside boundaty of both the soils GIS database and the stands GIS database was exacdy the same, therefore no areas covered in either database are excluded in the resulting stands/soils GIS database. The

resulting stands/soils GIS database contains more polygons (47 polygons) than what was fo und in the original

stands GIS database (31 polygons) and soils GIS database (7 polygons) . Upon inspection, you may find that may of

these polygons are actually spurious (small and irrelevant) polygons created simply due [Q the happenstance location of the boundaries of polygons from the original twO GIS

databases (Figu re 11.4). The tabular data contained in the resulting intersected

stands/soils GIS database contains all of the anribures of

GIS database Spatial features Tabular attributes

Input GIS databases

Database to be

~ attributes :

Intersected: stands basal area, volume per acre. vegetation type, age

Database used to

~ attributes:

perform the soil type, response intersection: soils to fertilization

------ ------ ------Output GIS database

Resulting database:

§i attributes :

stands, defined basal area, volume along original per acre, vegetation stand boundaries type, age, soil as well as soils type, response to polygon fertilization boundaries

Figure 1l.3 Intersecting the stands GIS database with the soils GIS database on the Daniel Pickett fo rest.

183

Spurious polygon

-;/jLy--J Height at this end: about 1.5 meters

Width: about 20 meters

Figun 11.4 A spuriow polygoo that was created during the intersect process.

each of the original stand polygons, and all of the attributes of each of the original soil polygons. Thus for each of the 47 polygons in the stands/soils GIS database, you now know the stand conditions above ground as well as the soil conditions below ground level. With this GIS database you can perform queries that rdate to both stand

and soil conditions. For example. based on a discussion with the foreSters associated with the Daniel Pickett foreSt, it may be decided that 20-30 year old Stands on soils that were amenable [Q a high forest growth response [Q a fenilization treatment would be the most preferable areas

What is a spurious polygon? When something is labeled as 'spurious'. it is meant [Q indicate that it is

counterfeit. false. fictitious. or not legitimate (Merriam-WebSter, 2007). Within GIS, spurious polygons are certainly genuine. and can be somewhat troubling to eliminate. These polygons arise simply because the compmer and GIS software are taking two GIS databases and combining them according to the instructions provided by the user. They are only doing what they were told to do: break polygons along intersecting lines and create new polygons using the newly formed intersections. Spurious polygons might nO{ be considered legitimate. however. given the managemem needs of an organization. Most organizations. in fact. have what they term 'minimum mapping units'. and polygons below this size are eliminated by being


to ferti lize. To find these areas in the Stands/soils GIS

database, you might develop a query such as the following (for a review of queries, please refer back to chapter 5):

[Age;" 20) and [Age S 30) and [Fertresp = 'high')

where:

Age = the stand age attribute in the Daniel Picke[[ Stands GIS database, and Fenresp = the fertilization response a[[ribute in the Daniel Pickett soils GIS database

With this rype of query GIS will provide the locations of areas where the imersecrion of stand age and soil type meeting the criteria. As you can see in the case of the

Daniel Pickett forest (Figure 11.5), the potential fertilization areas may not correspond directly with the original stand boundaries. Natural resource managers using an analysis such as this will subsequently need to decide whether to fertilize whole Stands (not JUSt the part of a nand where me stand and soil conditions are appropriate. since stand boundaries are usuaIly easy to locate) or pans

of Stands. The advantage of fertiliz ing a whole Stand, when only a portion seems appropriate. is that you do nO{ need to waSte time trying [Q identify a vague transition in soil types. On the other hand, fertilizer, and the

included with other larger, adjoining polygons. In some cases. the spurious polygon could become a part of an adjoining polygon with which it shares the longest edge. The shared edge is essentially removed, and the difference between the spuriolls polygon and its adjacent neighbor is lost. When using GIS processes such as me intersect, clipping. and buffering processes. spu rious polygons will undoubtedly be created. How you manage them (e .g .• ignoring them, elim inat ing them. etc.) once they have been created is a matter of

personal preference. or perhaps a reaction to organization 5[andards. Most natural resou rce management decis ions will not be affected by the presence of spu rious polygons, however the presence of spuriolls polygo ns may become a database management problem and may detract from a message presented in a map.

184


CJ Stand boundaries

[-": ....... J Potential fertilization areas

Figure 11.5 Potential fertilization areas on the Daniel Pickett forest that consiSt of forest stands 20-30 years of age located on soils that provide a high response to a fertilil.3tion applic3[ion.

applicado n of fertilizer. costs money. and o rganizations

may want to operate as efficiently as possib le. Therefore,

rhe goal would be [Q apply treatments only where neces

sary. Ie is becoming increasi ngly common [Q provide a

fenilizar ion contraccor with rhe exact geographic coordi

nates that define rhe fertilization area, either with maps or

acrual GIS databases. If a fenilizarion contrac[Qr uses a

hel icopter system to disrribme the fertilizer, which is a

commo n pract ice when fertilizing forested stands, it is

possible that G PS technology may be employed to assist in locating the desired fertilization areas. The GPS data may facilitate the development of a digital map. which can help guide the pilo t or can be coupled with an internal guidance system to autOmatically apply fertil izer.

Identity Processes

When performing an identity process, you hope to

incorporate information about the overlapping area of

one GIS darabase into a second GIS database. While the term 'identiry' refers to a distinguishing character of a per

sonality (Merriam-Webster. 2007). this process is more likely similar ro the term 'identiry element', w here pares of

the o rig inal data are left unchanged when combined with

other data. This is partially true-some portion of the

original GIS data may be left unchanged {and present in the o utput)-where a second set of GIS data does not

co incide spatially .

Si milar to the intersect process, one GIS database is

physica lly laid onto another, yet there is usually a distinct

difference in the resul ting Ol1[Pur when compared w ith the in te rsect process . The resulting GIS database is

defined by the boundary of one of the input GIS databases, not by the boundary of the overlap between the

two GIS databases . Figu re 11 .6 illustrates that the resulr-



2,

~ ~ , , , , , , : 21 : 22 , , , , , 2, , , , , , , , , . 2, t 27

2,

--------- --------Input GIS Database #1


Output GIS Database

, , , , /+-- In #t and #2 __ __ __ .J ___ _ _

'--_ _ -./-'1--- In #1, outside 01#2

+---t- In #2 , outside 01 #1

, ......... :--...... _, ,/; : .~:+--+ In #1 and #2

" I I

2, 2,

Figun~ 11.6 An example of the processing of landscape fea lures during an idenucy process.

ing GIS database has a geogtaphic extent representing the same area as database #2, yet the arcs that defined the

polygon in database # 1 are present where the polygon from database #1 overlapped the polygons in database #2. The resul ti ng GIS darabase now includes 4 polygons. 12 arcs, and 9 nodes , as arcs 24• 26• and 27 were split into twO pieces (a and b) based on their intersection with arc 11>

and arc 11 was split into two pieces based on its intersec

tion w ith arc 24,

To extend the idenriry process exam ple (() the D aniel

Picken forest, again examine the case of the stands GIS

database and the fire GIS database. Suppose the intent was [Q develop a GIS database that contained the entire stands

data (geographic and tabular). but with the fire boundaries being integrated into the stands database. Using an ident ity process you ca n see that some stand polygons

have been split along the fire polygon boundary (Figure 11.7), thus the fire GIS database has an influence on the

structure of the resulting polygons. In addi tio n, 3nribure

185

GIS database

Oatabase to perform the identity process Oil : stands

Oatabase used 10 perform the identity process: fire

Spatial features

Input GIS databases

o ------ ------

Output GIS database

Resulting

~ database: stands, defined along original stand boundaries and along the booodaries of the fire

Tabular attributes

attributes: basal area, volume per acre, vegetatiOil type, age


------

attributes: basal area, volume per acre, vegetation type, age, day, month, ,ear

Figure 11.7 Performing an identity proass on the stands GIS database using the fire GIS database.

fields are present in the resulting GIS database [0 represenr those that were presenc in the fire GIS database. However, only the polygons within the fire boundary actually contain data related [0 the fire. The amibute fields from the fire GIS database related [0 polygons outside the fire area are empry and contain '0' values (Figure 11 .8).

A key concept when performing the identiry process is obviously determining the spatial extent of the [wo GIS databases that is [0 be retained. In the above example, the fire GIS database was overlaid on the stands GIS database, and the incem was [0 rerain the spacial extent of the stands

GIS database. If you were to reverse the order and overlay me stands GIS database onto the fire GIS database, the resulting GIS database would have quite a different look [0

it (Figure 11 .9), as the spatial extent of the resulting GIS database is defined by the spatial extent of the fire GIS database. Here, only the stand boundaries within the fire remain. While the stand-level data anributes associated with the stands in [he fire area are presenr in the resulting GIS database, no stand-level data attributes are available for the polygons outside of the area represented by the original stands GIS database (Figure 1l.l0).


Stand V

StandW

Stand VegType Basal Area

T C 120

U A 260

V A 260

W C 190

X B 20 y B 20

StandY

Age

5S

70

70

45

10

10

o Stand bouooaries

D Firearea

MBF Month Day

19.5 7 2

37.7 7 2

31.7 0 0

17.3 0 0

1.8 7 2

1.8 0 0

Year

2002

2002

0

0

2002

0

Figure 11.8 A more detailed examination of the results of the identity process of the fire GIS databue overlaid on the stands GIS database.

Union Processes

In a union process, the intent is to overlay one GIS database on top of another GIS database, and re tain all of the spatial boundaries of the landscape features contained within both GIS databases, A 'union' is the act of joining [wo or more features into one (Merriam-Webster, 2007).

Figure 11.11 illustrates that when using a union process. the resulting GIS database has a geographic extent representing the same area as both database # I and database

#2, yet the arcs that defined the polygon in database # I are present where the polygon from database #1 overlapped the polygons in database #2. The resulting GIS database now includes 5 polygons. 13 arcs, and 9 nodes, as arcs 2 ... 26 • and 27 were split into two pieces (11 and b)

186


GIS database

Database to perform the identity process on: fire

Database used to perform the identity p(ocess: stands

Resulting database: fire, defined along original fire boundaries and along the boundaries of the stands

Spatial features

Input GIS databases

o

output GIS database

Tabular attributes




Figure 11.9 Perfo rming an ide ntity process on the fire GIS database using the n ands GIS database.

Stand M

\ Stand N

Stand VegType Basal Area Age

L C 120 30

M 0 0

N C 190 45

U A 260 70

X B 20 10

MBF

5.6

0

17.3

37.7

1.8

o Fire area outside of Daniel Pickett forest

D Fire area inside 01 Daniel Pickett forest

Month Oay Vear

7 2 2002

7 2 2002

7 2 2002

7 2 2002 7 2 2002

Figure 11 . 10 A more detailed examination of th(' results of the identity process of the standt G IS database overlaid on the fire G IS database.

Input GIS Database "

Input GIS Database .2

2,

~ ~ , , , , , , : 21 : 22 , , , , , 2, , , , , , , , ... 26 ... 27

2,

-------------Input GIS 8: .. ---In#1 and #2 Database" _____ J ____ _

~ In ", outside 01112

Input GIS Database '2

Output GIS Database

+---t- In '2, outside oil'

" " ;---.... ,,' : \~f--+-

./ I In 111 aOO'2

2,

/' Figure 11.11 An example of the processing of landscape features during an union process.

based o n the ir intersection with arc 11. and arc II was split into twO pieces based on its imersection with arc 24,

To illustrate a union process with a more realistic nat

ural resource management problem. suppose a union process were to be performed on the Daniel Picket[ forest fire and stands GIS darabases. The resul ting GIS database has the combined geographic extent of rhe (wo GIS databases, and co ntains similar landscape fea tu res as were

fo und in rhe origi nal fire and stands GIS darabases (Figure I 1.12). This iliumares one adva ntage of using a union process: rhe sparial del inearion of rhe polygons in rhe resul ring GIS database is a fu nction of both of the origi nal GIS databases. thus polygon boundaries from both original GIS databases are retained. However, it also suggests a disadvantage of the process: rhe result ing GIS database may comain landscape features outside of [he boundary

187

GIS database

Database to perform the union process on: fire

Database used to perform the union process: stands

Resulting database: fire and stands, defined along original fire boundaries and along the boundaries of the stands

Spatial features

Input GIS databases

Output GIS database

Tabular attributes




Figure: 11.12 Performing a union process using the fire GIS database and the stands GIS database.

of interest, and thus perhaps may include unnecessary

landscape features. Although some of the fire polygon lies outside the Daniel Pickett forest, this area will be represented in ,he OUtput G IS database. The attribute fields contained in both of the original GIS databases may also be present in the resulting GIS database, however some

data cells will likely be empty in the attribute table (Figure 11.13). The union process is useful for those situations in which you want to preserve all of the spatial and non-spatial data that is present in twO input GIS databases.

More complex analyses can be performed using the intersect, identity, or union processes than simply bringing together the characteristics of twO GIS databases. For example, suppose you were interested in locating areas suitable for a certain type of agricultural praccice in the Pheasant Hill planning area of the Qu'Appelle River Valley in central Saskatchewan. While the databases we will use in th is example are dated (\980), they are rich with information and they allow us to examine (he usefulness of the union process for a natural resource manage-

Chapter 11 Overlay Processes 1 n

o Stand attributes, no fire attributes

o Stand attributes, fire attributes

o No stand attributes, fire attributes

Figure 11.13 Illustration of completeness of a tabular database after a union process of the fire and stands GIS database.

ment analysis. In locating [he suitable areas, you decide that the criteria should include locating a certain type of soil (loamy soils), on a ce rtain slope condition (flat slopes), where the area is currently wned for agricultural use, and where there are few limitadons for using the land

to grow agricultural produces. In this assessment, there are four distinct anribures about the land: so il type, slope condidon, zoning code, and land classification . These four attributes are comained within four different GIS databases associaeed with the Pheasant Hill planning area, and are delineated using polygons that do noc necessary

coincide (spatially) from one G IS database to the next. One way {Q accomplish this overlay analysis is {Q use an iterative union process to bring all fou r databases together so chat all of the anribures are available in a single, combined GIS database. Initially, two of the GIS databases would be unioned, then a third would be unioned to the union result of the first two. Finally, the fourth database would be unioned to the union result of the first three GIS databases. Using a query thac involved the criteria

listed below, the areas suitable for the praccice you had in mind could be identified (Figure 11.14), since the union of the four GIS databases would contain the attributes of al l of the original GIS databases, and since the polygons would be split along the boundaries of the original polygons.

188


- - . -= .. ,pg."

.IiOi'_

_ Areas suitable for an agricultural practice

c=J Other areas that do not meet the criteria for an agricultural practice

Figwe 11.14 The result of a query on the union of soils. topography, land classification, and zoning GIS databases developed for the Pheasant Hill planning area of the Qu'Appclle River Valley. Saskatchewan (1980).

Query criteria

Soil_type ", Canora Loam

or Soil_type ", Indian Head Clay Loam or SoiLrype ,. Oxbow Clay Loam or Soil_rype ,. Oxbow Loam

or Soil_type" Rocanvillc Clay Loam or So il_type II: Whiresand Gravelly Loam

Topography = FLAT

Class" No Significant Limitations or Class", Moderate Limitations

Zoning = Agriculture Priority I

Original database

soils soils soils

soils

soils

soi ls

topography

land classification (eLi) land classification (eLI )

zoning

Incorporating Point and Line GIS Databases into an Overlay Analysis

Although the overlay examples thus fa r have focused on the analysis and manipulation of polygon GIS databases, it is also possible to incorporate other types of features (points and lines) into overlay processes. For example. point and line GIS databases can be used in association with polygon databases when performing the intersect and idemity processes. The union process. however, requires that all GIS databases of interest be composed of polygon features. When using the imersect and identity overlays. the input GIS database can be composed of points, lines, or polygons but the overlay GIS database must be composed of polygons with one exception. With in some GIS software. the intersection of two line databases is possible. with the result being a new point database that has captured all intersection locations. When point or line databases are involved in an overlay process with a polygon. the resulting Output GIS database will be of the same feature type as the first input GIS database (point or line).

As an example. if you use a GIS database containi ng lines as an input GIS database. and then intersect it wi th a GIS database containing polygons (Figure 11. 15), the resulting GIS database wi ll be composed of line features _ The line features will be split at all intersections with the boundaries of the polygons in the polygon GIS database,

Input GIS , ,

Database '1 , , , , , , , .. ,

Input GIS , , 2, , 2, , ,

Database In , , , , • , , , , , ,

2, 2, • , 2, ,

• , •

2. 2,

- - -- - - - -- - --- -1,

Overlay of 2, 2, Database #2

2, on Database.1 1,

2, 1, I ,

2,

2. 2,

--- ---- -- - - - -- -Output GIS Database

1,

Figure 11.15 An example of the manipulation oflandsc.1pe features during an intersect overlay of line and polygon databases.

189

and only lines that full within the extent of the polygons will be retained in the outpUt GIS database. While this

represents a process similar [0 the clip process, the resul t

ing lines contain the information (attributes) of the poly

gons within which they fell. In the example in Figure 11.15, a line (I) that represents a road (perhaps) is being

overlaid by the two polygons from previous examples in this chapter. The line initially comains two nodes, but when the overlay process occurs, it is broken into four

arcs (II' I" 1" and 1. ) with 5 nodes. The pieces of the original road that fall outside the area covered by the polygons (I I and 1, ) are subsequently eliminated. The

resulting GIS database comains a ponico of the original

line (sections 12 and 13), and each portion of the original

li ne contains the anributes of the associated polygon within which it was contained.

An idemity process using point or line GIS databases as

the input database results in a GIS database that concains

all of the original point or line features, yet the landscape

features would contain the 3ucibure information of the polygons within which they fell. Lines would also be split at polygon boundaries as in (he previous example. The primary purpose of the identi ty process would be (Q dis

tribute anribute data from an overlaid polygon GIS data

base (Q a poim or line. T he idenrity process with a point

GIS database as the input GIS database is similar, in fact,

to a point-in-polygon query, although the results here are not temporary.

Applying Overlay Techniques to Point and Line Databases

We presenr here some examples of applying overlay

processes (Q point and line databases (Q demonsuate

potential overlay applications. Our examples make use of

the Brown Tract databases described earlier in the texc. In

the first example, cons ider the distribution of research

plots as they relate to the Brown Tract forest stand

boundaries (Figure 11.16). It might be of interest to

determine rhe distribmion of land allocation categories

(described in the forest stand layer) that are associated

with each research plot. A point in polygon intersect over

lay is one approach an analyst could use to determine this

information . The results of the imersecr overlay in this

case create a new point layer that comains all plot loca

tions and an expanded list of data fields. Each of the research plot records would contain both the original data fields and the same data fields and values of the individ-


.. <, 1.'1.

", 1.'. '..-: ".~>

~ Research Plots [:=:J Forest Stands

Figure 11. 16 Research plots and forest stands on the Brown Tract.

ual stand boundaries in which they were located. A statis

t ical frequency could be generated from the new database and could demonstrate the distribution of land alloca

tions conta.ined in the research plot locations (Table

11.1). In this case, the majori ry (48 of 57) of research plots are located in even-aged stands. Given rhe geometry

of the research plots and stand boundaries, an identity

overlay process berween these flies should produce the same results as the inrersect process,

As mentioned earlier, overlay operations involving line

databases are also possible, As an example consider the

arrangement of streams relative (Q stands in the Brown

Tract (Figure 11.17). There are portions of the Brown Tract where the stream network extends beyond the for

est boundaries. In this case, the intersect and identity

overlay commands would lead (Q different output data

bases. The intersect overlay between these layers would

result in an output stream system that would be reduced

in extenr from the original; only those stream segments

that overlapped the stands would be retained in the out

put database. In addition, all data fields in both databases would be populated in the output database. The identiry

TABLE 11.1

Land allocation

Research

Uneven-aged

Frequency distribution of land allocation categories in research plot locations within the Brown Tract

Number of research plots

48

6

3

190


-- Streams

Forest Stands

Figure 11.17 Streams and forest stands on the Brown Tract.

overlay would retain all the Stream segments, bur we would only find stand data in a resulting database for a ponioo of [he stream segments. The selection of which

overlay to use-intersect or identity-will depend on the analysis goals. Let'S assume that the analysis goals suppon retaining aU st ream segments in a final streams database. An identity ourpm database between streams and stands contains 398 stream records since geometric intersections are created at all coincident locations in the input databases. The distribution of land allocation values fo r these streams is represented in Table 11.2. Note that there are 40 stream segments that do not have a land allocation

value. These missing values represent streams omside the Brown Tract boundaries.

Although point and line spatial databases can be used with overlay processes to derive information from polygon databases, polygon databases are unable to serve as

TABLE 11.2

Land allocation

Even-aged

Meadow

Oak Woodland

Research

Unevcn·agcd

<No Value>

Frequency distribution of .. land allocation categories ill relation to stream segments within the Brown Tract

Number of research plots

281

2

3

7

65

40

the omput product of overlay analysis in these cases. In order to assimi late information from point and line data

bases into a polygon database, other approaches such as

tabular or spatial joins must be considered.

Additional Overlay Considerations

Each of the three overlay processes discussed in this chap

ter are designed for different end products (Figure 11.18). Although different outputs will usually result depending

on which overlay is chosen, there are situations where the ompm of two overlay processes will be the same, regard

less of which process is used. For example. if two polygon databases have the same spatial extent, it will not matter whether an intersect, identify, or union process is used. In addition, jf one spatial database is contained completely within the extent of another database and is used as the

primary overlay database, an identity or intersect command should produce the same resulr.

Input GIS dalabase 1 Input GIS database 2 Output GIS database

~ 0 bJ Intersect Output

~ 0 ~ Identity Output

0 ~ LlSJ Identity Reverse Order Output

~ 0 ~ Union Output

Figure 11.18 Summary of results for intersect, identity, identity reverse order. and union overlays for the stands and fire GIS databases.

191

The order in which GIS databases are selected for an

overlay process may also have significance on the ompur

database, depending on your GIS software. The identity

overlay will take into accoum (he spatial extent of one of your databases, and reduce other layers (Q the same spatial

extent. For this reason it is imponam (Q recognize how

your soft\vare selects (he inpur database that is used to

determine the idenriry spatial exrenL The union and

intersect overlays. on the other hand. are less discriminat

ing as to the order in which spatial exrencs are considered.

The union output wi ll include the emire spatial extent of

all layers while the intersect OUtput will only include the

common areas. Some GIS sofrware. however, may only

include by default the attributes of the initial input layer

in the outpm database unless the user signifies otherwise.

Depending on your GIS software, you may also be able

co involve more than [Wo layers in an overlay process.

Nthough this ability may be particularly useful for some

applications, the complexity of the Output product will

increase with the number of layers that are involved.

Overlay ompm databases cake into account the topology

Summary

With the conclusion of this chapter you have examined

most of the common vectOr processes available within GIS

software programs. As we have shown in this and previous

chapters, to address natural resource management prob

lems a number of different courses of action can be used,

each utilizing different techniques or different sequences

of GIS processes. For example, the following three

processes might be employed ro develop a summary of

forest resources within owl buffer areas:

I. Buffer owl nest locations. Clip the owl buffer areas

from a stands GIS database. Summarize appropriate

statlS[1cs.

2 . Buffer owl nes( locations. Intersect a stands G IS data

base with the owl buffer GIS database. Summarize

appropriate statistics. 3. Buffer owl nest locations. Overlay a stands GIS data

base on the owl buffer GIS database using an identity

process. Summarize appropriate statisdcs.

The GIS toolbox available to readers of this text, as it

relates [Q vector GIS processes, should now contain a


and attr ibutes of all input layers depending on operaror

choices. Bear in mind that overlay processes are complex

operations, particularly when many features are present in

input databases. As such, and depending on computing

resources, some overlay omput databases may take more

time [Q complete than oilie r, less intensive. GIS processes.

Users may have [Q be patient in awaiting overlay ompm

results. Finally. many GIS sofrw-are programs allow users to

select the attribute fields from both the input GIS database

and the polygon overlay GIS database that are ro be carried

into the resulting output GIS database. Often you will find

that selecting a subset of attribute fields for the desired

analysis will reduce the time requirements related to inter

preting and analyzing the results of overlay processes.

Given the considerations expressed above, you should

always carefu lly consider the objectives of your analysis to

dete rmine which overlay process [Q use. In some cases,

knowing whether input vec[Qr databases represent point,

line, or polygon spatial features, or some combination

thereof, will help lead ro an appropriate overlay command

choice.

number of useful rools. The challenge lies in deciding

which tool(s) to use to address each natural resource man

agement issue. The intersect, identity, and union

processes each result in different outcomes, and you need

to match the process to the type of information you

desire. While the union process is restricted to polygon input and outpm databases. point and line vec[Qr data

bases can be used in intersect and identity process. The

intersect process only provides information for features

that overlap. An identity process breaks the featu res con

tained in one GIS database along the lines provided in a

second GIS database, yet retains the full extent of features

contained in the first database. In chis case. some of the

fearures in rhe first database will not contain information

from the second where the features in the second are

absent . The union process also breaks the features con

tained in one GIS database along the lines provided in a

second G IS database, yet retains the full extent of features contained in both databases. Where features overlap, rhe

attributes of both databases will be present in the attrib

Ute rable. Where features did not overlap, only arrributes

from one of the GIS databases will be present.

192


Applications

11.1. Fertilization Plan. You have been asked to help develop a fertilization plan for the Brown Traer. In next year's plan of action, the managers of the forest may

consider fert ilizing some forest stands. However, the

managers need [Q know how much area of forest could

be ferr ilized, and what casc co anticipate. After a short discussion with the Brown Tract managers, the follow~

iog criteria for ident ifying potential ferrilization was determined:

• Only stands;:>: 25 years old and $; 35 yea rs old should be considered.

• Only stands on soil types leading to a high fertil ization response should be considered (so il types 'PR' and ' ON').

• Only stands oU[side of a 50~me[er stream buffer

(around all types of streams) should be considered.

The forest managers wam co know the following:

a} How much area of land could be ferti lized, given the criteria noted above?

b} Assuming the forest will develop a contract for the fertilization project, and assuming the contractor

will lise a hel icopter to spread the fenilizer, how

much area of land would you recommend, and

why? Consider the following in your recommenda

tion: (I) Are some of the stands of the appropriate age bisected by (WO or more soil types? (2) Would it be possible fo r the helicopter pilot to recognize these changes from the air? (3) Are some of the areas suggested by the GIS analysis for fertilization

tOO small to be worth rhe effort? (4) Is there a minimum size assumption would you make regarding pocenrial fenilization areas?

c) If the COSt of fertilization was $250 per hectare, what might be the total COSt of the project?

d) How much fenilizer is needed if you expect [0 use

450 kilograms of fert il izer per hectare (about 400 pounds per acre)?

e} Please provide the staff with a map of the potential areas that you are recommending to be fenilized

(from part b above). Include rhe Brown Tracr roads and streams on the map.

11.2. GIS processing (1) . There are a number of processes that can be used to arrive at the info rmation

requested in Application 11.1. For example, you may

have taken advantage of the intersect process, and obvi

ously used a buffer process at some point. To illustrate the

steps raken to accomplish the analysis associated with

Application 11.1 , develop a flow chart similar to the one below (Figure 11 .19) that describes the process you used .

11.3. GIS processing (2). Use a flow chart to describe (WO other (different) processes that could also be used to

generate the same results as those generated for

Application I\,\, You may want to actually perform the process and check your results to see wherher the results

are identical to those obtained in Application I \.\.

11.4 Fire losses. How much forest area, by vegetation

class, burned on the Daniel Picke(( forest during the July 2, 2002 fire? Use the '070202_fire' GIS database to describe the boundary of the fire.

11.5 Operations around research plots. Assume that the managers of the Daniel Picke(( forest have decided to clearcU( sta nd 28. They noticed, JUSt prior to allowing

Vegetation GIS

database

Ouery process

Buffer streams

50 meters

Figure 11 . 19 Hierarchy of intermediate and final GIS databases created in the development of a GIS database describing the older forest vegetation within 50 meters of all streams.

193

loggers to begin operatio ns, that a research plm (number

3) was locared in me srand. If a 100 merer buffer was left around the research plot,

a) How much fo rese area in srand 28 will acwally be clearcur?

b) If rimber values were $400 per rhousa nd board fee< (MBF). whar is the value of rimber thar will remain around rhe research plor [h int: arrribure 'Mbf' represents thousand board feet of timber volume per acre)?

c) What is the djfference in dmber volume and value

between leaving the buffer around the research plor and not leaving me buffer?

d) Develop a map thar describes the vegetation condirions on the forest after the harvesting operation

has comple<ed. Assume rhar rhe 100-merer buffer was maintained around the research plot. I1Jus[rare

References

McHarg. l. (I 969). Design with nature. Garden Ciry. NY: Natura l Hiseory Press.


on the map (either with annotation or with a shading scheme) the basal area of all of me stands.

11.6. Integrating streams and vegetation data. Develop a strea ms GIS database fo r (he Brown T ract

where each stream contains the data related to the vegerarion polygon within which it is located. Using this data, develop a thematic map that illustrates one of the vegetation characterisrics of the forest around each stream. In this exercise, change the appearance of the Streams to

illustrate the condit ion of [he forest.

11. 7. Fish bearing streams. a) How many stands in the Brown Tract contain fish

bearing streams? b) How many srands are within 100 merers of fish

bearing streams?

Merriam-Webseer. (2007). Mtrriam- Webster online uarch. Rerrieved April 29. 2007. from hrrp :/Iwww. m-w .coml cgi-binl dictionary.

194

Chapter 12

Synthesis of Techniques Applied to Advanced Topics

Objectives

As you may have found during your various interactions with GIS, there are a number of spatial processes (or mix(Ures and arrangements of methods) that can be used to

find the appropriate solution to a namral resource management problem. As we near the end of Parr II of this book, this chapter seeks (Q integrate and synthesize the

GIS processes introduced in previous chapters. and apply them (Q more complex namral resource management

problems. At the conclusio n of (his chapter, you should be familiar with:

1. how a set of complex management ru les or assumptions can be synthesized into quantitative information [hat can be used in a GIS analysis;

2. how a number of GIS processes can be integrated to

allow you to develop a spatial representation of a value (ecological, econom ic, or social) rhar represems some aspect of a landscape; and

3. how ecological , economic, or social descriptions of a landscape can be developed [Q provide managers and other decision-makers with informat ion regarding the currem sratus of a landscape.

Many of the GIS processes presented in previous chapters can be inregrated to allow users of GIS [Q perform complex landscape analyses. As we progressed through chapters 5 [Q 11, we buih upon GIS processes to show how they may be complementary. For example, query

processes allowed you to quamitatively examllle the results of analyses that required buffering processes (chap

ter 7). In anorher example, clipping and erasing processes were used in tandem to create a new database of select features (chapter 8). Chapter 12 presents some examples of advanced topics in natural resource management for you to consider, and each requires an imegrarion of the GIS

techniques presented in ea rlier chapters. The roo Is acq uired by working through the previous chaprers should be more than adequate to address the problems introduced in th is chapter; rhree rypes of advanced management problems are presented and the 'Applications ' at

the end of the chapter provide an opporruniry for readers to perform similar analyses themselves. Once again, there

are a num ber of paths you can take to ap proach each management problem presented in the app lications section. You should approaeh each problem according to

your preference of methods and techniques. H owever, only one ser of answers to each narural resource manage

ment problem exists, and no maner what process you select, you should ultimately locate the appropriate answers.

We begin with land classification, where separate and

disr incr categories of land are delineated, each suggesting a d ifferent level of natural resource management acriviry wi ll be allowed. Land classifications are, in fact. common starr ing points fo r [he development of land managemenr plans. You may need to use the querying, buffering, erasing, and other GIS processes to parse a land ownership into distinct non-overlapping classes that co mpletely

195


cover a landscape. A similar problem is then addressed. where a landscape is delineated into Recreacion Oppor~

tunity Spectrum (ROS) classes. ROS classes are designed to describe and emphasize the potencial recreation opportu

nities across a landscape, ranging from primicive recre

ation opporcunicies with few visitors to chose where momrized vehicles and many visirors may be prevalenc.

Again. the querying. buffering. erasing. and other GIS processes may be used to delineate the non-overlapping

ROS classes. Finally. wildlife habitat suitability index measures across a landscape are examined. Here, suitabil

ity is a function of [he condition of the landscape, and

how far each [}'pe of vegetation is from the road system. A hypothetical habitat suitability index is presented to

provide you with a challenging spatial analysis. A number of GIS techniques can be used to develop the habitat suitability index values. including complex mathematical calculations within the attribute table of GIS databases.

In conjunction with these advanced narural resource management problems. we emphasize the use of Row charts to mainrain order in (he analysis process. The flow

charting process may be useful for your own GIS analyses,

as a means of developing a logical approach to addressing management issues. Since many intermediace (cemporary)

GIS databases are created and used. tracking the process of an analysis wich a flow chart may prove co be very useful ,

rhus several examples are provided.

Land Classification

Land can be classified by vegetation. soils. range. habitat. landform (physiography). and other measurable physical or socio-economic characceriscics. Any map chac delin

eaces unique pieces of land can be considered a land clas

sificarion. Land classifications have many pu rposes in nac

ural resource managemenc. from serving as a basis for

assessing che scams ofland resources [0 serving as a frame

work for assessing me local management opportunities

(Frayer et al.. 1978). Land classifications are necessary for providing both policy direction (knowing what types of resources are available) and for assiscing wich policy

implemenracion (knowing where che resources are

located) . Land classification systems are generally based o n landscape characreristics char can be seen and meas

ured. and they ideally would be based on flexible. logical. general. and professionally credible concepts. and th us would be described with a quamifiable set of ru les (Frayer et al.. 1978).

Land classifications can be made from a managemenc

perspeccive where srracificacion of land is based on eco

nomic and managemenr-relaced variables (roads. screams.

erc.) . The example we provide below caprures (he essence

of this type of land classification. Alternatively. land classifications can be made purely from an ecological perspective, using vegecation, soils. wacer, climare. and ocher

physica l variables. The fou r ecological classifications used in Canada are a good example of these. They are designed in a h.ierarchy and range from broader ecozones co smaller

ecodiscriccs. Each of rhe c1assificacions have ecosysrems rhar are predominantly woody vegeracion, and are delin

eaced wichouc regard co commercial va lue (Canadian

Forest Service. 2007). Mosr narural resource managemenr organizarions

establish their management plans with in a land c1assificacion framework. Therefore. one of che inicial sceps in che

development of a managemenc plan is co describe che

resources ([he land) that are managed. and the type of managemenc appropriace [0 each portion of me land base.

After a land classification has been performed. the goals. scracegies, and implemenration of management can be

planned and implememed accordingly. For example. afrer classifying the land base. an organization may decide co

exclude cercain managemenr acrivicies in some areas, limic

the types of activities allowed in other areas. or allow full consideration of silviculrural or operacional activities in

other areas (Table 12. I). For example. the Washington State Parks and Recreation Commission (2006) uses a socio-economic, ecological land classificacion syscem mar

inregraces physical land feacures with porential human

TABLE 12.1

Class I ( R~servcd)

An example of a management-related land classification sys tem

Administratively withdrawn areas (offices and orh~r facili ties related to resource managemem)

Wilderness ar~as Areas of special concern Rock pits Ponds or lakes Viewsheds Other areas where managem~nt activities are precluded

Class 2 (Limit~d management) Riparian ar~as

Visual qualiry corridors around trails Areas designated as buffers around wi ldlife habitat Other areas where management is limi(~d

Class 3 (G~nera1 managemelll) Areas not classi fied as Class I or 2

196


uses of the land. Associated w ith each land class is a description of the philosophy of each dass, the appropriate physical featu res used to delineate the land class. and a matrix of allowed and prohibited activities within each

land class area. A land dassification, in addition to guiding the devel

opment of a management plan. may also be a requirement for participation in volunta ry stewardship programs. For example. organizations [hat need ro comply with the Sustainable Forestry In itiative@ (SFJ) (American

Forest & Paper Association , 2002) are required to dassify thei r land according [0 the SF! land classification system. This requirement may be in addirion to {and different

(han) {he land classification a nacura i resource manage

ment organization may develop during their normal

course of management planning. The number of classes withi n a land classification may

vary. For example, the Washington State Parks and

Recreation Commission (2006) land classification system uses six classes, while the Oregon sta te fo rest land classification system (Oregon Department of Forestry, 2007) contains three: general stewardship, focused stewardship, and special stewardship. Each land dass should be described by key quantitative rules that allow physical identification. There is an 'order of precedence' abom a land classificatio n: the highest (most restrictive) classes should be identified first , then the next most restrictive,

and so on. Land classified into the highest class cannor subsequently be catego rized as lower classes. This systematic approach to land classification helps avoid categorization errors.

Soils data are, in some cases, the drivers for land classification systems. In most cases, these are agriculw ral land classification systems. One such system (American Farmland Trust, 2006) uses soils characteristics, past irri

garion practices, (Opography, and vegetation cover to describe the quality ofland for farming practices. The US Bureau of Redamation (195 1) uses soi l and topographic conditions along with economic opporwni t ies to classifY land. The main goal in this system is to express the anticipated influence of mappable phys ical features on the potential productivity of a farm ing enterprise, the cost of fa rm production, and the cost of land developmem. The USDA Natural Reso urces Conservation Services uses a land capability classification for interpreting soil groupings. The land capability dasses for a rable soi ls describe thei r potencial for sustained production of cultivated crops that do not require specialized site preparat ion. The land capability classes for non-arable soils (those unsuit-

able for long-term cultivated crop management) a re grouped according to their potential for production of

permanent vegetation cover, and grouped according to

the potential risk of soil damage (Klingebiel & Montgomery, 1973) .

The Canada Land Inventoty provides broad examples

ofland classifications related to foresny, agriculture, land use, recreation, and wildl ife. The developmenr of land capabil ity classes related to forestry uses is based on a national classification system where land is fated accord

ing to its capability to grow trees for commercial uses. The raring system considers land that has not undergone

improvements (such as fertilization or drainage acrivicies), and focuses on seven tree productivity classes:

1. Land with no limitations on the growth of commercial forests, where soils are deep. have good water-holding capacity, and are high in fertility. Productivity of tree

growth is greater than 7.77 m3 per hectare per year (J 11 ft' /aelyear).

2. Land with slight limitations on the growth of com

mercial forests. where soils are again deep and have good water-holding capacity, yet there is some limitation on growth (dimate, rooting depth, low fertility ,

for example). Productivity of tree growth is between 6.37 and 7.76 m' per hectare per year (9 1-110 ft ' l aelyear).

3. Land with moderate limitat ions on the growth of commercial forests, where soils are shallow ro deep and have good water-holding capacity, yet they may be

slightly low in fertility, and have periodic water imbalances. Productivity of tree growth is between 4 .97 and 6.36 m' per hectare per year (71-90 ft' /ac/year).

4. Land with moderate to severe limitat ions on the growth of commercial fo rests, where soils are shallow to deep and have other highly variable characteristics. The main limitations are roo much or too li ttle mois(Ure, restricted roming depth, and low fertility, Produc

tivity of tree growth is between 3.57 and 4.96 m' per hectare per year (5 1-70 ft'/aelyear).

5. Land with severe limitat ions on the growth of commercial forests , where soils are shallow and poorly drained. The main limitations are roo much or roo little moisture, restricted rooting depth. low fertility, excessive rock content, and high levels of carbonates. Productivity of tree growth is between 2.1 7 and 3.56 m' per hecta re per year (3 1-50 ft' /aelyea r).

6 . Land with severe limitations on the growth of commercial forests. where soils are shallow, excessively

197


uses of the land. Associated with each land class is a description of the philosophy of each class. the appropriare physical featu res used to delineate the land class. and a matrix of allowed and prohibieed activities within each

land class area.

A land classificario n, in addition to guiding the development of a management plan. may a]50 be a require

me nt for partic ipation in volu n[ary srewardship pro

grams. For example, organizalions thar need to comply

with the Sustainable Forestry Initiative'" (SFI) (American Forest & Paper Association, 2002) are required to classify thei r land according to the SFI land classification system. This requiremem may be in addidon ro (and different (han) the land c1assificacion a narura! resource manage

menr organizadon may develop during their normal

course of management planning.

The number of classes within a land classification may vary. For example. the Wash ington State Parks and Recreation Commission (2006) land classification system us<os six classes. while the Oregon state forest land classifIcation system (Oregon Department of Forestry. 2007) conrains three: general stewardship, focused stewardship,

and special stewardship. Each land class shou ld be desc ri bed by key quantitative ru les that allow physical identification. There is an 'order of precede nce' abom a

land classificalion: the highest (most restrictive) classes sho uld be identified first, rhen the next most restrictive,

and so on. Land classified into the highest class cannot subsequently be categorized as lower classes. This systemadc approach EO land classification helps avoid categori za

t.ion errors.

Soils data are, in some cases, (he drivers for land clas

sification systems. In most cases, these are agricultural

land classification systems. One such system (American

Farmland Trust, 2006) lISes soils characteristics, past irri

gation practices. topography, and vegetation cover to

describe the quality ofland for farmi ng pcacrices. The U Bureau of Reclamation (195 1) uses soil and ropographic conditions along with econom ic opportunities CO classifY

land. T he mai n goal in this sysrem is ro express the antic

ipated influence of mappable physical features on rhe potential productivity of a farmi ng e nrerprise, the COSt of

farm production. and the cost of land development. The USDA Narural Resources Conservarion Services uses a

land capability classification for inrerprecing soil groupings. The land capabiliry classes fo r arable soils describe their pQ[emial for susrained production of cultivated

crops that do not require specialized si te preparation. The

land capability classes for non-arable soils (those unsuit-

able for long-term cultivated crop management) are

grouped according to their porential for production of

permanent vegetarian cover. and grouped according [0

the potential risk of soil damage (Klingebiel & Montgomery. 1973).

The Canada Land Inventory provides broad examples ofland classificarions related ro forestry. agriculture. land use. recreation, and wildlife. The development of land capabiliry classes related [0 forestry uses is based on a

national classification system where land is rated accord

ing to irs capability [0 grow rrees fo r commercial uses .

T he rating system cons iders land that has nor undergone

improvements (such as fert il ization or dminage accivicies),

and focuses on seven tree productivity classes:

1. Land with no Limitations on [he growth of com mercial

forests, where soils are deep. have good water-holding capaciry. and are high in fertiliry. Productivity of tree grow[h is greater rhan 7 .77 m' per hectare per year

(1 11 fr' /aclyear). 2. Land with slight limitarions on the growth of com

mercial forests, where soils are again deep :tnd have

good water-holding capaciry. yet there is some limitation on growth (dimate. rooting depth. low fertility. for example). Productiviry of tree growth is between 6.37 and 7.76 mJ per hec tare per year (91-1 10 ft'l aclyear).

3. Land with moderate limitations on the growth of

commercial foresrs. where soiJs are shallow co deep and

have good water-holding capaciry, yet they may be slighrly low in ferti~ry. and have periodic water imbalances. Productiviry of tree growth is between 4.97 and 6.3601' per hectare per year (71-90 fr3/aclyear).

4 . Land wirh moderate to severe limitations on the

growth of commercial forests. where soils are shallow

to deep and have other highly variable charactetiStics. The main limitarions are [00 much or roo little mois

ture, restricted roming depch. and low feniliry, Produc

tiviry of tree growth is between 3.57 and 4.96 01 ' per hectare per year (5 1-70 fr' /aclyear).

5. Land w ith severe limi tations o n [he growrh of com

mercial forests, where soi ls are sha llow and poorly

drained. The main limitations are roo much or roo lit

tle mo isture, restricred roming depth , low fertility,

excessive rock coment, and high levels of carbonates.

Productiviry of tree growth is berween 2.1 7 and 3.56 mJ

per hectare per year (31-50 fr' /aclyea r). 6 . Land with severe limitations on the growth of com

mercial fores[s, where soils are shallow, excessively

Chapter 12 SyntheSiS of Techniques Applied to Advanced Topics 187

drained, and are low in fertility. The main limitadons are resuicted rooting depth. low fertility, excessive rock content, excessive soils moisture. and high levels of soluble sa lts and exposu re. Productivity of tree growth is between 0.77 and 2.16 m' per hectare per year (11-30 f" /adyear).

7. Land with severe limitations on the growth of commercial forests, where soi ls are shallow and may conta in toxic levels of soluble salts. A large portion of these areas include poorly drained organic soils. The main limitations are restricted rooting depth, low fertility, excessive rock content, excessive soils moisture. and high levels of soluble salts, and exposure. Productivity of tree growth is less than 0.77 m' per hectare per year (1 1 ft3/adyear).

The land classification is based on potential natural tree growth and soil characteristics. The proximity of some areas of land to the ocean may also affect the class ification of forest land. These seven main classes can be fU rther subdivided into subclasses that are based on climate, soil moisture, rooring depth. and orher soils characteristics. An example of the broad-based land classification for a portion of southern Ontario (using data obtained from Natu ral Resources Ca nada, 2000) is provided in Figure 12.1.

An important aspect to consider in any land classification process is that the sum of the area in the various classes should equal the sum of the area in the landscape being managed. For exam ple, using the State of Oregon system, the sum of rhe area in the special, focused, and general stewardship land classes should equal the total area of the landscape being classified. If not, one or both

of the following situations exist: (1) there is some overlap among the landscape features (polygons) in one or more of the land class GIS dacabases, or (2) some area of che landscape is not being represented by any of the stewardship classes.

As an example of developing a management-related land c1assificacion for a managed property, we wi ll illustrare the application of the general classes represented in Table 12.1 to the Brown T ract. First, some quantitative assumptions about the three land classes need to be made to allow us to delinea te rhe areas on a map . We will assume that class 1 (reserved) areas will contain meadows. research areas, rock pits, and oak woodlands. C lass 2 (limited management) will be those areas of land that are within 50 m of streams, 100 m of hiking trails , 100 m of homes, and 300 m from any owl nest locations. Finally, class 3 (general managemenr) areas are assumed to contain the land that remains after class 1 and class 2 management areas have been delineated. To delineate these three classes, a series of GIS techniques such as querying, buffering, clipping, and erasing processes may be requi red (Figure 12.2) . However, other arra ngements of GIS processes could have also resulted in the same solution. The polygons rep resented in the resulting three land classes (Figure 12.3) should not overlap, which means that no single unit of land wi ll be co unted twice. Put another way, each unit of land can only belong to a single land class. In this example of a management-related land classification, class I consists of 229 hectares (567 acres), class 2 consists of671 hecrares (1 ,657 acres) , and class 3 consists of 1,222 hectares (3,020 acres). When all three of the land classes are added togecher, chey equal che size of the Brown Tract.

Port Rowan

Classes 1-2 t Class 3 Classes 4-5 Nor1h Classes 6·8

Figun: l2.. Land d usification cxamplt: for a portion of soumt:rn Ontario.

198


(a) Class 1 stewardship areas

0"" process

(e) Class 3 stewardship areas

Erase process

Erase process

(b) Class 2 stewardship areas

Buller process

Buffer process

Buffer process

Buller process

Buffered trails

GIS database

Merge process

Clip process

Eras. process

Figure 12.2 Hierarchy of intermediate and final GIS databases created in one process that facilitates me development of the Brown Tract land classification.

Recreation Opportunity Spectrum

A number of classification processes for outdoor recrearion have been developed, including the Recreacion

Opponunity Spectrum (ROS), carry ing capacity, limits of accep table change. and the Tourism Opportunity Spectrum (Burler & Waldbrook, 1991 ). The latter system

is based on (he Recreation Opportunity Spectrum, and includes aspects of accessibility (i.e., transportation systems) , murism infrastructure, social interaction, and other non-adventure uses. The ROS was developed by the USDA Fo rest Service and the US DI Bureau of Land Management as a tool for managing recreation and

[Quflsm on fede ral land. and for integrating recreation and tourism with other land uses (Clark & Stankey,

1979). ROS is used to describe and identi fy recreational serrings. and to illustrate the likelihood of recreational opportunities along a spectrum that is divided inca several classes. This system combines the physical landscape characteristics of location and access (Q allow you [Q delineate areas of land that may be used for differenc recreational purposes. For exam ple, in the most recent forest plan for the Hi awatha National Forest in norrhern Michigan (US DA Forest Service, 2006) it is suggesred that recreation-rela ted development, activities, management practices, and access will be consistenc with the delineated

199

Chapter 12 SyntheSiS of Techniques Applied to Advanced Topics 189

Land classification _ Class 1

c:::::::J Class 2 c:::::::J Class 3

Figw~ 12.3 A land dusification of th~ Brown traCt.

ROS class for each area. Thus. the recreation opportun ities will be provided in a manner consistent with the ROS designation for each management area. The ROS classes represent a wide range of recreational experiences. from rhose rhar include a high likelihood of self-reliance. solitude. challenge. and risk. ro rhose rhar include a relarively high degree of resource development and interaction with

orner people. A recreation opportunity class. therefore. is an area of land that may yield certain experiences for recreationists in a specific landscape setting.

Consider an activiry such as cross-country skiing. a

popular recreational activiry in western North America. Cross-country skiing experiences in and around cities. such as Bend. Oregon. are likely to result in experiences

that are exercise-oriented yet include a high frequency of interaction with other people and developed resources. However. cross-country ski ing experiences in the backcountry. such as the nearby Deschutes National Forest, while also exercise-oriented. are more likely to include elements of solitude. risk. personal challenge. and will likely have a lower frequency of interaction with people. Therefore. the same activity. cross-country skiing. can be associated with different experiences in d ifferent land-

scape settings. As a result. there is a need to delineate

those areas spatially, so that management activities related to recreational activities (and other management objecrives) can be planned accordingly.

The original version of rhe ROS classificarion (Clark & Stankey. 1979) divided land areas into six classes:

I. Wilderness (now called primitive) 2. Semi-primitive non-motorized 3. Semi-primitive motorized 4. Roaded narural 5. Rural 6. Urban

Category 4 has since been expanded to two classes, roaded natural and rooded modified. alrhough me exacr classes used seems to vary from one management situation to the next. In the most recent forest plan for the Hiawarha Narional Forest (USDA Forest Service. 2006).

the o riginal six classes are used.

The ROS classes suggesr rhar specific kinds of recreation activities and experiences owing to certain physical (e.g .• size). social (e.g .• encounters with orher people). and managerial (e.g .• legally designared wilderness area) characterist ics can be supported. The rules that define the

ROS classes can include spatial relationships. For example. to be considered a primitive area, land must be more [han

1.5 miles from any road (Table 12.2). Further. some spa-

TABLE 12-2

ROS class

Primitiv~ (P)

Semi-primitive.

non-momriud (S PNM)

S~mi-primi cive,

motorized (S PM)

Roaded natural (RN)

A subset of rules with spatial consideratioDs for delineating recreational opportunity spectrum (ROS) classes

RuI,

Areas ofland great~r than 1.5 miles from

a road.

Areas of land that ar~ greater than 0.25 mi les

from a road , hav~ forest stands ~ ;0 years of

ag~. and ar~ ~ 202.3 hectares (500 acres) in aggregat~ siu.

Areas of land that ar~ grear~r than 0.25 miles from a pawd road. hav~ forest stands

<!: 50 yeltS of ag~. and are ~ 202.3 hectares

(500 acres) in aggrega[~ size:.

Areas of land with stand ages ~ 50 years. and

2: 16.2 hectares (40 acres) in aggregat~ siu.

Road~d, manag~d (RM ) Ar~as that do nor fit imo any of rh~ other

classes.

200


[ial aggrega[ion of polygons may be necessary. The semiprimitive, non-motorized ROS class suggestS mat there muse be a[ leas[ 500 comiguous acres (202.3 hec[ares) of land of certain forested conditions before land can be classed as such. Here, you may have co add cogecher [he

area of several contiguous polygons to determine how much area rhey represent in aggregate.

As with rhe land classification example described eacIier, [he sum of [he landscape area in [he ROS GIS da[abase(s) (Figure 12.4) should equal [he co[al area of [he landscape being classified. If no[, one or borh of [he fol

lowing sicu3rions exist: (1) there is some overlap among the landscape fearures in onc or more ROS classes, or (2) some area of rhe landscape is not being represented by any of rhe ROS classes. A process you might use co create the primitive and semi-primitive portions of an ROS map migh[ resemble [he flow chan illusera[ed in Figure 12.5.

As you may gather from this set of processing steps,

Erase process

Buffer roads

1.5 miles

Erase process

Aggregate or combine

process

ROS classes I:J!!!It!!I Primitive (none) rz::::iiiI Semi-primitive, non-motorized (none) _ Semi-primitive, motorized c::J Roaded, natural c:::J Roaded, mCYlaged

Figure 12.4 Recreation OpponunitySpecuum (ROS) classes for the Brown Tract.

Erase process

Buller m.ds

0.25 miles

Query process

(age ~ 50)

Query process

(size :<!: 500)

Figure 12.5 Hieruchy of intermediate and final C IS databases created in the development of the primitive and semi-primitive portions of a Recreation Oppo rtunity S~ctrum map.

201

Chapter 12 Synthesis ofTechniques Applied to Advanced Topics 191

when you consider the development of the full range of ROS classes, rhe sec of processing steps may become cum

bersome and confusing. Developing a flow chan to

describe the processing steps you used, and co identify the intermediate and final GIS darabases, will alleviare some of this confusion.

Habitat Suitability Model with a Road Edge Effect

Habitat suitability models provide natural resource managers with a glimpse inca rhe potencial of a landscape co suppOrt habitat for a specific species of wildlife, or group

of wildlife species (Morrison et ai., 1992) . These models generally describe habitat suitability as the geometric mean of two or more variables that represent (or inAu

ence) the occurrence and abundance of a particular

wildlife species. A geomerric mean is calculated by raking rhe nth root of the product of a group of numbers, where

n is equal to rhe number of observations. Rempel and

Kaufmann (2003) define habitat as rhe set of foresr struc(ural conditions that provide some means (e.g. , nesting.

reproduction. foraging) for a species of wildlife during its life history. Each forest stand in a vegetarion database is

assigned a single habitat value based on rhe structural

conditions thar exisr in (and perhaps around) the srand during some period of time. conditions which can

change as management activicies are implemented. The

suirabiliry of habirat is generally scaled becween 0 and I, and wildlife managers are called upon to determine what

levels are appropriate (0 describe optimal habitat. While there is considerable debate concerning the usefulness and accuracy of habitat suitabiliry models (see Brooks, 1997), when well-developed and validated, they do allow natural resource managers (Q examine rhe relative quality

of one area versus another with respect to some species of

inceresc. To illustrate the developmenr and display of a habirar

suitabiliry map, a hypothetical habirat suitabiliry index (HSI) is developed for a fictional species of vole. The model will allow you CO evaluate habitat suitability as a function of foresr basal area, age, and the distance of habitat from roads.

HSI = j(basal area, age, disrance from roads)

The HSI incorporates rhese three paramerers into a

single non-linear model that is used ro calculate rhe

1.2,-----------------,

1.0

0.8

Basal area 0.6 sco<,

0.'

0.2

Basal area per acre (sQuare feet)

Figure 12.6 BanI area scores for a range of stand basal areas.

geometric mean of the scores of each variable. The pur

pose of rhis exercise is to obtain a graphical descriprion of

the landscape features that are important in describing rhe habirar of the vole, given an undemanding of rhe vole's habitat requirements.

HSI calcularion = (basal area score X stand age score X

distance from road score) 113

In order [Q calculate each of the individual parameter

scores, a ser of quantirative rules is needed. These rules

are generally developed rhrough research , lirerature

reviews, or perhaps are based on the advice of biologists

who are experts on rhe life history of voles. Since rhe vole assumed here is a fictional species, the set of rules have

been developed by the authors of rhis book, and are hyporhetical. The basal area score, for example, is a funcrion of rhe square of srand basal area (fr2 per acre) mulriplied by a consrant, ro indicate a non-linear posirive

response of vole abundance to more heavily s[Qcked tim

ber stands (Figure 12.6).

Basal area score = 0.0000 I 15 X (basal area) 2

If Basal area score> 1.0, then Basal area score = 1.0

The stand age score is a linear funccion of stand age

(Figure 12.7), where age is multiplied by a co nsranr. When combined with the basal area score, me [wo por

tions of rhe HSI favor older srands that are well-stocked (where both rhe basal area score and stand age score are

high), over older srands rhat are not well-stocked, or younger srands rhat are over-stocked (where one score is

high and the other low) .

202


1.2

1.0

0.8

Stand a,. 0.6 score

0.4

0.2

0.0 1----,--,----,--,----,--,----,--,----r----1 10 W M ~ W ~ ro W 00 @

Stand age (years)

Figure 12.7 Stand age scores for a range of stand ages.

Scand age score = 0.01 X (scand age) If Scand age score> 1.0, chen Scand age score = 1.0

While the basal area and stand age parameters may more likely describe the abundance of rhe vole, the dis

tance from road parameter describes rhe potential occur

rence of the vole species. In other words , the distance to

road factor is used {Q represent the assumption that areas

near roads will represent lower habitat quality than simi

lar areas of land fa[(her away from roads (Figure 12.8) .

Distance from

road score

1.2

1.0

0.8 Distance from 0.6 road score

D ••

0.2

0.0 0 ~

N

0.25 if wichin 15.24 m (50 feec) of any road, or 0.50 if 15.25 (0 30.48 m (50.1 (0

100 feec) of any road, or

I I

I

Distance from road (feet)

Figure 12.8 Distance from roads scores for a range of distances from the road network.

Habitat scores

_ 0.80H.000 _ 0.601- 0.800

c::J 0.401-0.600 c::J 0.201-0.400 c::J 0.000-0.200

Figwe 12.9 Habitat suitability scores for a vole on the Brown Tract.

0.75 if 30.49 CO 45.72 m (100.1 (0

150 feec) of any road, or I .00 everywhere else

G raphically displayed, che map of HSI for che vole indicates the relative quality of vole habitat across the land

scape (Figure 12.9). A score of 1.0 represen" opcimal

habitat , a score of 0.0 represents the poorest quality

habicar. Somewhere along che 0.0-1.0 range, biologisrs will

need to determine the threshold levels that separate good

habitat from poor habitat. One process that can be used to arrive at these scores is presented in Figure 12.10 .

Here, che roads are buffered chree cimes (15.24, 30.48, and 45 .72 m). Two of rhe buffer GIS databases are chen subjected to an erase process. resulting in a buffer band

around each road (a 15.25-30.48 mecer band and a 30.49-45.72 mecer band). The 0--15.24 mecer buffer is then combined with these MO buffer bands (Q create a

GIS database that represents three of the buffer distances

by polygons, wich no overlapping polygons presem. The fourth buffer distance, as you can imagine. is everything

nor included in chese chree buffer discances. The buffers are then overlaid on the vegetacion GIS database. breaking

vegecacion polygons ac che buffer boundaries. The basal area, stand age. and distance from road scores can then be

calculaced in che cabular ponion of che resulcing GIS dacabase. The final HSI score can be calculaced as a funcrion

of the basal area, stand age, and distance from road scores. and a thematic map can be developed to illustrate the dis

tribution of vole habitat across the landscape. In addition,

che final GIS dacabase can be queried co develop a cable of

area by habicar class. 203


Iv~m~ I L R_ / I Roa~ I I Roads I GIS GIS GIS GIS da!abase database da!abase database

i ~ ~ ~ Calculate Buffer Buller Buffer basal area roads roads r_

score 15.24m 30.48 m 45.72 m

~ ~ ~ ~ Calculate I( BUllere,,'; I( Buffered III I( Buffered III stand age

(o-~~m) (o-~~ m) roads score (0-45.72 ml

~ Erase

process

Erase ~ process I Bullered I roads

~ (3Q.48-45.7Srn) I Buffered I r_

(15.24-30.48 m)

Combine process

~ Overlay / Road I process / buffers

~ I HSI IL Calculate calculate Develop GIS road HSI mapaf

database II score score HSI scores

Figure 12.10 Hierarchy of intermediate and final GIS d:ltabue$ created in the devdopmcnt of an analysis of potential wildlife habitat suitability (HSI) areas for a vole on the Brown Tract.

Summary

This chapter illustrates juSt a few of [he more com plex spatial analyses that may be performed (or requested) by natural resource managers. The number and arrangemem

of GIS processes could vary in addressing analyses such as these, and may include buffering. clipping, erasing, and querying of landscape features. Therefore. the chapter represents a synthesis of the tools readers have acquired from previous chapters in this book. It should be apparent by now that it is important to explicitly define the quantitative rules and the GIS processes that might be used to address complex spatial analyses. For example, the

need fo r quantirative rules and a logical set of GIS processes [0 separate one set of landscape featu res from another in an analysis of ROS classes is important because a single un it of land must be assigned only o ne ROS class, and all units ofland must be assigned a class. The graphical display of the result of a complex GIS analysis. such as the ones illustrated in this chapter. is also important because land managers rypical ly use these products to help them visualize and make decisions regarding the management of natural resources.

204


Applications

12.1 Land classificat ion. Becky Blaylock, manager of

the Brown Tract, wants you to develop a management

related land classification for the forest. She asks you to

develop GIS databases for each of three classes using the

following rules: 1. Special stewardship areas will consist of the follow

ing landscape features: Oak woodlands, Meadows,

and Rock pits. 2. Focused stewardship areas will consist of the follow

ing landscape feamces:

a. streams buffered according co the Oregon State Forest Practices Act (30.48 meters [100 feet]

around large fish-bearing st reams [Size = 'Large'

and Fishbearing = 'Yes], 21.34 meters [70 feet]

arou nd medium fish-bearing streams, 15.24

meters [50 feet] around small fi sh-bea ring

streams, 2 1.34 meters [70 feet] around large

non fish-bearing streams, 15.24 meters [50 feet]

around medium non fish -bearing streams, and

6.10 meters [20 feet] around small non fish

bearing streams);

b. a buffer of 100 meters around all water sources

tha t are not culvert spills or water towers;

c. a buffer of 100 meters around all authorized

(rails; and

d. research areas. 3. General stewardship areas will consist of whatever

land remains.

Do the following:

a) Develop and illustrate a process (How chart) for

accomplishing the task of defining the land classi

ficat ions of the Brown Tract forest according to the

rules listed above.

b) D etermine how much land area is contained in the

special stewardship land classification .

c) Determine how much land area is contained in the

focused stewardship land classification.

d) Determine how much land area is contained in the

general stewardship land classification .

e) Produce a map of the entire Brown Tract, ill ustrat

ing the three land classifications.

As a general strategy, you may want to follow [his process: • Develop a special stewardship GIS database.

• Develop a focused stewa rdship GIS database by

performin g the appropriate buffer and query

processes, and then intersecting these GIS databases.

(Why would you nOt use a merge process here?) • Erase the special stewardship GIS database features

from the focused stewardship GIS database features.

• Erase both the special stewardship GIS database fea

tures and [he focused stewardship GIS database fea

tures from the stands GIS da tabase, creating the

general stewardship GIS database.

12.2 Recreat ion O pportunity Spectrum. The Dimict

Manager associated with the Brown Tracr (Becky

Blaylock) would like you to determine how much area

might be classified in the five recreation opportunity spec

trum (ROS) classes (see T able 12.2) . Based on this subset

of rhe ROS criteria,

a) How much land area is contained in the primitive

class? b) How much land area is contained in the semi

primitive, non-mororized class? c) How much land area is contained in the semi

primitive, motorized class? d) How much land area is contained in the roaded

natu ral class?

e) How much land area is contained in the roaded

managed class? f) Develop a thematic map illustrating the five ROS

classes on the Brown Tract.

g) Draw a flow chart [Q describe the p rocesses lIsed [Q

develop the ROS classes, including the GIS operations and all GIS databases used (original, interme

diate, and final GIS databases).

12.3. Visual quality buffers. You have been asked by

the manager of the Daniel Picken forest [Q evaluate the

potential impact of two proposed organizational policies

for the forest resources found there. It seems that the

owners of the property are becoming very concerned with

the public perception of management on the forest, thus they are interested in the trade-off's associated with alter

native management pol icies.

Policy #1: Buffers next to neighboring landowners.

Assume for this example that even-aged forest manage

ment is practiced across the property. This potential

pol icy suggests that c1earcllt harvesting activities adjacent to neighboring landowners of the Daniel Pickett

forest will be restricted.

205

Chapter 12 Synthesis ofTechniques Applied to Advanced Topics 195

a} If a 50-meter uncut buffer were to be left adjacent co all other property owners, how much land would this require as a volumary contribution [0

rhe pCQ[ecrion of adjacent landowners resources?

b} How much timber volume of vegetation class A would be found in the buffer (vegetation class A is the older timber class. perhaps that which can be harvested in the near- term). and what perce mage

of rhe [Ocal volume in this vegetation class would

be affected?

Policy #2: Buffers next to paved public roads. This potential policy suggeSts that visual quality buffers may be maintained along paved roads within rhe Daniel Pickett forest. These buffers will not be managed. bur rather treated as reserved areas, where har

vesting is precluded. a} If a 50-meter buffer were required around all paved

roads, how much land area would this involve, and

how much timber volume in vegetarion class A would it affect?

b} If the State decided to convert the North-South paved road on the Daniel Pickett foreSt to a highway, and required a 1 DO-meter wide corridor [Q he transferred to State ownership, how much land area would be affected?

c} If bare land values were assumed to be $200 per hecta re. and timber volumes $400 per thousand

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a) Calculate the amount of land area on the Daniel

Pickett forest in the following habitat suitabil ity classes: 0.000-0 .200 (low quality). 0.201 -0.400 (low/moderate quality). 0.401-0.600 (moderate qual ity). 0.601-0.800 (moderate/high quality). 0.801-\.000 (high quality).

b} Develop a map illustrating the habitat quality for the vole by suitabi lity class.

c) Draw a flow chart of rhe process yo u used to develop the habitat suitability classes.

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Rempel. R.S .. & Kaufmann. G.K. (2003). Spatial modding of harvest co nsuainrs on wood supply ve rsus wildli fe habicat objecrives. £Ilvironmeruo/ Management. 32. 646-59.

US Bureau of Reclamation. (1951). Land classification. Bureau of reclamation manull~ vol. V, irrigated land use. part 2. Denver. CO: US Bureau of Reclamation.

USDA Forest Service. (2006). Hiawatha national form. 2006 fomt plan. Milwaukee. WI: USDA Forest Service. Eastern Region. Retrieved February 18.2007. from http://www.fs.fed.us/r9/hiawarhalrevision/2006/ ForPlan .pdf.

Washington Scate Parks and Recrearion Com mission. (2006). WAC 352-16-020 land classification. Olympia. WA: Washington Srate Parks and Recrea(ion Commission. Reuieved Februa.ry 17. 2007. from htrp:/iwww. parks.wa.gov/plans/lowerhoodcanaI/Srare%20Parks% 20Land%20Classificat ions.pdf.

Chapter 13

Raster GIS Database Analysis

Objectives

The skills and techniques you'll learn in this chapter should provide insight inco the examination and applicadon of raster GIS databases for natural resources research,

and how raster GIS databases might be included in suppaning natural resource management decision-making. At the conclusion of this chapter, you should have an understanding of:

1. how landscape comour GIS databases are created from

a OEM;

2. how landscape shaded relief GIS databases are created from a OEM;

3. how slope GIS databases are created from a OEM; 4. how to calculate slope gradients for a linear landscape

feature, such as a road, trail, or stream;

5. how (0 conduct a viewshed analysis for a parrion of a landscape; and

6. how to create a watershed boundary based on digital elevation data.

As mentioned in chapters 1 and 2, there are {Voto gen

eral types of data structures used in GIS coday: vector and

raster. Unci l now, we have focused on vec[Qr GIS data

bases and the GIS operations related to the typical kind of applications performed in natural resource organizat ion

field offices. This chapter now delves into rhe use of raster

GIS databases for namral resource applications. and a few

of the GIS operations that can be performed using them.

An emphasis is placed on how raster GIS databases might

be used in field offices to support natural resource man

agement decisions. Although we will rurn our attemion in

the next chapter to other raster database applications. the

primary raster GIS database that is considered in th is

chapter is a digital elevation model (OEM) . Many difTerem types of landscape information can be cultivated from

a single OEM database.

Digital Elevation Models (OEMs)

As their name implies. OEMs comain information related

to the elevation of a landscape above sea level or relative to

some other datum point. T hey are different than the typ

ical USGS Quadrangle maps discussed in chapter 4, in that they are in digital form. As with other raster GIS databases, each unit on the landscape is typically represented by a landscape-related value (or set to a null or 'no data' value),

and each unit is exactly the same s ize and shape as the

other units (F igure 13.1). The most prevalent OEM databases available in the US are the USGS 30 meter OEMs (US Department of Interior, US Geological Survey, 2007). Within Canada, Natural Resources Canada (2007) provides access to digital topographic data. Raster databases are onen described in terms of their spatial resolu

tion, as in the phrase '30 m OEM'. This infers that each grid cell in the OEM database is 30 m by 30 m in size in terms of on-the-ground area that it represenrs. Many

regions in the US also have 10m OEMs available for areas within federal and state agency administrative boundaries.

In some cases OEMs for states. provinces. or other large

regions can be purchased from commercial entities.

DEMs can be used for a variety of analytical purposes,

bur the most general of these purposes is simply ro view

the rel ief of a landscape. OEMs can use shades of color or gray rones co illustrate differences in e1evadon through a

208


Figure 13.1 Ruler grid cells from a digital elevation mode (OEM).

separation and c1assificarion of elevation values . Figure

13.2 illuscrates a gray tone color-shading scheme applied to a 10 m OEM of the Brown Tract, and uses a twelvecategory equal-inrerval classification scheme CO highl ight (he cha nges in elevation. The equal-imerval classificat ion

takes the disrriburion of elevadon data values found

within the 10m OEM and divides it equally into twelve sub-sets of elevat ion ranges. Most GIS software will allow users to define [he number of elevation range categories shown visually. and offer choices for color and gray tone schemes [0 illusrrate distinctions between elevation C3rcgones.

Brown Tract

OEM Values (feet)

D 136-150

0 151 - 200

D 201-25O

D 251 - 300

~301- J50

IH;11 351 -"OO 111 401_ 450

_ 451 -500 _ 501- 550

_551-600

_801-850

_651-700 _ No Data

Figure 13.2 Elevation ca t~ori~ for the Brown Tract using a 10 m OEM.

One notable category rep resented in the legend in Figure 13.2 is the 'No Data' category. This category is necessary because raster GIS data must be stored as a set of grid

cells that combine ro form a rectangular or square shapethe width and height of the image is defined by the number of grid cells. Therefore, when landscape fearures of interest do not match a rectangular shape (e.g., the shape of the Brown Tract) , the grid cells that are nOt associated with [he landscape features of interest are given a nuil , or No Data, value. For example, all of the grid cells that represent areas outside the boundary of the Brown T ract con

tain no data. For mapping purposes, the symbolization

used to display cells with a null value can be assigned a transparent shade. Almost all raster GIS software programs allow the recognition of a null value, yet are designed to ignore this va lue in analytical computations. When performing a multiple GIS database overlay analysis, some raster GIS software programs are also designed ro ignore any cells that overlap null cells in any other GIS database being analyzed. For example, if twO spatially coincident raster databases were overlaid on each orher and any portion of either dacabase comained cells with no darn, any

database resuhing from an overlay or comparative analys is that involved borh databases would also comain no data cells at rhe same locations. This result would occur even if actual values were present in a portion of one of the raster databases. Whi le th is functionality may or may not be appropriate for particular G IS analyses, users should be cognizant of how null values assigned to grid cells will be handled within their selected GIS software program{s).

Elevation Contours

OEMs can be used to create devation contours, or lines that indicate a constant or nearly constam elevation across a landscape. Contour lines are created adjacent ro each other such that elevation represented in even increments, such as every 30, 50, or 100 m. An elevation contour GIS database is usually represemed through a vector dara

srruCture based on a user-defined elevation interval. Contour lines allow YOll to examine the relarive relief of a landscape and to make inferences about landscape ropography ro support managemem decisions. For example. contour intervals can be used ro delineate likely hydrologic drainage patterns and watershed boundaries. When designing road systems, engineers rypically need to keep the slope of each road below some maximum gradient. since slopes too steep will either prevem the movement of certain rypes of veh icles (if the vehicle is travelling uphill),

209

A contour. [Q most people. represents the ou dine of some figure or body. Contour intervals. as used on maps. represent the oudine of all areas thar have the same elevation. It would be as if you were to slice (horizontally) the landscape every 100 feet (or whatever interval was chosen) in verrical elevation. Contour plowing is a common practice in agriculture. where

or will be too dangerous to travel (if the vehicle is travelling downhill) . With many raster GIS software programs, users have the ability [Q choose a contour interval and the starting elevation value at which contours will be created.

To creare contour lines a process such as rhar described in Figure 13.3 can be used. In cases where e1e-

Select contour interval

Setect base

elevation value to

start contours

Verify elevation

units

Figure 13.3 A general process for the development of a contour line GIS database from a OEM.


plow lines are laid parallel to the contour of the landscape [Q reduce the erosion potential of rhe agricultural practice. This also reduces strain on farm machinery; this practice encourages moving laterally rather than perpendicularly through elevation gradients. Each section of a plow line is. theoretically. at about the same elevation as every other section of the line.

vatlon umts (i.e. meters. feet) do not match horizontal mapping units. a unit conversion factor may be needed in order to bring the units into agreement. Within ArcGIS a OEM must be opened as a layer, the Spatial Analyst Extension must be activated. and the Spatial Analyst menu must be opened. From the Spatial Analyst menu. choose Surface Analysis. then choose the Contour option. This should open the Contour dialog box, which will prompt you for the Input Surface. conrour dimensions, and an output database name. Using the Brown Tracr 10m DEM, a COntour interval of 50 feet with a base contour elevation of 1 00 feet was chosen (Figure 13.4). Using (he 100-foot base elevation should result in comour lines that o riginate from 100 feet and incremem in 50-foot steps. The contour line GIS database that is created is a vector GIS database, and each line contains an att ribute describing the elevation . Users can then modify this vector GIS database to display different color shades or line thickness for differenr contour lines of interest.

Brown Tract

ContoIS Interval

- 50'''' OEM Value. (feet)

0 100- 150

o 1S1-200

W 2{)1 -25O

2S1-lOO _ 301 - 350

_ 351--400

_4()1-~

_.Sl -5OO _ 501-S50

_ 551-600

_ 601-MO

_ 6S1 -700

Figure 13.4 A contour line GIS database for the Brown Tract displayed on top of the Brown Tract 10 m OEM.

210


Whenever boch horizontal coordinate positions and vertical elevations are processed simultaneously. as is {he

case in the creation of contour lines discussed above. it should be ascenained. [hat both cypes of measurements use rhe same units. TypicaUy coordinates and elevations will he recorded using meters, international feet, US survey feet, or some combination of these units . It is not uncommon within rhe US, however, to discover OEMs chat have coordi nate values in meters bur (hat srore eleva

tion val ues in survey feec. A GIS analyst might mistake rhe resulring contour line va lues (a represent meters, rhus over-representing (he elevat ions alo ng co ntou rs. The

Spac ial Analyst Contour option dialog box conta ins an input box w here users can specify whether elevation units

differ from coordinate units within a OEM.

Shaded Relief Maps

Another product that can be derived from a OEM is a

shaded relief map. Shaded relief maps are intended ro simulate the su n-lit and shaded areas of a landscape when

assuming thar the sun is positioned at some location in

rhe sky. Landscape fearures that face roward the sun will appear more brightly lit than objects facing away. For raster GIS software programs that provide the abiliry co

create a shaded relief map. the resulr of performing a

shaded relief map process is a raster GIS database, and

each grid cell typically contains an attribute value describing a gray cone rangi ng from light (facing cowards rhe

sun) ro dark (f..ci ng away from the sun). The shaded relief map is useful for illustraring rhe ropography and provides a th ree-dimensional perspective of the landscape.

Shaded relief maps can be created with the genera l process described in Figure 13.5. The azimuth selected specifies the d irection from which rhe sun is sh in ing. An

azimuth of 90°, for example, indicates that the sun is posi

tioned in rhe eaSt, and an azimuth of 1800 indicates that

rhe sun is positioned in the south. The altitude defines the

angle of the sun above the landscape. An alti tude of O· typically indicates that the sun is located directly over

head, whereas an altitude of 90· wou ld indicare thar the sun is at the horizon. With in ArcGIS a OEM must be

opened as a layer, the Spatial Analyst Extension must be act ivated, and the Spatial Analyst menu must be opened. From [he Spatial Analyst menu, choose Surface Analys is.

then choose rhe Hillshade option. This should open rhe Hillshade dialog box, which will prompt you for the Input Surface. Azimuth, and Altitude amOunts. Options

are also provided for vertical unit conversion to horizon-

Selecl azimuth that

represents the sun's location

Select altitude of

the sun in the sky

(raslerl

Figure: 13.5 A gene:ral process for the: devdopme:nt of a shade:d re:lie:f GIS database: &om a OEM .

tal map unit (Z factor), an ompur cell size. and an output

database name. Usi ng the Brown T ract 10m OEM, an

azimurh of 210·, and an altitude of 45·, a shaded rel ief map is created (Figu re 13.6) that shows (relatively speaking) how much sunl ight reaches each parr of the landscape in the late afternoon (the su n azimurh of 21 0° indicates

[hat the sun is located directly co the southwest of the

FigllR 13.6 Shade:d relief map of the: Brown Tract using a 10m OEM, an illumination azimuth of 210°, and an illumination altitude of 45°.

211

The oriemacion and presentation of 'direction ' has not been discussed co great extent in this book, however, it is important for readers to know the difference between an azimuth and a bearing. Why? Because compasses used in fieldwork either represent

di recdon as azimuths or bearings . In some cases , both types of measurements will be represented. Azimuths are degrees of a circle, with No rth being 0° (or 360°), East being 90°, South being 180°, and West being 270°. A compass line indicating an azimuth of 353°, therefore, indicates a d irect ion of

almost due North. A bearing is represented as any angle of 900 or less from eithet the North or South

landscape, and the 45° altitude indicates a sun position halfway between 'direcdy overhead' and 'setting'). With this shaded relief analysis, you can obtain a sense of the varied ropography of the Brown T ract. Other landscape features. such as study areas, roads, and streams, might [hen be displayed on tOp of the shaded rel ief GIS database to allow an exam ination of how chese resources might be

influenced by landscape tOpography.

Slope Class Maps

A third product that can be derived from an analysis of DEMs are GIS databases that represent the slope class, or gradient, of each portion of a landscape. Slope class values are measurements chat indicate me steepness of a landscape, and provide insight into the rate at which other resources, such as water, vehicles. or people. are likely to travel over [hose portions of [he landscape. Since each

grid cell in a DEM contains both horiwntal (e.g., latitude and longitude) and vertical (elevat ion) measurements, the slope of each grid cell can be computed based on the posit ion and height of the neighboring grid cells. MoSt raster GIS software programs have the abi li ty to compute slope classes. and are able to express slope class as an angle (degrees) or as a percentage of the difference in elevation of each grid cell as compared to the neighboring grid cells.

lr is important to understand that there are a number of different methods used in choosing the values for slope class calculations. In a raster GIS database. each raster grid cell will have eight neighbors that share a portion (a side or a point) of its boundary: fou r neighboring cells will share


(and directed towards the East or West). Thus an azimuth of 353° represents a bearing of N7°W. since the angle would arise from the North half of a compass, and is directed towards the West r. Similarly, an azimuth of 89° represents a bearing ofN89°E (the angle arises from the North half of the compass and is directed towards the East 89°). and an azimuth of 190° represents a bearing of S I OOW (the angle arises from the South half of the compass and is d irected towards the West 10°). Property deeds, commonly used within North America co legally state ownership of a land area, often use bearings to describe the land boundary locations.

a corner point and four neighboring cells will share a side. Theoretically, eight possible grid cell values can be used to

calculate the slope class. Many raster GIS software programs lise a formula that takes into account me values of these neighboring grid cells in calculating the average slope elass of a single grid cell (Burrough & McDonnell, 1998, pp. 190-3). In Figure 13.7, the weighted average slope gradient between the cell of interest (the center cell with a

293 m elevation) and the eight neighbors can be calculated to determine the slope class change by computing the elevation change among the cells. You can imagine. however. that perhaps only the cells that share a side might be used to calculate the slope class for the cell of interest, or a broader window can be used. (e.g .. one more ring of cells around the cell of interest, or 24 neighboring cells) .

To create a layer representing slopes within ArcGIS a DEM must be available, the Spatial AnalySt Extension muSt be activated, and the Spatial Analyst menu mUSt be opened. From the Spatial Analyst menu, choose Surface Analysis, then choose the Slope option. T his should open the Slope dialog box, which will prompt you for the Input Surface

1 2 302 m 300m

4 293 m 290 m

6 7 287 m 288m

3 298 m

5 295m

8 290 m

Legend

12~m l 1293 m 1

Neighboring cell (3) and elevation (298 m)

Cell lor which slope class will be computed

Figure 13.7 Slope class computation within a raster GIS environment.

212


and whether degrees or percent slope is desired. Additional options include vertical unit conversion {Q horiwnral map

unit (Z faccoc), Output cell size, and OUtput database name.

G iven [hat slope is a d irect function of distance and eleva

tion comparisons, it is imperative with the slope process

rhat users know whether (he measurement units of coordi

nates and elevations are [he same. If the measurement units ate nO[ the same (e.g. meters for coordinates and feet for elevations), the Z factor input can be used co reconcile differ

ences. T he slope class GIS database created from the Brown Tract 10 m DEM (Figu re 13.8) shows that slopes are repom::d in degrees, and are divided into nine categories. The darker-shaded slope class categories represent areas

where slopes are steep, and the lighter-shaded slope class categories represent areas where slopes are gentle.

Many natural resource management organizations pre

fe r CO work with slope classes expressed as a percentage,

and thus it may be important for CO know how to perform

this conversio n:

slope class (percent) = tan (a) X 100

where

tan = tangent tr igonometric function

0: = slope in degrees

Brown Tract slope (degrees)

0 0-2.3 - 5.3-6.5 -0 2.4-3.8 - 6.6-7.8 -3.9-5.2 - 7.9-9.1 -9.2-10.8

10.9-13.1

13.2- 21.8

Figure 13.8 Brown Tract slope class GIS database' created from aIOmDEM.

50 feet

Angle (degrees) = 30' Angle (percent) = (28.9 feet /50 feet) = 57.7%

Tree height = tan (30') - 50 feet or 28.9 feet

tan (30°) • 100 = 57.7, providing a Quick conversion from degrees to percent slope

Figure 13.9 A simple example of the' con'lo'ersion process from degrttS to percent slope.

T o prove this rather simple conversion from degrees co

percent slope, assume that a person was standing on flat

ground (Figure 13.9) and needed to determine the percent slope from their location to the cop of a tree. By knowing

the angle (30') from their position to the top of the tree, and the distance from their location to the tree, the person

can calculate the height of the tree (28 .9 feet). The slope from the person to the top of the tree, as expressed in per

centage terms, is then the rise (the height of the tree, or

28.9 feet) divided by the run (the distance the person is from the tree, or 50 feet), or 57.7 per cent. And, by simply inserting the angle into the equation noted above,

slope class (percent) = tan(30') X 100

you can arrive at the same conclusion, D epending on (he

GIS sofrware program being used, you may need to con

ven between degrees and rad ians, since the angle reponed

after a slope class calculation may be reported as a radian.

An examination of the software documentation will reveal whether this consideration is necessary,

Interaction with Vector GIS Databases

There are a number of methods by which you can perfo rm a GIS analysis using both vector and raster GIS databases simultaneously. This ability has traditionally been uncommon in many desktop CIS software programs, bur as technology progresses you will see the expansion of

these capabili ties. and field personnel (those with access

primarily to desktop GIS software programs) will be able to perform more complex analyses. Two types of analyses 213

that combine vector and raster GIS databases will now be

explored: an examination of the slope class characteristics of land management units , and an examination of the slope class characteristics of streams.

Suppose you were ass igned rhe rask of developing a management plan for an area the size of the Brown Tract,

and one where there was significant amount of rel ief associated with the landscape. The set of management activities appropriate to each management unit defined on the landscape may vary based on rhe slope class wirhin each unir. For example, if you were to consider planning a for

est thinning operation on the Brown Tract, it would be

useful to know the locations of areas where thinning operarions should use a ground-based logging sysrem (e.g., fell-bunchers skidders, harvesrers, forwarders, ere.) and the locations of areas where the thi nning operations should use a cable-based logging sysrem. Since groundbased logging sysrems are appropriare fo r rhe gender slopes, slope class measuremenrs will help identifY those management units that have the steeper slopes more appropriare for cable logging sysrems. The slope class condition of a management unit can be measured in the field

with clinometers or other surveying instruments or hypsometers, or the slope class condition can be computed using a OEM in conjunction with the vector GIS database that describes the management units. In the case of the Brown Tract, rather than having field crews spend several days collecting slope measurements, the average slope class of each management unit can be calculated with GIS


Selecl attribute that

uniquely identifies stands

Summarize slope

conditions

Slope class report

FigUJ"~ 13.10 A g~n~ra1 prouss for tb~ developm~nt of a sJop~ class condition information for each stand (manag~m~nt unit) on a landscape.

using a process similar to that described in Figu re 13 .10. A rabular reporr is generared by the process described in

Figure 13.10, and provides a summary of the slope class condition for each of the management units. An annotated vers ion of the output, showing information for the first ten stands of the Brown Tract, is provided in Table 13. 1.

The first variable in the table represents the a((ribure that was selecred ro uniquely identifY each srand (rhe srand

TABLE 13.1 Output of percent slope values for management units

S .... d Count A=- M;" Max Range Mean Std Sum

3 19 343603 0.11 15.44 15.33 5.3 1 3.81 1692.78

2 2186 2354595 0.34 23.55 23.21 9.41 3.76 20564.20

3 770 829386 0.44 22.46 22.02 10.22 4.15 7866.61

4 2884 3 106428 0.28 23.01 22.73 9.54 3.66 27521.07

5 533 574107 1.71 19.80 18.09 8.34 3.14 4446.68

6 1195 1287164 0.44 23.72 23.28 8.51 4.24 10168.51

7 338 364068 0.20 15.15 14.95 6.20 3.52 2096.76

8 2494 2686349 0.15 26. 11 25.95 13.65 4.27 34040.15

9 337 362991 3.20 25.4 1 22.21 15.03 3.9 1 5066.74

10 2395 2579714 1.55 24.25 22.70 11.52 3.90 27591.07

CoUnt:o number units (10 m grid cells) in Range ,. (maximum value - minimum valu~)

the database Mean z averag~ slope Area .. squar~ feet Srd '" standard deviation o f values in [he database Min. minimum valu~ in [h~ da[abas~ Sum = sum of the slope for aU units Max .. maximum valu~ in me darabas~

214


number}. With this value, you could join the tabular data

co the stands GIS database. using a one-to-one join process

(see chapter 9 for a review of join processes), and facilitate

a graphical display of the slope class for each managemenr

unit. The variables 'Counr' and 'Area' list the number of

grid cells from the slope class GIS database that F...l1 within

each management unit, and the area mat the grid cells rep

resent. The 'M in', 'Max', and 'Range' provide the minimum slope class value within each management unit, the

maximum slope class value, and the difference becween these [wo values for each management unit. The 'Mean' is

the average percent slope (what was hoped to be obtained

for the thinning opportunity analysis) and the 'Std' variable is me standard deviation of the slope class values of rhe grid cells located within each managemem unic. The stan

dard deviation provides information on rhe disrcibucion and variat ion of slopes classes within each management unit, Large standard deviations indicate a wide variadon of slope class values whereas small standard deviations indicate a narrow varia don.

In the managemenr of namral resources, the condition

of a stream system may also be imporram [0 know from

Conversion to raster database

both a hydrologic and fisheries perspective. For example,

you might need [0 understand the abilicy of Streams to support fish populations, o r to understand the potential

water runoff implicarions from extreme rainfall evems.

Stream slope class (gradient) is one common measure of the condition of a stream system. Stream slope class can be calculated by field personnel using clinometers or other

surveying instruments or hypsometers, yet this requires a visit to each stream to provide measures for the entire landscape, a very costly and time consum ing proposition. The slope class conditions of streams across a landscape

can, alternatively, be estimated rather quickly if a OEM and a streams GIS database is available for the landscape.

The straightforward approach to calculating slope val

ues for streams would be to fo llow the previous example of

supporting a thinning operation. and use the slope class GIS database for the entire Brown Tract. However. si nce the slope class GIS database was created for the entire land

scape and only a small portion of the landscape is of inter

est (the streams), a different approach might be appropri

ate (Figure 13 .1 1). One solution would be to create a

raster GIS database of the streams. A raster database of

Overlay analysis

Select attribute that

uniquely identifies streams

Summarize slope

conditions

Slope class report

Figun: 13.11 A general proces.s for the development of slope inform:uion for each individual stream on a landscape.

215

Intervisibility is a term used to describe the number of

viewpoints with a view of each unit of land. For exam

ple, a number of homes may be within the viewshed of

a property being managed by a natural resource organization. These homes could be considered viewpoims.

By performing the viewshed analysis described above. however, you do not necessarily understand the imer

visibility of the landscape. or the number of homes that acmally have a view of each unit of land. In fact, SOffie

streams that matches the spatial extent and resolution of

other raster GIS databases previously developed for this landscape would fucilitate future overlay analyses. The grid cells that contain accuai values (other than 'no data')

should only be those that overlap a stream in the vector screams GIS database. An overlay analys is is then per

formed in conjunction with a OEM CO enable the creation

of a raster GIS database thar describes the elevation of each

grid cell represent ing a stream. Now that only those raster

grid cells that touch a stream have been identified. and the elevation of each is avai lable. you can calculate the average

slope class values for each of the streams.

Si milar to the previous analysis, the result of this

analysis is also a tabular database. By using the attribme

that uniquely identifies each stream. you could join this tabular data with the streams GIS database to facilitate the

display of the slope classes for each stream. A similar processing approach could be used to identifY slope or elevation characteristics for any vector GIS database that con

tains lines (e.g .. roads, trails, facility corridors, etc.) .

Viewshed Analysis

The maintenance or enhancement of aesthetic values is

becoming increasingly important in natural resource man

agement. Research has shown that the experiences of recre-


areas of the viewshed may be visible to a single home.

From a management perspective, this information may

be impartanr. and allow natural resource managers to

focus thei r public relarions efforts to rhe directly affected homeowners. If you were to develop a GIS database that describes the intervisibility of a landscape. the phrase 'cumulative viewshed map' (rather than

'viewshed map') mighr be used. to illustrate the number of homes viewable from each land unit.

adonal visitors are influenced by the visual appearance of

the landscape. and thus can be negatively affected by managemem activities that leave visible impactS (Ribe. 1989) . A key to reducing (or heading off) potential public relarions problems in natural resource management is to ascertain

what pardons of a landscape are visible to recrearional vis

itors, and to adjust the management plans associated with

those areas accordingly (Wing & Johnson. 2001). Vicwshed analysis can facilitate an understanding of

the portions of a landscape that are visible from specific

landscape features of interest. For example. observation or

viewing si tes (overlooks) may be represented in a GIS database by poinrs. and the location and elevation of the

points would be used to determine which other parts of

the landscape would be visible. If rhe viewing sites were described by lines, the vert ices (points where the line

direction changes) of each line would be used for the viewshed analysis. Fo r example. lines that represent a road

or trail through a landscape. could be used to determine which other parts of the landscape can be seen from those

featu res. The landscape in a viewshed analysis is represemed by a

OEM or a TIN (see chapter 2 for a description of a TIN).

The objective of a viewshed analysis is to calculare rhe line of sight berween the viewing sites (e.g., observation points,

homes) and other landscape features (F igure 13. 12) .

Figure 13.12 Line of sight from a viewing site to the surrounding landscape.

216


Features [hat are identified as being in [he line of sight of the viewing sites are considered visible, whereas all other

landscape feamces are not considered visible. A number of considerations must be taken into

account when conducting a viewshed analys is . Ahhough a OEM associated with a landscape may have been

acquired (perhaps from rhe US Geological Survey), an assessmem of its fitness (Q represent the landscape in a

viewshed analysis should be performed. I n heavily forested landscapes. the OEM may nor represent the effect of tree heights on your view, as [ree canopies extend above

the elevation surface (the ground. as represented in the

OEM) . The current management of rhe landscape will also affect a viewshed analysis because different aged stands have different heights, and therefore [he (Op of rhe current canopy (which is what is usually seen in a scenic view) may be misrepresenced. One solution to this prob

lem is co acquire a vegetation GIS database that contains

tree height information, and [Q incorporate these meas

urements inco the DEM elevations. Another consideration

is to make sure that the DEM surface covers the entire

landscape area between a viewing site and the landscape

being analyzed. In some applicat ions. a managec might be interested in determin ing the visibility of a resoucce from

surrounding viewing sites (homes oc roadways) but only

has access to a OEM that describes the area managed (such

as the OEM for the Brown Tract, as shown in Figure

13.2). To perform a viewshed analysis, i[ will be necessary to acquire a OEM that covers not only the landscape of interest ([he land being managed), but also contains [he areas whece the viewing sites are located.

One consideration in viewshed analyses often ovec

looked is [hat G IS users can generally modify rhe height of the viewing site. For example. the height of the viewing

site is genera lly considered to be the acrual ground-level

elevation at the location of the viewing site. If the viewing

si tes were meant to represent people who were standing

and viewing [he landscape, then an observation height of five or six feet higher than the ground elevation at the

viewing s ite might be more realistic than assuming a

ground level view of 0 feeL This relatively modest change in viewing elevation can have significant impacts on view

shed resules when large land areas are involved. As viewing elevation rises, the amount ofland returned by a view

shed analys is will typically also increase. Orher considerat ions in performing a viewshed analysis might

include sening limits as to how far viewing sites are allowed to 'see' across the landscape. and limits on view

ing angles.

To illumate [he development of a viewshed analysis, a GIS database representi ng home locat ions surrounding

the Brown Tract will be used as the viewing sites. A cur

sory investigation of [he homes GIS database reveals [hat [here are multiple homes along rhe eastern half of [he Brown Tracr (Figure 13.13). A viewshed analysis will allow you to determine what portions of the Brown Tract

are visible by residents of [he n ... rby homes. One of [he first steps in the viewshed analys is is to define the observer

height. You might assume that the average person has a

view height (above ground level) of 5.5 fee<, and [hat owners of [he homes around [he Brown Tract could likely view rhe foreS[ from [heir second Aoor windows (add 10 feet), resulring in an adjusted observer heighr of 15.5 fee<.

To perform the viewshed analys is. you might pursue a

process similar to that described in Figure 13.14. Since [he original OEM for rhe Brown T racr was clipped (0 the ownership boundary of the tract, you could acqu ire USGS

OEMs and creare a new OEM GIS database rhat includes rhe Brown Tract and the areas that cover the homes. As mentioned earlier, addressing vegetarian height in the

viewshed analysis might be appropriate. since much of

[he Brown Tract is foresred . The stands GIS database contains a variable named 'Height' that provides the average

height of each of the management units. By convert ing

this vector GIS database to a raster GIS database format.

height information can be added to the Brown Tract

D Brown tract tocation • Homes

•

• •••• . .. , . ... ..

•••• • • • •

• • •••

Figure 13.13 Locations ofhome.s near the Brown Tr:lci.

• • • •

•

• • •

217


Create observer height

attribute

OEM (raster)

Overlay analysis

Viewshed analysiS

(raster)

Stands GIS database

Conversion to raster database

GIS database (raster)

Figure 13.14 A general process for the development of a vicwshed analysis for the Brown Tract based on a set of vicwshed sites (nearby homes).

OEM values co get an adjusted elevacion that includes the average tree heights for each management unit contained in the stands GIS database (Figure 13.15). The Spatial Analyst extension within ArcGIS contains a convert menu that offers a 'feamres to raster' utility for this purpose. The dialog box for this option will prompt for the database to be converted and a variable co be represented in the raster (height in this case). Once converted the stand height raster can be added to the DEM ro create a newelevation surface that contains updated elevation data for the Brown Tract and surrounding areas (Figure 13.16).

,

StnI..,{tI) 0-" "..., ..... " ... 81-100 101-120 lZ1-140 141-180 ISHel

"' ''''

After performing the preliminary database development steps, the acmal viewshed analysis can be performed by computing the areas of the landscape that are visible from each home. With in ArcGIS, this can be accomplished by using the Viewshed command. under the Surface Analysis roo Is within the Spatial Analyst extension . A dialog box will open and offer prompts for the inpuc surface (OEM or other modified elevation surface) and observer points (locations from which the input sur-

Figure 13.15 Average tree heights for managemcO( units contained in the Brown Trace stands GIS database.

218


Figure 13.16 OEM GIS databuc fo r the Brown Tract and. surrounding areas that includes me :average tree hcighu of the management uniu contained in the stands GIS database.

face will be analyzed). Depending on the size of the DEM,

this processing dme may be lengthy, however, afrer pro

cessing is complete. a raster GIS database is created (hat

illustrates the areas with in and around the Brown Tracr that are visible from the nearby homes (Figure 13.17).

The areas within [he Brown Tract that are vis ible from

Figwc 13.17 Arc:as (dark shade) within and surrounding the Brown Tract that are visible from the ncarby homes.

the nearby homes are located mainly along [he trac[ bor

ders, however a significam amount of land within [he tract boundary is also visible from one or more homes

(a lthough you cannot, from chis analys is determine how

many homes have a view of each piece ofland).

Watershed Delineation

GIS has come to be a standard tool for hydrologic research

and water resource modelling. GIS applicat ions for water

resources are common and vary from simply mapping

water resources (Q sophis ticated analyses that consider

water quality infl uences and [umce water ava ilabi lity

(Wilson et aI., 2000; Marcin e< al., 2005). A primary use

of GIS for water resource managemenr is that of watershed delineation. A watershed can be defined as a land

scape area [hat shares a common drainage. This definition

assumes that if water were able [Q flow freely over a land

scape area, jt would follow a downhill trajectory and exit

(he landscape a rea at a common painr. W atershed bound

aries are commonly used by federal . St3te, and provincial

organizations ro separate a landscape inro smaller man

agemenr areas. In addition, watershed councils-groups

that help (Q determine management acdvities within a

specific watershed-are becoming more common in North America. Some refer {Q watersheds as 'catchmems'

but the intent is rhe same: {Q describe an area according to

the fl ow of water over its surface. The delineation of a

watershed boundary is a func[ion of topography and

changes in landscape relief. You must decide on the scale

of the watershed to be created and have access to topO

graphic information in order to begin delineation. Previous to digi[al GIS and the availabili<y of DEMs,

watersheds were created primarily by using available con

tour maps and using the visual clues provided by comour

shapes to draw or digitize a watershed. boundary. Clues

from contour shapes included saddles. peaks as indicated

by closed COntours, and [he direc[ion of funnel-like con

tour shapes that represent water fl ow paths o r ridges

(Figure 13.18). If existing meam ne<works were present

on the comour map or on additio nal maps, these features

also provided clues as to watershed boundary locations.

The delinea[ion process was usually begun from [he perceived lowest elevation point of the watershed and then

continued uphill , with ,he goal of identifYing the ridge lines that su rrounded a watershed and separated it from

other watersheds. Contour line funnels indicated either water flow paths or ridges. with water flowing from the

bottom of [he funnel (u- or v-shaped end) to the open

219


~ .. ~ ..... ....... ~ .. ::::------:::::

... E) .. a b c

Figure 13.18 Watershed boundary location and Stream flow patterns derived from contour inu:rvai shapes: (a) watcrsh~ bound2.ry parallels contour saddles and (b) splits peaks as indicated by a dosed contour homes, and (e) waler Rows from the bonom of conlour funnels through the tOp.

end. Closed conrour shapes were ro be spl ir rhrough rhe

middle as [hey indicared a peak. and boundaries should

split CQmour saddles in a parallel orientarion. This required those involved in the delineating watersheds co continually ask themselves 'which way would water flow if a bucket of water were dumped at a parcicular location?'

The delinearion process could be drawn direcdy on rhe

coorour map or accompl ished rhrough heads-up digiriz

ing on a monico[ with background databases of streams and contours being displayed . Nonetheless, manual watershed delineation is a tedious process with significant opportunities for human error. If a common drainage point. line. or area can be digitaUy represented in a GIS and a OEM exists of the area. many GIS can automatically generate a watershed area boundary given some basic data manipulations.

In addition to a feature that represems the drainage locarion(s) and OEM. a darabase represeming flow direc

tions is usually required for watershed delineation. A flow direction database is represemed through a raster database structure and assigns an expected direction of water flow to each raster cell. Fortunately, a flow direction layer can usually be creared from a OEM rhrough an a1gorirhm rhar

evaluares rhe e1evarions of each rasrer cell and all neighbor

ing cells. The possible direcrions include rhe parh co each

poreorial neighboring raseer cell (eighr direcrions) as derer

mined by rhe lowese devarion among rhe neighbor cells.

In addition, a 'no direction ' choice is possible and is referred to as a 'sink'. A sink occurs when a raster cdt has

an elevation value mat is less than all surrounding cells. Any ponion of a landscape can be analyzed for irs

watershed boundary given that a OEM is ava ilable of the emire pmential watershed extent. If the OEM does not cover the entire potemial wa[(~:rshed area, an incomplete

watershed boundary may resulr without any warning from rhe supporting GIS software as (Q its incompleteness. As an example of GIS-based warershed delinearion. a

portion of the stream network within (he Brown Tract that falls within the smaller incerior private ownership area is considered (Figure 13. I 9). A logical sequence of

steps for the watershed creation process is represented in Figure 13.20. A separare darabase muse firsr be creared

that represents only this stream network portion, or at

least the lowest elevation point within the porrion. This lowest elevation point in this database will serve as the watershed source, sometimes referred to as a pour point. Rather than risking a misidentification of the lowest point, it's probably more expedient and reliable to clip, or otherwise separate, the ent ire portion of the stream network into a new GIS database. Within ArcGIS, you can use the Select Features tool to select all of the stream features and then export (he selected feawres into a new

da<abase (righr click on rhe layer and choose Dara. [hen

Expon Dara) . Some GIS software will require rhar line

and polygon features intended to serve as a watershed source must be converted inm a raster database, Within ArcGIS, once the selected streams database is ready. the Spatial Analyst Convert menu contains a 'featu res to

raster' command that will make this conversion.

' ... .' :

\~.j 1.,;"; .. ,.'

J

""

··~··· ·'J·····,··.>m \·

.'

Figure 13. 19 The portion (in dark bold) of the stream network within the Brown Trace for which a watershed area is to be created .

220


separate Stream sections

of interest

Cooversion to raster database

(raster)

Create Flow direction

database

(raster)

Watershed analysis

Figure 13.20 A general process for the ddineation of a watershed area for a portion of the stream network in the Brown Tract.

The next step is to create a flow direction raster based

on the Brown Tract OEM. This process can be accom

plished wirhin ArcGIS by using rhe rasrer calcularor and flowclirecrion com mand. Assuming that (he extended

OEM for [he Brown Tract is used. (he raster calculator syntax would be ' f]owdirecrion ([browndemex])'. The result of this command should be a temporary raster file

with cells ass igned to one of nine possible values: one for

each direction to the eight neighboring cells and a sink or 'no lower elevation' value. A si nk can be caused by a nat

ural fearure. such as a lake or mher water body with no

surface o utl et, o r can be the result of a OEM error.

Summary

The examples in this chapter have demonstrated several

GIS operations [hat are possible when using raster GIS databases, and have concentrated on [he wealth of oppor

tunities associated with using a OEM. From the basic ele

vation information contained within a DEM, you can gen-

Regardless, it may be necessary to detect and eliminate

sinks within the OEM before flow direcrions can be calcu

lared. Wirhin ArcGIS, rhe fill command can be used to

manipulate porential sinks bur must be run through the

Sparial Analysr Hydrology rools in rho ArcToolbox or rhrough rhe command line inrerface. The fill command will create a new raster with elevation values for sink loca

tions raised to the minimum elevation found in sur

rounding cells. The modified ('filled') rasrer can rhen be

used to calculate flow directions. Once the flow direction

raster is complete, the watershed area can be derived.

Within ArcGIS the raster calculator can be used with rhe

Watershed command to create watershed boundaries. The syntax is 'watershed ([flowraster, streamsecrion])'

where flowraster is the name of the f1owdirection raster

and srreamsection is the name of the raster version of the

portion of the Brown Tract Stream network for which a

warershed will be creared. The warorshed croared should not be very large in co mparison to the Brown Tract

boundary (Figure 13.21 ).

.~.

Figure 13.21 Th~ watersh~d (i n gray) for a portion of the Brown T net stre2m network.

erate contou r lines. a shaded relief image. and a slope clas

sification map. Rasrer GIS databases can be integrated in spatial analyses to calculate slope classes for management units as well as gradiems of streams. and to create a data

base of me viewshed relared to nearby landscape feamres,

221

such as the homes located around the Brown Tract. In addir ion. DEMs can be used to analyze landscape topography and can create additional raster databases to support

watershed delineation . These examples are but a small

Applications

13.1 Shaded relief map. You have been asked by the manager of rhe Brown Tract ro create a shaded relief

map that simulates rhe sun 's lighr on rhe landscape in early morning.

a) What values would YOli use to approximate the sun's azimuth and altitude?

b) Develop a map that illustrates the shaded relief.

13.2 Slope map of the Brown Tract. During one of your monthly planning meetings. it was suggested that a slope map would be of value to the foresters who plan timber sales on the Brown Tract. This map would be of equal value [0 (he recreadon manager, who is interested in

potendal trail systems throughom the forest.

a) Create a slope GIS database of the Brown Tract that has 80 ft con [Our lines that originate from a

300 fr base elevation . b) Develop a map illuStrating the slope classes.

13.3 Viewshed analysis planning. Your supervisor has approached you and asked that you assist in a viewshed

analysis for part of your resource management area. She is

interested in knowing what port ions of your resource

management area are visible from a nearby road. She

needs the information quickly and has asked you to scope our the project with regard to how this mighr be accomplished in GIS (e.g .• what GIS databases are necessary. what processes are necessary, etc.). Provide a brief descrip

rion that lists required GIS databases, techniques, and

potential pitF.Uls of conducting the visibility analysis.

13.4 Road gradient analysis planning. Your supervisor is interested in knowing the average slope gradients of a set of roads within your organization's management area. She has asked you for a one-page description of how this might be accomplished in GIS. Prepare a reporr for her that describes the necessary GIS databases, potential techniques, and considerat ions that would be involved in

using GIS for this purpose.

13.5 Road gradients on the Brown Tract. The forest


sample of the types of applicat ions that are possible with a single raster GIS database in terms of examining landscape (Opography. We turn our anemion in rhe following chapter to exploring orner poremiai raster data applications.

engineer associated with the Brown Tract has asked you

to calculare road slope gradients (percent slope) for the roads in the Brown Tract using two GIS processing

methods. For the first method. the slopes should be calculared using [he entire DEM available for the landscape. For the second method. the slopes should be calculated using only those raster grid cells that overlap the road

network. The engineer wants to understand the differ

ence, if any, (hat would be observed in the average

slopes gradients when using these two methods. Prepare

a short report to convey this information (0 the forest

engIneer.

13.6 Brown Tract viewshed analysis. The manager of the Brown Tract would like you to develop a viewshed analysis using only those homes located on the north side

of the Brown T racr. Use the assumptions and processes

described in the example provided earlier in this chapter.

How do the viewshed results differ from [hose of the example presented earlier in this chapter?

13.7 Brown Tract watershed analysis. The hydrologiSt for the Brown Tract, Samantha Wasser, has asked for

your help in identifying a watershed for a portion of the Brown Tract stream network. The lowest elevation

stream segment in the portion is identified through a

numeric value of 123 in the stream variable within the

streams database. Develop a watershed for this stream

network portion and report the resulting watershed area

in both acres and hectares.

13.8 Brown Tract beaver watershed analysis. T he Brown Tract manager has asked you to delineate the

watershed area that Aows into a large beaver pond. The

beaver pond location is described in the water sources

database as a point location. Create a warershed for this

point and repOf[ the resulting watershed area in both

acres and hectares.

13.9 Brown Tract stand watershed analysis. The Brown Tract manager has asked you to delineare rhe

222


watershed area that flows into a stand inside rhe Forese.

The stand is identified as number 36 with the stand field in rhe Brown T faCt stands database. Create a watershed

for this stand and repon rhe resulting watershed area in

bmh acres and hectares .

13.10 Basic characteristics of the Brown Tract. Becky Blaylock. the manager of rhe Brown T ract, is interested in

some basic knowledge of the landscape. Specifically. she wants to know rhe following:

a) What is rhe m inim um, maximum, average. and

standard deviation of slope (degrees) fo r the Brown Tract?

b) What is rhe m inimum, maximum, average, and

standard deviation of slope (percent) for rhe Brown

T racr?

References

Burrough. P.A .• & McDonnell. R.A. (1998). Principles of geographical information systems. Oxford: Oxford University Press.

Martin. P.H .• LeBoeuf. E.J .• Dobbins. J.P .• Daniel. E.B .• & Abkowirz. M.D. (2005). Interfacing GIS with water resource models: A state-of-the-art review. Journal of the American Water Rtsourus Association, 41 , 1471-87.

Natural Resources Canada. (2007). Mapping. Retrieved May 26. 2007. from hrrp:llwww.nrcan-rncan.gc.cal com/subsuj/mapcar-eng.php.

Ribe. R.G. (1989). The aesthetics of forestry: What has

13.11 Combining raster GIS databases. Wha t is the result when two raster GIS databases are combined. yet

'no dam' values are present in some portions of rhe land

scape in one of the [wo raster GIS databases?

13.12 Highest and lowest elevation stands. Which stand in the Brown Tract is located at (he lowest average elevation?

Which stand is located at the highest average elevation?

13.13 Elevation information for a single stand. What are the average, minimum , and maximum elevations of

the stand described in question 13.9 (stand 36)?

13. 14 Water source elevations. What is the average ele

vation for each of the points described in the water

sou rces database?

emp ir ical preference resea rch taught us? Environmental Managemen t. 13.55-74.

US Department of Interior. US Geological Survey. (2007) . USGS Geographic data download. Retrieved May 26. 2007. from hrrp:lledc2. usgs.gov/geodatal index.php.

Wilson. J.P .• Mitasova. H .• & W right. D.J. (2000). Water resource applications of geographical informa

tion systems. URlSAjouma!, 12(2).61 -79. Wing. M.G .• & Joh nson. R. (2001). Quanti /Ying forest

visibility with spatial data. Environmental Management. 27. 411-20.

223

Chapter 14

Raster GIS Database Analysis II

Objectives

Chapter 14 builds on the previous chapter and further explores raste r data for spatial analysis. Like chapter 13, this chapter also involves [Opographic applications of raster data bue broadens the (reatment of raster data applications. In addition, more technical detail is provided for

raster data processing techniques and analysis. Ar the con

clusion of this chapter, readers should understand and be able (0 converse aOOm:

1. the potential applications of caster data for natural resource problem analysis.

2. how distance functions can be applied to raSter data. 3. the types of statistical summary search functions for

raster data,

4. the capabilities and applications of density operations, 5. raster data reclassification and map algebra processes,

and 6. data structure conversion considerations.

Raster Data Analysis

We further develop raster data analysis in [his chapter by describing some general considerations for raster analysis

working parameters. examples of raster analysis functions. and applications of processing commands for manipulating raster data. We then describe some application examples that make use of select raster funct ions. The raster analysis functions we discuss include distance, statistical sum mary search, and density functions. In some cases, rhe functions can accommodate both vecror and rasrer databases as input. General processing commands cov-

ered in this chapter involve raster reclassification. rasrer map algebra. and data structure conversions. We also provide more detailed information abour the procedures within the world 's most popular GIS software-ArcGISthar can be used for raster analysis. An assumption for rhese procedures is that the Spatia l Analyst software extension for ArcGIS is available. This extension is specifically designed for raster data analysis and provides a set of

menu choices and commands that make some of primary raster capabilities more readily available. If readers are using different raster software (i.e. , Imagine, GRASS. Idrisi , etc.) for their applications, the procedures described in this chapter should provide a template for

applying the functions and analyses that we present.

Raster Analysis Software Parameters

Some raster software will allow you to set working environment parameters that the software will observe during use. These conditions establish resolurion, analysis, ourpur. and other conventions that affect Output raster databases that are created during processing. One of the distinguishing characteristics of raster databases in com

parison to vector data is that they are cypically larger in size in terms of digital storage space. Raster working environment parameters can help constrain raster databases to

specific study areas and resolutions so thar Output files do not become overly large in size. Some raster software will

only create temporary raster databases unless otherwise specified by a user. These remporary raster databases will be removed from the hard drive once the GIS software is closed, which can cause some distress for unsuspecting

224


users the nex( time [he software is resrarred. Depending on the raster software, working environment parameters

may need co be reestablished ar rhe beginning of every analysis session.

An important rasrer parameter (Q consider is an anaJy~

sis mask. An analysis mask, as esrablished by rhe dimensions of anQ[her GIS layer. will restrict processing to on ly

those areas coincident within the layer. The extent oprions allow you co further resrricr the areas that will be considered when new raster output is created . Depending on you r software, possible choices include existing layers. spa

rial combinarions of exiscing laye rs. or a bounding ser of

coordinates. In addition to raster databases. vector databases can usually also be used (Q create an analysis mask.

Another critical choice for processing out pm raster databases is cell resolution. Typically, working parameter choices will allow users CO set a designated raster cell resolution for Output databases. This can typically be set [Q

match orher analysis layers. combinations of other layers. user-specified cell resoiurions, or a user-specified set of columns and rows. Sening a cell size and analys is extem

[Q match an existing layer will ensure that our pur raster cells are registered to the existing layer. and that output raster cells from both layers will be coincident and nor offset from one another. These potential parameters described here, and orhers rhar may be available depending on software, can help make raster analys is more efficiem. These choices can help establish a common resolution, analysis area, and output location for raster processing results. The appropriate options should be set prior [Q any raster analysis session.

Distance Functions

Distance functions calculate the measured distance [Q fea

tures of interes(. There are va rious ways of computing distances, ranging from a straight line measurement to more involved approaches (i .e., shorrest paths) that use constraims of moving across a landscape due [Q changes in relief or other impediments. Distance functions can be used to help determine the next closest feature, areas that might be ecologically sensitive because of their proximity to namral features. or the most expedient route to take from one feature to another. Typ ical dista nce funct ions include the straight line, allocation distance, cost weigh red disrance, and shorresr parh (Theobald, 2003).

The stra ight line distance function will create a raster output wim direct (also called Euclidean) distances ro rhe

closest feature. The function can be appl ied to either vecror or raster databases. As an example, consider the water sources poim locations within and around the Brown T racr (Figure 14.1) . Ir mighr be beneficial for emergency response units combating wildfire to know how far away

the closest water sources are located, parriculariy if helicopters will be used (0 carry water from sources to a forest fire and stra ight line distances were of imerest. A distance function cou ld demonstrate relative distances [Q the nearest water source (F igure 14.2) .

An allocat ion distance functio n assigns areas [Q the closest feature. In this case, raSter cells will be assigned a pixel value that recognizes the nearest wne of influence, similar to the vector representation of a Thiessen polygon. A Thiessen polygon is created for each feature in a spatial database and represents the area that each feature is nea res(. In the case of the nine potential water sources, nine allocation zones are identified with each water source hav

ing irs own zo ne of influence (Figure 14.3) . A cost weighted distance function allows for ass igning

different weighting to raster cells that take into account

what is required for a pathway to cross through the cell (Chrisman, 1997). This fill1crion mighr be used ro assess the amount of time needed for water to flow through one end of a srream nerwork to anorher, the relative costs of materials [Q construct or resurface a trail , or resources necessary to develop access ro areas in mountainous te rrain. The COSt weighted function requires that in addition to

I

D

... Water sources

Figure 14. 1 Water source5 in and around the Brown Tract.

225

... Waler sources W,t.r source distance (ft) 0 0- 1.000 LIZJ 2.000-3.000 .. 4.000-4.000 _ 5.000-5.000 _ 6.000-7.000 _ 8.000-8.000 _ 9.000-10.000

Figure 14.2 Straight line distance categories to the nearest water source in the Brown T raet.

the raster database representing desired destinations, the cells contain values representing ilie weights. The weights represent the cost inherent in accessing the cell, be it symbolic of steepness. required construction materials, or some other value that has significance. The COStS are then added togecher. start ing at the destination(s) and moving outward. to form an output raster that symbolizes the entire COStS or resources from each cell to the desired des[ination(s) . A cost weighted distance surface wi th the water sources as destinations and the Brown Tract slope values as weights is shown in Figure 14.4 .

,---------- ----- ------- ----- ------A------ ------------- - -- ----,

// I r-''''-o!''/ i

!

... L= _____ = _____ ±:~ __ = ____ = ___ = ______ = _____ = __ _:b ___ = ______ = ____ ...L.L

... Water sources

~ ...... J Allocation areas

Figure 14.3 AJlocation areas for water sources in the Brown Tract.


... Water sources Cost weighted slope values

U 1 low slope aco..mu/ation

!2iJ 2

3 _ 4 medium Slope accumulation .' .' • 7h~" _ "",mula""

Figw e 14.4 Cost weighted slope values to water sources in the Brown Tract.

The shortest path function is intended to do what the name implies: identify the path of least distance or resistance to a desired dest ination. The shortest path function requires supporting databases representing cost weighted distances and least cost directions. The least cost direction raster is generated for each raster cell and is used to designate which of the eight neighboring cells is upon the least COSt path.

Statistical Summary Search Functions

Srariscical summaries can be generated from individual raster databases. multiple raster databases. and combinations of raster and vector databases using raste r-based search funccions . These fu nctions search within a database and recurn summary values based on search criteria provided by the user_ The search crireria may be based on areas within a second coincident database, a search area within a given pattern and size, or values comained within multiple raster fi les_ Three general types of raster statistical summary search functions include cell. neighborhood. and zonal statist ics options (DeMers. 2002) _ Cell and neighborhood search functions are sometimes called local and focal searches, respectively. Each search function requires user input to direct the search extent and the con[em of statistical summary information that is returned .

Cell, or cellular, statistics allow multiple raster databases to be evaluated in the creation of a new raster database. Each coincidem raster cell in all input databases is considered and a statist ical summary of cell values is selected fo r output into the new database. Stads[ical summaries can include average. summary, minimum. maximum. standard deviadon. most common value. or other

226


possibilities. Values within input darabases are also restricted to numerical formats . The cel lular srar iscics

capability represents a type of raster overlay operation but

all values in all input databases are treated equally according CO [he statist ical summary selected. Potential applica

tions of cellular slatisrics involving multiple raster data

bases include adding ground and structure elevations co

create a surface elevacion layer, developing a composite

fire risk index by summarizing poremial fuel and land

scape qualities. and developing an average temperature

given a set of databases that contain annual temperature

measurements. Neighborhood statist ics funct ions are for single raster

o r vector files and allow for statistical summaries based on

a rea searches within a single database. The search will

consider the entire extent of (he database bur will consider

each feature {point. line, or polygon} or raster cell and

apply the search area parameters for each. Regardless of

raster or vec(Qr input. a new ou tput raster is created that

conrains the results of a rea evaluations in a summary

value within each output raste r cell.

Neighborhood search functions can have several dif

ferenr shapes including rectangles, circles, and wedges. In

addition , an annulus shape is possible which has the func

rional appearance of a donut, with an inside area (donut

ho le) that is ignored in searches and an outside a rea

(donut) that is evaluated. The user designates the size of

the neighborhood search area shapes. Within each a rea, a

statisrical summary is possible of any si ngle numeric field

in the inpur database. Statistical numeric summaries

include rhe average, summa ry. minimum, maximum,

standard deviation, mOSt common value, and other fre

quency evaluations. Possible uses of neighborhood search

functions include identifying the number of available

water sou rces occurring within a 100 km radius or any

feature, the number of nest locations at least 50 m away

but less than 2,000 m disrant. and the maximum tree

height of all trees contained within a 20 m by 20 m plot.

Zonal statistics, applied in examples in rhe previous

chaprer. are similar (Q neighborhood search functions in that a location or area is being evaluated. In cOntrast (Q

neighborhood funccions, zonal statistics functions offer

the advantage of allowing twO databases (Q be evaluated

simultaneously. A new o utput raste r database is created

that provides a numerical summary of values in one database that are coincident with areas in a second database. The areas in the second database are considered (Q be

analys is or summary zones for which values in coincidenr

locatio ns in the other database will be analyzed. The

zones can be designated by an attribute value in either

numeric or categorical format, such as counry names,

land cover categories, or road numbers. Output includes

a statistical summary in tabular format for each idenrified

zone and contains the number of coincident raster cells

and associated a rea. In addition, numerical summaries

include the minimum, maximum, range, average. stan

dard deviation, sum, variety, majority. minoriry, and

median values found within each zone. Zonal statistics

offer the advanrage of allowing raster databases to be sum

marized in relation (Q zones described within a vector or

raster database in a single analysis . The vector database

can be point, line. or polygon format. Examples in the

previous chapter included calculating slopes for forest

stands and streams. Other potential applications include

determining maximum elevations of precipitation gauges.

average aspect of a srream. and the average elevation In

the home range of a wildlife species.

Density Functions

The intensity or frequency with which something occurs

across a landscape or portion of a landscape can be

demonstrated through a density function . Implicit in the

requirements of density calcu lation is the tabulation of

some resource in magnitude or location relative to some

area quantity (Chang. 2002). Within many raster-based

GIS programs. density can be calculated for point and line vector layers with ourput results being written to a ras rer

database. The creation of smoothed densiry surfaces is

a lso possible for point and line featu res (S ilverman,

1986). Density estimates are useful for describing the road

system within a forest. assess ing the relative quali ty or

suitabi lity of habitat areas given the number of wildl ife or

other features present in an area, and for demonstrating

conglomerations or 'hot spots' of an activity or resource

condition when many features are present in a database.

Hot SpOts a re apparent locations or areas where stronger

concentrations of some conditio n are observed. When

thousands of locations are presenr in a database, it may be

challenging ro map and determine locations that have

heavier concentratio ns than o thers (Wing & Tynon ,

2006). This is particularly true when point features are of

interest and many occur in the same location. A si ngle point in this situation can obscure other points when

pioned on top of one another on a map or compurer monitor. I n these situations, density functions ca n be

applied to create shades that demonstrate concentration 227


possibilities. Values within input databases arc also restricted to numerical formats. The cellular statistics capabili~ represents a cype of raster overlay operation bur all va lues in all inp"' databases are "eated equally according to the statistical summary selected. Potential applicap

tions of cellular statistics involving multiple raster databases include adding ground and srructure e1evarions (0

creare a surface elevation layer. developing a composite fire risk ind<x by summarizing potential fuel and landscape qualities , and developing an average temperature given a set of databases that con[ain annual temperature

measurements.

Neighborhood statistics functions are for s ingle raster

or vecmr files and allow for statistical summaries based on area searches within a single database. The search will

consider the emire ex;,rem of the database bur will consider

each feature (point, line. or polygon) or rasrer cell and apply [he search area parame«rs for each. Regardless of raster or veccor input, a new Output raner is created that

contains rhe results of area evaluadons in a summary

value within each output raster cell .

Neighborhood search funcdons can have several different shapes including rectangles. circles. and wedges. In addi[ion, an annulus shape is possible which has [he functional appearance of a donut. with an inside area (donut

hole) (hat is igno red in searches and an outside area

(donu,) that is evalua<ed. The user designates the size of ,he neighborhood search area shapes. Within each area, a statisrical summary is possible of any single numeric field in the input database. 5tadsdcal numeric summaries

include me average. summary. minimum . maximu m.

standard deviation. mOSl common value. and other fre

quencyevalua<ions. Possible uses of neighborhood search functions include identifying the number of available

water sources occurring within a 100 km radius or any

feature, the number of nest locacions at least 50 m away

but less than 2,000 m distant, and ilie maximum tree heigh, of al l trees contained wiiliin a 20 m by 20 m plot.

Zonal stadsdcs, applied in examples in the previous

chapl er, are si milar to neighborhood search functions in (hal a locadon or area is being evaluated. In contrast co

neighbo rhood fun ctions, zonal sratistics functions offer

,he advantage of allowing rwo databases to be evalua<ed s imul taneously. A new outpUl raster darabase is created

(hat provides a numerical summary of values in one database that are coincidenr with areas in a second database. The areas in the second database are considered co be

analysis or summary zones for which values in coincident

locat ions in the other da<abase will be analyzed . The

wnes can be designa<ed by an attribute value in either numeric or categorical format , such as county names,

land cover categories, or road numbers. Qurpuc includes

a statistical summary in tabular format for each identified

zone and contains che number of coincident raster cells

and associated area. In addition. numerical summa ries

include me minimum, maximum, range, average. stan

dard deviation) sum, va riety. majority, minority, and

median values found within each zone. Zona] statistics

off'er ilie advantage of allowing ... ster databases ro be summarized in relation to zones described w ithin a veccor or

raSter database in a single analysis. The veccor database can be point, line. or polygon format. Examples in the previous chapcer included calculating s lo pes for forest

srands and streams. Oilier potential applications include determining maximum elevations of precipitation gauges. average aspect of a scream, and me average elevation in

the home range of a wildlife speeies.

Density Functions

The intensity or frequency with which something occurs across a landscape or portion of a landscape can be

demonstrated through a density function . Implicit in the requiremenrs of density calculation is the tabulation of

some resource in magnitude or locat ion relative to some area quantity (Chang, 2002). Wiiliin many raster-based GIS programs. density can be calculated for point and line vecror layers wirh output results being wrinen to a rasrer database. The creation of smoothed density surfaces is

also possible for poinr and line fearures (Silverman, 1986).

Density estimates are useful for describing rhe road

system within a forest. assessing the relative quality or

suitability ofhabirat areas given ilie number of wildlife or other fC'dtures present in an area, and for demonstrating

conglomerations or 'hot spars' of an acriviry or resource

condition when many features are present in a darabase.

Hot SpOts are apparent locations or areas where stronger

concentrations of some condir io n are observed. When

thousands oflocarions are present in a database, ir may be

challenging to map and determine locations rhat have

heavier co ncentrations than others (Wing & Tynon.

2006). This is panicularly true when point features are of interest and many occur in the same location. A single

point in [his sicuacion can obscure other poims when ploned on top of one another on a map or computer monimr. In rhese siruations, density function s can be

applied (0 create shades that demonstrate concentration

What is a hot spot? In terms of GIS analysis. hot SPOtS

are locadons where some feamre of interest is occurring with greater frequency. A hot SpOt can be indicared through increased density of point, line, or polygon features in a vector database. Groupings of

raster cells can also mark hot spots. Beyond the density of spatial features, an 3nribuce field that contains

one or more features with increased or heightened anribure values in comparison (Q mher feature values

intensities and can more quickly draw ones attemion [0

likely hot Spots. Densicy functions within GIS cypically allow a user to

select an attribute within a layer (0 serve as the population

quamiry for density calculation. If no attribute is selected, it is then the number of points or length of line within a

search distance that is quantified. Two types of densities

are usually available: simple and smoothed. For each cype, a search radius and area unit must be specified. and Outpur

results are wrinen to a raster database. The search radius

determines how far from each raster cell in the output

database in which CO search for features. The area unit

will be the size of the landscape unit area in which fre

quencies are assessed: per square meter, square kilometer,

or other area unit. The simple density method will result

in a raster Output in which each cell shows the number of

features per unit area. If the user chooses a populat ion field, the field quanticy is used as the number of times in which CO count each feature in the density summation.

The smoothed densicy also uses the same approaches but stretches or 'smoomes' the results such that density will be

highest at each feature location but reduced gradually to

D at the outside radius of the designated search distance. The output of the smoothed densicy is cypically more aesthetically pleasing than the simple densicy output, bur it is less precise in its demonstrat ion of density. Some refer

to the smoothed dens icy approach as the kernel dens icy method.

An example of a simple density raster is shown in

Figure 14.5. In this example, the roads in the Brown Tract have been selected as the source to create for the density raster. A 2,000 fe radius was selected as the search

distance and areas with greater road density are displayed

using darker shades.


can also create a hotspot. In other words, a subset of

attribu te values might differ markedly from other a([ribure values. The locations of the features would

then be used to identify the hot spot's location. Hot spots are frequently mentioned in spatially-based crime research and a number of analyt ical techniques

have been developed to identify hot SpOt locations. These techniques can also be app lied to natural resource applications.

Raster Reclassification

Raster cell values can represent almost any numerical or

categorical value, ranging from reAected electromagnetic

energy co descriptions ofland cover categories. It may be necessary to recode raster values so that they represent a

modified range or more representative range of values,

given an analysis objective. I t may also be necessary that raster values need co be aggregated to form a smaller set of

values. The reasons for needing to reclassify raster values

vacy and include:

I. Values within a raster may have been updated through

additional data collection .

2. Numerical values are needed instead of current values

that are described using categorical or nominal values .

3. A more detailed description of categorical raster values

may be desired.

Simple road density surface

low dens.,

moderate density -_ high density

Figure 14.5 Simple density surface for roads in the Brown Trac[ wing a 2,000 ft search radius.

228


4. Raster values may need [ 0 be rescaled in order [0 sup· pore a single raster analysis.

5. Raster va lues may need co be rescaled in o rder ro support a multiple raster analysis.

Reclassification is differenc from altering the symbology or legend of a raster database for display purposes in that it goes beyond simply altering rhe appearance of a raster database. Regardless of the reasons for reclassifYing a raster database, rhe reclass ification process leads to a new raster database with pixel values ceRecting rhe recoded values. The new raster database and its reclassified values can then be used for mapping and analysis purposes.

Within the ArcGIS Spatial Analyst, a reclassifY command is available. Selecting the reclassifY command will open a dialog box that prompts rhe selection of a raster database, reclassification field , and input cells for reclassification values. By default, this interface will use the exist

ing symbolization used to display the raster. For example. should current raster values be displayed using five ranges or categories, these same five classifications would be displayed on the reclassifY cell options. To select different classifications from which to begin the recoding, the 'classifY' button can be chosen.

Raster Map Algebra

The ability to access and evaluate values within multiple raster databases and output results to a new raster database presents powerful analysis oppormnities. Raster map algebra involves a mathematical evaluation that is applied to one or more raster databases to create a new database

(DeMers, 2002). Mathema, ical evaluations for single raster databases may include adding or multiplying raster cells by a constant value, or applying trigonometric. logarithmic, and other mathematicaJ transformations to raster values. A common example of single raster map algebra might be taking an elevation-based raster and multiplying the elevation values by a conversion facto r to move from one measurement unit to another, such as converting elevation values from meters to feet. Mathematical evalua

tions for multiple raster databases might involve adding or multiplying raster values, comparing mulriple raster databases and remrning the largest value for all coincidental cells, or using raster database values within a formula that capmres landscape processes such as that for water velocity and discharge rates.

Whether one or more raster databases are used, raster

map algebra resulrs in a new raster database that contains the results of the mathematical evaluations specified by the user. Some forms of map algebra can be accomplished through techniques discussed earlier, such as cellular statistics that a llow for statistical summaries to be calcu lated from multiple raster databases. Operations that require mathematical manipulations of single or multiple raste r databases, however, requi re more direct approaches. Within the Spat ial Analyst function of ArcGIS, the raster calculator is provided in support of raster map algebra, and also provides access to additional raster related

funcdons.

Database Structure Conversions

It may be necessary to convert a spatial database from a vector to raster or from a raster to vector data structure for

a variety of reasons. Potential reasons include:

1. supporting a GIS process or ana lysis that only accom

modates vector data. 2. supporting a G IS process or analysis that only accom

modates raster data, 3. sharing data with a colleague who can only access one

data structure type. 4 . meeting the data requirements of a client or funding

organization, and 5. database storage size considerations.

As your work with GIS conti nues. it is likely that you will have to convert spatial data from one srrucmre to another. Regardless of which Structure you are converting to. there are decisions that mUSt be made fo r either (Cansformat ion . The good news is that a second database is typically created through the conversion process. This

usually ensures that if the transformation is unsuccessfut subsequent attempts can be made until a satisfactory product is created.

The creadon of point. line, or polygon features is of interest in converting vector databases to raster. Usually an attribute field is selected as the information to be car

ried into the new raster database. Depending on the software you use, the outpUt raster format might be integer or floating poine. Integer and floating point are the twO primary types of raster data formats supporred within typical GIS software. A key distinction between these twO types is that integer raster databases will usually accommodate

229


4. Rascer values may need to be rescaled in order to supP O f[ a single raster analysis.

5. Raster va lues may need to be rescaled in order [Q SUp

porr a mulciple raster analysis.

Reclassi fication is differem from altering the symbology or legend of a raseer database fo r display purposes in that ic goes beyond simply altering the appearance of a raseer dacabase. Regardless of the reasons for reclass ifYing a raster database, the reclassificat.ion process leads to a new raster database with pixel values reflecting the recoded val ues. The new raster database and its reclassi

fied values can then be used for mapping and analysis purposes.

Within the ArcGIS Spatial Analyst. a reclassifY command is available. Selecting the reclassify command will open a dialog box [hat prompts the selecdo n of a raster database. reclassification field. and input cells fo r reclassification values. By default. this interface will use the ex isting symbolization used m display the raster. For example. should current raster values be displayed using five ranges or categories. these same five c1assificacions would be displayed on the reclassifY cel l options. To select different classifications from which to begin the recoding. the 'classifY' butmn can be chosen.

Raster Map Algebra

T he abilicy m access and evaluate values within multiple raster databases and Qurpm results [Q a new raster data

base presents powerful analysis opponunities. Raster map

algebra involves a mathematical evaluation that is applied

to one or mo re raster databases [Q create a new database

(DeMers. 2002). Mathematical evaluations fo r single raster databases may include adding or multiplying raster

cells by a constant value, or applying trigonometric, logarithmic. and other mathemarica1 transformarions co raster

values. A common example of single raster map algebra might be raking an elevation-based raster and multiplying the elevation values by a co nversion factor ro move from

one measurement unit co anomer, such as converting ele

vation values from meters to feet. Mathematical evalua

rions for multiple rascer databases might involve adding or multiplying raster values, comparing muhiple raster

databases and returning the largest value fo r all coinci

demal cells, or using raster database values within a for

mula that captures landscape processes such as thar fo r

warer velocicy and discharge rates.

Whether one or more raster databases are used, rasrer

map algebra results in a new raster database that coma ins the resul" of the mathematical evaluations specified by the user. Some fo rms of map algebra can be accomplished through techniques discussed earlier. such as cellular sta

tistics that aHow for sracisri cal summaries to be calcu lated

from mu ltiple raster databases. Operations that require

mathematical manipulations of single or multiple raster

databases , however. requi re more d irect approaches.

With in the Spatial Analyst function of ArcGIS. the raster calculatOr is provided in suppo rt of raster map algebra.

and also provides access to additional raster related

functions.

Database Structure Conversions

It may be necessary fO convert a spatial database fro m a

vector to raster or from a raster to vec(Or data structure for

a variety of reasons. Potencial reasons incl ude:

I . supporting a GIS process or analysis that on ly accommodates veccor data.

2. supporting a GIS process or analys is that only accom

modares raster data.

3. sharing data with a colleague who can only access one data structure type.

4. meeting the data requirements of a client or funding

organ ization. and

5 . database srorage size considerations.

As your work with GIS conti nues, it is likely that you will have to convert spatiaJ data from one structure to

another. Regardless of which Structure you 3re convening

to, there are decis ions that must be made fo r either trans

formation. The good newS is that a second database is

cypically created through the conversion process. This usually ensures that if the rransformadon is unsuccessful .

su bsequent attempts can be made uncil a satisfactory

product is created .

The creation of point , line, or po lygon features is of

imeresr in conven ing Vec[or databases [Q raster. Usually

an arrribure field is selected as the information (0 be car

ried into the new raster database. Depending on the soli:ware you use. [he OutpUt ras ter format might be imeger or

Roaring point. Integer and Aoat ing poi nt are {he two pri

mary rypes of raster dara formats supported within typical

GIS software. A key distinction between these twO rypes is

that inreger raster databases will usually accommodate

mulriple fields or variables, and can accommodate non

numeric dara. Floating point raster databases are typically used for numeric data that incl udes precis ion. as evi

denced by decimal values. Floating point raSter databases are usually restricted [Q one aruibure value. rather than mulriple. being associated with each raster cell. In some

cases, it may be possible [Q convert a floarin g poin[ raster

database to an integer fo rmat database. or vice versa. bur you must consider what types and formats of data are

intended fo r the final product. In addi tion [Q the attribute field ro carry into the new

raster database. a raSter cel l resolution must also be

selected. T his is a critical choice and it will have a large influence on the specificity of the representation of vector features in the raster database and the scale at which sub

sequent analys is can occur (Mitchell , 2005). Equally important is that this choice will also impact the amount of digital storage space required by the resulting raster database. A smaller cell resolurion will represent vec[Qr features more precisely. but will require more sro rage

space. If a study area is large in size, and analysis goals are oriented more rowards a landscape or regional scale, then a larger cell resoiurion may be an appropriate choice [Q

consider. If a study area is small in size. o r if more detail is desired in the representation oflandscape fea tures, then a smaller cell resolution should be chosen . Increasing a cell resolution by half, however, resul ts in a four-fold increase in the number of raster cells. Keep in mind that

many raster analysis processes will either require or depend on raster databases having the same cell resolution in order to produce reliable outpur results . Raster resam

pIing involves changi ng the resolut ion of an existing raster layer and is a commonly used process among rasterbased GIS users. However. decisions about how cell values are to be treated in the resampled product must be made.

When the resampling of a raster database increases the spatial resolut ion. such as moving from a 30 m [Q a 10m spatial resolution, the value for each resulting cell can be taken from the 'parent' cell directly. When the resampling of a raSter database decreases spatial resolution. such as

moving from a 30 m to a I km resolut ion. the val ue for each cell wi ll require some statisdcal summation of input raster ce lls that contain numeric values. The statistical

summation might be the average or highest value of input cells. For raster databases with categorical values. such as land cover or tree species, the choices will be more limited in how values are resampled. The value in the nearesr or mosr completely coincident cell, o r the most commonly


occurring value in cells that will be combined in the out

put product, might be chosen . In moving to a vecror structure from a raster database.

a raster value will need to be selected for (he transforma

t ion product if [he raster supportS multiple fields. A key choice wi ll be the vector feature type for the output database. Users will usually have (Q choose from point, line, or

polygon feature types. The choice of one feature type over another wi ll be a function of the inpur raster database and

analysis objectives.

• Rasterization is the process of creating a rasrer database

from a vector darabase.

• Vectorization is the process of creating a vector data

base from a raster database.

The spat ial resolution of the raster database in either

process may influence the quality of the resulting GIS database (Bettinger et aI., 1996).

Getting Started with the ArcGIS Spatial Analyst

The Spati al Analyst extension software for use with

ArcGIS must be purchased in addition to the base sonware, and will not work independently of the base sonware. The Spatial Analyst extension must be enabled within an open ArcGIS session in order for rhe extension

software to work. As with all ArcGIS extensio ns. the Spatial AnalySt is enabled by selecting [he Tools menu, selecting the Extensions option. and selecting the check box next to the extension . The Spacial Analyst roolbar must then be enabled through either the T oolbar choice under the View menu, or by right clicking on an open location o n one of the menus, then selecting the Spatial Analyst. Once the Spatial Analyst toolbar is avai lable, session parameters ca n be established by accessing the options cho ice at the bottom of the Spatial Analyst menu.

The general options allow you to set your working directory. where newly created raSter databases will be saved. and also the parameters of an analysis mask. The analysis mask can be set to the spat ial dimensions of another GIS layer and processing will only occur in coincident areas. The extent options, avai lable in the second tab of the oprions dialog box, allow you to furthe r customize the

designation of areas considered for raster ourput. Choices include existing layers. spatial combinations of existing layers, or a bounding set o f coordinates. Both vector and

230

mulriple fields or variables, and can accommodate nonnumeric data. Floating point raster darabases are rypically used for numeric clara char includes precision. as evi

denced by decimal values. Floating point raster databases are usually restricted [0 one 3nriburc value. rather lhan mulriple, being associared wim each rasrer cell. In some cases, it may be possible {Q ccnven a fl oating point rasrer

darabase to an integer foemar database. or vice versa, but

you must consider what types and formars of dara are

intended for rhe final producr. In addition to the arrribure field [0 carry inca the new

rasrer database. a raSter cell resoimion must also be selecred. This is a critical choice and ir will have a large influence on the specificiry of me represemarion of vecroc feacures in rhe raster database and the scale at which sub

sequenr analysis can occur (Mirchell, 2005). Equally important is (har this choice will also impacr rhe amounc

of digiral storage space required by me resulting raster darabase. A smaller cell resolution will represent vecmr features more precisely. bur will require morc storage

space. If a study aJea is large in size, and analysis goals are

oriented more towards a landscape or regional scale. men

a larger cell resolution may be an appropriate choice co consider. I f a study area is small in size. or if more detail

is desired in the representation of landscape features, then

a smaller cell resolution should be chosen. Increasing a

cell resolurion by ha lf, however, results in a four-fold increase in the number of raster cells. Keep in mind that

many raster analysis processes will either requ ire or

depend on rasrer databases having the same cell resoludon in o rder to produce reliable ompur resulrs. Raster resam~

piing involves changing rhe resolucion of an exisring

raster layer and is a commonly used process among rasterp

based GIS users. However, decisions abou t how cell va lues are to be [realed in the resampled product must be made.

When me resampling of a raster darabase increases rhe spatial resol ution, such as moving fro m a 30 m (Q a 10 m

spatial resolution. rhe value for each resulring cell can be taken from the 'parenr' cell direcrly. When the resampling of a raster database decreases spatial resolurion, such as

moving from a 30 m to a I km resolution. the value for each cdl will require some s(3tistical summarion of in pur rasrer cells that contain numeric values. T he stat istical

summation migbt be me average or highest value of input cells. For raster databases with categorical values. such as

land cover or tree species, lhe choices will be more limited in how values are resam pled. The value in the nearest or

most completely coincident cell , or the most commonly


occu rring value in cel ls mat wi ll be combined in me our

pur producr, might be chosen. tn moving to a vector structure from a raster database,

a raster value will need co be selected for {he transforma

tion producr if rhe raster suppOrtS multiple fields. A key choice will be the vecror fearure rype for the outpur darabase. Users will usually have ro choose from point, line, or polygon fearure rypes. The choice of one feature rype over another wi ll be a function of the input rasrer database and

analysis objectives.

• RasreriZ3tion is the process of crearing a rasrer da[3base

from a vector database.

• Vecwrizacion is the process of creating a vector data

base from a raster database.

The spacial resolution of the raster darabase in eilber

process may influence ri,e quality of the resulting GIS database (Bettinger et a1 ., 1996).

Getting Started with the ArcGIS Spatial Analyst

The Spatia.! Analyst extension sofrwa re for use with

ArcGIS must be purchased in addition ro rhe base software, and wi ll nor work independently of rhe base software. The Sparial Analyst exrension must be enabled within an open ArcGIS session in order for rhe exrension

software to work. As with al l ArcG lS extensions, the

Spatial Analysr is enabled by selecting the Tools menu, selecdng the Extensions option, and selecting the check

box next ro the extension . The Spa rial Analyst roolbar must then be enabled through eirher rhe T oolb.r choice under the View menu, or by right" clicking on an open

location on one of the menus, then selecting the Spatial

Analyst. Once ,he Spatial Analyst toolbar is available, session pa rameters can be established by accessing rhe options choice a[ (he borrom of the Spatial Ana lyst menu .

The general options allow you to set your working direc

cory. where newly created raSter databases will be saved.

and also me paramerers of an analysis mask. The analysis mask can be set [0 the spada! dimensions of another GI S

layer and processing will only occur in coincident areas.

The extent options. available in the second tab of (he

oprions dialog box, allow you to furrher customize me designation of areas considered fo r raster am pul. C hoices include exiSting layers, spatial combinauons of existing

layers. or a bounding set of coordinates. Both vector and


raster databases can be used. The cell size options are pro

vided in the third tab of the options dialog box. and can

be used CO establ ish the resolution of Output raSter databases. Settings include other analysis layers. minimum or

maximum area covered by all input databases. and userspecified resolutions. The number of columns and rows in [he raSter can also be chosen . Setting a cell size to

match an existing layer will lead to spatial agreement

berween the layer and any output raster databases. This is

a key choice that can help ensure consistent spatial registration of raster databases, an attribute that helps support

reliable analysis output.

The default raster format with in the Spatial Analyst

extension is an ESRl grid. The grid data structure bears similarities to that of ESRI coverages in terms of transpon, storage, referencing, and naming conventions.

These convemions are nor very forgiving but are manageable given a few ground rules. ESRI grids should be

named using no more than 13 characters and should not stan with a number, should not contain spaces, and

should not use unusual characters such as an ampersand

(&) or dollar symbol. These same rules should be consid

ered for the naming conventions of folders under wh ich

grids are smred. An underscore can be used in place of a

space. JUSt as with an ESRJ coverage, an ESRl grid is actually composed of two folders and all information in both

folders must be stored under the same directOry. For th is

reason. an Ardnfo interchange file with an .eOo file exten

sion is often used to transport ESRJ grids and coverages, much as a common zip file format performs when it is

used to compress and transport multiple files. The majority of the raster analysis and processing

functions described are available under the primary Spatial Analyst menu or within sub-menus. The previous chapter explored the majority of functions available in the surface analysis sub-group. We turn our attention to (WO

examples that involve raster data processing and analysis functions.

Determining the Most Efficient Route to a Destination

Let's assume that the Brown Tract staff would like [Q take

rocky fill material placed in one of the rock pits (described in rhe Brown T racr srands GIS database as Stand 282) and

transpoC[ it to another location near the southeast entrance of the Brown T racr boundary (Figure 14.6). The material will be used to create a new trailhead ro accom-

Brown Tract • Rock pit

• Southeast entrance

- Roads

Figure 14.6 Rock pit, south~ast ~ntr.tnc~. and road 5yst~m in th~ Brown Tract.

modate the growing use within the forest. Mulriple trips with a dump truck will be required and the staff would

like m minimize the impact on the forest road system

during the fill hau ling process. A potential logical

sequence for the GIS operations to support the shortest

path creation is represented in Figure 14.7.

The roads database is in a vector data struCtu re and describes the road surface rype according to one of three

possible values: paved. rocked. or dirt (unpaved). These

values will be used to ass ign a cost to each of the Brown

Tract roads. The idemificarion of the most efficient route will assign costs of I. 5. and 10 to the three road rypes.

respectively. The slopes of the Brown Tract roads will also

be considered in the analysis. Due to the relatively coarse

resolution of the Brown Tract OEM (10 m). road slopes will be divided into three broad categories with COSt val

ues of 1 for mild slopes (< 5 per cent). 5 for moderate

slopes (5-1 0 per cent). and 10 for greater slopes (> 10 per

cent) . Although the choice ofa range between 1 and 10 is

somewhat arbitrary. choosing values from a larger range will help differentiate possible routes more distinctly than

will values from a smaller range. The cwo layers are then

added to each other through raster map algebra to form a

si ngle database with coSt values for each cell. COst weighting and direction are then calculated for the Brown Tract

road ne(Work in order to reach the rock pit. After the support ing databases have been developed a shortesr path

function can be used to identify a preferred transportation route for the fi ll material (Figure 14.8) . Although the

road system represented in the Brown Trace database is not substantially large in extent, many forest and other natural systems have exrensive transportation ne[Works.

231


Slope Rock pit GIS database GIS database

(vector)

Conversion Reclassify to raster slope database categories

Reclassify Combine road type values

Develop cost path &

cost direction (raster)

Best path

algorithm

(vector)

Figure 14.7 A general process lO identify the shortest path between two locations on the Brown Tract.

In these simations, the number of potenrial fOUCes can surpass (he ability of transportation planners to sysremar· ically evaluate and select from a full range of options.

Brown Tract • Rock pit

• Sootheast entrance

- Shortest path

- Roads

Figure 14.8 Shortest path between rock pit and southC:l$t entrance of the Brown Tract, given cost weights for road surfaa and road slope.

Shorrest path algorithms can assist planners and managers in making sound decisions.

Creating a Density Surface for the Number of Trees Per Acre

Density functions can be used to demonstrate [he relative abundance or strength of the locations of features and anributes, The stands database for the Brown Tract contains an attribute named 'trees_acre', This attribute has a

relative weighting of the trees that you would expect to

find in each stand within the Brown Tract on a per acre

basis. It may be of interest to determine the areas in the forest where this field is strongest, indicating where higher numbers o f trees are more likely to be fou nd. Deter

mining this info rmation could be done through plotting the polygons and using shaded symbols to demonsttate intensity values of individual polygons. This approach, however, would neglect the influence of neighboring

232


What is a centroid? A centroid is a coordinate pair that is intended co represent me mid-point of a feature or group of fe-drures. A centroid could be creatai co represent {he center of a group of points by taking the average of all the longirude and latitude coordinates. In terms of a line feature, a centroid position is easily determined. by dividing the tQ[al length of the line in half and using a coordinate

pair [Q represent the half-way point. A polygon centroid can be more difficult to determine if the polygon shape is

polygons in irs representation . A more helpful approach might be to create a densi ty surface which would search

surrounding areas and determine inrensicies that take into account (he nllmber of trees for each stand while consid

ering the number of trees in neighboring stands.

A density surface must be created from a point or line

feature type. In order [Q apply the density function to the stands layer in the Brown Tract, we'll need ro conven the

stands polygon feamce rype. A point representation is probably preferred over a line feature type for the con

vened stands. A common method for representing poly

gons as poims is {Q calculate the cemroid, or middle of a

polygon's extent, which is determined geometrically if the shape is basic, such as ,hat described by round, square, or

rectangular fearures. For irregular polygon shapes, cen

troid determinat ion must be accomplished with more rig

orous mathematical techniques. Most GIS software sys

tems will offer routines for cemroid determination and

can quickly create a cemroid represemation of a polygon

or line feature with one oUCput point created for each

input feature. In addition, all of the attribute values will be carried into the poim anribuce table. Within [he

ArcGIS software, ,he ArcToolbox has a 'Featu re to Point'

conversion command that will accomplish this cransfor

mation. The XTools extension software, a popular low

COSt ArcGIS extension program, has commands that also

support centroid creation.

After the stand polygons have been converted to

poims, the density surface can be created. Figure 14 .9

shows the resuh of a simple density surface based on the number of trees per acre. The darker shaded areas highlight the areas where greater numbers of crees would be expected. The search rad ius was set to 1,000 ft and den

sity circles were created for each stand cencroid to demon-

irregular or non-homogenous (is not round, square, rec

tangular, triangular, etc.), patricularly if some or all of the

boundaty that make up a polygon contains curves, as is orren the case when describing narural features. The cen

troid of such a polygon is determined through mathematical integration (calculus) with the goal of determining where the center of gravity of the polygon is located.

The center of gravity can be thought of as the point at which the polygon would balance if set flat upon a pole.

strate the detected densities. Figure 14. 10 shows the Output that results for a smoothed density surface using the

same stand centroids and a 1,000 ft search radius for trees

per acre.

Simple density surface Trees per Icre o lOw density

CJ -_ moderate density --_ hIgh density

Figure 14.9 Simple density surface for num~r of trees per acrc based on a 1,000 ft search rndius.

•• i ..

Smoothed density surface Trees per Icre o Iowdenslty CJ II!llI _ moderate density --_ high denslty

Figure 14.10 Smoothed dcns ity sUrhce for num~r of trees per acre based on a 1,000 ft search radius.

233

Summary

We demonstrated in (his chapter how raster databases can

be manipulated and analyzed (0 solve questions related [0

natural resource applicadons. A host of functions are

available ro supporc analysis including distance, sracisrica1 search summary. and density functions . The functions

differ in their application and in the types of database structures that can be used for analysis . Omput may be tabu lar, veC(Of, or raster depending on the function .

Several common procedures within most raster-based

software include raster reclass ification, raster map algebra.

and conversion routines between vector and raster dar3-

Applications

14.1 Straight line discance function for points. A!; parr of your position as a natural resource manager. you manage

the research areas on the Brown Tract. Concerned abom

their dimibu<ion across ,he landscape, you decide rhac a simple rasrer analys is mighr shed lighr on rheir spacia l arrangement. Create a straight line dismnce raster database

for research plot pointS conrained in the Brown Tract.

14.2 Straight line distance function for lines. The density of stream systems can be used to define their char

acter. Given the streams GIS database for rhe Brown

Tract , creare a srraight line distance rasrer database for

streams contained in the Brown Trace.

14.3 Straight line dinance function for polygons. A!;

another analysis related {Q the distribution of research

areas, create a straight line distance raster database fo r the

"and polygons in ,he Brown Traer where ,he LANDAL

LOC field is designared as 'Research '.

14.4 Allocation distance. As we mentioned earlier, in

developing an allocation distance, ras ter cells are ass igned

a pixel value that recognize the nearest zone of inAuence,

si milar to the vector representation of a Th iessen polygon.

Create an allocation raSter database for researc h plot

po ints contained in the Brown Tracr.

14.5 Ceu statistics. To furrher you r undersranding of the spatial distribution of research plots on the Brown

Tract, you decide (Q embark on a series of raster analyses

to determine their venical distribution.

a) What is the average elevation of the resea rch plots?

b) Which research plo< (use rhe numbers in rhe field


bases. The procedures facilitate spatial analys is and supPOrt ,he abili ty [Q prepare spa,ial daubases so ,har ,hey are su ited for specific analytical purposes. In additio n, we

presented [Wo potential applications in which some of the

functions and procedures discussed earlier in the chapter

were applied. The raster analysis processes and examples

we presented by no means represent the extent of the

porential of raster analysis. Rather, these processes and

functions describe some of the more usefu l and commo n

commands and processes for namra1 resource analys is

with raster data.

' PLOT' to describe) has rhe highesr e1evar ion and what is the elevation?

c) Which research plor (use ,he numbers in ,he field ' PLOT' ro describe) has rhe lowest e1evarion and what is the elevation?

14.6 Neighborhood statistics. Using ,he elevation layer for the Brown Tract as an analys is extent and a template

for Output cell reso lution, what is the longitude and lati

rude of ,he cenrer of rhe ,hree by rhree grid cell neighborhood wirh rhe highest e1evarion'

14.7 Zonal sta,inics. Assume ,har a fire has s<arced in srand 140 of ,he Brown Trace. Ifhand crews canno[Control the fire, water must be acquired from nearby sources

to help extinguish the flames. What is the average dis

tance to the nearest water source for stand 140?

14.8 Zonal statistics . What is the average distance {Q

the nearest water source for so ils polygon 166? Use the

SOILS_ field to determ in e where this stand is located

wirhin the so ils database in the Brown Tracr?

14.9 Density surface for basal area. Crea,e borh a simple and smoorhed density surface for [he Brown Trace stands. Base rhe density upon (he basal area field con

tained with (he stands attribute table.

14.10 Density surface for research plots. Creace borh a simple and smoothed dens ity surface fo r rhe Brown Tract

research plors. Base rhe density upon [he field named 'sl' which contains a numeric value for the site index of each

ploe. 234


References

Bettinger, P., G.A. Brndshaw, & Weaver, G.W. (I 996). Effects of geograph ic information system veC[Q f- rasrer

vector data conversion on landscape indices. Canadian journal ofFomt Restarch, 26,1416--25.

Chang, K. (2002) . Introduction to geographic informa

tion systems. New York: McGrnw-Hill. Chrisma n, N . (1997). Exploring geographic information

systems. New York: John Wiley & Sons, Inc. DeMers, M.N. (2002). GIS modeling in raster. New

York: John Wi ley & Sons, Inc.

Mitchell, A. (2005). The ERSI guide to GIS analysis. Volume 2: Spatial 1nf!asurements and stah'sties. Redlands, CA: ERSI Press.

Silverman, B.W. (I986). Density estimation for statistics and data analysis. New York: Chapman and Hall.

Theobald, D.M. (2003). GIS conupts and ArcGlS methods.

Fort Collins, CO: Conservation Planning Technologies. Wing, M.G., & Tynon , J.F. (2006). Crime mappi ng

and spatial analys is in National Forests. Journal of Forestry, 104(6),293- 8.

235

Part 3

Contemporary Issues in GIS

G t!ographic Information Systems: Applications in Natural Rt!sourus Managemt!llt focused

on the background and development of GIS in rart I and delved into GIS applications in Pan 2. In Parr 3. we try [Q provide a glimpse of where GIS use may be heading in

the nea r future. Trying to look ahead and predict what may happen is a difficult task because both GIS-related technology and the society that surrounds it are changing rap

idly. Nonetheless. in chapter 15 we discuss some trends that 3re associated with GIS in

namra1 resource management. These trends are related CO technological developments. rhe

handling and sharing of spatial data, and the legal issues that may impact organizations that use GIS. C hapter 16 makes note of how the increased ava ilability of GIS has trans

formed rhe delivery and struc(Ure of GIS operations in many organizations. Also important in chapter 16 is rhe discussion of possible barriers ro successful GIS impiemenrarion

and how implementation effectiveness can be assessed.

We also consider some of the current challenges within the GIS communicy in Parr 3 .

T he final chapter, chapter 17, examines the on-going and sometimes contentious discus

sion of how the GIS profession should be defined and recognized. There are a number of

other established professions that are also involved with measuring and mapping features

and there has been friction at times in agreeing on me capacity in which certain professions

should apply GIS. Such discussions are evidence that GIS and GIS professionals have an important and necessary ro le in today's society. While many other professions have well

defined activities, competency standards. and governi ng bodies that describe and guide irs

members. the GIS community has only recently developed some initial pathways for cer

tifying GIS competency. Criticism has been leveled toward these initial efforts on the

grounds mat sufficiently rigorous processes to establish competency have not been devel

oped. The discussion ofeIS competency arose initially from concerns voiced. from me land

surveyi ng and engineering communities about the potential for GIS users to perform tra

ditional surveying and measuremenr activities while not actually having professional license as a surveyor or engineer. The final chapter probes the issue of whether GIS users should be licensed. a hot ropic of conversacion particularly when you try to define what a 'professional' GIS user is and what sorts of activicies (hat person is qualified ro perform.

236

Chapter 15

Trends in GIS Technology

Objectives

GIS technology is constantly evo lving, adapting. and changing according to the needs and capabilities of GIS users , panicularly (hose within the field of namral resou rce management. While namral resource managers

may only represent a portion of the tOtal population of GIS users, natural resource management also benefits from the influence other fields (t ransportatio n, utility

management, public planning, etc.) have on [he evolucion of GIS technology. This chapter provides a discussion of so me of the current trends associated with GIS technol

ogy and use. When we initially developed th is chapter in 2004 (Bettinger & Wing, 2004), we co ncluded that fore

casring the direction and success of trends was challenging, but allowed you [0 consider what potential applications might ex ist within the area of natural resource management. As you will see, some of the trends in GIS

tech nology have remained over the past duee years, while others have recently appeared.

After considering the topics presented in this chapter, readers should have a reasonably firm understanding of a number of issues related to the trends in GIS technology and lise. As a result, readers should be able to describe and debate the associated strengths and weaknesses of:

I. the common trends related ro GIS technology, and how these might be applied in natural resource management,

2. the oppo rrunities for strengthenin g GIS technology and applications within natural resource management o rganizations. and

3. the current and potential technological developments that might promote or hinder the adva ncement of GIS

as an effective problem-solving roo l.

Integrated Raster/Vector Software

For many years, GIS and other spatial software systems have been defined by their ability to wo rk with either

raster or vecror data. In fact, many GIS software programs either rest ricted lIsers ro working with one data structure o r the other, or aiiowed users to conduct analyses with one data strucrure and limited the use of the other data Structure to rudimentary purposes (e.g., viewing only) . Recently, however, almost all traditionally raster-based

GIS software programs have begun to include algorithms and techniques to allow the capability of managing vector GIS databases, and similarly, almost all traditionally vector

based GIS software programs have begun to include algorithms and techniques to allow the capability of managing raSter GIS databases (Faust, 1998).

The primaty hindrances to providing the capability to use both data srrucrures for spacial analyses were {he

marked differences between the twO data structures and in part icular, how they were stored. To further complicate marters, software manufactUrers created their own proprietary formats for raster and vector data structures that were best suited to their product. In addit ion, each data structure could also be described by more than one format. The di ffe rent structures (and formats of srruc~

cures) overwhelmed the computing capabi li t ies and software design efforts of earlier GIS software com panies. As

237

computer technology and software programming languages evolve. a (Orally imegrated system, one that would

be able to incorporate both vecrof and raster GIS data

structures simultaneously in the spatial analysis of natural

resource issues, is a trend in software development that

will continue [0 drive the direction offurure GIS software

programs. For example. such a system would allow the use of vecmr GIS databases (0 assist in image classification,

whereas previously only raster-based GIS databases would allow a system to perform GIS operarions such as buffer

ing. overlays. and proximity operations. with hoth raster

and vector processes in a seamless and efficient manner. In a totally integrated GIS, processes such as vecrof-toraster or raster-[Q-vector conversion (as described in chap

ter 3) and analyses that use both raster and vector data simultaneously (as described in chapter 13) would therefore be transparent to users of GIS (Faust, 1998).

Software such as ENVI (ITT Corporation, 2007) and Erdas Imagine (Leiea Ceosystems, LLC, 2007) not on ly provide a vast suite of raster-based analytical tools (e.g. ,

image classificacion, terrain analysis), bm also allow you

to integrate vec[Qr data with raster data and perform the

buffering, digicizing, and edicing funcrions that were dis

cussed earlier in this book. Erdas Imagine also allows you to clean and build the tOpology of vector GIS databases, which is useful when editing vectOr GIS data. Coogle Earth (Coogle, Inc., 2007) is a similar system but it may be more appropriately considered as a geospatial explo

ration program at this point in rime. The Google Earth

system has the ability to integrate vector and raster data

to a limited extent, but its real value lies in allowing users

to easily visualize landscapes through an Internet

browser.


Linkage of GIS Databases with Auxiliary Digital Data

While we think of GIS as a system for displaying and manipulating geo-referenced maps and images. we have

[he ability in some GIS software programs to associate

spatial data with other non-spatial data. Of course, data in

an atuibme [able, or data from a non-spatial joined table

can full into this category as well, but what we refer to

here is the association of an image that is nO( georefer

enced with some spatially-referenced data. For example.

in the field of urban forestry, you might capture the spatial position of (rees within a ciry as a set of vector points.

These points may be attributed with tree characteristics

(species, height, etc.) and other local landscape variables. The points that represent the trees can also. in some CIS

software programs, be atuibuted with a link [Q a picture

and, when you select a point representing (he tree, the

picture of the tree is presented (Figure 15.1). The linkage of GIS databases to this type of auxiliary

data is generally made using a hyperlink. A hyperlink is a navigation element that allows. when selected, [he view

ing of the referenced information associated with the link.

Hyperlinks are used widely on the Internet for navigation

purposes, but they are nO( limited to Internet usage. obvi

ously. They were designed as a way for you to link to specific portions of related documents without having to

open each new document at its beginning and search for

the desired page. Hyperlinking is a useful way to associate pictures. documents, videos, or any other relevanr data to

a mapped feature. thus allowing for a more comprehen

sive use of information systems. The urban tree example

is bur one of many logical and valuable uses ofhyperlink-

Figure 15.1 A GIS database of urban treC$, and an associated hyperlinked picture of a trcc (Courtesy of Andrew Saunders).

238

228 Part 3 Contemporary Issues in GIS

ing non-spatial data to GIS databases. Having the abi li ty to view (with actual photos) oblique perspectives of landscapes from various vistas or overlooks represents another

valuable use of hyper/inking data for natu ral resource management purposes.

High Resolution GIS Databases

New areas of research and development, called 'precision

fo restry' or 'p recision agriculture', have recently been introduced in natural resource management. These areas

of research and development seek co use digital technologies for improving, and making more efficient. namral resource management activities. 'Precis ion tech niques' might include using GPS as a navigational aid for farm or

forestry equipment. capturing remotely-sensed imagery co describe the status of soil properties (e.g .• the need for fertilizer or pesticides), or using digital aerial photography to

record crop planrings and outcomes. Precision agriculture

techniques have been actively used and recognized as a

discipline for at least a decade. In contrast. the first formal recognition of precision forestry occurred in June 2001 at

the University of W ashingwn's Precis ion Forescry

Symposium. High among the list of goals of precision forestty is the identification of methods fo r the collection. analys is. and use of highly accurate and precise data from

the Earth's surface w facilitate bener management of nat

ural resources. Examples of precision fo restry techniques

might include using electronic distance measuring (EDM)

(Ools ro capture the precise spatial position of forest land

scape features, capturin g precise and timely satellire

imagery (0 ass ist in monitoring threats (0 forest health

(e.g .• fire or disease). or developing precise. fine-scale DEMs to identify steep forested areas susceptible ro poten

tiallandslide activ ity.

The main obstacle ro implementing precision forestry

or agriculture techniques in natu ral resource management

remains that of obtain ing accurate. prec ise. and timely

spatial data of landscapes . Some applications of precision

technology may be implemented more easily in differing

land uses, however. For example. in COntrast ro many

agricultu ral applicatio ns, forests are characterized by a

dense canopy cover and sometimes by mountainous ter

rain, which can limit the types of technologies that can be used {Q coll eCt precise spatial data. A thick canopy cover,

for instance, often hinders CPS reception. and hillsides can prevent satel lite signals from reaching a CPS receiver. There are ways to avoid some of these problems, such as

by co llect ing CPS data during rhe winrer months when

the tree canopy is least dense. or by scheduling GI'S missions during periods when an increased number of CPS satelli tes will be available.

Spadal data collect ion technology conti nues to evolve.

and natural resource managers are likely to see new tech

niques that improve upon present CPS, satellite imagery,

and LiDAR data collection methods in the upco ming yea rs. One of the most promising secrors o f improved

data collection technology is related to high spatial resolution GIS data. GIS databases developed from the rasterization of color ae rial photography and developed from satelli tes such as IKONOSTM (GeoEye. 2007) are becoming available at 1 m to 4 m spatial resolutions (Figure

15.2). While geo-registered color aerial photography of large land areas can now be collected, processed . and

made avai lable to clients the following day. acquiri ng satell ite imagery at 1 m resolution requires a longer time

period (generally 10 days or more) and depends on the area and time frame of interest. Another promising area of

improvement in data is high speccral resolution raster

databases. Normally. aerial photographs that are converted into digital o rrhophotographs cover a 0.4 to

0.9 micrometer range of the electromagnetic spectrum.

Some satellites systems capture energy in longer wave

lengths. but usually within 10 distinct bands (ranges of energy) or less . Higher spectral resolut ion data implies

FigllR 15.2 IKONOS satellite image at 4 m resolution of Copper Mountain located in the Colorado Rocky Mountains (Imagc.s courtesy of GeoEye).

239

that many mOte bands of enetgy have been captured, and can be used co mo nito r and evaluate the Earm's surface.

AVIRIS (National Aeronautics and Space Administration, 2007) is an example of one such high spectral resolution system, although it has been avai lable for almost 15 years. With this system, 224 bands of data can be captured for a single landscape at one point in time, aIlowing scientists

and managers to use the appropriate spectral reflecrances

for analyzing various nacurai resource management issues.

Still among [he primary concerns of most nam ral

resou rce management organizations is (he COSt related (Q

the acquisi tion of high-resolution spatial data. While initially relat ively high, as new technologies and data sources become available the cost and ava ilability of highresolution GIS databases might be as low as $0.03 per acre. Anomer issue of concern is the data scocage require

ments. H igh-resolutio n images can require mass ive

amounts of computer hard dtive space. Although large hard drives (I 00 GB and above) have become the norm, storage space can be filled quickly as images are gathered and stored digitally.

Managed and integrated properly, high-resolution GIS

databases w ill help facil itate the creation and maintenance

of databases (hat have tremendous potencial for organiza

tions that manage large land areas. These GIS databases can be associated with methods char keep land cover

informacion current, and allow you co conduct temporal

analysis of land cover conditio ns. Two of the challenges

[0 usi ng high-resolution GIS databases wi ll be in deciding how often to acquire new data. and how CO integrate new

GIS databases with existing GIS databases. These challenges are markedly different from those experienced in

the recent past, where most natural resource management

organizations struggled due to a lack of data. Now, the amount of data available to an organization can become overwhelming. The high resolution of these GIS databases w ill also help provide a more accurate representadon of

natural resources, which has typically been one of the

drawbacks of using raSter GIS databases (Faust, 1998) .

Distribution of GIS Capabilities to Field Offices

There are a number of reasons why the use of GIS has.

and cominues (0. spread from a centralized organizational

office to field offices: more and more people are becoming comfortable using GIS. colleges and universities are educating students in the use and application of GIS in natural resources, and natural resource o rganizarions are rec-


ognizing that more timely analysis and map products can be obtained if the work is more closely situated co the end user (Bett inger, 1999; Wing & Bettinger, 2003). [n addition, the increased power of computer technologies (speed and memory) and [he advancements made in the operating systems of perso nal computers have both

allowed a wider user base to use GIS technology (Faus[, 1998). Since computer systems are relatively inexpensive

(a GIS workstation can now be purchased for unde r

$2,500) , and GIS software has been developed with end users (e.g., field personnel) in mind, the trend is [Oward a GIS system where, in larger organizations, data develop

ment and maimenance tasks are performed at a cemeal

office, and data analysis and map production tasks are

performed at remote field offices. In smaller organiza

tions, the d istribution of processes may be less clear.

because the distinction between a central office and field

offices may be blurred (or non-existent). In some o rganizations, approved person nel who work

in the field perform the data maintenance tasks . C hanges

to GIS databases can be made at the field office, sent electronically co the cemral office for verification and integration, and eventually passed back to field offices. In systems such as these, o nly one person can be making

changes at each point in time. As a result, data being

edited is 'checked out' (like a library book) umil [he editing has been completed. The transfer of updated information to [he field offices would ideally be instantaneous, but it is not. A delay of 15-30 minutes is required for centralized systems to complete the ir tasks.

The benefits of a distributed GIS system are aimed at enhancing local or field office productivi ty and decisionmaking. Two of the main benefits include a more timely

response co analysis and map productio n needs of field

offices, and a decreased work load on a centralized GIS

office (al lowing more time and effort to be devoted to GIS

database quality and maintenance). Within a distributed

GIS system, clearer channels of communication should

exist, since generally speaking, (he customers (those

requesting maps or analysis) and suppliers (those performing the analysis or making maps) are in the same office (or

perhaps are the same person). This face-eo-face communi

cation is often more effecdve in meecing the goals of a map

or analysis request than communication processes that rely on e-mail or phone ca lls. [n addition , field personnel involved in GIS analysis and map production are likely (0

feel as if they have a greater investmenc in [he GIS program.

and perhaps will develop a greater sense of responsibil iry for maintaining accurare GIS databases (Berringer, 1999) .

240

that many mOte bands of enetgy have been caprured, and can be used to monitor and evaluate the Earth's surface. AV1RIS (National Aeronautics and Space Administration, 2007) is an example of one such high spectral resolution system, although it has been available for almost 15 years. With this system, 224 bands of data can be captured for a single landscape at one point in time. aUowing scientists and managers [0 use the appropriate spectral re£lecrances for analyzing various namrai resource management issues.

Sdll among the primary concerns of most natural resource management organizacions is the COSt related [0

the acquisition of high-resolution spatial data. While initially relatively high , as new technologies and data sources become avai lable the cost and availability of highresolution G IS databases might be as low as $0.03 per acre. Anorher issue of concern is the dara s[Qrage re'luiremenrs. High-resolution images can require massive amounts of computer hard drive space. Although large hard drives (100 GB and above) have become the norm, storage space can be filled quickly as images are ga<hered and stored digitally.

Managed and integrated properly, high-resolution GIS

databases will help facilitate the creation and maintenance of databases mat have tremendous pmential for organizations that manage large land areas. These GIS databases can be associated with methods mar keep land cover informacion currene, and allow you [Q conduct remporai analysis ofland cover conditions. Two of the challenges to usi ng high-resolution GIS databases will be in deciding how often ro acquire new dara, and how (0 imegrare new GIS databases with exisring GIS databases. These challenges are markedly differenr from [hose experienced in the receor pasr, where mosr narural resource managemem organizations Struggled due 10 a lack of data. Now, the amounr of dara avaiJable [0 an organizarion can become overwhelming. The high resolution of these GIS databases will also belp provide a morc: accurale representation of natural resources, which has typically been one of the drawbacks of using raster GIS databases (Fa uS!, 1998).


There are a number of reasons why me use of GIS has, and conrinues ro, spread from a cemraliz.ed organizational office to field offices: more and more people are becoming comfortable using GIS. colleges and universiries arc: educadng srudc:ms in the use and application of GIS in nacuraj resources, and namral resource organ izarions are rec-


ognizing that more timely analysis and map products can be obtained if the work is more closely situated to the end user (Bettinger, 1999; Wing & Berringer, 2003) . In add irion , rhe increased power of compurer [echnologies (speed and memory) and the advancements made in the operaring systems of personal compurers have borh allowed a wider user base to use GIS technology (Faust, 1998). Since computer systems are relatively inexpensive (a GIS worksrarion can now be purchased for under $2.500), and GIS software has been developed with end users (e.g., field personnel) in mind, the trend is toward a GIS sysrem where, in larger organizacions, dara developmem and maintenance casks are performed at a cemral office, and clara analysis and map production [asks are performed at rem ore field offices. In smaller organizarions, the distriburion of processes may be less dear. because [he distincrion becween a central office and field offices may be blurred (or non-existent).

In some organizations. approved personnel who work in the field perform the data maintenance tasks. Changes to GIS databases can be made at the field office, sent electronically co [he central office for verification and integration, and eventually passed back to field offices. In systems such as these, only one person can be making changes at each point in time. As a result , dara being edited is 'checked out ' (like a library book) until the ed iting has been completed. The transfer of updated information ro the field offices would ideally be instantaneous, but it is not. A delay of 15-30 minutes is required for centraljzed systems co complete [heir rasks.

The benefits of a distributed GIS system are aimed at enhancing local or field office productivity and decisionmaking. Two of me main benefits include a more timely response to analysis and map production needs of field offices, and a decreased work load on a centralized GIS

office (allowing more time and effort to be devoted to GIS

database quality and maintenance). Within a distributed GIS system. dearer channels of communication should exist , since generally speaking, the customers (rhose requesting maps or analysis) and suppliers (those performing the analysis or making maps) are in the same office (or perhaps are the same person). This fuce-to-fuce communication is often more effective in meering rhe goals of a map or analysis request chan communication processes mat rely on e-mail or phone calls. In addition, field personnel involved in GIS analysis and map production are likely to

feel as if rhey have a gre-arer investment in (he GIS program, and perhaps will develop a greater sense of responsibility for maintaining accurate GIS databases (Bettinger, 1999).


Given the ongoing technological {software and hardware} advancements related co GIS. and the proliferation

of GIS training, it is highly likely that the distributed GIS model of capabilities wiH continue to grow, and become

more prevalent than the centralized model. At some point, the distributed model may replace the centralized model completely in many organizations. The challenge in man

aging this paradigm shift will be to ensure that organizational prococols and monitoring are in place to protect distributed users from using spatial data improperly.

Web-based Geographic Information Systems

The widespread use of Google Earth has suggested to many that GIS can be both affordable and easy to use across the Internee. Some organizations have recognized

the need to provide GIS data management services over

the Internet as a way [0 more rapidly update databases and provide information to users in the field. Ideally, a natural resource organization would maintain a system where data

being updated by an employee in a field office can be 'checked our' over the Internet and managed (updated or

modified) . While the data is being modified remotely, other users would have the abilicy to view the data, but not

simultaneously modify the data. Once the data has been checked back in, orner users in the organization can then

use the modified data. Overall, [his type of process has the potential for substantially reducing the time required to

traditionally update GIS databases (see chapter 10). However, there may be some data qualicy issues related to

remotely-performed modifications of databases that are

not consistent with organizational standards.

As an example of a system such as this, the Virginia

Department of Forestry has recently implemented the

Integrated Forest Resource Information System (IFRJS).

Many organizations have developed their own Intranet.

These are private, networked computer environments

in which only members within me organization have

access. Imranets look and act like the Internet, and can

range in complexity from the vety simple (a set of folders or subdirectories containing data or other organiza-

The benefits of the system include reduced paper-related processes {e.g., transfer of maps and data to a centralized

office} , and an empowerment of people to adopt and use

new technology at the field level. Users in field offices can update GIS databases using heads-up digitizing, and submit data and repons on progress in managing the state's

forests. Future endeavors include extending the real-dme

capability to hand-held data recorders equipped with GPS technology to all ow an immediate data capture and

update to occur, which can increase the efficiency of map

ping and reporting wildfires, water quality problems, and insect and disease outbreaks. Although the intent of the system is to make administrative functions of state land

managers more efficient, as with the previous discussion,

there may arise some data quality issues related to

remotely-performed modifications of databases that are

not consistent with organizational standards. Only time

will tell whether these advances will result in COSt- and

time-savings and in increased productivity with no sub

stantial reduction in data quality.

Data Retrieval via the Internet

As we discussed in chapter 3, the Internet is becoming a

common source for acquiring GIS databases, GIS meta

data, and other information regarding the acquisition of

GIS databases. In fact, the current popularity and preva

lence of GIS can at least be parely attributed to the Internet. As public agencies began to produce and make

GIS databases available, customers who wanted the data

were often required to pay for the storage medium (tape, co, etc.) and for the time required to place the GIS database{s) on the medium. Since the media had to be mailed to the customer, this process also required a period of sev

eral days (to weeks) before the user could actually use the GIS databases. Presently, most public agencies offer their

tion information) to the sophisticated {those that offer graphical interfaces for access to data, software, or links to other organizational services}. Each of these services is

provided through an internal (co the organization) website. Inrraners are a method that can facilitate a disrrib

ured GIS system within an organization.

241

non-sensitive GIS databases over the Imernet, allowing

GIS users the opportunity to quickly acquire the data at

no COSt. In cases where GIS databases are very large and therefore nO( practical for Imernet transfer (for example, as wirh DOQs or orher high-resol urion rasrer imagery) . public agencies may still require that consumers pay for data transfer COSts. However, data compression software technology conrinues to improve, which reduces the need

for non-Internet transfers. Private organ izations that marker and sel l GIS darabases also allow cusromers ro download the databases from the Internet. In these cases, cus

tomers usually need to be registered w ith rhe private organization because access to the data is restricted.

Portable Devices to Capture, Display, and Update GIS Data

[n rhe pasr decade rhe use of hand-held dara collecrors and personal digiral assisranrs (PDAs) has become quire common for collecting forest inventory data and other attributes oflandscape features. GPS receivers, in faCt, use

hand-held dara collecrors ro allow you ro caprure rhe spatial anributes oflandscape features. Inregration of the two philosophies. allowing you ro collecr sparial locarional information about landscape features and to coliect anribute data, results in positive benefits to a natural resource

management organization. Traditionally, data collected for natural resource inventories would be recorded in a field notebook or on a map, and would requ ire manual dara enrry inro a spreadsheer or GIS darabase (rhrough anributing spatia l landscape features). This manual process is time consuming and presents several opportunities for human error to be incroduced into a GIS database. The integration of digital technologies allows info rmation to be recorded in a computer database while a person is in rhe field. Hand-held dara collecrors and PDAs are able co conneCt to wired or wireless computer systems, allowing rhe dara co be rransferred ro GIS. This grearly exped ires rhe rransfer of field-collecred dara ro a GIS darabase where rhe dara can be analyzed and mapped. This process also removes some of the error opportunities that might occur through rhe manual coding and inputting of clara. Dara collectors also offer users the abil ity to examine maps and

images of landscape fearures as rhey are being measu red. Field personnel can use this heads-up d isplay ro visually determine whether their measurements are in agreement with the landscape features being measu red. Digital orthophotoquads o r digital raster graphics are two com-


man GIS darabases rhar can also be used ro visually check measurements, as well as to faci li tate a traverse of rhe

landscape. Hand-held dara collecrors are moderarely expensive

($1.000 ro $5.000). depending on rhe quality of rhe instrument and the functions they allow. POAs are less expensive (around $500). and can be used as dara collectors, but they are generally less rugged and more prone to damage from environmental factors (e .g., rain) and

human error (e.g .• dropping rhe device). Some GIS consulranrs have developed software rhar will run on PDAs. ArcPad (Environmental Systems Research Inst itute, Inc., 2006) is perhaps rhe mosr widely known produce. A growing list of accessories can also be purchased to make POAs more durable and useful in inclement weather and

under other conditions.

Standards for the Exchange of GIS Databases

The deve10pmenr and use of srandards for exchanging GIS databases may seem like a trivial exercise for governmental employees and university researchers, since theorerica1ly dara ((ansferred among US federal governmenr o rganizations (for example) must adhere to federal data srandards (hrrp:llwww.fgdc.gov/srandards) . These srandards specifY dara formars rhar are inrended co facilirare the sharing of spatial data among organizations. Many university researchers also utilize th is protocol (or something very similar) in some cases because they interact with federal granting agencies during the course of research. However, most private natural resource management organizations are not bound by these data standards. Thus acqu isition and modification of GIS databases by private natural resource management organizations proceeds undocumented; transformations

and re-projections regularly occur to allow an integration of rhe acquired GIS darabases inro rhe organizarion's system, since the type and format of data exchanged can vary considerably (Figure 15.3) . Moving co a srandard dara excha nge fo rmat usually suggests that o ne of twO organizational policies will be used: ( I) organizations convert all of the GIS databases currently in use to a standard format, thus avoiding rhe need to convert GIS databases when data exchange processes occur, or (2) organizations convert GIS databases to a standard exchange format on ly when data exchange processes occur. There is a cost associated with both policies, and it is a function of how often

242

non-sensidve GIS databases over the Internet, allowing

GIS users the opportunity [0 quickly acquire the data at no COSt. In cases where GIS databases are very large and therefore nO{ practicaJ for Imernet transfer (for example,

as with OOQs or other high-resolution raster imagery). public agencies may still require that consumers pay for data cransfer costs. However. data compression sofrware technology condnues [0 improve. which reduces me need for non-Inrernet transfers. Privare organizations that market and sell GIS databases also allow customers to download the databases from the lnrernet. In these cases) cus

[Omers usually need ro be regisrered with rhe private organization because access ro the data is restricted.

Portable Devices to Capture, Display, and Update GIS Data

I n the past decade the use of hand-held data collectors and personal digital assistants (POAs) has become quite common for collecting forest inventory dara and other anribures of landscape fearures. G PS receivers, in faCt, use hand-held data collectors [0 allow you [0 capture the spatial attributes of landscape features.lntegradon of the rwo philosophies, allowing you to collect spatial loeational infortflalion aboullandscape features and to collect a([fibute data, results in posirive benefits to a namral resource

management organiz.ation. T radicionaHy. data coUected for narural resource inventories would be recorded in a field notebook or on a map. and would require manual data entry into a sp readsheet or GIS database (through attributing spatial landscape fea(Ures). This manual process is time consuming and presents severaJ opporcunities for human error co be introduced into a GIS database. The integration of digital technologies allows information to be recorded in a computer database while a person is in the field. Hand-held da,a collectors and POAs are able to conneCt to wired or wireless computer systems, allowing ,he data to be transferred to GI . This greatly expedites the transfer of field-collected data to a GIS database where the da,a can be analyzed and mapped. Th is process also removes some of me error opportunities char might occur through ,he manual coding and inputting of data. Da,a coHeetors also offer users the abil iry ro examine maps and

images oflandscape features as they are being measured. Field personnel can use this heads-up display to visually determine whether their measurements are in agreement with the landscape features bei ng measured. Digital onhophotoquads or digital raster graphics are two com-


mon GIS databases that can also be used to visually check measurements, as wel l as to faciJir3re a uaverse of {he

landscape. Hand-held data collectors are moderately expensive

($1.000 to $5,000) . depending on the quality of the instrument and the functions they allow. POAs are less expensive (around $500) , and can be used as data collectors. but they are generally less rugged and more prone to

damage from environmental facto rs (e.g. , rain) and human error (e.g., dropping the device). Some GIS consultants have developed sof1ware that will run on POAs. ArcPad (Environmemal Systems Research InstitU[c, Inc. ,

2006) is perhaps the mosr widely known product. A growing IiSl of accessories can also be purchased to make POAs more durable and useful in inclemen, weather and under other conditions.

Standards for the Exchange of GIS Databases

The development and use of standards for exchanging GIS databases may seem like a trivial exercise for governmcnral employees and university researchers, since meoretically data transferred among US federal government organizations (for example) must adhere to federal data standards (J1[[p:llwww.fgdc.gov/standards) . These standards specifY data formats that are intended to facilitate the sharing of spatial data among organizations. Many universiry researchers also urilize this protocol (or something very sim ilar) in some cases because they imeract

with federal granting agencies during the course of research. However, most private namral resource managemenr organizations are not bound by these daca standards. Thus acquisition and modification of GIS databases by private natural resource managemenr

organ izations proceeds undocumenced: transformations and re-projections regularly occur CO allow an integration of the acquired GIS databases into rhe organization's system, since the rype and format of data exchanged can vary considerably (Figure 15.3) . Moving to a standard data exchange formac usually suggescs thac one of cwo organizational policies will be used: (1) organizarions conven all of [he GIS darabases curren dy in use [Q a srandard format. rhus avoiding the need to conven GIS databases when data exchange processes occur, or (2) organizarions convert GIS databases ro a standard exchange format only when dara exchange processes occur. There is a cost associated with both policies, and i, is a function of how often


I I

, , , \ I

Siale organizations

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companies

,

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Forestry consultants

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....... Federal data standards implied when transferring data

- - .... Other data standards generally used when transferring data

Fi~ 15.3 Datab~ transfu and implied standards among organiutions.

an organizacion perceives that it might exchange data

with orner organizacions. If an o rganization positions irs internal data standard closely to that of the data exchange standard, the cost w ill be minimized when acquiring GIS

databases from. or sharing GIS databases with. federal agencies. If an organization does not plan (Q acquire GIS

databases from, or share them with, federal agencies. the cost of deviating from the federal standard may be minimal.

Even if a natural resource management organization

avoids implementing a data exchange standard a few common data exchange formats are used by other organ

izations. For example. it is not uncommon for public

agencies to make GIS databases available in both ArcInfo export file format (eOO) and ArcView shapefile format. Among com purer-assisted design (CAD) software programs, the DXF (drawing exchange format) format is

commonly used to exchange files. Most GIS software manufacturers recognize that users will need to accommodate data formats designed by other software vendors . For

this reason, it is not unusual for a GIS software program

[Q make ava ilable conversion and integration processes

that make it possible [Q view other GIS database formats.

particularly those (hat are used by the most common GIS

software programs.

Legal Issues Related to GIS

Legal issues confront (he GIS community on several fronts

including issues related to privacy. liability. accessibility,

and licensing. Some of these issues are relatively new,

while others have been associated with GIS since its incep

tion . In either case, the issues will continue to evolve as

GIS sofrware becomes more widely used. Licensing and

certification of GIS professionals is an issue of currene concern to many GIS users and other professionals, and

will be discussed in more detail in chapter 17. Therefore issues related to privacy. liability. and accessibility are presented here.

GIS data is being co llected at an ever-increasing pace,

and used in novel ways as people begin to understand the power of connecting information to spatial position. For

example. some organizations now rely on the ability co relate data abom purchasing decisions with demographic

and location information (Cla rke. 2001). This information is used by businesses to direct mass mailings, CO sug-

243

gest the location of new facilities. and to place phone caUs to porenriai customers in [he evening (Q inquire whether

they may be interested a purchasing a product or service. Although federa l and State legislation exiSts to protect the

privacy of information collected from individuals by public organizations, very little legislation currently exists to

prevent non-public organizations from sell ing or sharing the information that is gathered during regular consumer transactions. GIS has thus enabled organizations to culti

vate business using spatial analyses. In this way. GIS has become a business tool. and like it or nor, an eff'eccive one. As private organizations co ntinue to forge new

ground in the collection, sale. and exchange of spacial data that describes the economic and social behavior of individuals. sociery will be challenged in establishing the laws and regulations that relate to privacy srandards.

In (he US, the federal government and state agencies

have spent millions of dollars of public funds collecting and processing spatial data. The Freedom of Information Act (FOIA) was authorized in 1966 to grant taxpayers the

right to access information related to the functioning of the government (Korte. 1997). Certain rypes of information, such as that related to security and law enforcement investigations, among others, are exempt from the FOIA.

Other types of information, however, must be provided to the person making the request, usually at some minimal cost to cover the cost of processing the data and pro

viding the media upon which the data is exchanged. Most states in the US have developed laws based on the FOIA that also require state governmental agencies to make government information avai lable. Unfortunately, new threats to public safety and national security have emerged in recent years, and have necessitated a closer

scrutiny of the types of government information made available to the public. More than likely, access to certain GIS databases describing such landscape features as water

supplies and power facilities will be curtailed in the future. and access to other GIS databases will be delayed

due to new security protocols. Legal liability issues are associated with circumstances

where a service or product provided by a producer is not sadsfactory to the customer receiving the service or product. Onsrud (I999) identifies twO rypes of liabiliry that

are pertinent to GIS: contractual and tort liability. Contractual liability issues arise when a contract be[Ween

[WO parties has been breached. For a private organization that provides GIS products or services, this might involve a software product not behaving as advertised or a GIS

database that does not adhere to a data accuracy standard.


Tort liability issues arise when a party (person or organization) becomes injured (sustains a physical injury, loses business, etc.) as the result of another party's actions or

products. An example might be an accident at sea as a result of using an inaccurate GIS database for navigation.

Private organizations that provide GIS products and services are responsible for adhering ro, and demonstrating, a level of competency associated with their discipline. When others are injured as the result of incompetence,

the organization providing the service or product may be liable for damages. In determining incompetence or negligence, a private organization may be responsible for pro

ducing inaccurate or insufficient data, bue this alone does not prove incompetence. Rather, the courts have sought to establish incompetence by comparing services or products to those that would be produced from an organization that is acting 'reasonably' (Onsrud. 1999).

Government agencies have typically been immune to litigation or responsibility for providing inaccurate spatial data due (0 sovereign immunity. An exceprion is made among agencies that produce goods or services that are

considered discretionary. Discretionary services or prodUCtS have resulted in government agencies being held liable for damages (Korte. 1997).

Bo(h private and public organizations that are involved in providing GIS products and services can act to limi t their liability risk. One method fo r lim iring risk is to

include information or disclaimers [hat accompany a

product in order to describe irs intended use, data accuracy, data reliability, and a wa rning that there may be errors in the data (as described in chapter 4) . Organizations can further protect themselves by ensuring thae all relevant parries have signed a clearly defined contract

for products and services, and that the organization performs the specifics of the contract compeeently. If project requirementS necessitate actions (for example. the development of other products or services) other than what is contained in the original cOntract, the organ ization providing the productS or services should contact the other

panies involved immediately to reach agreement on rhe specifics of the additional produces and services (costS, time frame, etc.) before beginning to develop those products and services (Beardslee. 2002).

Licensing of GIS data products is another legal issue.

and is tied directly to digital rights management (Cary. 2006) . Many organizations require payment for use of data and sof[Ware that they produced and, without payment, organizations using the data risk violating [he terms

of agreements that may have been implicit when the

244

gest rhe location of new facilities, and to place phone calls co porenrial customers in the evening [a inquire whether they may be interested a purchasing a product or service. Although federa l and state legislation exists to protect rhe privacy of information collected from individuals by public organizations. very litt le legis lation currently exists [0

prevem non-public organizations from selling or sharing (he informacion that is gathered during regular consumer

transactions. GIS has mus enabled organizacions [0 cubvate business using spatial analyses. In this way. GIS has become a bl1siness rool , and like if or nor, an effective one. As private organizations co nrinu e (0 forge new ground in the collection. sale. and exchange of spatial data that describes rhe economic and social behavior of individuals, society will be challenged in establishing rhe laws and regulations that relate ro privacy smnclards.

In the US. tbe federal government and state agencies have spent millions of dollars of public funds collecting and processing spacial data. The Freedom of Informacion Act (FOIA) was aurhorized in 1966 to grant taxpayers the right (Q access informacion relared to the functioning of the government (Korte. 1997). Certain types of information. such as mar related to security and law enforcement investigacions. among others, are exempt from the FOIA.

Other types of informacion, however, must be provided [0 the person making the request, usually at some minimal cost (Q cover the cost of processing the data and pro

viding the media upon which rhe data is exchanged. Most states in rhe US have developed laws based on the FOIA

that also require S[3.te governmental agencies (Q make government informadon avai lable. Unfortunately, new

threats to public safety and national security have emerged in recent years. and have necessitated a closer scrutiny of the types of government informacion made available (Q the public. More than likely, access [Q cerrain GIS databases describing such landscape features as water supplies and power facilities will be curtailed in the future. and access to orher GIS databases will be delayed due to new security protocols.

Legal liability issues are associated with circumstances where a service or product provided by a producer is not satisfactory ro the customer receiving the service or produCt. Onsrud (1999) identifies tWO types of liability that are pertinent to GIS: cOIHraCtUaJ and [Ort liability. Contractuall iabiliry issues ar ise when a comracr berween

twO parries has been breached. For a private organization (hat provides .IS products or services, this might involve a software produCt not behaving as advertised or a GIS database mat does not adhere [0 a data accuracy standard.


Tort liability issues arise when a party (person or organizacion) becomes injured (sustains a physical injury, loses business. etc.) as the result of another party's actions or

products. An example might be an accident at sea as a result of using an inaccurate GIS database for navigation.

Private organizations that provide GIS products and services are responsible for adheri ng [0, and demonstrating, a level of competency associated with their discipline. When others are injured as the result of incompetence.

the organization providing the service or product may be liable for damages.ln determining incompetence or negligence, a private organization may be responsible for pro

ducing inaccurate or insufficient data, hue this alone does not prove incompetence. Rather, the courtS have sought to establish incompetence by comparing services or products to those that would be produced from an organization ,hat is acting 'reasonably' (On stud, 1999). Governmem agencies have typically been immune to lit

igarion or responsibility for providing inaccurate spatial data due to sovereign immunity. An exception is made among agencies that produce goods or services that are

considered discretionary. Discretionary services or prodUCtS have resulted in government agencies being held liable for damages (Korre. 1997).

Both private and public organizarions that are involved in providing GIS products and services can act to

limit rheir liabil ity risk. One method for limiting risk is to include information or disclaimers that accompany a product in o rder to describe its intended use, data accuracy. data reliabili ty, and a warning that there may be erro rs in rhe data (as desc ri bed in chapter 4) . Organizations can hlfdler protect themselves by ensuring that all relevant parties have signed a clearly defined contraer for products and services, and [hat rhe organization performs the specifics of the contract competently. If project requiremenrs necessitate actions (for example, the developmen [ of other products or services) other than what is con[3.ined in the original contract, the organization providing the products or services should comact the other

parties involved immediately to reach agreement on the specifics of the additionaJ products and services (costS, time frame. etc.) before beginning to develop rhose products and services (Beardslee. 2002).

Licensing of GIS data products is another legal issue, and is tied directly to digital rights management (Cary, 2006). Many organizations require payment for use of dara and software that they produced and. without paymem, organizations using [he data risk violating the terms of agreements that may have been implicit when the


information was shared. While the appropriate model for

licensing GIS data is currently being debated, the problem lies with the ease of copying digital data and sharing it with o thers in the absence of an agreemenr (s imi lar to

sharing music files). New types of data sharing arrangements wi ll likely be formulated that are based on limited data sharing licenses. Cary (2006) suggests a system where users of GIS are granted access to certain GIS data based

on the locations of acruallandscape feacures or proximity

CO other featu res . This rype of proximal data sharing

could balance the need for openness (as desired by the user) with the need for confidentiality (as desired by the producer).

GIS Interoperability and Open Internet Access

Interoperabil ity in terms of GIS refers to ability of differen t geospadal insrrumenrs, databases, and techniques to

work together on applications. Inreroperabil iry involves crearing standard terminology. data formats. and software

inrerfaces that are borh recognized and used by organiza

tions involved in geospariaJ applications. T he need for

inreroperabili ry shou ld not be surprising for any disci

pline that becomes popular amo ng a wide number of

potenrial users, such as has been witnessed by the rapid

growth of GIS applications over the past two decades. The rapid growth of GIS gave rise to a number of GIS software inrerfaces and data formats that were proprietary and therefore not designed so that others could easily and freely access and exchange data with the proprietary for

mats. This inabi lity led co frustration among GIS users

and gave rise to the need for GIS interoperability. The Open Geospatial Co nsortium was founded in 1994 (Open Geospatial Consortium, Inc., 2007) and has 34 1 member o rganizations as of2007. The aGe represems a

coalition of both private and public organizations. The

goals of the aGe are to promote public accessibility to

geoprocess ing tools and o ther location-based services.

Sign ificant accomplishments of the aGe include the standardization of terms for GIS features (po inrs, polylines, polygons), the creation of the Geography Markup Language (GML) that provides an open source language for describing spat ial data. and the developmem of stan

dards fo r how geographic data can be requested and accessed from Internet servers (Longley et aI., 2005) . T he aGe has had a profound influence on making geospatial tools and services available to I mernet users and continues

to work on promoting geospatial software accessibil ity.

The aGe will likely continue to develop innovations thar Increase public access to locatio n-based info rmat ion

services.

GIS Education

Attention is likely to increase around the methods and

approaches instructors use [Q teach geosparial ski lls to stu

dents, not only at the university level, but also in high school and within the professional workforce. GIS capabilities are now essential for natural resource organiza

tions, as well as for other disc iplines throughout society

(Wing & Sessions, 2007). The primary tra ining ground for GIS ski lls is currently with in the university sYStem but geospatial skills are also being taught to srudenrs during

e1emenrary school years. In addition. train ing opportuni

ties for in-career professionals appear [Q be growing.

Many disciplines. such as fo restry. engineering, and

surveying have accreditat ion bodies that review and

appraise the curriculums of universities and co lleges that

offer related degrees . The accreditation bodies eithe r

approve the curriculum or repon why it cannot be

accredited and what steps are needed to gain accredita

tion. The accreditation process helps ensure that instruc

tion supporting the necessary knowledge and skills to

enter in to professional disciplines is being delivered

appropriately. The accreditation process helps educational programs become recognized and can be a significant incemive for drawing srudents. No such accred itation

process exists specifically for geospatial technology instruction per se, although some engineering and sur

veying programs focus heavi ly on measuremems and have

accreditation through the American Board of Engineering

Technology (ABET). Currently, a wide variety of methods and approaches are used to teach geospatial ski lls. Th is results in considerable variation in achieved learning Out

comes (Longley et aI., 2005) . A recent publication titled Th, Geographic Information Science and Technology Body of Knowledge (Di Biase et aI. , 2006) has attempted to define critical concepts and ski lls that relate to geographic information science and technology. Written through

extensive collaboration among GlScience researchers and

educators, this work represents an initial attempt to pro

vide a unified description of copies imponant for establishing geospatial ski ll competency. A second edition is planned that will provide additional detail and instructio n that supports key concepts and skills.

245


information was shared. While the appropriate model for licensing GIS dara is currencly being debated, the problem lies with the ease of copying digital data and sharing it wi th o[hers in me absence of an agreement (similar (0

sharing music files) . New types of data sharing arrangements wiU likely be formulated that are based on limited dara sharing Licenses. Caty (2006) suggests a system where users of GIS are gramed access to certain GIS data based on (he locations of acmallandscape fearures or proximity

to other fearu res. This type of proximal dara sharing could balance the need for openness (as desired by the user) wirh rhe need for confidentiality (as desired by rhe producer).

GIS Interoperability and Open Internet Access

Interoperability in rerms of GIS refers to ability of different geosparial inSlrumenrs, databases. and techn iques ro work together on applications. Interoperability involves creati ng standard terminology. data formats, and sofrware interfaces that are both recognized and used byorganizations involved in geospatial applicarions. The need fo r interoperability should not be surp rising for any discipline that becomes popu lar among a wide number of potencial users, such as has been wi tnessed by rhe rapid growrh of G IS applications over the past two decades. The rapid growth of GIS gave rise co a number of GIS software interfaces and data formats that were proprierary and therefore not designed so tha t others could easily and freely access and exchange data with the proprietaty formars. This inabili ty led to frustration among GIS users and gave rise co rhe need fo r GIS interoperabil ity. The Open Geospatial Consorrium was founded in 1994 (Open Geosparial Consorrium, Inc., 2007) and has 341 member organizarions as of2007. The aGe represents a coalition of both privare and public organizations. The goals of the aGe are to promore public accessibil ity to

geoprocessing (Ools and other location-based services. Significant accomplishments of the aGe include rhe standardization of rerms for GIS features (poin rs, polylines, polygons), the creation of rhe Geography Markup Language (GML) thar provides an open source language for describing spatial data, and the developmem of standards for how geographic data can be requested and accessed from Interner servers (Longley er aI., 2005). T he aGe has had a profound inAuence on making geosparial rools and services available co Internet users and conti nues

ro work on promoring geosparial software accessibi lity. T he aGe will likely continue to develop in novarions rha[ increase public access co locatio n-based informat ion

services.

GIS Education

Auention is likely to increase around the methods and

approaches instructors use [Q [each geospatial ski lls [0 sru

dents, not only ar the university level, but also in high school and wirhin rhe professional wotkforce. GIS capabiliries are now essential for natural resource organiza

tions, as well as fo r other disciplines throughour society (Wing & Sessions, 2007). The primary training ground for GIS ski lls is cu rrently within the university system but geosparial ski lls are also being taugh[ to students during e1ementaty school years. In addir ion, training opportunities for in-career professionals appear to be growing.

Many disciplines, such as fores[ty, engineering, and surveying have accreditation bodies rhat review and

appraise the curriculums of universities and co lleges that offer related degrees. The accreditation bodies eirher approve the curriculum or repon why if cannor be accredited and what steps are needed (Q gain accreditarion. The accreditation process helps ensu re that insrruc[ion supporring rhe necessaty knowledge and ski lls to

enrer in tO profess ional d isciplines is being delivered appropriarely. T he accreruta[ion process helps educarional programs become recognized and can be a significant incentive for drawing sruden{s. No such accredjtation process exists specifically fo r geospa[ial [echnology instruction per se, although some engineering and surveying programs focus heavily o n measurements and have accredi rarion [hrough the American Board of Engineering Technology (ABET) . Currendy, a wide variety of methods and approaches are used ro [each geospatial skil ls. This results in considerable variation in achieved learning Out

comes (Longley er al ., 2005). A recent publication titled The Ceographic Infomlation Sci",,, and Technology Body of Knowledge (DiBiase er aJ. , 2006) has arremp[ed [0

define critical concep" and skills that reiare co geographic information science and technology. Written through

extensive collaboration among GIScience researchers and educators, this work represents an initial attempt to provide a unified description of topics important for estab· lishing geospatial skill compereney. A second edit ion is planned char wi ll provide additional detail and instructio n that su pporrs key concepts and skills.

Summary

GIS technology is evolving almost as quickly as general computer systems (hardware and software) evolve. People

are thinking about natural resource managemem issues in

ways unimagined juSt a few years ago, and spatial data is facilitating these effons. Natu ral resource management

o rganizations are actively engaged in testing and imple

menting new [Ools for collecting and analyzing spatia l data. Organizations are also making GIS technology available to a large portion ohheir workforce. and me Internet

Applications

14.1 Local regulations regarding GIS distribution. Select an agency in yo ur area (state. province. city ,

coumy, townsh ip, etc .) that uses and mighr distribute GIS

databases. a) What types of GIS databases are publicly avai lable? b) Are the GIS databases available for download over

the I merner? c) What data exchange form ats are available?

d) Is <here a COSt related to acquiring the G IS databases? e) Are there specific laws that relate to GIS database

distribution , and how do they affect your abi li ty (Q

acqu ire GIS data from rhis agency?

14.2 Product liability (1) . Yo u work fo r a private organizat ion that makes GIS databases available to cus

tomers. How might you help prQ[ec( your organization

from liab il ity that could arise from CUS(Qmers that pur

chase your services and products?

14.3 Product liability (2). You work for a public agency that makes spatia l data available to customers.

How might you help protect your organization from lia

bility thar could arise from customers that use you r serv

ices and products?

References

Beardslee, D.E. (2002). Do it! Or, call them before they call you. Proftssional Surveyor, 22(2), 20.

Bettinger, P. (I999). Distributing GIS capabil ities to forestty field offices: Benefits and challenges. Journal of Forestry, 97(6), 22--6.


has c reated an efficient avenue [Q make GIS databases

available in a timely manner. These developmems have had a posicive effect on the acceptance and use of C IS. and

encourage furthe r GIS technology development. As GIS

technology and use evolves. however, other issues (pri

vacy. access ibil ity. li ability. etc.) arise that must be

addressed. These issues may require a close examination of policies and practices related to the use of GIS in naru

ral resource managemenr.

14.4 Precision forestry and agriculture. You work for the Bureau of Land Management (BLM) in central Colorado, and your supervisor, Mark Miller, has recently learned of precision forestry and agriculture. He has asked

you how precision techniques might benefit the manage

menr of BLM land in Colorado. Write a brief memo that

out lines the GIS databases, hardware, and sofrware related

to precision techniques. and how rhey might benefit the

management of BLM land in Colorado.

14.5 Distributed GIS. A timber company in the southeastern United States has JUSt hired you as a field forester.

You are eager to use the GIS skills you have learned in college to help yourself (and others) make informed forest managemenr decisions. The timber company has a cen

rraIized GIS deparrmem and five remote field offices and

they are in the midst of developing a system whereby personnel (foresters, biologists, hydrologists, etc.) in field offices (where you are located) can use desktop G IS software to make their own maps and pe rform their own

analyses. What can you do to ensure that the distribution

of responsibilit ies (map development and analysis) to your field office will be successful?

Bettinger, P., & Wing, M.G. (2004). Geographic information sysums: Appfications in forestry and natural resources management. New York: McGraw-Hill, Inc.

Cary, T. (2006). Geospa[ial digita l rights management. Geospatial Solutions, /6(3), 18-2 1.

246


Clarke, K.c. (200 I). Getting started with geographic information systems (3rd ed.). Englewood Cl iffs, NJ: Premice-Hall, Inc.

DiBiase, D., DeMers, M. , Johnson, A., Kemp, K., Luck, A., Plewe, B., & Wentz, E. (Eds.). (2006). The geographic information science and technology body of knowledge. Washingwn. DC: University Consortium for Geographic Information Science, Association of American Geographers.

Environmental Systems Research Institute, Inc. (2006). ArcPad - Mobile GIS software for field mapping applications. Redlands, CA: Environmental Systems Research Institute, Inc. Retrieved May 8, 2007, from http: // www.esri.comlsofrware/arcgis/arcpadlindex.html.

Faust, N. (1998) . Raster based GIS. In T.W. Foresman (Ed.), The history of geographic information systems: Perspectives from the pioneers (pp. 59-72). Upper Saddle River, NJ: Prentice-Hall, Inc.

GeoEye. (2007). GeoEye imagery products: IKONOS. Dulles, VA: GeoEye. Retrieved May 9, 2007, from http://www.geoeye.com/prod uctsl i magery/ikonosl default.htm.

Google, Inc. (2007). Google Earth. Mountain View, CA: Google, Inc. Retrieved May 9, 2007, from http: // earrh.google.com.

ITT Corporation. (2007). ENVI- The remote sensing exploitation platform. Boulder, CO: ITT Corporation. Retrieved May 9, 2007, from http: //www .ittvis. com/envil.

Korte, G.B. (1997). The GIS book (4th ed.) . Santa Fe, NM: On Word Press.

Leica Geosystems, LLC. (2007). Erdas Imagine. Norcross, GA: Leica Geosystems LLC. Retrieved May 9, 2007, from http: //gi.leica-geosystems.com/LGISubIx33xO.

aspx. Longley, P.A. , Goodchi ld, M.F., Maguire, D.J., &

Rhind, D.W. (2005). Geographic information ,ystems and science (2nd ed .). Chichester, England: John Wiley & Sons, Inc.

National Aeronautics and Space AdminiStration. (2007). A VlRfS: Airborne visible / infrared imaging spectrometer. Pasadena, CA: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology. Retrieved May 9, 2007, from http: //aviris.jpl. nasa.gov.

Onsrud, H.J. (1999). Liability in the use of GIS and geographical datasets. In P.A. Longley, M. F. Goodchild, D.J. Maguire, and D.W. Rhind (Eds.), Geographical Information Systems, Volume II (2nd ed.) (pp. 643-52). New York: John Wiley & Sons, Inc.

Open Geospatial Consortium, Inc. (OGe) . (2007). Wayland, MA: Open Geospatial Consortium, Inc. Retrieved May 21, 2007, from http: //www. opengeospatial.org.

Wing, M.G., & Bettinger, P. (2003). GIS: An updated primer on a powerful management (001. Journal of Forestry, 101(4),4-8 .

Wing, M.G., & Sessions, J. (2007) . Geospatial technology education. Journal of Forestry, 105(4), 173-8 .

247


Clarke, K.c. (200 I). Getting started with geographic information systems Ord ed.). Englewood Cliffs, NJ: Premice-Hall, Inc.

DiBiase, D., DeMers, M., Johnson, A., Kemp, K., Luck, A., Plewe, B. , & Wenrz, E. (Eds.). (2006). The geographic information science and technology body of knowledge. Washing[Qn, DC: University Consortium for Geographic Information Science. Association of American Geographers.

Environmenral Sysrems Research Institute, Inc. (2006). ArcPad - Mobile GIS software for field mapping applications. Redlands, CA: Environ menraJ Systems Research Instirute, Inc. Retrieved May 8, 2007, from http:// www.esri.com/sofrware/arcgis/arcpadlindex.html.

Faust, N. (1998). Raster based GIS. In T.W. Foresman (Ed.) , The history of geographic information systems: Perspectives ftom the pioneers (pp. 59-72). Upper Saddle River, NJ: Prenrice-Hall, Inc.

GeoEye. (2007). GeoEye imagery prodncts: IKONOS. Dulles, VA: GeoEye. Retrieved May 9, 2007 , from http: //www.geoeye. com /products/ i magery/ikonos/ deFaulr.htm.

Google, Inc. (2007) . Google Earth. Mountain View, CA: Google, Inc. Retrieved May 9, 2007, from http:// earth.google.com.

ITT Corporation. (2007). ENVI-The remote sensing exploitation platform. Boulder, CO: ITT Corporation. Retrieved May 9, 2007. from http://www.ittvis. com/envi/.

Korte. G.B. (1997). The GIS book (4th ed .). Santa Fe. NM: OnWord Press.

Leica Geosystems. LLC. (2007). Erdos Imagine. Norcross. GA. Leica Geosystems LLC. Retrieved May 9. 2007. from http://gi .leica-geosystems.com/LG (Sub I x33xO. aspx.

Longley , P.A .• Goodchild. M.F .• Maguire, D.J. , & Rhind, D.W. (2005). Geographic information systems and science (2nd ed.). Chichester. England: John Wiley & Sons, Inc.

National Aeronautics and Space AdminiStration. (2007). A VIRIS: Airborne visible / inftared imaging spectrometer. Pasadena. CA: Narional Aeronautics and Space

Administration, Jet Propulsion Laboratory. California Insci tute of Technology. Rerrieved May 9, 2007, from http: //aviris. j pI. nasa.gov.

Onsrud, H.J. (I 999) . Liabi lity in the use of GIS and geographical datasets. In P.A. Longley, M. F. Goodchild, D.J. Maguire. and D.W. Rhind (Eds.). G<ographicai Information Systems, Voillme II (2nd ed.) (pp. 643-52). New York: John Wi ley & Sons. Inc.

Open Geospatial Consortium, Inc. (OGe). (2007). Wayland, MA: Open Geospatial Consorcium, Inc. Retrieved May 21. 2007. from http: //www. opengeospadal.org.

Wing, M.G., & Bettinger, P. (2003). GIS: An updated primer on a powerful managemenr [001. Journal of Forestry. 101(4).4-8.

Wing. M.G., & Sessions. J. (2007). GeospaciaJ technology education. journal of Fomtry, 105(4). 173-8.

Chapter 16

Institutional Challenges and Opportunities Related to GIS

Objectives

This chapter offers some thoughts on rhe imegration and

use of GIS within narural resource management organizadons. There are a number of issues related to rhe successful development of a GIS program within an o rganization, and programs in many organ izadons are continuously evolving as people. technology, and organizational struc

[Ures change. At me conclusion of th is chapter, readers

should have an undemanding of a number of issues that relate co rhe challenges facing the implemenracion and use

of GIS in nacural resource managemenr, including:

t. an understanding of rhe potencial challenges mat are ahead for successfu l and efficient GIS applications within and among namra l resource management organIZatiOns;

2. an understanding of the challenges that exist for

organizations thinking of distributing GIS capabilities

to field offices. a move becoming more prevalent as recent natural resource graduates likely will have GIS experience in cou rsework. and exposure or uaining in the field; and

3. an understanding of how to assess the benefit of using GIS. a measurement process that will likely be necessary to develop more efficiem business operacio ns. higher qualiry products. and more timely managemenr decisions.

The development and use of GIS in natural resource

management has evolved considerably in the past 20 years as field foresters. biologists. hydrologists. and other natural resource professionals have become empowered with this technology. and are able to perform many of their own spatial analyses and map production tasks (Wing & Bettinger. 2003). In addition. it is increasingly likely that natural resource professionals wi ll have cou rsework

involving GIS and related geospatial technologies during undergraduate or graduate studies (Wing & Sessions. 2007) . While GIS technology continues to evolve. natural resource management organizations are faced with several lingering issues related to the GIS use. and several nC\v

challenges have arisen with the availability of GIS technology to field offices. Natoli et aJ. (2001) describe a set of

challenges for GIS implementation and use within (mainly) municipal organizations. and Bettinger (J 999) describes some of the challenges associated with implementing a disuibuted GIS system in forestry organiza{ions. This chapter ex{ends the discussion presented by these twO sets of wo rk. and adds to it some additional

points that are relevant to forestry and natural resource management.

Sharing GIS Databases with Other Natural Resource Organizations

As mentioned in chapter 15. one aspect of the use of GIS in natural resource management is the notion mat some GIS databases can be shared among other natural resource

248


organizations. The potential to collaborate with other organizations. and rhus the need (0 share GIS data, repre

sents an interesting dynamic that is evolving in narural

resource management. Public organizations. such as rhe USDA Forest Service and the USDI Bureau of Land

Management. provide a wide variety of GIS databases to

rhe public at no COSt; as we discussed in chapter 3. many of rhese databases can be accessed over rhe I nrerner. State-level namral resource management organizations generally provide a more narrow range of GIS databases to the public, if they provide any at all. State-level GIS dacabase clearinghouses, on the ocher hand. generally provide a wide variety of GI S databases (O EMs, digital

orthophotographs, land cover, etc.) to the public at no (or low) cosc, Private natural resource management

o rganizations generally trear their GIS databases as propri

etary-that is, they are not freely ava ilable to the general public (anyone outside of the organization) . W hile the original source of the data for many of the private organ

ization GIS databases may have been a public organization, once the private investment has been made in main

taining and updating the data, concern arises about how the cost can be recouped. and whether competitors may gain insight into the management practices being used

(e.g .. management COSts. productive capacity), thus gaining a competitive edge in the marketplace. Although some may argue that organizations, public and private, should share dara so that more informed decisions can be made for a landscape, the goals and objectives of each organization wi ll guide the development of policies

related to data sharing. As a result, many private natural resource organiza

tions do nO( share GIS databases with those outside of rheir organizarion , unless they can place a value on doing so. There are a number of cases, for example. of private organizarions sharing GIS databases through cooperarive planning and management effons. For example, in Washington State, the Depanment of Natural Resources (WADNR) has taken an ownership role in the development of several state-level databases: roads, streams, and the public land survey to name a few. The WADNR has developed protocols whereby both public and private nat

lIral resource organizations can share their GIS data with rhe WADNR in an effo rt to improve rhe state-level GIS databases. While rhe process is open for contributions of information and knowledge from those more directly tied to the management of particular areas of land. it cOlHains fairly rigid guidelines for contributors. The GIS databases shared with the WADNR, for example. must have been

developed using standards and protocols developed by the WADNR, to maintain the quality of data in the WADNR

distributed GIS databases, and to minimize the costs asso

ciated with updating and managing the state-level GIS

databases. There are also a number of cases where private natural

resource organizations actually have shared data with one another. For example, a number of watershed analyses were conducted in Washington State in the late 1990s. In

performing a watershed analysis, one of the major landowners in the watershed generally coordinates the compilation of the GIS databases associated with the watershed. All other landowners interested in helping

develop the watershed analysis can comribure GIS databases to this 'lead' organization. More than likely, the amount of GIS data shared amongst different private organizations will be limited to a level that is suffic ient to

complete rhe analysis. For example, the spatial extent of

the GIS databases shared will likely be limited to the boundaty of the watershed being analyzed, and the attribute data associa ted with landscape featu res may be

reduced to a small subset of the rotal set of attributes prior to sharing the data. In addition, it is likely that the projection and coordinate systems for each shared GIS database will need to be changed to conform to the system used by the lead organizarion.

An example of multiple public o rganizations joining

tOgether to creare a significant spatial database is the Puget Sound LiDAR consortium (PSLC, 2007). The PSLC

formed in 1999 and now includes metropol itan, academic, couney, state, and federal o rganizat ions. The group's original goal was to use LiDAR to develop public domain high-resolution topographic data initially for significant parts of Washin gtOn. More recendy, public organizarions within Oregon have also joined the PSLC. and large portions ofland in Oregon are also included in

data acquisition plans. Funding has come from the operating budgets of group members and also from grants that members have received co suppOrt landscape hazard mitigation efForrs. This unique collaborative effort will result in LiDAR data being available for most of the population centers in Washington and Oregon , and surrounding lands. The joint efforrs of these organizations have undoubtedly resuhed in significant cosr savings for LiDAR data, particularly in light of the fragmented ownership patterns represented by all groups. Aerial data acquisition is more efficient when continuous swaths of land can be imaged rather than disjoinred sections, which reqUire additional fli ght lines.

249


organizations. The potencial to collaborate with other

organizations, and thus the need (Q share GIS data. rep resents an inrerescing dynamic that is evolving in natural resource management. Public organizations. such as the USDA Forest Service and the USDI Bureau of Land

Management. provide a wide variety of GIS databases ro

the public at no COSt; as we discussed in chapter 3 . ma ny

of these databases can be accessed over rhe lorefncr.

State-level natu ral resource management orga.nizarions

generally provide a more narrow range of GI databases

to the public, if they provide any at all. State-level GIS database clearinghouses. on the other hand . generally

provide a wide variery of GIS databases (DEMs, digital

orthophorogrdphs, land cover, etc.) to the public at no (or low) cose. Private namral resource management

organizations general ly [rear their GIS databases as propri

erary-that is, rhey are not freely ava ilable [Q rhe general

public (anyone outside of the organization) . While the original source of the dara for many of the privare organ

ization GIS darabases may have been a public organiza

tion, once me private investmenc has been made in main

taining and updating (he data, concern arises abom how

me cOSt can be recouped, and whether competirors may

gain insight into the management practices being used

(e.g., managemem COStS, productive capaciry), thus gain

ing a competitive edge in the marketplace. Although some may argue that organiz.arions, public and private. should share data so that more informed decisions can be made for a landscape, the goals and objectives of each o rgan ization wi ll guide ,he development of polic ies

related ro da ra sharing.

As a result, many private natural resource o rganiza

tions do nor share GIS databases wim [hose outside of

rheir organizadon, unless they can place a value on doing

so. There are a number of cases, for example, of private

organizations sharing GIS databases through cooperative

planning and management efforts. For example. in

Washington State, the Department ofNarural Resources (WADNR) has taken an ownership role in the development of severn I stare-level databases: roads, Streams, and

the public land survey to name a few. The WADNR has developed protocols whereby both public and private nat

ural resource organizations can share their GIS data with the WADNR in an effort [Q improve [he state-level GIS

databases. While the process is open for contributions of

information and knowledge from those more di recdy tied

to the managemenr of particular areas of land, ir comains Fairly rigid guidelines for co nrributors. The GI databases sha red with the WADNR, for example. must have been

developed using standards and protocols developed by the WADNR, to maintain the qualiry of data in the WADNRdistributed GIS databases, and [Q minimize the costs asso

ciated with updating and managing the state-level GIS

databases. There are also a number of cases where private natural

resource organ izations actually have shared data with one

another. For example. a number of watershed analyses were conducted in Washington State in the late 1990s. In

performing a watershed analys is. one of the major

landowners in the watershed genera lly coo rdi nates the

comp il ation of the GIS databases associated with the

watershed. All other landowners interested in helping develop the watershed analysis can contribute GIS databases to this ' lead' organization. More than likely, the amount of GIS data shared amongst d ifferent private

organizations will be limited to a level that is sufficienr to

complete [he analysis. For example. the spatial exrent of

the GIS databases shared will likely be limited to the boundary of the watershed being analyzed, and the attribute data associa ted with landscape featu res may be

reduced to a small subset of rhe roral set of attributes prior

to sharing the data. In addition, it is likely that the projec[ion and coordinate systems for each shared GIS database

will need to be changed to conform to the sYStem used by

the lead organization. An example of multiple public organizations joining

together ro create a significant spadal database is the Puget Sound LiDAR consortium (PSLC, 2007). The PSLC

fo rmed in 1999 and now includes metropolitan, academic, county, state, and federal o rganizations. The

group's original goal was to use LiDAR to develop public domain high-resolution topographic data initially for significant pans of WashingtOn. More recenrly, public

organizations withi n O regon have also joined the PSLC, and large port ions of land in Oregon are also included in

data acquisition plans. Funding has come from the operating budgets of group members and also from grants that members have received ro suppOrt landscape hazard mit

igation efforts . This unique collaborative effon w ill resulr

in LiDAR data being available fo r moSt of the population centers in Washington and Oregon , and surrounding

lands. The joint efforts of these organizations have

undoubtedly resulted in significant COSt savings for LiDAR data, particularly in ligh' of the fragmented ownership patterns represented by all groups. Aerial data acquisition is more efFicienr when cont inuous swaths ofland can be imaged rather than disjoinred sections. which reqUire

addit ional fl ight lines.

Chapter 16 Institutional Challenges and Opportunities Related to GIS 239

Sharing GIS Databases within a Natural Resource Organization

The issues of GIS database ownership, maintenance, distribution, and data quality within an o rganization are problematic, and partly a result of how a GIS database

development and disuihurion system may have been designed. For example, a road engineer may be the most

appropriate person (0 own and maimain a culverr GIS database, and a wildlife biologist may be most appropriate person to own and maintain a GIS database related to

locations of threatened or endangered species. However, the readiness of each person to perform these tasks within GIS, and the time they have available to do so may be

limited. Therefore ass igning the ownership, maintenance, and mher tasks to either a GIS technician, GIS manager, or a GIS contractor or consuhant may be more logical. More than likely, GIS databases will be shared at some stage to facilitate GIS database management, as a verificacion step in the maintenance stages of GIS database management, or [Q facilitate namral resource managemem

(after the GIS databases have been updated). If you were co assume that a namral resource managemem organizadon was structured in a tradidonal manner, with a cemral

office and a set of field offices, the GIS da tabase sharing process should be viewed as more than a one-way transaction between a cemralized GIS departmem and everyone else (Figure 16.1), because the roles related to ownership,

Updates neede for database

d

I,d Upd, datab as,

Field office 1

Centralized GIS office

Field office 2

Role: Use,

Updat ed as, datab

Updat lorda

Roles : Ownership Maintenance Distribution Ouality

es needed tabas<!

Role: User

a) Forest vegetation (stands) GIS database

maintenance, d istribution, and data quality may be

shared. Defining the appropriate roles for each person in an organ ization can result in a difficult negotiation process, part icularly when the roles are considered for change.

Establishing a process for sharing GIS databases at all levels within an organization is becoming more impor

tant as both field personnel and upper-level managers are becoming interested in using GIS technology. Data sharing can be as simple as routing a com purer d isk from person-to-person or office-to-office, or as sophisticated as placing all GIS databases on an organization's Internet (or Intranet) site or FTPserver. In the laner cases, the ab ili cy

to access the Internet sites and FTPservers may be limited to authorized personnel.

Technology and processes for sha ring GIS data are advancing, however, leading to the potential for two-way transac tions of data within a namraJ resource organization and nearly real-rime updates of the information. For example, some organizations allow field managers [Q

update GIS data directly in the corporate databases, a ro le

tradirionally held by a centralized office. These updates are sent electronically m the centralized office, and after data verification and consistency filrers have been applied,

redi rected to other field offices. In an efficient system, the other field offices can acquire these updaces in a matter of minutes. The importance of using up-m-dare informacion is perhaps most imponant when dealing with rime-

ated Upd database

led Olstribu database

Field office 1

Centralized GIS office

Fjeld office 2

Distri

Rotes : OwnerShip Maintenance Quality User

buted ase datab

Roles: Coordination Distribution

Updal ,d as, d,tab

Roles : Ownership Maintenance Quality User

b) Road culvert GIS database

Figun: 16.1 Example pathWll}'S of actions when sharing data within an orga.niu tion. for (a) a typ ical forest v~getation (stands) GIS database and (b) a road culvert G IS database.

250


The following story indicates nor only rhe motivation of field office personnel !O use GIS [<chnology, but also

a residual negative effeer of the abi lity !O share GIS databases with in an organization.

One natural resource managernem organization the authors were famil iar with had a central office where

30- 40 'corporate' GIS databases were mainta ined:

roads, streams, timber scancis, and others. T he organ i

zation had a distributed system. where GIS users in

field offices could access a server and download (copy) rhe GIS darabases !O their field office computers. Field

foreSters, biologiSts, and hydrologisrs subsequently did juSt rhat-rhey copied rhe GIS databases !O rheir personal computers and used [hem fo r a variety of pur

poses related ro rhe management of rhe land. water, wildlife. and recreation resources. Some of these GIS

darabases were even updated with peninenr informa-

sensitive decisions. such as those related [0 wildfire con

trol and suppression. With GIS capabilities now available in hand-held computers, managers in the field can make

more timely and informed decisions when necessary.


With rhe development of desktop GIS software the ability of every employee in an organization to use GIS is now

possible. Bettinger (I 999) has noted that the deployment of software and GIS databases to field offices is a significant challenge, and, as nmed in chapter 1 S. that is a trend

that will likely continue. Some of the challenges to naru

ral resource management organizations interested in

imp lemenring a distributed GIS system, as noted by Bettinger (I 999) include:

Where people are concerned • Provide the appropriate training to reduce computer

related anxiety.

• Provide a statemem of purpose behind implementing a distributed system.

• Implement methods to reduce the resistance to

change.

• Provide mmivation ro use the system .

tion that should have been incorporated imo the cor

porate GIS databases. As the d iscussion grew related to

sharing the modified corpo rate GIS databases wirh the

corporate office, it was determ ined that there were

over 1,000 variations of the corporate GIS databases stored across the organization's computer systems.

Each GIS database required Storage space on hard drive

and it was likely that hundreds of megabytes of space were dedicated for th is effort. I n some cases, field personnel believed that their modified version of the cor

porate GIS database was of higher qual ity than other user's versions (even better than the corporate version

of the GIS darabase) . As expected, a number of d iscussions regarding the quality of these mod ified GIS darabases arose, and the process of changing the GIS darabase maintenance policy for the corporate databases

was begun.

Where GIS databases are concerned • Clarify GIS database ownership and mall1tenance

Issues .

• Outl ine data acquisition protocols.

Develop data distribution systems.

• Develop quality control measures.

Where the technology is concerned • Facilitate the acquisition of appropriate hardware and

software.

• Control the purchase, update. and maintenance of

software.

Where the leadership of the organization is concerned • Demonstrate strong suppOrt for the program.

• Make a long-term budgetary commitment to the

program. • Adjust reward systems for added responsibilities of

field managers. • Document the direction and strategic goals for the

system.

Most young profess ionals in natural resource managemem o rganizations will likely be called upon to provide

assistance in the development of maps and GIS databases

to facilitate the day-to-day management of a landscape. 251

Chapter 16 Instrtutiooal Challenges and Opportunrties Related to GIS 241

This is a relatively new job expectation (hat may not ordinarily be placed on [he more seasoned foresters and natural resource professionals. At the onset of a program such

as [his, you should acknowledge [hac i[ adds responsibility [0 [he field manager fo r [he development of his or her own management-related maps. Tasks traditionally per

formed by specialises in a cemralized office will be transferred [0 field offices. This [ransfer of responsibility could lead [0 anxiety on [he parr of field managers, however [he main advantage of the program is in reducing the amount

of time required [0 produce [he produces (maps and analyses) needed CO make management decisions. Once field personnel are sufficiendy rrained and become confident in their abilities. time savings across {he organization should be realized.

Perhaps one of (he most important conuibutions young professionals can make is [Q ask peninem ques

tions about the organization within which they work:

What role do I play in each process [hac involves GIS

databases? How are the GIS databases created or acquired? Who claims ownership ovec the maintenance and disrri p

bu[ion of each GIS database? What [echnology (hardware) is avai lable [0 display [he resul" of an analysis? What [echnology (software) is available [0 perform an analysis? How and when does the organization value GIS analysis

in supporcing management decisions? By asking these questions, you indicate your willingness to understand

how GIS has been implemented within an organization. and imply [hac you understand [hac GIS is a valuable [001 in natural resource management.

Technical and Institutional Challenges

One of the most expensive and time-consuming aspects related [0 using GIS is the effore (hat is required to creatc

a GIS database. Dupl icating previous data collection effores in [he creation of a GIS database should always be avoided, thus a lack of awareness of existing GIS databases

is a serious challenge [0 confront . To prevem duplicating

GIS database development efforts, GIS users within an organization should be made aware of [he types of GIS

databases that the organization can easily access. This

might include GIS database produces [hac were developed internally within an organizadon. GIS databases devel

oped by GIS contractors or land surveyors. or GIS databases that are available through agreements or relationships with other nacural resource management

organizations. Information regarding avai lable GIS data-

bases could be seored in a searchable database or it might be ca[alogued in a less formalized manner. Regardless of how this information is gathered. and stored. personnel in

an organization who use GIS should be able co easily iden[ify and loca[e exiseing GIS databases [hac might facilita[e [he rasks [hac [heir jobs require.

Metadata, or information documenting the specifica[ions and quality oflandscape feacures in a GIS database, have become an important aspect of GIS databases in the

pase decade (Dobson & Durfee, 1999). In order co de[ermine the fitness of a GIS database for a particular use, the

me[ada[a related co me GIS database should be considered. In panicular, when a GIS database is acquired from anomer organization, me me[ada[a should be relied upon co verify [hac [he condition of [he GIS database is what was expected when acqui red. In many instances. however,

little metadata exists to describe the qualities of GIS data

bases. The [wo hypothetical forests used extensively in

this book are prime examples. In faCt, more often than

not you may find [hac GIS databases developed or main[ained by non-federal organizations lack, or have insufficient. metadata. Thus. nacural resource professionals must

be careful when basing decisions on GIS databases where

the level of qualiry is uncertain.

For organizations that are involved in producing and

distributing GIS databases to other natural resource man

agement organizations, guidelines or protocols should be in place that address all aspects related to the distribution of the GIS databases. Without guidel ines. organizations

are likely to be working with an inefficient distribution

syseem, and may be prone co liability problems. Guidelines should include a pricing struccure for all available GIS databases; chis structure will need to reflect the

organization's views on cost recovery. In the case of pub

lic organizations. there may be no need or desire co

recover more than the delivery costs . Some public organ

izarions. however. do utilize contractors to collect and

develop GIS darabases, and may need co recoup some of the COSts of doing so. For most private natural resource

management organizat ions, the pricing struccure w ill

likely need co [eflecr [he accual coses of collecting [he data. Organizations [hat distribute GIS databases will also need co develop a comprehensive liability policy [0 pro[ecr themselves from li[iga[ion. A liability policy will likely need co be [ailored [0 each particular GIS database because the content. accuracy. and uses of databases will vary. A method of providing GIS darabases to cuStomers will also need [0 be identified. As discussed in chapter 15, organizations [hac provide GIS data [0 [he publ ic should make

252

Chapter 16 Inst~utional Challenges and Opportun~les Related to GIS 241

Thls is a relatively new job expectation that may not ordinarily be placed on [he more seasoned foresters and natural resource professionals. At the onset of a program such as this. you should acknowledge that it adds responsibility to the Reid manager for the development of his or her own management-related maps. Tasks tradicionally performed. by specialists in a centralized office will be transferred to Reid offices. This transfer of responsibili ty could lead to anxiety on the part of field managers. however the main advantage of the program is in reducing {he amount of rime required to produce the products (maps and analyses) needed (Q make managemem decisions. Once field personnel are sufficiently trained and become confident in their abilities. time savings across the organizacion should be realized.

Perhaps one of [he most impona.nr contributions young professionals can make is ro ask pertinent quesdons about the organiz.arion within which [hey work: What role do I play in each process thar involves GIS databases? How 3.fe [he GIS databases created or acquired? Who claims ownership over the maintenance and disuibution of each GIS database? What technology (hardware) is avai lable to display the results of an analysis? What technology (software) is available to perform an analysis? How and when does rhe organization value GIS analysis in supporcing management decisions? By asking these questions, you indicate your willingness [0 understand how GIS has been implemenred within an organ izarion, and imply that you understand that GIS is a valuable (001

in narural resource management.

Technical and Institutional Challenges

One of the most expensive and time-consuming aspects related to using GIS is the effon that is required to create a GIS darabase. Duplicating previous data collection efrorts in rhe crearion of a GIS database should always be avoided. thus a lack of awareness of existing GIS databases is a serious chaJlenge to confrom. To prevent duplicating GIS datab.se development efrons. GIS users within an organization should be made aware of the types of GIS databases that (he organization can eas ily access. This might include GIS database products that were developed imernaJly within an organization, GIS databases developed by GIS contractors or land surveyors, or GIS databases that are available through agreements or relationships wiTh other natural resource managemem organizarions. Information regarding avai lable GIS data-

bases could be stored in a searchable database or it might be catalogued in a less formalized manner. Regardless of how ffiis informadon is g:nhered and stored, personnel in an organization who use GIS should be able to easily identifY and locate existing GIS da tabases that might lacilitate the tasks that their jobs require.

Meradara, or informadon documeming [he specifications and quality oflandscape features in a GIS database. have become an important aspect of GIS databases in the past decade (Dobson & Durfee. 1999). In order to derermine the fitness of a GIS database for a particular use. the metadata related to the GIS database should be considered. In particular. when a GIS database is acquired from another organization. the metadata should be relied upon to verifY that the condirion of the GIS database is what was expected when acquired. In many instances, however, Iirde mecadata exises to describe the qualities of GIS databases. The twO hypothetical foresrs used extensively in this book are prime examples. In lact. more often than not you may find that GIS databases developed or maintained by non-federal organizations lack. or have insufficient, mecad:Ha. Thus, natural resource professionals musr be careful when basing decisions on GIS databases where the level of qualiry is uncenain.

For organ izarions that are involved in produci ng and distributing GIS databases [0 orner namral resource management organizations, guidelines or protocols should be in place that address all aspecrs related to the disrriburion of the GIS databases. WithoUt guidelines. organizarions are likely ro be working with an inef-hciem distribution system, and may be prone to liability problems. Guidelines should include a pricing Structure for all avai lable GIS databasesi (his structure will need [0 reAecr [he organization's views on COSt recovery. In me case of publie organizations, {here may be no need or desire to recover more than me delivery costs. Some public organizations, however. do utilize conuacmrs m collecr and develop GIS databases. and may need to recoup some of the coses of doing so. For most private natural resource managemem orga niza tions. rhe pricing srrucrure will likely need to reflect the actual COstS of collecting the dara. O rganizations that distribUte GIS databases will also need to develop a comp rehensive liability policy to protect t11emselves from litigation. A liability policy will likely need to be tailored to each particular GIS database because [he conrent, accuracy. and uses of databases will vary. A method of providing GIS darabases (Q cuStomers will also need [Q be identified. As discussed in chap[er IS, organ izacions that provide GIS data to the public should make


GIS databases avai lable in an expedient manner and use cu rrent Internet technologies such as creating new wehsites chat allow users (0 browse darabase offerings and

download both data and metadata. In addition. file transfer protocol (FrP) can be used to make data available. but this data-sharing system is less user-friendly.

For organizations that are nor involved in producing and developing GIS databases for other namrai resource management organizations. the high COSt associated with crearing GIS databases can result in a reluctance [0 share

databases with other organizations. For example. certain GIS databases may contai n sensit ive information. and

might reveal the location of landscape features (such as endangered species nesting locations or archeological sites) that might be disturbed or destroyed should the

locations become public information. Two examples of these GIS databases include the location of endangered species nest sites, and the locacion of genetically-modified [fee field trials. The GIS databases may also comain information about the status oflandscape resources that would be of value to another organization with which it competes, providing the other organization an advantage in the market place. This is clearly importam tOday as the number of land sales has skyrocketed. and potential (a nd

perhaps hostile) investors desire complete information regarding a land asset.

A reluctance on the part of federal public organizations in the US to share or publicize data holdings is that all federal agencies are subject to the Freedom of Information Act (FOIA). FOIA was signed into law in

1966 and later amended in 2002. FOIA makes it possible for individuals (Q request access to federal agency records,

To facilitate a recem landscape analysis research project, a private natural resource managemem organiza

tion agreed to provide a highly detailed GIS database that desc ribed the management units within their

ownership boundaries. The GIS database had been assembled at great COSt and effort, and revealed considerable information abom the natural resources that the private organization managed. The private organization required that a confidentiality agreement be signed prior to making the GIS database available (Q

the research project. As noted in the confidemialicy

except in cases where records are protected from disclosure. These requestS can also be made for spatial databases [hat were produced by federal agencies. In some cases,

spatial databases may comain information that the agency considers to be sensicive or potenrially damaging (Q the resources it manages. One example of sensitive informa

tion might be spatial records of vandalism that occur in public recreation areas. Evidence of high vandalism

occurrence might deter visitors from staying in affected areas, and potemialiy reduce revenue that is gathered from day-use permits. In addition, there may be hesitation to draw additional arremion to 'hot-spotS' of crimi

nal activiry in case the arrenrion may encourage others to

visit these locations our of curiosiry or to cause additional

vandalism. Other hesitations may involve the presence of unusual features, such as special habitat areas, or archeological sites, and the potencial damage that too many visimrs may bring to these areas. For various reasons, federal

agencies may not openly advertise the types of spatial databases that they have produced. A primary hesitation to do so is likely because of the uncertainty of what will happen with the information within the databases should

rhey become widely circulated. Understanding that there are factors that may hinder

an organization's willingness to share a GIS database is

important prior m requesti ng the database. For data of a sensitive nature, it may be possible to enter into a confidentiality agreement to gain access (Q the GIS database. Ultimately, it may be necessary (Q pay a large sum of money ($5.000 to 10.000) for a GIS database. and for

some organizations, it may be difficult m locate the neces

sary budget resources appropriate for this type of purchase.

agreement, access to the GIS database was limited to

the primary scientists of the research project, the sha ring of the GIS database with others was prohibited. and protocols for distribming information drawn from the GIS database were outlined. Without the confidentiality agreement, which facilitated the sharing of the GIS database, knowledge of [he status of resources located within the private natural resource management organization 's ownership would likely have been less accurate, reducing the confidence placed on the landscape analysis results.

253

Chapter 16 Instrtutional Challenges and Opportunities Related to GIS 243

Benefits of Implementing a GIS Program

The decision to implement a GIS program (the entire suite of hardware, software, and personnel related CO the

use of GIS within an organization) can be intimidating for

natural resou rce management organizations. There are

many factors to consider, including investments in soft

ware, hardware, personnel, and CIS databases. Since nat

ural resource management organizations cypically rely on

maps or mapped data to assist in making decisions, GIS

can allow an efficiem storage of maps, and can fac ilitate

the generation of multiple versions of maps in a timely

manner. In addition, GIS allows landscape features to be

measured, analyzed, and integrated with other GIS data

bases in an expedient manner. New technology has pro

vided [he pQ[entiai to convey information [Q field man

agers very quickly. These capabilities, if managed

properly, can allow natural resource management organi

zat ions to make bener management decisions, more accu

rately gauge the effore and cost of potencial namral resource managemem projec(S, and increase the efficiency

of tasks performed by their employees.

Successful GIS Implementation

Saving money, reducing the amount of time spenr in the

office analyzing options. and thus saving resources for Q[her [aSks and management activities are common goals

of namral resource managers. Distributing GIS capabili

ties to field offices has been suggested as one way to

address some of these issues. Successfully implememing

and managing a GIS program can be difficult, as the costs

of implementation and management vary from one

organization to the next. Perhaps the strongest ingredient

for success in implementing a GIS program is in esrablish

ing an organizational commitmenr within the upper lev

els of managemenr of an organization . This commitmenr

needs to view the GIS program as more than JUSt a short

term experimenr that can be discarded after early, d isap

poinring results, since initial GIS products and experi

ences are likely to identify implementation problems.

Unfortunately, upper-level managers tend to be less

Summary

What would the management of natural resources be

like without a few challenges? With the imroduction of

fumiliar with GIS technology than the actual GIS users.

For this reason, GIS users should communicate their sup

porr of GIS in terms that are comp rehensible to the

upper-level managers and help them understand that

when technical difficulties do arise, program implemenra

cion plans must be adjusted. Upper-level managers, in

turn, should promote the G IS program as a way of mak

ing more efficient the tasks required of natural resource

management.

Organizations must also be realistic about the time,

effort, and budgetary resources that individual GIS proj

ects or analyses wi ll require. Proper planning requ ires that

project objectives be clearly defined. Objectives provide a

project mission and ca n help keep personnel focused,

should setbacks occu r. Project objectives a re also impor

tant for establishing standards that allow you to gauge the

success of a GIS project or analys is . Achievement bench

marks are also critical in justifying the continued and

expanded use of GIS within an organization. Allowing

users of GIS to become involved in the planning and

implementation of GIS projects is also importanr, since

they may be among the best qualified to assess whether

GIS can accomplish the appropriate project tasks, which

may lead to an improved level of efficiency in the man

agemenr of natural resources.

Finally, GIS user training is an importanr considera

tion for the success of a GIS program within a natural

resource managemenr organization. Most recenr college

graduates from natural resource programs will likely have

a rudimenrary knowledge of how GIS can ass ist in natural

resou rce management, but will likely lack the level of

experience you gain from using GIS periodically for

actual, on-the-ground, management purposes. To acceler

ate the development of personnel, organizations can pro

vide GIS training inrernaIly, or can allow personnel to

auend cominuing education courses or pursue other

training opportunities. Geospatiai training courses and opportunities appear to be increasing within natural

resource disciplines (Wing & Sessions, 2007). This

investment in continuing education increases the knowl

edge level of personnel and demonstrates an organization 's commitment to its personnel, which hopefully

leads to increases in work efficiency and productiviry.

new computer-related measurement technology and accompanying GIS databases, natural resource manage-

254


menr o rganizat ions are facing numerous challenges related to GIS data management. Given that GIS-related technology continues (Q evolve to meet (he increasing

needs of society. an optimistic person might expect that the challenges described in this chapter can be over-

Applications

16.1 Sharing GIS databases with people outside your organization. What issues sho uld a natural resou rce management o rganization address when considering making GIS databases available to other organizations. including perhaps competitors?

16.2 Managing GIS technology. A seaso ned professional (Mary Swarthmore) who manages another department (Accounting) in your natural resource o rganization

is considering a career change. This change will result in Mary managing your organization's GIS department. Mary has never managed a GIS department before, nor

been involved in the creation. acquisition. o r use of GIS databases. She has approached you fo r some advice regarding the tough issues G IS managers face when concerned abom successful implementadon of a GIS program. What might you advise Mary?

16.3 Sharing GIS databases within an organization. You've heard that a colleague in another private natural

References

Bettinger, P. (I999). Distributing GIS capabi lities to fo restry field offices: Benefits and challenges. journal of Fomtry, 97(6), 22-6.

Dobson, J.E., & Durfee, R.C. (I 998). A quarter century of GIS at Oak Ridge National Laboratory. In T.W. Foresman (Ed.), The history of geographic information systems: Perspectives from the pioneers (pp. 231-63). Upper Saddle River, NJ: Prentice-Hall.

Natoli, J.G ., Pelgrin, W.H., Oswald, B. , & Montie, K. (200 1). Geographic Information Systems: The wave of the fmure for information analysis. Public Works, May, 22-9.

come, even as new and va ried issues arise. As we have

noted, many of [he challenges facing the use of GIS within natural resource management organizations are

related to GIS databases, technology, people, and organizational issues .

resource management organization has developed a GIS database that may contain info rmation that could assist you in some part of your job. Under what conditions should you expect your colleague to provide you access to

the GIS database?

16.4 Distributed GIS program. You work for a large integrated forest products company that has a central office and five district offices. The company has been

attempting to shift GIS capabil ities to the field offices by pu rchas ing the appropriate hardware and software resources. and insist ing that field perso nnel (foresters. biologists. managers. and others) use it to make maps associated with their management activities . After twO

years, only one of the five offices has successfully implemented the program.

a) Why do you think the other four offices have been less than successful?

b) Why might the one office have been successfu l?

Puger Sound LiDAR Consortium (PSLC). (2007). Puget Sound Lidar Consortium: Public-domain high-resolution topography for Western Washingron. Retrieved April 23, 2007, from http://pugetsoundlidar.ess.washington. edu/ index.htm.

Wing, M.G., & Bettinger, P. (2003). GIS: An updated primer on a powerful management rool. journal of Forestry, 101(4),4-8.

Wing, M.G., & Sessions, J. (2007). Geospatial technology education. journal of Forestry 105(4), 173-8.

255

Chapter 17

Certification and Licensing

of GIS Users

Objectives

The progressio n of GIS use in natural resource manage

mem has been evolving from development and implementation of systems. to (he distribution of analytical capabilities to field offices, to porting spatial technology into the resource setting. The evolution of GIS can be

viewed from (he perspective of a single organization or

from [he perspective of national or worldwide GIS com

munities. From a global perspective. GIS is facing one of its greatest challenges: mat of implementing certification

and licensing processes to define and recognize 'profes

sio nal' GIS users. In recem years, concerns have been

voiced from the land surveying and engineering communiry regarding the definition of surveying activities and whether GIS practitioners impede upon traditional sur

veying accivities when colleccing o r mapping spatial data

(Gibson, 1999). These concerns have foStered debate among surveyors. engineers. and GIS users regarding the

types of measuremem and analytical activicies that might

be required to competently perform specific accivit ies in

co njunction with the analysis. Thus, in an effon to gain

credibi lity and recognicion among other professions. the

GIS commu ni ty has pondered the issue of cenificatio n

and licensing.

After reading th is chapter and exploring the questions posed in the applications sect ion. students should have

an awareness o f:

1. why certification and licensing of GIS users is being

debated,

2. what organ izations might be relevant in certification

and licensing discussions. and

3. how cenification and licensing issues might affect the

typical GIS user in a natural resource organizacion .

During the last 10 years, one of the primary goals of the GIS comm unity has been to educate other profession

als and the general public about the power and usefulness of GIS beyond its map production capabilities. Many people in natural resource organizations (as wel l as academia)

view GIS only as a map-making tool and may have limited understanding of its analytical power. The American

Society for Photogrammetry and Remote Sensi ng (ASPRS) and the University Consortium for Geographic Information Science (UCGIS) are perhaps the most active groups in educating those who use GIS, as well as the pub

lic, about the capabi lities of GIS. At the ASPRS 2007 Nacional Convemion in Tampa, one AS PRS member was

heard to remark ' If you claim that GIS technology is vital to society you should also promote the need for certifica

tion and licensing among GIS users. ' The UCGIS conducts nadonal meetings rwice a year to identify research and

other act ivi ties that wi ll idencify and promote rhe use of

GIS as a problem-solving tool for society. At a narionai

meeting in June 2000, a member of the UCGIS asked the other delegates. 'Now that everyone seems co know about

GIS , what are we going to do about it ?' These seemingly

innocent observations speak positively about society's growing awareness of GIS while also indicati ng a potent ial

pitfall within natural resou rce managemenr: GIS has been

embraced by natural resource organizations in a way that

256


facilitates open use by any professional who might be interested in the technology. Only recently has the GIS community begun co discuss in depth whether professional standards must puc in place to ensure professional

competency for data development. analysis. and other tasks. Given that ocher professions initiated these discus

sions, me leaders and champions of the lise of GIS in nat

ural resource management have found themselves at rimes

in a defensive position when addressing issues related CO

the call for licensing and certification of GIS users. Throughout the US, some professional land surveyors

and engineers have perceived that GIS users were vio lating

state surveying laws when usi ng CPS to coliect spatial

data, and when reponing positional accuracies of collecred measurements. Some of these GPS data collection

activities have actually led to legal disputes, particularly when the collection and mapping of spatial data refercoees land ownership locations. In California, state legis

lation was developed to clarify the activities involving spa

tial data collection (Korte, 1999). These activities full into (wo categories: (1) those that constitute 'land surveying',

and hence require professional licensing, and (2) all other activities, wh ich do not require professional licensing.

Most stares have registration boards mat license and reg

ulate land surveyors and engineers. These boards often

have the ability to interpret existing statutes and laws that

govern the collection and representation of spacial data.

and may also initiate. support. or approve legislation

regarding spatial data collection activities. In addition.

state-level professional land surveying sociecies are active

in promoting or modifYing laws regarding spatial data

collection, and they may occasionally engage political lobbyists to assist in influencing the legislative process.

In contrast to the land surveying and engineering

fields. the GIS community is not direcdy represented or

controlled by a nationally- or state-recognized licensing

board in most cases. There are GIS-related professional

societies at the state or province level but these societies

typically have nOt been in existence for very long. and are

generally nor very active in influencing legislation related to spatial data co llection. The main objective of state-level

and nat iona.l-Ievel GIS societies has been to communicate

information related to the collection. maimenance. and

analysis of GIS databases to inrerested users.

Current Certification Programs

In terms of nationally-recognized GIS certification pro

grams in the US. there are primaril y (wo current options.

The more established and rigo rous of me options is that made by the American Society for Photogrammetty & Remote Sensing (2006). The ASPRS has created a Mapping Scientist certification for GIS users and has also

created certification programs for remote sensing and

photogrammetry. The Mapping Scientist cerrification

requires applicants to develop a statement of accomplish

ments, which are peer-reviewed. and to pass a written

exam. Currently, only about 50 people are certified as Mapping Scientists through the ASPRS. The APSRS has also creared technologist-certifications for GIS, remote

sensing, and photogrammetty. These GIS technologist certifications require less work experience than the

Mapping Scientist and other full certifications (three years as opposed co six) . There are currendy s ix certified

G IS/LIS Technologists. The Urban and Regional Info rmation Systems

Association (URlSA) initiated a GIS professional certification program in 2004 . The GIS Certification Institute

(GISel, 2007) manages the certification program, which results in qual ified applicants becoming recognized as a

certified geographic information systems special ist (GISP).

Three categories of experience must be demonstrated in

order to qualify. The primary experience necessary in

ga ining GISP cert ification is a documented work history

involving GIS and other spatial tools. T he second experience category is an education background that can be sat

isfied by attending conferences and workshops, as well as completing formal education programs or earning certificates. The third category is described as 'contributions'

and includes GIS publications. conference planning or

presentations. and volunteer activities related to GIS.

Ai; of October 2007, there were 1,709 certified GISPs, giving the GISCI program visibility beyond the certification programs of the ASPRS. Although the creation of the GISP certification is noteworthy. the experienced-based

portfolio approach to qualifYing as a GISP lends itSelf to criticism (Longley et aI. , 2005). It remains to be seen whether a certification approach that does not include a

written examination will receive respect and recogn ition

from other professions and disciplines. In addition, there

is no clear method for addressing unprofessional activities

related to GIS, should they occur. Given the emphasis on self-reporting of experience, another issue of discussion is

whether any applicants have been denied GISP status. Many colleges and universities now offer certification

degrees rhat are related to GIS. as well as other spatial data collecrion and analysis rechnologies. however no stan

dards exist for what should be offered in those curricu- 257

246 Part 3 Contemporal'( Issues in GIS

faci li[a[es open use by any professional who might be inreresred in rhe <echnology. Only recently has [he GIS community begun [Q discuss in depth whether professional standards musr put in place (0 ensure professionaJ competency for data development. analysis. and other (asks. Given that o ther profess ions initiated these discus

sions, me leaders and champions o f rhe lise of GIS in nat

uraJ resource managemenr have found themselves at times in a defensive position when addressing issues rdated (0

rhe call for licensing and cerrificarion of GIS users. Throughour rhe US, some professional land surveyors

and engineers have perceived that GIS users were vio lating

scate surveying laws when usi ng GPS [0 collecr spacial

data. and when reporting positional accuracies of col

lecred measurements. Some of these GPS dara collecrion acdviries have acrually led ro legal dispures, parricularly when [he collecrion and mapping of sparial dara references land ownership locado ns. In California, state legis

lation was developed to clarify the accivities involving spa

rial dara collecrion (Korte, 1999). These acriviries fuJI inro [wo caregories: (I) those thar consrim<e ' land surveying', and hence require professional licensing, and (2) all other activ ities. which do nor require professional licensing.

Most states have registration boards (hat license and reg

ulate land surveyors and engineers. These boards often

have rhe abili ty to interprer exis ting starutes and laws that

govern the collection and rcpresemarion of spatial data,

and may also initia te, suppocc, or approve legislat ion

regarding spatial data collection activities. In addition,

state-level professional land surveying societies are active

in promoting or modifying laws regarding spatial dara

collecrion, and they may occasionally engage political lobbyists to assist in influencing the legislative: process.

In contrast to rhe land surveying and engineering

fields, the GIS community is not directly represented or

controlled by a narionally- or state-recognized licensing

board in mosr cases. There are GIS-relared professional societies at the srate o r province level but these societies

typically have nOt been in existence for very long, and are

generally nor very acrive in influencing legislation rdared

to spa rial data collection. The main objective of stare-level

and narional-Ievel GIS socieries has been to communicate

information related to the collection, maintenance, and

analysis of GIS databases (Q interested users.

Current Certification Programs

In terms of nationally-recognized GIS cerr.ification pro

grams in rhe US, there are primarily twO current options.

The more esrablished and rigorous of the oprions is that made by [he American Sociery for Phorogrammerry & Remore Sensing (2006). The ASPRS has crea<ed a Mapping Scientist certification for GIS users and has also

created cen ification programs for remote sensing and

photogram merry. The Mapping Scientisr certification

requires applicants to develop a sratement of accomplish

mems, which are peer-reviewed, and (Q pass a written

exam. Currenrly, only about 50 people are cerrified as Mapping Sciemisrs rhrough rhe ASPRS. The APSRS has also crea red technologist-certifications for GIS, remote

sensing, and phowgrammerry. These GIS rechnologisr certifications require less work experience than the

Mapping Scienrisr and other full certifications (three years as opposed [0 six). There are currently s ix certified

GIS/LIS Technologists. The Urban and Regional Informar ion Sysrems

Associarion (URISA) iniriared a GIS professional certificarion program in 2004. The GIS Certification Innirure (G ISel, 2007) manages rhe cerrification program, which resulrs in qualified applicanrs becoming recognized as a certified geographic informarion sysrems specialisr (GISP). Three categories of experience must ~ demonstrated in

o rder to qualify. The primary experience necessary in

gaining GISP cert ification is a documented work history

involving GIS and orher spa rial rools. The second experience category is an education background (hat can be sat

isfied by arrending conferences and workshops, as well as completing formal education programs o r earning cenifi

cates. The third category is described as 'conuiburio ns'

and includes GIS publications , conference planning or

presentations, and vo lunteer activities related to GIS.

As of October 2007, rhere were 1,709 certified GISPs, giving the GISel program visibiliry beyond [he certificarion programs of rhe ASPRS. Airhough rhe creation of the GISP certification is noteworthy. the experienced-based

portfolio approach ro qualifYing as a GISP lends irself ro criricism (Longley er aI. , 2005). I[ remains ro be seen whether a certification approach rhar does not include a

written examinarion w ill receive respect and recognition

from other profess ions and disciplines. I n addition. there

is no clear merhod. for addressing unprofess ional activicies

reJa<ed to GIS, should [hey occur. Given the emphasis on se lf-reporting of experience, another issue of discussion is

whether any applicants have been denied GISP status.

Many co lleges and universities now offer certification

degrees mar are related ro G IS, as well as other spatial data

collectio n and analysis technologies. however no Stan

dards exist for what should be offered in those curricu-

Besides the current course that you are taking (that

hopefully uses this book), what other GIS courses does your university, college, or community college offer? Although there are many educational instim

tions that offer coursework or curriculum related to

GIS, these programs can look quite different from one instimtion to [he next. At most educacional instim

[io ns, for example, GIS courses are located within the geogra phy department. H owever, special ized GIS cou rses may be found within departments such as

lums. The National Center for Geograph ic Information & Analysis (2000) has produced, and suggested for use, a core curriculum to serve as a foundation fo r studies in GIS. Typically, GIS users can earn a GIS certificate after tWO yea rs of part-time study. While this option does include organized coursework (and perhaps exams to evaluate competency), programs that offer cert ificat ion

degrees lack a recognized set of certification gu idel ines and are generally not accredited by a professional GIS or remote sensing society. As mentioned in chapter 15, the GIS&T Body of Knowledge (DiBiase et aI., 2006) was recently published in o rder to define critical concepts and skills that relate to geographic information science and technology (G IST) . This document was created through the joim efforts of many GIScience researchers and educators, and is an initial attempt to define the skills that you can use to describe geospatial competency. A second edition is intended that will provide detail fo r instructional activities that suppOrt important geospatial concepts and skills.

The NCEES Model Law

The National Council of Examiners for Engineering and Surveying (Ne EES) has developed a set of guidelines described in a Model Law document to help states with licensing issues related co land surveying and engineering (National Council of Examiners for Engineering and Surveyi ng, 2006). The Model Law contai ns reference to GIS activities associated with spacial data collection and use. The Model Law contains 29 sections that are designed to help state boards and other legislative bodies create or amend laws for land surveying and engineering.

Chapter 17 Certification and licensing of GIS Users 247

fishe ries, wildlife, oceanography, forestry , soi ls, rangeland resources, and others. More than likely, GIS courses offered in departments other than geography will provide a different perspective on wha t is

important (Q smdents pursu ing namral resource degrees. If a university or college does not offer courses related co GIS, students can still learn about

the capabilities of GIS through Internet courses, selfstudy of GIS texts, and volunteer wotk with local agencies or government offices.

The fi rst section of the Model Law clarifies the necessity for guidelines by stating that the practices ofland surveying and engineering are a matter of pubic interest. The

decisions made (or recommended) by people employed in these professions can potentially affect the life, health, and property of the general public. Section 2 defines the tasks that are associated with surveying and engineering and no longer refers directly to GIS, as it did in an earl ier version of the Model Law. Section 2 does state that mapping involves the configuration of the Earth's feamres. the subdivision ofland, the location of survey control points, reference points, or property boundaries, and thus people performing 'mapping' are performing the 'Practice of Surveying'. The Model Law suggests that these people should be registered as professional surveyors before engaging in those act ivit ies .

The most d irect pathway co becoming a professional surveyor is to fi rst graduate from an accredited fou r-year college program in engi neering or surveying. Then you must successfully pass an e ight-hour written exam cover

ing surveying fundamentals. Once the fundamentals exam has been successfully passed, four years of surveying experience under the supervision of a licensed surveyor must be accumulated before admittance is allowed to an eight-hour written comprehensive exam covering surveying principles and practice:. Once this comprehensive exam has been successfully passed, and all other state-level requirements are satisfied, you are qualified to become a professiona l land surveyor. Those who grad uate from four-yea r surveying cu rriculums that are not accredited must spend an addirional rwo to four years working in the land survey profession before they can be admitted to the fundamentals exam. T his process of attai ning

258


licensing is daunting, and requires a long-term commit

ment on [he pare of poten tial surveyors or others who

w ish to comply with me Model Law. For many cu rrent

natural resource professionals who use GIS. act ive engage

ment in a career combined with family and communiry

commitments offer few realist ic opportunit ies [0 pursue

an engineering degree or to work under the guidance of a

land surveyor.

Earl ier versions of the Model Law definition of surveying that comained direct mention of GIS were criticized as

being (00 stringent and expansive in its descriptio n afG IS

applica tions within surveying activities. Several promi

nent organizations related (0 surveying and GIS co

authored a repan that suggested modifications [0 the Model Law (American Congress on Surveying and

Mapping [ACSM] et aI. , 2001). The report urged the

NeEES to dro p explicit reference to GIS as a data manip

ulation and mapping tool. to refine the definition of sur

veyi ng so that it was less broad, and [0 specifically include

and exclude certain GIS-related accivities in its defini tion

of surveying. The NCEES appears to have acted a( leas t in

parr to these recommendations. Previous to the Model

Law changes. the broader definitions of su rveyi ng created

difficulries for some stares that tried to incorporate the

Model Law's definitions of surveying inco their own state

statutes and regulations (Thurow & Frank, 2001).

The Need for GIS Certification and Licensing

Chief amo ng the argumenr for GIS certifica tio n is an assessment of how GIS activities might impact society's

welfare and safety (Gibson, 1999). The surveying and

engineering professions have long been involved in dete r

mining how best to accurately and precisely collect and

analyze Earrh and structural measurements. Since land

values in North America will likely concinue to increase as

the human population also increases. land areas that are

incorrectly measured can resulr in large monetary losses or

gains for land owners. Knowing the reliabi lity of land

measurements (exp ressed through uncertainty es timates)

will allow land managers ro make bener decisions. While

you may argue that a distinct parr of natural resource data

collection and analytical processes involves quantifying

and expressing the uncertainty that is associated with

those measurements. this quantification is rarely used to rate the reliab ili ty of measu rements collected. Thus the

surveying and engineering professions may be better posi-

tioned to provide information regarding the inherent

uncertainty in land measurements.

Land surveyors are also charged with locating or estab

lishing roads and other utilities such as power lines and

fire hydranrs. Surveyors have argued that it is inappropri

ate for unlicensed surveyo rs to operate CPS equipment for

this purpose since locational errors can potenriaIly affect public safety. In addition, since professional surveyo rs

must absorb the on-going costs associated with maintain

ing licensing and liability insurance, they a re likely to

charge mo re for their services than unlicensed CPS opera

tors. Thus, surveyors have stated that it is unfai r for unli

censed GPS o perators to compete with professional land

surveyo rs in offering these types of d ata collection

servICes. Licensing varies throughout the world and may also

vary by stare o r province. In add ition. it appears that the number of licensed professions is in transicion . In

Canada. professions that require licenses are referred to as

regulated occupations. There are approximately 50 di f

ferenr regulated professio ns in Canada (Government in

Canada, 2007). Within the US, the number of profes

sions thar requ ire a license fo r participario n has been

gradually increasing. Doyle (2007) found that abou(

20 per cem of all professions in rhe US require licensing,

up from abour 5 per cent during the early 1960s. About

50 professions have a regist ration process (hat is recog

nized in all 50 stares. Some people criticize (he licensing

process and claim thar it resulrs in higher prices for serv

ices without an equivalent gai n in the quality of rhe serv

ice or good provided. In additio n. some peo ple see enrry into the profession as being prohibi tively limited once

licensing is in place. While Kle iner (2000) found (hat

incomes from licensed occupations were higher for those

occupations that required more education and training.

faster employment growrh was evidenced in many

licensed professions, such as engineering and law. when

compared to no n-licensed professions. Kle iner (2000)

also reports that empirical evidence addressing whether

licensing results in greater societa l goods. such as increased safety, is currently lacking.

Issues of conrrol and enforcement are additional

aspecrs of licensing and certification . Most stares have

clear definitio ns of what constitutes acceptable surveying

practices. These rules typically cover what clients should

expecr from a professional land surveyor's services, in

terms of producrs. as well as erhical considerations.

Ethical considerarions nOt on ly address the surveyor-

259


licensing is daunting, and requires a long-term commit

menr on rhe part of potential surveyors or others who wish to comply with the Model Law. For many current natural resource professionals who use GIS. active engagement in a career combined with family and communiry

commitments offer few realisdc opportuniries to pursue

an engineering degree or to work under rhe guidance of a

land surveyor.

Earlier versions of the Model Law definition of surveying thar contained direct mention of CIS were criticized as

being [00 srringenc and expansive in its description of GIS applications wi thin surveying acrivities. Several prominent organizations related [0 surveying and G1S co

authored a repon that suggested modifications [0 the Model Law (American Congress on Surveying and Mapping [ACSM] et aI., 200 I). The report urged lhe NeEES co drop explicit reference [Q GIS as a data manip

ulation and mapping tool. to refme the definit ion of sur

veying so that i[ was less broad, and ro specifically include and exclude certain GIS-related activities in its definition

or surveying. T he NCEES appears ro have acted a[ leaS[ in pan to these recommendations. Previous to the Model

Law changes, the broader definitions of surveying creared difficulties for some states char tried to incorporate the

Model Law's definitions of surveying into their own state

starutes and regulations (Thurow & Frank, 2001).

The Need for GIS Certification and Licensing

Chief among the argumenr for GIS certification is an

assessment of how GIS activities might impact sociery's

welfare and safery (Gibson, 1999). The surveying and engineering professions have long been involved in deter

mining how best to accllrarcly and precisely collect and

analyze Earth and strucwral measurements. Si nce land

va lues in North America w il i likdy conrinue to increase as

the human popularion also increases, land areas that are

incorrectly measmoo can result in large monetary losses or

gains for land owners. Knowing the reliabiliry of land measurements (expressed through uncertainty estimates)

will allow land managers to make better decisions. While

you may argue that a distinct parr of natural resource data

collection and analytical processes involves quamifying

and expressing the uncertainty that is associated with

those measurements, this quanrification is rarely used to

rate the reliability of measurements collected. Thus the

surveying and engineering professions may be better posi-

tioned to provide information r~garding the inherem

uncertainry in land measurements.

Land surveyors are also charged with Jocaring or estab~

lishing roads and other utilities such as power lines and

fire hydrants. Surveyors have argued [hat it is inappropriate for unlicensed surveyors to operate GPS equipment for

this pu rpose since locational errors can potentially affect

public safery. In addition, since professional surveyors

must absorb rhe on-going costs associated wi th ma i ntain~

ing licensing and liabiliry insurance, they are likely to

charge more fo r their services than unlicensed GPS opera

tors. Thus, surveyors have stated that it is unfair for unl i

censed GPS operaro rs to compere with professional land su rveyors in offering these types of data collection

servIces.

Licensing varies throughout [he world and may also

vary by scate o r province. In addition, it appears that the

number of l icensed professions is in transition . In

Canada, professions mat requ_ire licenses are referred to as

regula red occuparions. There are approxima[e1y 50 different regulated professions in Canada (Govern ment in

Canada, 2007). Within [he US, the number of professions thar require a license for participation has been

gradually increasing. Doyle (2007) found lha[ about 20 per cent of all professions in the US requi re licensing, up from about 5 per cent duri ng the early 1960s. Abou l 50 professions have a registration process that is recog

nized in all 50 srates. Some people criticize the licensing process and claim thal it results in higher prices for services without an equivalent gain in the quality of the serv

ice or good provided. In addition, some people see entry into the profession as being prohibitively limited once

licensing is in place. While Kleiner (2000) found thar incomes from licensed occupations were higher for those

occupations [hat required more education and training,

faster employment growth was evidenced in many

licensed professions, such as engineering and law, when

compared to non-licensed professions. Kleiner (2000) also reports that empirical evidence address ing whether

licensing results in greater societa l goods, such as

increased safety. is currently lacking.

Issues of conrrol and e nforcement are additional

aspects of licensing and certification . Most states have

clea r defi n itions of what constitutes acceptable surveying

pracrices. These ru les rypica lly cover what clients should expect from a profess ional land surveyor's services. in

terms of products. as well as ethical considerations.

Ethical co nsiderations nor only address the surveyor-

client relationsh ip but also offer advice on the professional

relationships that should exist becween surveyors. Most states also have developed a process [0 fac il itate the sub

mirral of complaints against land surveyors. All state licensing boards have the power ro revoke a surveying license if disciplinary infractions occur as a resulr of com

plaines. Th is power encourages most land surveyors [0

become familiar with board rules for professional conduct and [0 adhere to the rules as they engage in survey act ivities. Such rules do not exist in general GIS use in natural resource management.

Crit icism has also been weighed against unlicensed GIS and GPS users due to [he lack of an accredited educational curriculum. Most professions, including forestry and wildlife. have identified an educa[ional and professional background that is necessary for accreditat ion or licensing within their fields. The accred itation process usually suggestS the coursework, minimum competency standards, professional standards, and imegration with other disciplines [hat should be provided to students pursuing degrees in [hose fields (Huxhold. 2002). An accreditation process for G IS could be developed (and perhaps is currently under development). However. the dilemma is thac if you view GIS as a field of study. [he students in [hose cu rricula will graduate with a geography degree. and be considered professional geogra phers. Most natural resource organizations hire biologists, foresters. soils scientists, or other associated professionals. as well as geographers, to assis t in the management of natural resources. These organizations increasingly expect all of thei r personnel to util ize GIS, not JUSt those who have obtained a degree from an accredired geography program.

GIS Community Response to Certification and Licensing

As you might expect. some members of the GIS community have voiced opposition to the suggestion that certa in GIS activit ies be included in the lis t of functions only to

be performed by land surveyors. C riticism has been directed toward the sometimes-broad definitions of land

surveying. and whether GIS databases developed and distributed by public agencies should require management by a licensed surveyor GofFe. 200 1). In addition. some proposals for certificatio n and licensing have also been viewed as exclusionary. and co uld prevenr natural resource professionals from performing the GIS activities that rhey historically performed. Thus the opposirion to


certification and licensing from the natural resource GIS community. largely composed of natural resource managers. bio logists. foresters, and others. is to be expected.

Defining those areas of spatial data collection and mapping that porentially afFecr public welfare and safery is easy in some cases. and challenging in orhers. Clearly. GIS databases used for activ ities such as navigation, locating underground facilities when excavating, or ensuring that property boundaries have been accurately located and used in calcularing land areas. should fall under [he purview of a professio nal land surveyor or enginee r. Activ ities that involve display ing data for illustrative pur

poses, recreatio nal gu ides, or in activit ies that may only affect the person{s) or agency responsible for the associared decision. could perhaps fall ourside [he purview of a professional land surveyor or engineer. Although these examples illustrate distinctions becween activicies chat clearly afFecr public welfare and safery and rhose [har do not, there are many other examples that are less clear and will require furthe r discussion before agreements between the surveying and GIS communities are reached. In cases where mapping products might affect public welfare and safety, several scenarios fo r making map consumers aware of pocenriallimitations have been suggested. Suggestions

include requiring that maps explicicly iden ci fy the source documents, associared metadata, appropriate use of map comenr. and any positional adj usrmenrs Goffe. 2001). Examples of these caveats and disclaimers were introduced in chapter 4. Whether these caveats will continue ro fall short of requiring a certified or licensed G IS professional ro develop the mapping products wi ll likely be an area of discussion.

Finally. and inreresringly. some people have argued that the surveying profession has traditionally not required tra ining in GIS in order to obtain professional surveying cert ification (American Congress on Surveying and Mapping. 1998). For [h is reason. they argue. i[ may not be appropriate fo r land surveyors to manage rhe development. maintenance. and use of GIS databases. However, the current exam syllabi for the national land

surveying exams (borh rhe fundamemal and professional pri nciples and practice) do include GIS and land information systems as potential exam topics .

MAPPS Lawsuit

In June 2006, the Management Associarion for Private Phorogrammerric Surveyors (MAPPS) and rhree other

260

client relationship but also offer advice on the professional relationships chat should exist berween surveyors. Most Sca tes also have developed a process [0 facilitate (he sub

minal of complaincs against land surveyors. All state

licensing boards have the power to revoke a surveying

license if discipl inary infractions occur as a result of com

plainrs. This power encourages most land surveyors to

become familiar with board rules for professiona1 conduct and (Q adhere to the rules as [hey engage in survey activi

ties. Such rules do nor exist in general GIS use in namral


Criticism has aJso been weighed agai nst unlicensed

GIS and GPS users due to the lack of an accredi ted educadonal curriculum . Most professions, including forestry

and wildlife, h.ve identified an education. I and professional background [hat is necessary for accredi ta tion or

licensing IlJithin their fields. The accreditation process usu

ally suggests me coursework, minimum competency sran

dards, professional standards, and imegration with other

disciplines that should be provided to students pursuing degrees in those fields (Huxhold, 2002). An accreditation process for GIS could be developed (and perhaps is currently under developmenr). However, me dilemma is rhat

if you view GIS as a field of srudy, the students in those curricula will graduate with a geography degree, and be considered profess ional geographers. Most "amrai

resource organizations hire biologists, foresters, soils sci

entists, or other associated professionals, as well as geogra

phers, to assist in the management of natural resources.

These organizations increasingly expect all of their per

so nnel to ucili1.e GlS. nor just those who have obtained a

degree from an accredited geography program.

GIS Community Response to Certification and Licensing

As you might expect, some members of the GIS community have voiced opposition co the suggestion thar cerrain

GIS activi ries be included in the lis t of funcrions only co

be performed by land surveyors. Criticism has been directed toward the sometimes-broad definitions of land surveying, and whether GIS databases deve.loped and distributed by public agencies should require management by a licensed surveyor Uoffe, 200 1). In addition, some proposals for certifica tion and licens ing have also been

\'iewed as exclusionary. and could prevenr natural

resource professionals from ~rforming the GIS acr iviries

tha t they histor ically performed. Thus the opposition to

Chapter 17 Certffication and Licensing of GIS Users 249

cert ification and licensing from the natural resource G I

communiry, largely composed of narural resource man

agers, biologists. foreseers, and others, is to be expected .

Defining those areas of spatial data collection and

mapping that potentially .ffect public welfure and safety is easy in some cases, and challenging in others. Clearly, GIS databases used for acdviries such as navigation. locat

ing underground Facilities when excavating. or ensuring

that property boundaries have been accurately located and used in calculating land areas, should full under the purview of a professional land surveyor o r engineer.

Acriviries (hat involve displaying data for illustrative pur

poses, recreational gu ides, or in activities [hat may only

.ffect the person{s) or agency responsible for the associated decision, could perhaps fall outside the purview of a professional land surveyor or engineer. Although these examples illusrra te distinct ions between activ it ies [hat

clearly affect public welfare and safety and those that do nor, there are many orher examples rhat are less clear and

will require lUrrher discussion before agreements between

rhe surveying and GIS communities are reached. In cases

where mapping products mighr affect public welfare and safety, several scenarios fo r making map consumers aware

of poremial limitations have been suggested. Suggestions

include requ iring (hat maps explicitly identify me source

documents, associated meradara, appropriate use of map

content, and any positional adjustments Uoffe, 2001). Examples of these caveatS and disclaimers were intro

duced in chapeer 4. Whether these caveats will continue

to fall shon of requiring a certified or licensed GIS professional to develop the mapping products wi ll likely be an area of discussion.

Finally, and interestingly, some people have argued that the surveying profession has traditionally not

required training in GIS in order to obtain professional

surveyi ng cert ification (American Congress on Surveying

and Mapping, 1998). For th is reason, they argue, it may nor be appropriate for land su_rveyors to manage the

developmenr. maimenance. and use of GIS databases.

However, the current exam syllabi fOr rhe narional land

surveying exams (both the fundamental and professional principles and practice) do include GIS and I.nd information systems as poremial exam topics.

MAPPS Lawsuit

In June 2006, rhe Managemenr Associarion for Private

Photogrammetric Surveyors (MAPPS) and three other


professional engineering associations filed a lawsu it

against the US government. The lawsuit was fi led on

behalf of the Federal Acquisition Regulation (FAR) Council and is referred to as the MAPPS lawsuit. The actual tide for the lawsuit is MAPPS et at. v. United States

of America. The lawsuit requests changes in interpreta

tion of the 1972 Brooks Act (US Public Law 92-582) which is intended to direct federal government policy in

select ing providers [0 perform architectural. engineering,

and related services. More specifically. the lawsuit seeks

co modify how the selection process for government coo

traccors is evaluated as it relates to mapping activities.

The resulrs of the lawsuit may impact how some US federal agencies award governmenr contracts. The MAPPS

lawsuit has been a considerable concern for many GJS

oriented organizations and has once again fueled the

debate over the definition of activities that can be con

sidered surveying and engineering, which activides influ

ence public welfare and safety, and the appropriate

geospatial certification and licensing requirements. The

participation of prominent organizations on both sides

of the lawsuit is evidence that the MAPPS lawsu it is not

a trivial legal exercise.

Plaintiffs of the MAPPS lawsuit included the American Sociery of Civil Engineers (ASCE). National Sociery of Professional Engineers (NSrE). and Council on Federal Procurement of Architectural and Engineering Services

(COFPAES). The Brooks Act established that price alone should nO( be used in the awarding of government con

tracts to individuals or firms. Instead. Qualificarions

Based Selection (QBS) is to be used. which involves evaluating professional qualifications and experience. in

addition to seeking a 'fair and reasonable' COSt co the gov

ernment, in awa rding contracts. Negotiations for an

acceptable price should begin with the most qualified firm. If negodat ions fail, then negociations should pro

ceed with the second most qualified firm , and so on . The

FAR COllncil applies the rules and laws related to the Brooks Act, and is supposed co ensure that the intentions

of the act are upheld. Although the Brooks Act includes 'surveying and mapping' among the list of architectural and engineering services that it is intended CO cover, fed

eral agencies have not been consistent in their interpreta

cion and adherence to stated protocols. The MAPPS lawsu it seeks to compel the FAR council to more rigorously

interpret and apply the Brooks Act in the select ion of

contractors for surveying and mapping services. In states where licensed surveyors or engi neers are required to per-

form su rveying and mapping activicies. the lawsuit contends that the FAR council should direct that licensed professionals be selected for government cOntracts involv

ing surveying and mapping. The im plications of the MArrs lawsuit are [hen potentially significant given that

many states do nOt draw a clear distinction between sur

veying and mapping in their laws and rules that govern

surveymg. The American Association of Geographers (AAG).

GISCI. Geospatial Information & Technology Association (GITA). VCGIS. and VRISA jointly submitted a briefing to the US District Court in Virginia

(Alexandria Division) that opposed the MAPrs lawsuit (Association of American Geographers. 2007). The brief stated that a successful lawsuit could cause serious con

cern nO( only for the GIS community but also fo r other

related activ ities and professions. Other related activit ies

included GPS data collection, lnrernet mapping, geospa

tial analysis , remote sensing, academic research that

involved mapping, and the broad activities encompassed

within cartography. The briefing claimed that the lawsuit's impact would greatly affect many activities and

industries that involve or rely on mapping activities and

information.

The US District Court ruled against MAPPS in June 2007 and issued a summary judgment in favor of the US Government. The judge in the case stated that the MArps plaintiffs failed to 'establ ish that an injury in fact was suffered by the individual surveyors or their firms'. In order for a case to be tried. a plaintiff (those hling the lawsuit) must establish standing. Standing is indicated by suffering a loss of some sort, be it monetary or otherwise. The

judge found that MAPPS and the other plaintiffS had nOt established sufficienr standing to suppOrt the lawsuir. Alrhough there ate opportunities to appeal the judge's decision, the judge's ruling appears to be strong enough

that an appeal was considered unlikely. There was great uncertainty in determining how a suc

cessful MAPPS lawsuit would have impacted the GIS community and many community members expressed relief

that the lawsuit came to an end. Doubtlessly. there will be similar disputes and uncertainty in the future regarding

licensing that involves (he court system. Although (his

represents a US example of the conAict that has ar isen

with the widespread use of GIS, you could reasonably envision this happening in other countries with an es(ab

lished land records system and regulations rela(ed to engi

neering and land surveying practices.

261


professional engineerin g associations filed a lawsuit

aga inst the US governmenr. The lawsuit W<1S filed on behalf of the Federal Acquisirion Regulation (FAR) Council and is referred to as the MAPPS lawsuit. The actual title for tbe lawsuit is MAPPS et al. v. United States of Am(ricft, The lawsuit requests changes in interpretation of the 1972 Brooks Act (US Public Law 92-582) which is intended (0 direct federal government policy in selecti ng providers to perform archi tectural. engineering,

and related serv ices. More specifically, the lawsui t seeks

[0 modify how rhe selection process for government coo

traccors is evaluated as it relates to mapping acriviries.

The resultS of the lawswr may impacr how some US federal agencies award government contractS. The MAPrs lawsuit has been a considerable concern for many GJS

oriented organizations and has once again fueled rhe

debate over the definicion of acdvities that can be con

sidered surveying and engineering, which acriviries inRu

ence public welfare and safery. and the appropriate geospatial cerrificarion and licensing requiremems. The

participation of prominenr o rganizations on both sides

of rhe lawsuit is evidence thal rhe MAPPS lawsui t is nor

a rriviallegal exerc.ise.

Plaintiffs of the MAl'l'S lawsuit included rhe American Sociery of Civil Engineers (ASCE). National Sociery of Professional Engineers (NSPE). and Council on Federal Procurement of Architectural and Engineering Services

(COFPAES). The Brooks Act established that price alone should nO( be used in [he awarding of governmenr con

rracts to individuals o r firms. Instead, Qualificadons

Based Selecrion (QBS) is to be used. which involves evaluating professional qualjficadons and expe rience. in

addirion to seeking a 'fair and reasonable' cosr [0 rhe gov

ernment , in awa rding contracts. Negotiarions for an

acceptable price should begin with the most qualified firm. If negotiations fail , then negoriarions should pro

ceed with the second most qualified firm . and so on . The

FAR Council applies the rules and laws related ro the Brooks Act, and is supposed ro ensure mar the intentions

of rhe acr are upheld. Although rhe Brooks Acr includes 'surveyi ng and mapping' among me list of architectural

and engineering services thar it is intended to cover, fed

eral agencies have nor been cons istent in their interpreta

rion and adherence to stated protocols. The MAl'l'S lawsu it seeks [0 compel the FAR counci l ro more rigorously

inrerpre[ and apply rhe Brooks Act in the selecrion of

contractors for surveying and mapping services. In states

where licensed surveyors or engi neers are required ro per-

form surveying and mapping activicies, the lawsuit con

tends that rhe FAR council should di rect that licensed professionals be selected for government contracts invo lv

ing surveying and mapping. The implicarions of rhe MAPPS lawsuit afe then potentially significanr given thar

many states do nO[ draw a clear distinction berween sur

veying and mapp ing in their laws and rules that govern

surveYing. The American Association of Geographers (MG).

GISCI. Geosparial Information & Technology Associarion (GITA). VCGIS. and VRISA jointly submitted a briefing (0 the US District Court in Virginia

(Alexandria Division) thaL opposed the MAPl'S lawsuit (Association of American Geographers. 2007). The brief stared that a successful lawsuit could cause serious con

cern nm only for rhe GIS commu niry but also fo r mher related act ivities and professions. Other related activities

included GPS data collection, Internet mapping, geospa

tial analysis, remote sensing, academic research that

involved mapping, and the broad activities encompassed

wirhin cartography. The briefi ng claimed thar the lawsuit's impact would greatly affect many activities and

industries that involve or rely on mapping activities and

informacion .

The US Districr Court ruled againsr MAPl'S in June 2007 and issued a su mmary judgment in favor of the US Government. The judge in the case srated that the MAPPS plainrifFs failed to 'establish that an injury in faCt was suffered by rhe individual surveyors or their firms'. In order for a case to be tried. a plaintiff (those filing the lawsuit) must establish standing. Standing is indicared by suffering a loss of some sort, be it monetary or otherwise. The

judge found rhar MAPl'S and the other plaintiffS had nor established sufficienr standing to suppOrt the lawsuit.

Alrhough rhere are opportunities to appeal rhe judge's decision, rhe judge's ruling appears to be strong enough

tllar an appeal was considered unlikely. There was great uncenainry in determining how a suc

cessful MAPl'S lawsuir would have impacted the GIS commun iry and many community members expressed relief

that ti,e lawsuir came ro an end. Doubtlessly. there will be s imilar dispures and uncertainty in the future regarding

licensing that involves the coun system. Although this

represents a US example of the conRicr that has arisen

with the widespread use of GIS. you could reaso nably envision this happening in other counrries with an estab

lished land records system and regulations relared ro engineering and land surveying practices.

Summary

In some circumstances, certification and licensing may be

necessary for those involved in (he developmem and man

agement of GIS databases. to ensure that minimum stan

dards of competency exist and (hat standards 3re being mer in the development. maintenance. and applicacion of

G IS databases. Although many GIS activities related to namral resource managemem may have no bearing on public welfare and safery, some GIS acriviries result in maps or mapped data being sold or made available to the public. and thus may have direct or indirect implicacions on public welfare and safety. GIS has evolved into its own discipline, and is being integrated with other fields of

Applications

17.1. Licensing. Assume that the state or province in

which YOll work is considering the development of a

licensing board to oversee the licensing of GIS users. and

to regulate the use and managemenr of GIS databases

related to your field of namral resource management.

a) What benefits might a licensing board provide for professionals engaged in GIS activities?

b) What disadvantages for GIS users might result from the developmenr of a licensing board?

c) Describe three key dimensions of the licensing

process that the board should develop.

17.2. GIS certification programs. The owners of the Brown Tract have recently become aware that GIS is used

extensively in the management of the fo rese. They are

concerned about the credibility of their land management

team, and have suggested that all employees obtain GIS

certification .

a) What are the potential benefits of GIS certification for GIS users?

b) What are the potential drawbacks of GIS certification for GIS users?

References

Association of American Geographers (MG). (2007) . Amicus Curiae Brief of the Association of American Geographus, GIS Cutification InstituU, Geospatial


study. Guidelines should be developed for determining the experience. educarionaJ background. professional conduct, and cominuing education that defines (he qual ifica

[ions appropriate for those disciplines. Guidelines should

also be developed to define the extent [Q which persons

who are qualified within disciplines can appropriately

develop, manage, and use certain GIS databases. Until a

national certification or licensing program for GIS gains credibility and respect within society, GIS users in namra!

resource fields will find themselves struggling with local or national regularory groups. the legal system, and other

professions for control of GIS activities.

c) What elements would be necessary in order for a GIS certification to be more widely recognized and

respected within society?

17.3. NeEES Mood Law. Does the NeEES Model Law process seem like a reasonable or rational approach to

clarifYing the issue of G IS licensing? Why or why not?

17.4. The need for licensing or certification. Identi fY three GIS or GIS-related activities that could affect public

welfare. and that might suggest that those developing.

maintaining. or using the supporting data should be

licensed or certified.

17.5. GIS community response to certification and

licensing issues. Many professional (foresters. wildlife

biologists. hydrologists. engineers. etc.) working in nat

ural resource management currendy use GIS to ass ist in

making management decisions. Why might they be concerned about the issue of GIS certification and

licensing?

Information 6- Technology Association, University Consortium for Geographic Information Science, and Urban and Regionallnfonnation Systems Association in

262

252 Part 3 Contemporal)' Issues in GIS

opposition to plaintiffs notion for summary judgmt nt. Retrieved on June 6. 2007. from hnp:llwww.aag.org/ Donatellinks.html.

American Congress on Surveying and Mapping (ACSM). (I998). Should surveyors supervise GIS? A CSM

Bulletin. NovemberiDeumber. 26--3 I. American Congress on Surveying and Mapping (ACSM).

American Society of Civil Engineers-Geomatics Division. American Society for Photogrammcrry and Remote Sensing, Management Association for Private Phorogrammecric Surveyors, National Sociery of Professional Surveyors. National States Geograph ic Information Council. & Urban and Regional Information System Association. (2001 ). GIS/LIS addendum to rhe repan of the task Force on the

NCEES Model Law for surveying. Suroeying and Land Infonnation Syswm. 61(1). 24-34.

American Society for PhotOgrammerry & Remote Sensing (ASPRS). 2006. Certification and recertification guidelines for the ASPRS certification program. Bethesda, MD: American Society for Photogrammetry &

Remote Sensing. Retrieved June 7. 2007. from h rtp:l lwww.asprs.org / membership/cenific3rion l certi ficat ion_gu i de lines. h tm I#Certi fied_M a p pin l\Scientist_GIS-LIS.

DiBiase. D .• DeMers. M .• Johnson. A .• Kemp. K .• Luck. A .• Plewe. B .• & Wentz. E. (Eds.). (2006). The geographic information sciena and technology body of knowledge. Washington. DC: University Consortium for Geographic Information Science. Associarion of American Geographers.

Doyle. R. (2007). By the numbers: license to work. Scientific American. February 9. 28.

Gibson . D.W. (1999). Conversion is out. measurement is in-are we beginning the surveying and mapping

era of GIS? Professional SlIroeyor. 19(7). 14- 18. GIS Certification Insritute. (2007) . Are you a GIS practi

tioner or a GIS professional? Retrieved October 5. 2007. from Imp:llwww.gisci.orgl.

Government in Canada. (2007). Types of work. Retrieved October 5. 2007. from hnp:llwww.goingtocanada.gc. calT ypes_oC work_in_ Canada-en.htm.

Huxhold. W.E. (2002). GIS professionals-get a profession! Ceospatial Solutions. 12(2). 58.

Joffe. B.A. (2001). Surveyors and GIS professional reach accord. Suroeying and Land Information Systems. 61(1).

35-6. Kleiner. M. (2000). Occupational Licensing. journal of

Economic Perspectives. 14(4). 189-202. Korte. G .B. (1999) . The current controversy: GIS and

land surveying legislation. Part I . Point of Beginning. 24(6). 70-3.

Longley. P.A .• Goodch ild. M.F .• Magui re. D.J .• & Rhind. D .W . (2005). Ceographic information systems and science (2nd ed.). Ch ichester. England: John Wiley & Sons.

National Council of Examine rs for Engineering and Surveying (NCEES). (2006). Model law. Clemson. SC:

NCEES. National Center for Geographic Information & Analysis

(NCGIA). (2000). The NCGIA core curriculum in GIScience. Sama Barbara. CA: Nationa l Center for Geographic Information & Analysis. Retrieved June 5. 2007. from http://www.ncgia.ucsb.edu/education/ curricula/gisccl.

Thurow. G .• & Frank. S. (200 I). Coming to terms with the Model Law: The search for a definition of surveying in New Mexico. Surveying and Land Information Systems. 61(1).39-43.

263


opposition to plaintiffs 11otioll for mmmary jJldgm~llt. Retrieved on June 6, 2007, from Imp:llwww.aag.orgl Donatellinks.html.

American Congress on Surveying and Mapping (ACSM). (t998). Should surveyors supervise GIS? ACSM Bull,tin, NovnnberID,wnber, 26--3 1.

American Congress on Surveying and Mapping (ACSM), American Society of Civil Engineers-Geomatics Division, American Sociery for Photogrammeuy and Remme Sensing, Managemem Association for Private Phocogrammeuic Surveyors, Narional Sociery of Professional Surveyors. National States Geograph ic Information Council, & Urban and Regional Information System Association. (200 1). GIS/LIS addendum CO the report of the task force on the NCEES Model Law for surveying. Surveying and Land Information Sysm"" 61(1), 24-34.

American Society for Phorogrammerry & Remote Sensing (ASPRS). 2006. Certification and reunification guid,lines for Ihe ASPRS certification program. Bethesda, MD: American Sociery for Photogrammerry & Remote Sensing. Retrieved June 7, 2007, from http://www .aspes.o rgl mem beesh i pi cen i ftcat ion I cert i fica t io n_gu idel i nes. h tm I #Certi f1ed_M a p pin &Scientist_G IS-LIS.

DiBiase, D., DeMers, M. , Johnson, A. , Kemp, K., Luck, A., Plewe, B., & Wentz, E. (Eels.). (2006). TIlt gtographic information science and technology body of knowledge. WashingtOn, DC: University Consonium for Geographic Informatio n Science, Associadon of American Geographers.

Doyle, R. (2 007). By [he numbers: license to work. Scientific American, hbruary 9, 28.

Gibson , D.W. (1999) . Conversion is OUt, measurement is in-are we beginning the surveying and mapping

era of GIS? Proftrsional SlIrlltyor, 19(7), 14-18.

GIS Certification Institute. (2007). A" JOu a GIS practitioner or a GIS professional? Retrieved October 5, 2007, from Imp:llwww.gisci.org/.

Government ill Canada. (2007). Types of work. Retrieved October 5, 2007, from Imp:llwww.goingrocanada.gc. carr ypes_oC work_in_ Canada-en.htm.

Huxhold. W.E. (2002). GIS professionals-get a profession! Geospatial Solutions, 12(2),58.

Joffe, B.A. (200 I). Surveyors and GIS professional reach accord. Surveying and Land Information Systems, 6/(1), 35-6.

Kleiner, M. (2000). Occupational Licensing. journal of Economic Perspectives, 14(4), 189-202.

Korte, G.B. (1999). The current controversy: GIS and land surveying legislation, Part 1. Point of Btginning, 24(6), 70-3.

Longley, P.A., Goodchild, M.F ., Maguire, D.J. , & Rhind, D.W. (2005). Geographic information sysmm and scienet (2 nd ed.). Chichester, England : John Wiley & Sons.

National Council of Examiners for Engineering and Surveying (NCEES). (2006). Mod,l law. Clemson, SC: NCEES.

National Center for Geographic Information & Analysis (NCGIA). (2000). The NCGIA core curriwlum in GIScitl1U. Santa Barbara, CA: National Center for Geographic Information & Analysis. Retrieved June 5, 2007, from htrp:llwww.ncgia.ucsb.eclu/educacion/ curricula/gisccl.

Thurow, G ., & Frank, S. (200 I) . Coming [0 terms with the Model Law: The search for a definition of surveying in New Mexico. Survtyillg and Land Infonnation Systems, 61(1),39-43.

Appendix A

GIS Related Terminology

The following represents a brief treatment of [he termi

nology common to all eyp .. of GIS sofcwa re programs and processes. An attempt has been made to avoid [he varia

tions of definitions that rend [0 be more descriptive of [he

processes related to one (or more) particular GIS software

programs. It is important that natural resou rce profes

sionals gain an understanding of a common, generic lan

guage. The lines of communica[ion becween [hose highly versed in GIS and [hose wi[h a cursoey knowledge of GIS need to be clear when making natural resource manage

ment decisions.

Accuracy: The ability of a measuremenr to describe a

landscape feacuce 's [rue locatio n, size, or condition.

Adjacency: A spadal relacionship indicadng which landscape features share boundaries. or are w ith in cenain

discance of other landscape fearures. For example,

adjacency relationships for timber stands may indicare

which stands (Ouch other stands, allowing one to

model green-up requiremems.

Annotation: Text o r strings of characters and numbers

used co describe landscape feamres on a map.

Arc: A single s[[ing of X,Y coordinates (vercices), [hac when connected, form a line. A single line may con

rain many arcs, bur a single arc may only represem one

line. or parr of a line. Arcs have staning and ending

nodes (Q allow analys is of directional travel , such as in

stream systems, where water emers one end of the arc (the starr-node, or 'From-node') and leaves the other end of [he arc (the end node, or 'To-node).

ASCII: American Scandard Code for Informacion lneer-

change. Many eypes of text fi les are commonly referred to as ASCII files; some comain comma-delimited data

(e.g., I , 12, 3.45, 65 .2, 0.45), ochers coneain spacedelimi[ed data (e.g., e.g., I 12 3.45 65.2 0.45), and still others use other delimiters (0 separate individual

pieces of data. See Comma-dtlimited text filt and Space-dtlimited text file.

Attribute: A characteristic of a landscape feacure. A((rihutes can be represented by characters or num

bers (or a combination ofbo[h), and [hey describe various characteristics oflandscape feamres. For example,

the attributes of a timber stand could include the fol

lowing: stand number (or code). basal area, trees per

acre, vo lume per acre, dominam tree species, and so on. The amibmes of a research plo[ (a poine) could include: smdy eype, insta llacio n date, date last remea

sured, dare of nexr remeasure, etc. See Fi~ld.

Azimuth: A degree of a circle, wi[h North being 0° (or 360°), Easc being 90°, South being 180°, and West being 270°.

Azjmuthal project jon: A projeccion system where the

direct ions fro m a centra l point of origin are all

preserved. Bearing: An angle of 90° or less originating from either

[he North or Somh (and direc[ed towards [he Easc or West). Thus an azimuth of 3530 represems a bearing

ofN7°W. Boolean expression: A rype of expression used in a query

o r computer code that requi res a yes or no respo nse. AND, OR, and NOT are the three most common

Boo lean exp ressions. For example, a query us ing a

264

254 Appendix A GIS Related Terminology

Boolean expression could take this form :

stand_age ~ 50 AND land_allocation = 'Even-Aged'

BASIC computer code using a Boolean expression

would look like th is:

If (stand_age ~ 50) AND (land_allocation = 'Even_aged') Then

End if

Buffering (or buffer): A type of spat ial analysis of proxim i[}', where zones of a given distance are generated

around selected landscape feat ures . The result of a

buffering operation is one o r more polygons that rep

resem (he area within a specific distance (fixed. or variable, as defined through an attribute field) around landscape features.

Buffer rone: A set of one or more polygons that represent [he area within a specific distance around landscape

feamces. Cartesian coordinate system: A system that allows one

[Q locate any point on a planar surface divided by a set

of grid lines. Cen: see Grid cell. Character: A single attribute describing a landscape fea

ru re, such as a letter, number, or special symbol. o r a

type of data that indicates the attribute should be considered a piece of text (even though numbers and spe

cial symbols may be val id attributes) . Clipping process: The process of extracting from one

GIS database only those landscape features within the bounds of another GIS database (which could contain a single polygon or a set of polygons) . This is an action that essentially acts like a cookie-currer.

Column: A set of cells in a spreadsheet or database that

are verr ica lly aligned, usually representing a single

anribure of every fearure (reco rd) in the database.

Columns could contain column headers, or terms that

describe the data in the column, or in spreadsheets,

simply be represented by characters such as A, B, C,

etc. Often called ' Fields'; see Field. Related to

'Records'; see Ruord. Combine process: A process of eliminating the shared

edges or intersections between two landscape features. Comma-delimited text file: A text file created in a word

processing system, a text editor, spreadsheet, or database, and saved in a format where commas separate

items. The fo llowing, for example, could indicate

hab itat suitab ili ty index va lues for spec ific timber

stand polygons, with the first item of each line identifying the polygon, and the second item listing the habitat suitability index:

1,0.657 2,0.433 3,0.298

Completeness: A desc ription of the types and extent of landscape fearu res included in a GIS database, and

conversely, those that are omirred.

Conformal projection: A projection system where angles

on the Earth's surface are represented by approxi

mately the same angles on a map. thus the angles related to map fearures are preserved. Therefore. the

scale around any single poinr on a map us ing this pro

jection system is the same in every direction .

Conic projection: A projection system where a cone is

positioned so that it cuts through the Eanh's surface at

one lat irude, and comes Out at another (usua lly the

equator), and mapped features are projected OntO the

cone's surface, based on lines radiating outward from

the center of the Earth. Contour interval: The venica l distance that distingu ishes

neighboring contour lines .

Contour lines: Lines that are connected, and represent

locations of equal elevat ion.

Cy lindrical projection: A project io n system where

mapped features are projected onto a cylinder. then the cylinder is unrolled and the map becomes a planar

surface. Database: A collection of information that is managed

and stored as a single enticy. A spatial database in

cludes information regarding the spatial coordinates

of all of the landscape featu res in the database, as well as information regarding the attributes of each land

scape feature, usua lly in a tabular (spreadsheet) form . Database conversion: The translation of a database from

o ne forma t to ano the r. Fo r example, to conven

ArcView shapefi les to Maplnfo tables would requi re a database conversion.

Datum: A mathematical represenration of the Earth's su r

face fo rmi ng a control surface upon wh ich an ellipsoid

and other location data are referenced.

Destination table: One of twO tables used in a Join operation, the one where the information resides after the join operat ion. In (he case of linking {WO database

tables together, it is the table that is active just before

they are linked. 265


Boolean expression could take this fo rm:

stand_age ~ 50 AND land_allocation = 'Even-Aged'

BASIC computer code using a Boolean exp ression

wou ld look like this:

I f (stand_age ~ 50) AN D (land_allocation = ' Even_aged') Then

End if

Buffering (or buffer): A type of spatial analysis of proximity , where zones of a given distance are generated

around selected landscape features . The result of a

buffering operation is one o r more polygons [hat rep~

resem the area within a specific distance (fixed. or v'dri

able, as defined through an amibute field) around la ndscape featu res.

Buffer woe: A set of one o r more polygons that represenT rhe area within a specific distance around landscape

features. Cartesian coordinate system: A system [hat allows one

(Q locate any point on a planar surface divided by a set

of grid lines. Cell: see Grid a ll. Character: A single acrr ibure describing a landscape fea

rure, such as a le[[cr, number, or special symbol, or a

type of data that indicates the amibute should be considered a piece o f text (even though numbers and spe

cial symbols may be valid attributes) . Clipping process: The process of extracting from one

GIS database only those landscape features within the bounds of another GIS database (wh ich could contain a single polygon or a set of polygons) . This is an act ion thal essenrialJy acts like a cookie-currer.

Column: A set of cells in a spreadsheet or database that are vertica lly aligned , usually represeming a s ingle

anribure o f every featu re (record) in the database.

Columns could comain col umn headers, o r terms rhat

describe the data in the column, or in spreadsheers,

s imply be represeIHed by characters such as A, B, C,

etc. Often called ' Fields'; see Field. Related to 'Records'; see Rtcord.

Combine process: A process of eliminating the shared edges or inrersections between rwo landscape features.

Comma-delimited text file: A text me created in a word processing system, a {ext ediror, spreadsheer, o r database, and saved ina formar where commas sepa.rare

irems. The fo llowing, for example, could indicate

habitat suitab ili ty index va lues fo r specific timber stand polygons, with [he firsr item of each line identifying the polygon , and the second item listing the habitat sui tab il ity index:

1,0.657 2,0.433 3,0.298

Completeness: A description of the eypes and extent of landscape features included in a GIS database, and conversely, those that are omitted .

Conformal projection: A projecrion system where angles

o n the Earth's surface are represented by approxi

mately the same angles on a map, thus the angles related to map features are preserved. Therefore. the

scale around any single poinr on a map using this pro

jection system is the same in every direction .

Conic projection: A projection system where a cone is

positioned so that it cutS through the Earth's surface at

one lat itude, and comes o ut at another (usually the

equator) , and mapped features are projected OntO the cone's surface. based o n lines radiating ourward fro m

the cencer of the Earrh.

Contour interval: The verrical distance that distinguishes

neighboring conrour lines.

Contour lines: Lines (hat are connecred. and represent

locat ions o f equal elevation.

Cylindrical projection: A pro jection system where mapped featu res are projected onco a cylinder, then the cylinder is unrolled and the map becomes a planar surface.

Database: A collection of info rmat ion that is managed

and stored as a single entity. A spatial database includes information regarding the spatial coo rdinates of all of the landscape features in the database, as well as information regarding the atuibutes of each land

scape fea ture, usually in a tabular (spreadsheer) form. Database conversion: The translarion of a darabase from

one format to another. For exa mple, to co nvert

ArcView shapefiles to Mapln fa tables would require a database conversion.

Datum: A mathemat ical represenration of the Earth's su r

face forming a control surface upo n which an ellipsoid

and other location data are referenced .

Destination table: One of twO tables used in a Join operation, [he one where [he information resides after che

join operarion. In the case of linking twO darabase rabies together. it is ~e rable [hat is active JUSt before

they are linked.

Digital elevation model (DEM): A GIS database formed offeamres (rypically regularly-spaced as in a grid) [hac contain X.Y as well as Z (elevation) coordinates. Usually i[ represents a [opographic da[abase.

Digital orthophotograph: An image. usually a scanned phocograph. [hac has had removed some of [he displacemenc normally caused during (he aerial phoco

graphic process (tilt. terrain relief) . Digital raster graphics (DRGs): Digi[ally scanned repre

sen[ations of USGS 7.5 Minu<e Series Quadrangle maps.

Digitize (digitizing): To record the X.Y coordina[os of landscape feamres in a compucer file or system as dig

ital data. Digitizing can occur using a digitizing tablet, where maps are raped down, reference points are noted, and featu res (paines, lines, or polygons) are then traced, and their locations in space are known with some level of precision and accuracy. A looser form of digitizing can occur when using the 'heads-up'

technique, where you lise your compuccr's mouse co delineate landscape features on a computer screen, perhaps wi[h a digital orthophocograph as a backdrop. The heads-up cechnique is quicker [han regular digitizing. but usually comes with a COSt reflected in lower

precision or accuracy. or both. Dissolve: To remove the boundaries (arc, lines) between

adjacenc polygons. keying on [he fact [hat some of the polygons have the same value for some 3nrihure. thus [hey should be logically combined.

Dynamic segmentation: A vector data analysis process that cencers on the use of line segments. and attempts to link a nerwork oflines. based on a common attribute, so mat the lines 3re grouped or joined into cate

gories of interest. Easting: A measure of distance east of a coordinate sys

tem 's origin.

Editing: The process of modifying either the spatial shape or location of landscape features, o r the tabular data

that describes the attributes of each landscape feamre.

Elevation contours: Lines that indicate vertical elevation distances. or changes in elevation, across a landscape.

Ellipsoid: A spheroidal figure used co describe the shape of [he Ea"h.

Equal area projection: A projection system where the Earth's features are represented on a map using a constant scaling factor. thus the area of land features is

preserved. Erasing process: The process of extracting from one GIS

database only those landscape features outside the


bounds of another GIS da[abase (which could comain a single polygon or a ser of polygons). This is an action that essentially acts like a cookie-cutter with objects outside the cookie-cutter being retained.

Extent: The limits of the locations of all landscape features in a GIS database. Coordinates are used to define the lower-left and upper-right corners of a rectangular area tha[ would include all of the landscape feamres.

Field: As in a[[[ibme field. a column in a tabular da[abase that represents some characteristic of the landscape features in that database. For example. in a polygon database representing timber stands. stand volume. trees per acre, basal area, and dominam species type could all be fields in a [abular database. In an owl location da[abase. a field could be developed co represenc [he firs< sigh[ing. lasc sigh[ing. and number of owls ar certain points on the landscape.

File Transfer Protocol (FTP): A widely used method of transferring data over the Internet, allowing one to transfer computer files to other remote computers, or to download computer fi les from a remore computer.

Fixed-width buffers: Buffers tha[ do no[ vary in size and are applied uniformly co all landscape fea[ures .

Flattening ratio: A ratio used to describe the difference berween the shape of the Earth and a perfecc sphere. described by the relationship (a-b)/a. where a is the equatorial (or semi-major) radius and b is the polar (or semi-minor) radius of [he Earth.

Font: The rype of <ext sryle being used. such as Times New Roman, Arial. or Cour ier. l[ may also represem rypes of symbols commonly used in word processing or graphics programs.

From-node: One of the twO end nodes of a line or arc, the first one of the twO that was digitized. The mher is the to-node.

Geodesy: An area of mathematics that involves determining the precise shape and size of Earth features, as well as positions of features on the Earth's surface.

Geographic Information Science (GIScience) : The identification and study of issues that are related to

GIS use, affect its implementation. and that arise from ics applica[ion (Goodchild. 1992).

Geographic Information System (GIS): A syscem designed to capture, store, edit, manipulate. ana lyze. display, and export data related to geographic features, and includes not only the hardware and software necessary to accomplish these tasks, but also the databases acquired or developed. and the people performing the casks.

266

D igital elevat ion model (DEM): A GIS database formed of fea m res (typically regularly-spaced as in a grid) that contain X.Y as well as Z (elevation) coordinates.

Usually it represents a topographic database. Digital orthophotograph: An image. usually a scanned

photograph. [hat has had removed some of me displacemem normally caused during the aerial photographic process (tilt, terra in relief) .

Digital raster graphics (DRGs) : Digitally scanned representations of USGS 7.5 Minute Series Quadrangle maps.

Digitize (digitizing): To record the X.Y coordinates of landscape features in a compmer file or system as digital data. Digitizing can occur using a digitizing tablet. where maps are raped down. reference points are noted, and feacu res (points. lines, or polygons) are rhen traced . and theif locations in space are known with some level of precision and accuracy. A looser form of digitizing can occur when using the 'heads-up' technique. where you use your compmcr's mouse [Q

delineare landscape feat ures on a com purer screen, perhaps with a digital ormophotograph as a backdrop. The heads-up technique is quicker rhan regular digitizing, but usually comes with a COSt reflected in lower precision or accuracy, or both .

Dissolve: To remove me boundaries (a rc. lines) between adjacenr polygons. keying on the fact that some of the polygons have the same value for some attribme, rhus they should be logically combined.

Dynamic segmentation: A vec[Qr data ana.lysis process that centers 011 the use of line segments, and anempts to link a necwork of lines, based on a common attdb

ute , so that the lines are grouped. or joined into categories of inrerest.

Easting: A measu re of distance east of a coordinate sysrem 's origin.

Editing: The process of modilYing either [he spatial shape or locarion of landscape fearures. or me tabular data ma[ describes the attributes of each landscape fearure.

Elevation contours: Lines that indicate vertical elevation distances. or changes in elevation, across a landscape.

Ellipsoid: A spheroidal figure used ro describe me shape of me Earth.

Equal area projection: A projection system where the Earth's features are represented on a map using a con

scant scaling factor. thus (he area of land features is preserved.

Erasing p rocess: The process of exrracting from one GIS database only those landscape features outside [he


bounds of anomer GIS darabase (which could contain a single polygon or a ser of polygons). T his is an action rhat essemiaJly acts like a cookie-curter with objects oucsicle me cookie-cutter being retained.

Extent: The limits of the locations of all landscape features in a GIS database. Coordinates are used [0 define the lower-left and upper-right corners of a rectangular area mat would include all of the landscape fearures.

Field: As in amibure field. a column in a tabular database that represents some characteristic of the landscape features in mat database. For example. in a polygon database representing timber stands, stand volume, trees per acre, basal area, and dominant species type could all be fields in a tabular database. In an owllocation database, a field could be developed to represent the first sighting. last sighting. a.nd number of owls ar certai n points on the landscape.

File T ransfer Protocol (ITP): A widely used memod of transferring data over [he: Internct, allowing one [0

transfer compmer files to orner remote computers, or

[0 download computer fi les fro m a remore computer. Fixed-width buffers: Buffers [hat do nOI vary in size and

are applied uniformly to all landscape fearures. Flattening ratio: A ratio used to describe [he difference

between the shape of [he Earth and a perfect sphere. described by the relationship (a-b) /a. where a is rhe equatorial (or semi-major) radius and b is the polar (or semi-minor) radius of the Earm.

Font: The rype of text style being used. such as Times New Roman, Arial , or Courie r . lr may also represent rypes of symbols commonly used in word processing or graphics programs.

From-node: One of the twO end nodes of a Itne or arc. the first one of the (wo (hat was digitized. The other is the to-node.

Geodesy: An area of mathematics thar involves determ ining the precise shape and size of Earth features. as well as posidons of features on the Earth's surface.

Geographic Information Science (GIScience) : The identification and study of issues that are related to GIS use, affect its implementat ion. and that arise from irs appl ication (Goodchild. 1992).

Geograpb ic Information System (GIS): A system designed to captu re, store, edit, manipulate, analyze.

display. and export data reiated to geograph ic featu res. and includes nor only the hardware and sofTWare necessary to accomplish these tasks. but also the databases acquired or developed. and the people performing the tasks.


Geoid: An irregular shape that approximates Earth 's mean sea level perpendicular to the forces of gravity.

Global Positioning System (GPS): A system allowi ng one to locate their position on the Earth's surface by receiving signals from satell ites. The signals are used (Q

calculate a position based on triiarcration. and perhaps differemial correction, processes.

Graticule: The collection of meridians and parallels superimposed on the Earth's surface.

Grid: A geographic database made up of raster features, commonly called grid cells. Can also refer to a graticule.

Grid cell: The smallest unit in a raster database. or pixel.

Usually these are represemed by squaresi however, any regular form that fully covers an area (square. rectangle, triangle, hexagon, polygon, etc.) could be considered a grid cell.

Gross error: Sometimes referred to as human error, it is a blunder or other mistake made somewhere in {he data collection, map creation, or editing processes.

Heads-up digitizing: A method of developing vector GIS

databases (of points, lines, or polygons) where a user

creates landscape features using the com puter's mouse, generally with a rasre r image as a backdrop SO as to draw (or point out) those landscape featu res that are

important. For example, displaying a digital orthophotograph as a backdrop, one might use the computer's mouse to draw roads, or timber stands. This method of dara development is quicker than uaditional digitizing, bur usually less accurate, and does nor require a digitizing table.

Identity process: The acquisition of information within a GIS database concern ing the area represented by another GIS database. Here, like with an intersect process, one GIS database is physically laid onto another, yet the resulting third (new) GIS database is defined by the area of coverage of one of (he input GIS

databases. Intersect process: The acquisition of information con

cerning the overlapping areas of two GIS databases. In uti lizing an intersect p rocess, a third, new GIS database will be created that consists of only those areas where the two original GIS databases overlap, no more, no less.

Intervisibility: A descript ion (from each unit of land's perspective) of the number of viewpo ints that can be seen from a land unit.

Intranet: A network of computers similar to the Internet, except access is limited to a set of authorized people (usually internal to an organ ization).

Join process: A process designed to ei ther (1) associate an external table (wi th no landscape features) to a table of a spatial database using a join item, or (2) to associate

features from one spatial database to another spada) database, using point-in-polygon or nearest neighbor (echniques. When joining tables, the type of join can be a one-to-one join, a one-to-many join, a many-too ne join, or a many-to-few join.

Join item: A field (column) in a tabular database that contains similar information as a field (column) in a

second tabular database. For example, a field in one database may be called 's tand_number' and contain

timber stand numbers, while a field in a second database may be called 'stand' , and again contain timber

stand numbers. While the field names do not have to be the same, the informacion within each field must be similar, such as in this example. where both contain timber stand numbers. The join would then link the

two databases using timber stand number as rhe common element. so data for stand 1234 from one database will be matched with data for stand 1234 in the

second database. Labels: Like annotation, which is text or strings of char

acters and numbers, these are used to describe land

scape features on a map. They usually arise from some anribure field, or column, in the spat ial database's tabular database. where for each landscape feature, a description of that attr ibute (such as srand number,

volume per ac re, trail eype, road eype, etc.) exiscs. Legend: That part of a physical map that contains the

reference informatio n necessary to inrerprer the colors of polygons, and the styles and colors of lines and pOlms.

Line: A str ing ofX,Y coordinares (vertices) that make up a cominuous linear feature. A single line feature (a

road) can comain many small arcs thar are topologically linked at their nodes (end-points), since each arc stans and ends with a node.

Link A line that connects poims, as defined by nodes and vertices (somerimes caIled an arc) .

Link process: A process designed to ei ther assoc iate land

scape features from one database to another database. This process allows the selection and view of landscape features from database, and the inspection of associated (linked) data in the other database. Sometimes called a relate.

Logical consistency: A descrip rion of how well the relat ionships of difFerenr types of data fit together with in a system.

267


Geoid: An irregular shape mar approximates Earth's mean sea level perpendicular co the forces of gravicy.

Global Positioning System (GPS): A system allowing

one to locate their position on the Earth's su rface by receiving signals from satellites. T he signals are used to

calculate a posicion based on rrjlarerarion. and perhaps differential correction, processes.

Graticule: The collecrion of meridians and parallels

superimposed on rhe Ea"h's surface. Grid: A geographic database made up of rasrer features,

commonly called grid cells. Can also refer ro a graticule. Grid cell: The smallest unit in a ras ter darabasc. or pixel.

Usually these are represented by squaresi however. any regular fo rm that fully covers an area (square. rectangle, "iangle, hexagon, polygon, erc.) could be considered a grid cell.

Gross error: Sometimes referred [0 as human error, it is a blunder or mher mistake made somewhere in the dara collcedon. map creation, or editing processes.

Heads-up digitizing: A merhod of developing veccor GIS

darabases (of poines, lines, or polygons) where a user

creates landscape features using the com purer's mouse, generally with a raster image as a backdrop so as to draw (or poim our) rhose landscape fearures rhar are

imporram. For example, displaying a digital orrhophotograph as a backdrop, one might use the com purer's mouse to draw roads, or timber stands. T his method of dar a development is quicker than rradirionaJ d.igitiziog, bur usually less accu rate, and does not require a digitizing rable.

Identity process: The acquisition of information within a GIS database concern ing the area represented by another GIS database. Here, like with an incersecc process, one GIS database is physically laid onco anorher, yer rhe resulting ulird (new) GIS database is defined by rhe area of coverage of one of the inpm GIS

darabases.

Intersect process: The acquisition of information concerning rhe overlapping areas of rwo GIS darabases. In

mil iz ing an intersect process, a thi rd. new GIS database wi ll be created chat consists of only those areas wbere the twO original GI databases overlap, no more, no less.

Intervisibility: A descriprion (from each unir of land's perspective) of the number of viewpoints that can be seen from a land unit.

Intranet: A network of compucers similar ro the [nterner, except access is limited to a set of 3urhoriz.ed people (usually inrernal ro an organization).

Join process: A process designed co eirher ( I) associare an exrernal rable (with no landscape feamres) to a rable of a spatial database using a join item, or (2) to associate fearu res from one spatial database to another spatial database. usi ng poinr-in-polygon or nearest neighbor rechniques. When joining ,ables, the 'Ype of join can be a one-repone join. a one-tQ-many join, a manY-(Qone join) or a many-ro-few join .

Join item: A field (column) in a tabular darabase rhar contains similar information as a field (column) in a seco nd tabular database. For example) a field in one database may be called 'stand_number' and contain timber stand numbers, while a field in a second database may be called 'srand', and again contain rimber

srand numbers. While ,he field names do not have to be [he same, the jnformation within each field must be similar, such as in this example. where both contain rimber srand numbers. The join would rhen link rhe

twO databases using timber stand number as the common element. so data for stand 1234 from one darabase will be marched wirh dara for srand 1234 in rhe

second database. Labels: Like annotation) which is text or strings of char

acters and numbers, these are used [Q describe landscape karu res on a map. They usually arise from some anribme field. or column, in rhe spatial database's rabular database. where for each landscape feature. a description of that arrr ibute (such as stand number, volume per acre, trail rype, road eype, etc.) exists.

Legend: TIl.r parr of a physical map thar con rains rhe reference information necessary to interpret [he colors of polygons, and rhe sryles and colors of lines and poims.

Line: A String ofX,Y coordinares (vertices) rhat make up

a cont inuous linear feawre. A si ngle line feature (a road) can contain many small arcs that are ropologically linked ar rheir nodes (end-pointS), since each arc

starts and ends with a node. Linlc A line thar connects poims, as defined by nodes

and vertices (somerimes called an arc). Link process: A process designed [0 eimer assoc iare land

scape features from one database to another database. This process allows the selection and view of landscape feawres from database. and the inspection of associared {linked} dara in rhe other database. Somerimes called a relare.

Logical consistency: A description of how well the relarionships of different 'Ypes of dara fit cogerher wir;,in a system.

Logical operators: The operarors 'and', 'or', and 'not'

that allowing one to develop a complex query without having ro perform several single criterion queries in sequence.

Map scale: T he ratio of map distance £0 ground distance represented on the map. For example, I :250,000 map scale indicates that I un it (inch, perhaps) on a map represents 250,000 units (inches) on the ground represented by the map.

Merge process: A process that creates a single G IS database from a set (or subset) of one or more previouslydeveloped G IS databases. Point, line, and polygon databases can be merged together, however, database types are generally mixed. The resulting merged GIS

database may comain landscape feacures that overlap. Metadata: Data that summarize the characteristics of

databases (or 'data about data). Network: A collection of lines connected via their nodes,

and represeming possible paths from one location to anomer. For example, a stream network would include all of the streams, where the smaller headwater streams

connect to wider streams with more water flow. and so on to rivers, and perhaps the oceans. A road system is another network that is more complex in nacu re. because there may not be a logical Row of traffic from

one road £0 the next. Yet £0 determine optimum paths or alternative roUtes, you would need £0 know which roads connect (Q which orner roads. what types of roads they are, and what restrictions may be placed on them .

Node: One (of two) end-pointS of a line. Northing: A measure of distance nonh of a coordinate

system's origin.

Overlay analysis: The process of analyzing or combining multiple layers of information at one time.

Pan: To slide the viewable image to one side (left, right, up. down, or some combination of these). allowing a view of some portion of the landscape nOt viewable with the previous arrangement. Actively called 'panning,' or 'grabbing' when users are performing this action.

Photogrammetry: T he act of collecting measuremems of landscape features from a image or photograph.

Pixel: The smallest unit in a raster database, or a grid cel l. Formed by combining the twO terms 'picture' and 'element'.

Planar coordinates: X,V coordinates that relate £0. or are positioned on, a planar or horizomal surface.

Plane coordinate system: Rectangular coordinates that reference landscape features as displayed on a flat map of the Earth's surfilce.


Point: A single X,Y coordinate that represents a feamre on the landscape (such as a research plot, culvert, owl nest, or spring). These feamres are usually deemed tOO small (by an organization) £0 represent as a line or polygon.

Polygon: A multi-sided, closed spatial object that has an area. Polygons are formed by connecting lines (arcs) until a closed area is formed. They may define the boundaries of timber stands, so ils types, riparian buffers, wildlife habitat, and so on, using a set of logic consistent across the landscape. Squares. triangles, rectangles, and hexagons can be considered polygons. yet the term usually applies to irregularly shaped objects.

Precision: The degree of specificity £0 which a measurement is described. Can also refer to consistency.

Public Land Survey System (PLSS): Established in 1785 by the US Congress as a national system for the measurement and subdividing of public lands. At 24-mile intervals north and south of each baseline, standard parallels are established that extended east and west of the principal meridian. Guide meridians were also established at 24-mile intervals east and west of the principle meridian. The grid of meridians and parallels creates blocks, each nominally 24 miles square.

Townships are created within each block by forming range lines (run ning north and south) and township lines (running easr and west) both at six-mile inter

vals. Each £Ownship is six miles square. Each township is divided into sections, with each section measuring one square mile; there are 36 sections within a £Own

ship. Sections are numbered 1-36 starting at the upper right hand corner of a tOwnship.

Query: To select a subset of landscape features from a la rger set using some selection cri teria. These statementS can include a single piece of logic (stand_age S 40), or multiple pieces of/ogic connected by Boolean operators. For example. suppose you have 10.000 timber stands. and you are interested in those that could be commercially thinned. You could query the GIS

database for those stands of a certa in age, with a query such as (stand_age ~ 30 AND stand_age S 40) . Queries can be rather simple, or quite complex.

Random error: A narural by-product of how one measures and describes landscape featu res. No marter how well maps are developed or data are collected, error will usual ly exist in the representation oflandscape features.

Raster: A data structure based on cells organized in rows and columns. This is a grid-based structure where an entire area is represented by a cell, and a single land-

268

Logical operators: The operarors 'and', 'or', and 'not' rhar allowing one ro develop a complex query wirhour having to perform several single c riterion queries in sequence.

M ap scale: T he ratio of map distance [0 ground disrance

represemed on the map. For example, I :250,000 map scale indicates that I unit (inch, perhaps) on a map represents 250,000 units (inches) on the ground represemed by rhe map.

Merge prOeeM: A process rhat creates a single G IS database from a set (or subset) of one or morc previouslydeveloped GIS databases. Poinr, line, and polygon databases can be merged rogerher. however. database

rypes are generally mixed. The resulting merged GIS dacabase may contain landscape features (hat overlap.

Metadata: Data that summarize the characteristics of databases (or 'data about dara).

Network: A co llection oflines connecred via their nodes,

and representing possible paths from one location to another. For example, a stream ne[Work would include all of the streams, where: the smaller headwater Streams

connect co wider screams with more water Row, and so

on to rivers, and perhaps rhe oceans. A road system is

3.nQ[her network char is more complex in nacu re,

because rhere may not be a logical flow of traffic from one road (Q me next. Yet to determine optimum paths

or alternative routes, you would need to know which

roads connect ro which other roads, what rypes of roads they are, and whar restrictions may be placed on them.

Node: One (of two) end-points of a line. Northing: A measure of diStance norrh of a coordinate

system's origin.

Overlay analysis: The process of analyzing or combining multiple layers of inform ad on at one time.

Pan: To slide rhe viewable image to one side (left, right, up, down. or some combination of these), allowing a

view of some portion of the landscape not viewable wirh the previous arrangement. Actively caHed 'panning,' or 'grabbing' when users are performing (his acrion.

Photogrammetry: The act of collecting mC'.suremenrs of landscape fearures from a image or photograph.

Pixel: The smallest unit in a raSter database, or a grid cel l. Formed by combining the fWO terms 'picture' and

'element' .

Planar coordinates: X,V coordinates that relate co, or are

positioned on, a planar or horizonral surface. Plane coordinate system: Rectangular coordinates that

reference landscape fearures as displayed on a flat map of rhe Earth's surface.


Point: A single X,V coordinate that represenrs a feature

on the landscape (such as a research plot, culvert , owl neSt, or spring). These features are usually deemed roo small (by an organization) co represent as a line or

polygon. Polygon: A multi-sided, closed spatial object thar has an

area. Polygons are formed by connecting lines (arcs) u mil a closed area is formed. They may define the boundaries of timber stands, so il s types, riparian buffers, wildlife habitar, and so on, using a set of logic consistent across the landscape. Squares. triangles. rec

tangles, and hexagons can be considered polygons, yer the rerm usually applies to irregularly shaped objects.

Precision: The degree of specificiry to which a measurement is described. Can also refer to consisrency.

Public Land Survey System (PLSS): Established in 1785 by the US Congress as a na<ional system for rhe measurement and subdividing of public lands. At 24-mile intervals north and south of each basel ine, standard pa rallels are established that extended east and west of the principal meridian. Guide meridians were also

established ar 24-mile imervals east and west of the principle meridian. The grid of meridians and parallels creates blocks, each nominally 24 miles square. Townships are creared wirhin each block by forming range lines (running north and sourh) and townsh ip lines (running east and west) both at sLx-mile inrer~

vals. Each township is six miles square. Each township is divided into sections, with each section measuring

one square mile; there are 36 secdons within a town

ship. Secrions are numbered 1-36 sraning at the

upper right hand corner of a township. Query: To selecr a subset of landscape features from a

la rger set using some selection criteria. These state

ments can include a single piece of logic (stand_age $ 40), or multiple pieces of logic connected by Boolean operators. For example, suppose you have 10,000 timber stands, and you are interested in those chat could

be commercially rhinned. You could query rhe GIS

database for those stands of a cenain age. with a query such as (stand_age ~ 30 AND stand_age $ 40). Queries can be ramer simple, or quite. complex.

Random error: A narural by-product of how one measures and describes landscape fearures . No matter how well maps are developed or data are collected, error wiU

usually exist in the representation oflandscape features. Raste r: A data structure based on cells organized in rows

and columns. T his is a grid-based structure where an

entire area is represented by a cell, and a single land-


seape fearure (rimber stand. road. research plor) can be represcmed by one or more cell .

Rasterization: The process of converti ng veccoc data to

raster daca. usually by scann ing. Record: Each fearure (poin<. line. or polygon) in a GIS

database is represenred by a record in a tabular data

base. A record is a row in the tabular database, thus each row represems a feamce . Associated with each record arc one or morc fields (columns), which conrain the characrcriscics of each feature.

Region : A data structure that consists of vector features

(lines or polygons). yet allowing overlapping areas. Remotely sensed data: Raster dara acquired by a sensor

(camera, satellite) that is some d istance from the land

scape features being sensed. Root mean square error (RMSE): A measure of the error

between a mapped point and its associated nue ground position. Com mo nly used when assess ing spatial accuracy or digitizi ng a map, RMSE measures the posicional error inherent in the registration points on the hardcopy map.

Satellite imagery: Dara eaprured by a remote sensing device housed in a satellite that is positioned above me Earth's surface. Generally, the data consists of values

representing the relative degree of reflectance of electromagnetic energy in cereain wavelength categories (bands). T he imagery is srored as rasrer dara with a spacial resolution that can range from 1 meter to 1 kilometer.

Scale: The relarionship between a map displayed on a computer screen or printed on a type of media (paper, mylar. ere.). and rhe actual physical dimensions of rhe same area. For example, a township (36 square miles,

or 23.040 acres) drawn on a map where I inch on the paper represents 1 mile on the ground, is displayed at a scale where I inch represenrs 63.360 inches. This scale can rhen be expressed as a fraction (I :63360) or a equ ivalence (l" = I mile).

Scanning: The process of excraccing features from a map

or phorograph and sroring reAeered values generally as a raster database. Line fo llowing and text recognition processes are promising methods for converting analog maps to vector GIS databases.

Secant projection: A projection system where the Earth's surface intersects a map surface in marc than one locat ion.

Selection: A set of one or more landscape features (poinrs. lines. or polygons) from a single GIS darabase. chosen based on a Query or by manual methods

(poin<ing and clicking wirh a com purer mouse). The landscape features selected are usually done so for some reason, perhaps to perform some spacial operation on them (such as buffer some selected screams), or to simply visually inspect those landscape fea tures with

a characteristic ofimerest (such as being curious about where the culverts over 20 years old are located in a

forest). Shaded relief map: A map intended to simulare rhe sun

lir and shaded areas of a landscape when assuming rhar the sun is positioned at some location in the sky.

Slivers: Very small polygons thar resul r during overlay operations (union, identity, intersection) or during clipping or erasing processes. Sometimes these occur when common borders are represented differently,

from separate digitizing processes, and sometimes these occur simply as a result of the spatial process that

was anempred. Slope class : The gradien< of a porrion of a landscape. as

descr ibed by a disriner class (e.g .• 0-10 per een<. 11 - 20 per een<. ere.).

Spaee-delimited text file: A rexr file creared in a word processing system, a text ediror, spreadsheet, or darabase, and saved in a format where items are sepa rated

by blank spaces. The following. for example. could indicate habitat suitability index values for specific timber stand polygons, with the first item of each line identifYing rhe polygon. and rhe second item lisring rhe habitar suitability index:

10.657 20.433 30.298

Spatial database (or data): A database containing some information about an area or landscape, the relacionships among the features in the landscape. and perhaps some tabular or attribute (non-spacial) data abom each feature . T hese databases are usually stored in some known coordi nate system, thus each landscape

feature has one or more spat ial coordinates that defi ne where it exists.

Splitting process: A process of erearing mulriple landscape features from a single landscape feature.

Spurious polygons: Small fractions of polygons ereared as a result of a GIS process.

State plane coordinate system: A coordinate system developed in rhe 1930s by rhe US Coast and Geoderie Survey to create a unique set of planar coordinates for each of rhe 50 United Sures.

269


scape feature (timber stand, road, research plOl) can be

represented by one or more cell. Rasterization.: The process of converting veccor data (Q

raster data, usually by scann ing. Record: Each feature (point, line, or polygon) in a GIS

database is represented by a record in a tahu lar data

base. A record is a row in the tabular darabase, thus

each row represents a feacure. Associated with each record are one or more fields (columns), which con

tain (he characteristics of each feature.

Region: A da(3 structure mat consists of vector features

(lines or polygons), yet al lowi ng overlapping areas. Remotely sensed data: Raster data acquired by a sensor

(camera, satellite) that is some distance from the land

scape fearures being sensed. Root mean square error (RMSE): A measure of the error

berween a mapped point and its associated true

ground position. Common ly used when assess ing spacial accuracy or digitizing a map. RMSE measu res d1C

posicional error inherent in the registration poims on

the hardcopy map. SateUite imagery: Data capru red by a remote- sensing

device housed in a satell ite that is positioned above me

Earth's surfuce. Generally, the data consists of values

represeming the relative degree of reAecrance of electromagnetic energy in cenain wavelength categories

(bands). T he imagery is stored as raster data with a spatial resoludon that can range from I meter to I

kilometer.

Scale: The relationsh ip between a map displayed on a

computer screen or primed on a rype of media (paper, mylar, etc.), and the actual physical dimensions of tbe same area. For example, a township (36 square miles ,

or 23,040 acres) drawn on a map where I inch on the paper represems I mile on the ground, is displayed at a scale where I inch represems 63,360 inches. This scale can then be expressed as a fraction (I :63360) or a equivalence (i" = I mile).

Scanning: The process of extracting features from a map

or photograph and storing reAccted values generally as a raster database. Line following and text recognition

processes are promising melhods fo r convening analog maps to vec(Qr GIS databases.

Secant projection: A projection system where the Earth 's surface intersects a map surface in mo re (han one locat ion.

Selection: A set of one o r more landscape fearures (poims, lines, or polygo ns) fro m a single GIS database,

chosen based on a Query or by manual methods

(pointing and clicking with a computer mouse). The landscape features selected are usually done so for some reason, perhaps (0 perform some spatia] opera

tion on them (such as buffer some selected srreams). or

to simply visually inspect those landscape features with

a characteristic of interest (such as being curious about

where the culvens over 20 years old are located in a

fores t). Sbaded relief map: A map intended to sim ulate the slln

li t aod shaded areas of a landscape when assuming thaT the su n is posidoned at some location in the sky.

Slivers: Very small polygons that result during overlay operations (u nion , identi ty. incersecdon) or during

clipping or erasing processes. Sometimes these occur

when common borders are represented differendy,

from sepa rate digitiz ing processes, and sometimes

these occur simply as a result of the spatial process that was attempted.

Slope class: The gradienr of a port ion of a landscape, as described by a distinct class (e.g. , 0-10 per cent, 11 - 20 per cent, etc.).

Space-delimited text file: A text file created in a word processing system, a text edirar, spreadsheet , or data

base, and saved in a format where items are separated

by blank spaces. The fo llowing, for exampl e, could indicate hab irat su i(abili ry index vaJ ues fo r speci fic

timber stand polygons, with the first hem of each line

identifying the polygon, and the second irem listing the habitat suitabil ity index:

10.657 20.433 30.298

Spatial database (or data) : A database containing some information about an area or landscape, the relation

ships among the features in the landscape, and perhaps some tabular or attribute (non-spatial) data about each fea mre. These databases are usually stored in

some known coordinate system, thus each landscape

feature has one or more spat ial coordinates [hat define

where it exists .

Splitting process: A process of creating mult iple landscape features from a single landscape feature.

Spurious polygons: Small fractions of polygons created as a result of a GIS process.

State plane coordinate system: A coordina te system

developed in the 1930s by the US Coast and Geodetic Survey to creare a unique sel of planar coordi nates for

each of the 50 United States.

,

Systematic error: Sometimes referred [0 as instcumencal

error, it is propagated by problems in the processes and [Ools used (0 measure spatial locations or other attribute data.

Tabular data: The data in a GIS database that describe the attributes of each landscape feature. Usually displayed as rows (records) and columns (fields), where each row represents a landscape fearure, and each column repre

sents an attribute of that feature. When displayed, a tabular database often looks as if it is in a spreadsheet.

Tangent projection: A projection system where the Earth's surface (Ouches a map surface at one location

(a tangent). To-node: One of the [wo end nodes of a line or are, the

last one of the twO that was digitized. The other is the from-node.

Topology: An exp ression of the spatial relat ionships among landscape features in a GIS database.

Triangular irregular network (TIN) : A vecto r data model that descri bes the landscape using triangles. Each corner of each triangle is described by a set of values, such as elevation, aspect, and coordinates.

Unjon process: The acquisition of information within

twO GIS databases concerni ng the area represenced by both GIS databases. Here, like with an incersect process, one GIS database is physically laid onto another, yet the resulting th ird (new) GIS database is defined by the area represented by both of the input GIS databases.

Universal Transverse Mercator (lJfM) : The most common coordinate system used in the US, which divides the Eanh into 60 venical zones, each w ne covering 6° oflongitude. The zones are numbered 1- 60 staning at 180· longitude (the international date line) and proceeding eastward.

Update interval: The period of time between the performance of subsequenc update processes on a GIS database.

Update process: The methods used to maintain the cur

rent Status and description of landscape features contained in GIS databases.

References

Goodchild, M.F. 1992. Geographical information science. Internationaljournal o/Geographical information

Spurns. 6(1):31-45.


Variable-widtb buffers: Bu ffe rs that vary in size based on some anribu te of the landscape features being buffered.

Vector: A data structure commonly used to represent points, lines, or polygons. This is a coordinate-based structure (nor a regular grid), which may not enrirely fill an area, and each landscape featu re is represented by X,Y coordinate pairs. Attributes can be associated with each feature (point, line, or polygon).

Vectorization: The process of converting rasrer data to vectOr data.

Verification process: The processes that one would use

to find landscape features or attributes requi ring ed iting. With a verificat ion process, the goal is to ensure that a particular set of data is appropriate (or reasonable, or within some standard)

Vertex (Vertices pl.): One of a set ofX,Y coordinates that delineate an arc or line.

Viewshed analysis: The process of understanding the pan ions of a landscape visible from specific landscape features of interest.

X, Y coordinates: A set of values [hat represent a pain[ in

space, relative to the coordinate system be ing employed. A single X,Y coordinate could represent a point featu re. Lines (arcs) are characterized by a series of X,Y coordinates. Polygons are formed by a collection ofl ines (arcs), thus have many X,Y coordinates to

define their shape and posi tion on a landscape. Z coordinate: A th ird value associated with an X,Y coor

dinate usually indicating the elevation of the paine in space above some reference (such as mean sea level) . X,Y coordinates do not necessarily need to have a Z coo rdinate to be usefu l, whereas Z coordinates need their X,Y associates to be of use.

Zoom: To focus more closely on a smaHer or larger area of a spatial database, or to enlarge or make smaller an area of a spatial database, showing more or less detail. Zoom-in refers to focusing more closely on a portion of a spatial database. Zoom-out refers to focusing less closely on a portion of a spatial database.

270

Appendix B

GIS Related Professional

Organizations and Journals Compi/.d by Rongxia (Tiffany) Li

The following is a list of GIS-related professional organization and peer-reviewed journals. We apologize in advance for any om issions. and will gladly add any other o rganizations or journals to the list as they are brought (0

our attention.

Organizations

American Association for Geodecic Surveying. 6 Mont

gomery Village Avenue . Suire #403 Gaithersburg. MD 20879 USA. (h ttp://www.aagsmo.org/)

American Association of Geographers. 1710 Sixteenth Street NW. Washington. DC 20009-3 198 USA. (http: //www.aag.org)

American Congress on Surveying and Mapping. 6 Montgomery Village Avenue. Suite #403 Gaithersburg. MD 20879 USA. (http://www.acsm.net/)

American Geophysical Union. 2000 Florida Avenue NW. Washington. DC 20009-1277 USA. (http:// www.agu.orgl)

American Plan ning Association. 122 S. Michigan Ave.,

Suite 1600 Chicago. IL 60603 I 1776 Massachuserrs Ave .• NW. Washington. DC 20036-1904 USA. (http://www.planning.orgl)

American Society of Landscape Architects. 636 Eye Sueet. NW. Washington. DC 20001-3736 USA. (http: //www.asla.orgl)

American Sociery for Phomgrammercy & Remote

Sensing. 5410 Grosvenor Lane. Suite 210. Bethesda. MD 20814-2160 USA. (http://www.asprs.orgl)

Association of American Geographers. 17 10 16th Street. NW. Washington. DC 20009-3198 USA (http:// www.aag.org/)

British Cartographic Society. BCS Administration. 12 Elworthy Drive. Wellington. Somerset. T A21 9AT. England. UK. (http://www.cartography.org.ukl)

Canadian Association of Geographers. McGill University. Burnside Hall 805 Sherbrooke Sr. West. Room 425. Montreal. Quebec. Canada H3A 2K6 (http://www. cag-acg.ca/en/)

Cartography and Geographic Information Society. 6 Montgomery Village Avenue. Suite #403 Gaithersburg. MD 20879 USA. (http://www.carrogis.orgl)

Geographic and Land Information Society. 6 Montgomery Village Avenue. Suite #403 Gaithersburg. MD 20879 USA. (http://www.glismo.orgl)

Geographical Society of New South Wales. PO Box 162 Ryde NSW 1680. Australia. (http://www.gsnsw.org.aul)

Geosparial Information and Technology Association. 14456 East Evans Avenue. Au rora. CO 80014 USA. (http://www. gita.orgl)

Management Association for Private Photogrammetric Surveyors. 1760 Reston Parkway. Suite 515. Reston. VA 20190 USA. (http://www. mapps.orgl)

271

Appendix B GIS Related Professional Organizations and Journals 261

Narional Council of Examiners for Enginee ring and

Surveying. 280 Seneca Road. Clemson. SC 29633-1686 USA. (Imp://www.ncees.org)

National Society of Professional Surveyors . 6 Montgomery Village Avenue. Suite #403 Gaithersburg. MD 20879 USA. (http://www.nspsmo.orgi)

National States Geographic Information Council. 2105 Laurel Bush Road . Suite 200 Bel Air. MD 21015 USA. (http: //www. nsgic.orgl)

New Zealand Geographical Sociery. Department of Geography. The Universiry of Waikaro. Private Bag 3105. Hamilton. New Zealand. (http ://www.nzgs. co.nzl)

Remore Sensing and Phocogrammerry Sociery. c/o Department of Geography. The Universiry of Nottingham. University Park. Nottingham NG7 2RD. United Ki ngdom. (http://static.rspsoc.orgi)

Society of Cartographers. Mr Brian Rogers. Membership Secretary. Canographic Resources Unit, Depr of

Geographical Sciences. University of Plymouth. Drake Circus. Plymouth PL4 8AA. Uni ted Kingdom (http: //www.soc.org.uki)

University Consonium for Geographic Informacion Science. PO Box 15079. Alexandria. VA 22309 USA (http://www.ucgis.org)

Urban and Regional Information Systems Associacion (URlSA) . 1460 Renaissance Dr .• Suite 305 Park Ridge. IL 60068 USA. (http://www.urisa.orgi)

Journals

Annals of th. Association of Am"ican Ceographers (http:// www.blackwellpublishing.com/journaLasp?ref=0004-

5608) Asian Ceograph" (http: //geog.hku.hklag/default.htm) Asian Journal of Ceoinformatics (http ://www.a-a-r-s.

org/ajglindex.htm) Australian C.ographer (http://www.tandf.co.ukljournals/

carfax/00049182.html) Cartographica (http://www.utpjournals.com/carto/carto.

htm1) Cartography and C.ography Information Science (http://

www.cartogis.org/publications) Computational Ceosciences (http://www.springerlink.com/

content/ 1573- 1499) Computm and Electronics in Agricultur< (http ://www.

e1sevier.comlloC3te/compag) Computers 6- GloscilllUS (h [{p: //www.e1sevier.com/loca(e/

cageo)

Cybergeo. European Journal of Ceography (http: //www. cybergeo.presse.fr/)

Environmental Modeling and Softwar< (http ://www. e1sevier.com/loC3(e/envsoft)

Geocarto International (hnp:llwww .geocarro.com/ geocarto.html)

Ceographical Analysis (Imp://www.blackwellpublishing. com/journaLasp?ref=OO 16-7363)

Ceographical and Environmental Modelling (http://www. tandf.co.uk/journals/carfax/13615939.html)

Ceographical Research (Imp://www.blackwellpublishing. com/journaJ.asp?ref= 1745-5863&Site= 1)

Ceographical Systems (http://link.springer-ny.comllink/ service/journals/l 0 1 09Irocs.htm)

Ceography Compass (http://www.blackwellpublishing. com/journaLasp?ref= 1749-8198&site= I)

Ceol nformatica (http ://www.springer.com/west/home? SGWID=4-40 1 09-70-35704166-0)

CIScimce 6-Remote Sensing (http://www.bellpub.com/mses) IEEE Transactions on Ceoscience and Remote Sensing (http://

ieeexplore. ieee.org/xpIiRecenrlssue.jsp?punumber=36) International Journal of GeographicaL Information Science

(http: //www.tandf.co.ukljournalsIrf/13658816.html) International Journal of R.mot( Sensing (http: //www.

tandf.co.ukljournalslrf/O 1431 161.html) ISPRS Journal of Photo gramme try 6- Remote Sensing (http:

Ilwww.itc.nl/isprsjournall) Journal of C.ographic Information and Decision Sci",ce

(http://www .geodec.org/) Journal of Ceographical Systems (http://link.springer.de/

linklservice/journals/l 0 1 09/index.htm) New Zealand C.ographer (http://www.nzgs.co.nzlJournals

Online.aspx) Norwegian Journal of CfOgraphy (http: //www.tandf.

no/ngeog) Photogrammetric Engineering 6- Remote Sensing (http://

www.asprs.org/publicat ions/pers/index.html) Remote Slnsing of Environment (hnp://Wr.vw.clsevier.com/

locate/rse) Spatial Cognition and Computation (http://www.wkap.nl/

journalhome.htm/1387-5868) Surveying and Land Information Systems (http://www.

acsm.net/salisjr.html) The Bulktin of the Soci.ty of Cartographm (http: //www.

soc.org.uklbulletin /bulletin.html) Th. Canadian Ceographer (http://www.blackwellpublishing.

com/CG) The Cartographic Journal (http: //www.maney.co .uk/

journals/carm)

272

262 Appendix B GIS Related Professional Organizations and Journals

The Proftssional Ceographa (http://www.blackwell publishing.com/PG)

Transactions in CIS (http://www.blackwellpublish ing. com/journalsltgis)

Transactions of the fnstitllU of British Ceographers (hnp: Ilwww.blackwellpliblishing.com/journaLasp? ref= 0020-2754&si[<= I)

URISA Journal (http://www.urisa.org/urisajournal)

273

262 Appendix B GIS Related Professional Organizations and Journals

The Proftssiollal Geographtr Omp: llwww.b lackwell publishing.com/PG)

Transactions in GIS (hcrp:llwww.blackwellpublishing. com/journalsitgisl

Transactions of the Imti"''' of BritiJh Geographm (h((p: Ilwww .blackwellpliblishing.com/journal. asp? ref= 0020-2754&si,e= I l

URISA lo.mal (http://www.urisa.org/urisajournal)

Appendix C

GIS Software Developers Compiled by Rongxia (Tiffany) Li

The following is a list of organizacions-governmemal. university, and private-that develop and distribute GIS

related software programs. Included are many of the com

mon GIS software programs as well as contact informa

rion (e.g .• website addresses) for each, however. the list is

not exhaustive. In most cases, GIS software programs

must be purchased either from the developers, or from software diStributOrs, who are nor listed below. Sales representatives associated with (he developers may be able to

direct you to a loeru software distributor. We apologize in

advanee for any omissions, and wiil gladly add any other products to [he list as (hey are brought to our atremian.

GIS Software Program, Distributor, and Website

ArcGIS(Environmental Systems Research Institute, Inc.

[ESRI], 380 New York Street, Redlands, CA 92373-8100 USA) http: //www.esri.eom

Arclnfo (Environmental Systems Research Institute, Inc. [ESRI1 , 380 New York Street, Redlands, CA 92373-8100 USA) http://www.esri .com

ArcYiew (Environmental Systems Research insricuce, Inc.

[ESRI], 380 New York Street, Redlands, CA 92373-8100 USA) http://www.esri.com

ATLAS (Environmental Systems Research Insricute, Inc.

[ESRI], 380 New York Sueet, Redlands, CA 92373-8100 USA) http://www.esri.com

Auro CAD (Aurodesk Media & Emertainmem, Mumbai, 400052, India) http://www.aurodesk.com

ERDAS Imagine (Leica Geosystems Geosparial Imaging, 5051 Peachtree Corners Circle Norcross, GA 30092-2500 USA) http://gi.leica-geosystems.com/LGISubl x33xO.aspx

Geomatica (PC! Geomatics, 50 West Wi lmot Street, Richmond Hill, Omario, Canada, L4B IM5) http: //

. . www.pclgeomancs.com

GeoMedia (lntergraph Corporation, Huntsville, AL 35894 USA) http://www.intergraph.com

GRASS (Geographic Resources Analysis Supporr System) http://grass. i tc.i t

IORISI (Clark Labs, Clark University, 950 Main Street, Worcester, MA 0 I G I 0-1477 USA) hrrp:/Iwww. c1arklabs.org

ILWIS (lmernarional Institute for Geo-Information Science and Earrh Observation, [lTC], 7500 AA Enschede, The Netherlands) http://www.itc.nllilwis/

Manifold System GIS (Manifold Net Ltd., 1945 North Carson Street, Suite 700, Carson City, NY 89701 USA) http://www.manifold.net

Map Info (MapInfo Corporation, One Global View, T roy, NY 12 180-8399 USA) http: //www.map info. com

MGE Products (lmergraph Corporation, 170 Graphics Drive, Madison , AL 35758 USA) hrrp:/Iwww. inrergraph.com

SuperMap GIS (SuperMap GIS Technologies, Inc., 7th Floor, Tower B, Technology Fonune Center, Xueqing Road, Haidian District, Beijing, China, 100085) h[tp: llwww.supermap.com

274

Index

accred itation, 234, 249 accuracy. 9-10; relative, 9 adjacency, 45 Albers' equal area projection, 34

allocation distance function, 214

American Society for Phomgrammerry and Remote

Sensing (ASPRS), 245--{j analysis mask, 214 annotat ion , map. 76 arc: overlay process and, 171; vecroc

data and, 46 ArcGIS, 6--7, 8, 213, 219-20 Arisrode, 28 association: databases and. 144-56 attribure, 38, 49; definition of, 93;

editing, 60-1; errors in, 65--{j; selecti ng, 90-9; updating, 167

AVIRlS,229 axis, 30-1 azimuth, 200-1 azimuchal map projection. 32-4

baseline, 37 bearing, 20 I Bernard, A.M., and Prisley, S.P., 23 Berthier, Louis-Alexandre, 5 Bettinger, P., 237, 240 Boisrad, P., et al., 19 bounds, 36--7 Brooks Act, 250 buffering, 92, 106, 119-31;

concentric rings and, 125-6;

constam/fixed width, 121, 123, 125; individual , 121-2; out pur of, 127; overlapping, 121-2,124,126,137; uncontinguous/non-

concinguous, 121-2; variable width, 121, 123-4, 125

buffer zone, 119

cameras, 11 , 13-14 Canada Geographic Information

Sysrem (CGIS), 6 Canada Land Inventory, 186--7 Cartesian coordinate system, 30-1

canograms, 84 cartography, 3-4, 5, 72; com purer,

3-4, 5; see also maps Cary, T., 234 catchmems,208-10 caveats: maps and, 79-80 cells; see grid ce lls Census Bureau (US), 5--{j Cemral Imelligence Agency (CIA), 5 centroid, 222

certification, 245-52 Clarke Ellipsoid, 30 classification, land, 185-8 clipping process, 106--14 color: DEMs and, 197-8; maps and

77-8,81-2,83 combine process, 132-8, 144 compact discs (CD), 19 completeness, 64 computer aided drafting (CAD)

sohware, 3-4 compurers, personal, 7, 19-20,22-3 conformal map projection, 33-4 conic map projection, 32-4

connectivity,45

containment, 45

COntou r intervals, 4 1, 298-9 COntou r lines, 198-200; watershed

delineation and, 208-9

contrasr: maps and, 77-8 control: datum and, 29-30 coordinate pair. 45 coordinate systems: geographical,

30-2; negative values in, 34-5; planar, 34-7; rectangular, 34-7; vector data and, 45

coSt weighted distance function,

214-15 Cressie, N ., 63 cylindrical map projection, 32-4

Dangermond, J., 6 data: auxiliary, 227-8; collection of,

8-20, 228-9, 231; elevation, 30, 38-9; field collection of, 16--1 9; inconsistent, 62-3;

manipulation and display of, 19-20; missing, 62; output devices and, 20-2; sensitive,

242; data, soils, 186; spatial, 3, 9-10; storage technology and, 19

databases: acquiring, 54-7, 241-2; associating, 144-56; auxiliary

data and, 227-8; clipping, 106--14; combining, 144; conversion of, 218-29;

creating, 10-20,57-9; editing, 59--{j3; erasing and, I 14-16; format of, 55; high resolution, 228-9; joining, 144-53, 154; linking, 144, 153-4; merging, 132,137-8,140-2; official, 134; overlay with point and line, 178-80; ownership of, 239; permanent, 153; proprietary, 109, 238; raster,

275

89. 197-224; relating. 144. 153-4; resolution of. 51. 228-9; scale of. 51; sharing. 237-40; soils. 110-11; sparial. 27-53; temporary. 144; terminology for. 3; updating. 59-63.157-69.229-31. 239-40; vector. 89. 202-5; see also database struc[Ure

database managemem, 3-4

database struc[Ures, 38-50;

alternate. 48-50; conversions

and . 218-19; raster. 38-44. 47-8.49; vector. 38. 44--8. 49

datum. 29-30. 42; vertical. 30 degrees. 31-2 density functions. 216-17. 221 - 2 density surface. 221-2 Oem. B.D .• 82 Descartes. Rene. 30-1 de Steiguer. J.E .• and Giles. R.H .• 2 desrination rable. 145 differential correction, 17

digital elevation model (OEM). 13. 16. 38-9. 197-211

digital line graph (OLG). 6 digital orthophotographs. 15-16.

39-40; updating and. 166-7 digital orthophoto quadrangle

(OOQ). 39. 231 d igiral raster graphics (ORGs).

40-3.231 digital versatile discs (OVO). 19 digitizing. 57-8. 163; 'heads-up'.

40.57.58.163; manual. 10. 57- 8

OIME (Dual Independent Map Encoding). 6

direction, 201; map. 73; flow,

209-10 disclaimers. 233; maps and.

79-80 dissolve process. 133; see also

combine process

distance functions. 214--15, 220-1 dimibutions. 82-3 Dominion Land Survey. 37 Doyle. R .• 248 dynamic segmentation. 49-50

Earth: shape and size of. 27-8 eastings. 34--5 education. GIS. 234. 246-7, 249 Edwards. D .• 80-1 electronic distance measuring

devices (EOMs). 16 elevation, viewing. 206 elevation camours, 198-200 ell ipsoid. 29-30 engineering: v. GIS users, 246.

247-50 ENVI.227 Environmental Systems Research

Institute (ESRI). 6-7. 8 equal area map projection, 33--4 'erasing outside', 108

erasi ng process. 107. 114--16; merging and. 140

Eratosthenes, 28

Erdas Imagine. 227 errors: a[(cibure, 65-6;

compensating. 64; cumulative,

63; database. 59-60. 62-7; definition of. 64; gross. 63; human, 63; instrumental,

63; multipath. 17-18; positional. 64--5; random. 63-4; roer mean square,

65. 66; syntax. 102; sysrema ric, 63

ERSI grid. 220 event themes, 49

Federal Acquisit ion Regulation

(FAR). 250 fiducial marks. 14 field offices: GIS capabilities and.

229-30.239-40 File Transfer Protocol (FTP). 56 flattening ratio, 29

flight line, 14--15 float ing point raster databases,

2 19 Aow direction. 209-10 focal searches. 2 15 font: maps and. 77 Freedom of Information Act

(FOIA). 233. 242 fu nctions. 214-17. 220-2

Index 265

Galileo satellite sYStem. 18 geodesy. 28 Geodetic Reference System of 1980

(GRS80). 29. 30 geographic information science and

technology (G IST). 247 geographic informacion systems

(GIS) : applicarions of. 2. 7-8 ; definition of. 3-4; hiStory of, 4-7; natural resource

management and, 2, 7-8;

technology of. 8-23. 226-36 geographic information systems

specialiSt (G ISP). 246 geographical coordinate system,

30-2 Geography Network Canada. 55 geoid. 29. 30 global posirioning sYStem (G PS) ,

11-13. 16-19; errors in. 17-18; receivers fo r. 18-19

GLONASS (Global Navigation Satellite SYStem). 18

Google Earth. 7. 227 grat icule. 3 1. 32 gray tone: OEMs and. 197-8 grid cells. 38. 48. 217-18; OEMs

and. 197-8; null. 198; size of. 58. 66; slope class and. 201.205

grid cell resolution. 214. 219 grid cell search functions. 215-16

habitat: definition of. 191 ; wildlife. 185, 191-2

habitat suitabil ity index (HSI). 185. 191-2

high resolut ion databases. 228-9 'hot spots'. 216-1 7 hyperlinks. 227-8

identity process. 170. 174-5. 178-8 1

images. graphic. 22 input devices. 10- 20 insets. map. 75-6 Internet: databases and, 55-7; GIS

and. 7. 230-1; open. 234 interoperability. 234

276

266 Index

intersect processes, 170, 171-4. 178- 81

intervals: contour, 41,198-9; maps and, 82-3; update, 159

intervisibility, 205 Intranet, 229

inven selection technique, 97-9

join item/field, 145

jo in process, 144-53, 154; nearest neighbor, 150; non-spatial and spatial databases and, 145- 56; point-in-polygon, 150-1; tabular attributes and, 167

journals, GIS, 7, 261-2

Kaya, N ., and Epps, H.H., 77- 8 Kleiner, M., 248

Lambert conformal conic projection.

33-4,35-6 landmark bui ldings, 43 Landsat Thematic Mapper, II landscape features: contiguous,

similar, 135-6; discontiguous, 136-7; overlapping, 137- 8; selecting, 90- 105

latitude, 31 , 32, 35 legal issues, 232-4 legend: interval , 82- 3; map, 74-5 liability, 232-3, 241 - 2 licensing: data products and,

233-4; professionals and, 246, 247-9

LiDAR (light detection and ranging), 11-l3, 238

line, 44-5, 47 link, 46

link process, 144, 153-4, 171 local searches, 215 ' local shapes', 65 logical consistency, 64 logical operators, 94-5 longitude, 3 1, 32, 35

McHarg, I., 5, 170

Management Association for Private Phorogrammetric Surveyors (MAPPS),249- 50

Manual of Federal Geographic Data Products, 55

maps: ancillary information and.

78-9; ch loropeth, 8 1-2; common problems of, 85-6;

components of, 72- 80; contour, 83; design of, 71-88; design loop and, 85; digitizing of, 10; dot-density, 84;

graduated circle, 84; projeccion and, 27, 28, 32-4, 37; raSterbased, 84; reference, 80-1; scale of, 15, 17; shaded relief, 200-1; slope ciass, 201-2; thematic, 81-4; types of, 80-4

Mapping ScientiSt, 246 mapping unit: joining and, 150;

minimum, 133, 173 MapQuest, 7 Mercaco[ projeccion, 33--4 merge process, 132, 137- 8, 140-2

meridian: prime, 31; principal, 37 Merry, K.L. , et aI. , 8 metadata, 49, 55, 56-7, 67, 241

metes, 36-7 military grid sYStem, 35 minutes. 31 - 2 Model Law, 247-8

Naesser, E., and JonmeiSter, T. , 19 National Council of Exami ners for

Engineering and Surveying (NCEES), 247-8

National Geodetic Venical Datum of 1929 (NGVD29), 30

National Map Accuracy Standards (NMAS), 41, 43-4

National Soil Database of Canada, III

National Topographic Data Base (NTDB),40

Natoli, J. G. , et ai., 237

Natural Resources Canada, 55 NAVSTAR (Navigation Satellite

Tracking and Ranging), 18 neadine, map, 76

neighborhood search funcrions, 215- 16

Newton, Isaac, 28-9

'No Data' categoty, 198

node,45-6 North American Datum of 1927

(NAD27),30 North American Datum of 1983

(NAD83), 30, 42

North American Vertical Darum of

1988 (NAVD88), 30 northings, 34-5

Odyssey GIS, 6 Ohio code system, 42

Onsrud, H.J ., 233 Open Geospatial Consortium, 234 organizations: GIS use and, 237-44;

professional GIS, 260- 1

output devices, 20-2 overlay analysis, 3, 106, 170- 83;

manual, 4-5, 170; null cells

and, 198

parallels: PLSS and, 37 personal digital assistants

(rDAs),231 Peucker, T .K., and Chrisman,

N .,6 Phillips, R.J., et ai. , 77 photogrammetry, 13-16;

analytical, 15 photographs, 13- 16; scale of, 15;

vertical/oblique, 14; see also digital orthophotographs

pixels: raster data and, 38

planar coordinate systems, 34-7 plotters, 20-1 points, 44-5, 47 polygons: overlapping, 137-8;

regions and, 50; spurious.

133, 173; Thiessen, 214;

uncontinguous/non

conringuous. 121-2; vec[Qr

data and, 44-5, 47 Position D ilution of Precision

(PDOr), 17 pour point, 209 precise point positioning. 18 precis ion. 9-10; relative. 9 'precision techniques' , 228-9 printers, 20-1

277

266 Index

imersect processes, 170, 171-4. 178-8 1

imerva ls: contour, 41, 198-9: maps

and, 82-3, update, 159 inrervisibiliry.205 Imranet,229 invert selecdon technique, 97-9

join itemlfield, 145 join process, 144-53, 154, nearest

neighbor, ISO, non-spatial and spatial databases and, 145- 56, point-in-polygon, 150-1, tabular atrributes and, 167

journals, GIS, 7, 261-2

Kaya, N .. and Epps, H.H., 77-8 Kieiner, M., 248

Lamberr conformal conic projection.

33--4,35-6 landmark buildings, 43 Landsat Thematic Mapper, II landscape features: contiguous.

sim ilar, 135-6: discontiguous l

136-7, overlapping, 137- 8, selecting, 90-105

latitude, 31 , 32, 35 legal issues, 232--4 legend: interval, 82-3, map, 74-5 liabiliry, 232-3, 241 - 2 licensing: dara products and,

233--4, professionals and , 246, 247-9

LiDAR (light detecrion and ranging), 11 -13, 238

line, 44-5, 47 link,46 link process, 144, 153--4, 171 local searches, 2 15 'local shapes', 65 logical consisrency, 64 logical operators, 94-5 longirude, 3 1, 32, 35

McHarg, l., 5, 170 Management Association fo r Privare

Phorogrammetric Surveyors

(MArrS),249-50

Manual of Federal Geogtaphic Data Products, 55

maps: anci llary informacion and. 78-9, ch loroperh, 8 1-2; common problems of, 85-6, components of, 72-80, conrour, 83, design of, 71-88; design loop and, 85, digitizing of, 10; dot-densiry, 84, graduated circle, 84; projection and, 27, 28, 32--4, 37; ras<erbased, 84; reference, 80-1, scale of, 15, 17; shaded relief, 200-1; slope class, 201-2; rhematic, 81--4; rypes of, 80--4

Mapping Scientist, 246 mapping unit: joining and, I SO,

minimum, 133, 173 MapQuest,7 Ivterc3co[ projection, 33-4 merge process, 132, 137-8, 140-2 meridian : prime, 31; principal, 37 Merry, K.L., er ai. , 8 meradata, 49, 55, 56-7, 67, 241 meres, 36-7 mili tary gr id system, 35 minutes, 31-2 Model Law, 247-8

Naesser, E., and jonmeisrcr, T ., 19

National Council of Examiners for

Engineering and Surveying (NeEES),247-8

National Geodet ic Verrical Datum

of 1929 (NGVD29), 30 National Map Accuracy Standards

(NMAS), 41, 43--4 National Soil Database of Canada,

III National Topographic Data Base

(NTDB), 40 Natoli, J.G., et al., 237 Natural Resources Canada. 55 NAVSTAR (Navigation Satellite

Tracking and Ranging), 18 neadine, map. 76 neighborhood search func tions)

215- 16 Newmn. Isaac, 28-9

'No Data' category, 198 node, 45-6 North American Datum of 1927

(NAD27),30 North American Datum of 1983

(NAD83), 30, 42 Nonh American Vertical Datum of

1988 (NAVD88), 30 northings, 34-5

Odyssey GIS, 6 Ohio code system, 42 Onsrud, H.J., 233 O pen Geosparial Consortium, 234 organizations: GIS use and, 237-44;

professional GIS, 260-1 o utpU[ devices, 20-2

overlay analysis, 3, 106, 170- 83; manual, 4-5, 170; 111111 cells and, 198

parallels: PLSS and, 37 personal digiral assistams

(PDAs), 231 Peucker. T .K., and Chrisman,

N .,6 Phill ips, R.J., et ai. , 77 photogrammerry, 13-16;

analytical, IS phorographs, 13-16; scale of, IS,

vertical/oblique, 14, see also digital onhophotographs

pixels: rasrer data and. 38 planar coordinate systems. 34- 7 plorrers, 20-1 poinrs, 44-5 , 47 polygons: overlapping, 137-8;

regions and, 50; spurious,

133, 173; Thiessen, 214; u ncon ti nguousl no ncomi nguous. 121-2; vector

data and, 44-5, 47 Posit ion Dilurion of Precision

(PDOP), 17 pour poim, 209 precise paine positioning. 18 precision. 9-1 OJ relative, 9 'precision techniques'. 228-9 primers, 20- 1

privacy: as legal issue. 232-3

projections. map. 27. 2B. 32-4. 37 proximiry analysis, 120; su also

buffering

Public Land Survey System (PLSS). 37.43

public relations. 205 Pythagoras. 2B

Quadrangle maps; see USGS Quadrangle maps

Qualifications-Based Selection (QBS).250

queries. 90-1. 92-102.106; advanced. 102; combinations of. 9B-9; complex. 95; dynamic. 102; hierarchical. 95-7; multiple criteria. 94-5; single criterion. 94. 95-7; spatial. 99- 101

ranges: PLSS and. 37 raster database analysis. 197-224;

software parameters for.

213-14 raster database structure, 38-44,

47-B. 49; vectOr databases and. 202-5

raster map algebra. 21B raster reclassification, 217-18 raster resampling. 219 receivers. GPS. IB-19 Recreational Opponuniry Spectrum

(ROS). IB5. IBB-91 reference points. 57-B reflectance intensity. 12-13

region data system. 49 regions. specific geographic.

106--IB relate process; see link process

femme sensing. 3-4,11-13.38; see also satellite data collection

Rempel. R.S .• and Kaufmann. e.K .. 191

restricted/unrestricted areas, 115.

119.140--1 riparian areas: definicion of, 122 root mean square error (RMSE),

65. 66

satellite data collection. 11-12.

16--17. lB. 3B. 22B-9 scale: map. 74; photOgraphs and

maps. 15 scann ing. 10-11.57. 5B screen displays. 21-2 secant map projection. 33 seconds. 31-2

sections: dynamic segmentation and.

49; PLSS and. 37 selection: landscape fearuces and,

90-105 selective avai lability (SA). IB shaded relief maps. 200-1 shortest path distance function, 215.

220- 1 Sigrist. P .• et al.. 19 sink. 209. 210 slope class. 201-5 snapping. 137 Snow. Dr John. 5 software: computer aided drafting.

3-4; computer mapping. 5; desktOp. 7. 22-3; developers of. 263; GIS. B. 22-3; integrated vectO r/raster. 226-7;

maimenance charges and. 23;

map projecrions and. 37; raster analysis and. 213-14; vector data and. 46-7j workstation.

22-3 soil survey geographic database

(SSURGO). III source table. 145 Space Based Augmemation Systems

(SBAS) . IB Spatial Analyst. 2 13. 219-20 spatial data. 3; quality of. 9-10 specific geographic regions:

definition of. 106; obtaining information about. 106--IB

spectrum. e1ectromagnecic. 13

spli((ing process. 132. 136. 13B-40 standards: data exchange and. 231 - 2 standard deviacions. 204

state plane coordinate system (Spe ).

35-6 statist ical summary search functions,

215-16

Index 267

statistics, 3-4; zonal. 216

STATSGO soils database. III stewardship classes. IB6. IB7 straight line distance function. 214 Structured Query Language

(SQL). 101

surveying: v. GIS users. 246. 247-50 Sustainable Forestry Initiative

(SFI). I B6 symbology. map. 72-3 symax errors: queries and, 102

synthesis: G IS processes and. IB4-96

tables: joining and. 145. 153 tabular output. 22 tangem map projecdon. 33 target table. 145. 153 TerraServer. 7

Thiessen polygon. 214 TIGER (Topologically Integrated

Geographic Encoding and Referencing System). 6

wpology. 6; edit ing. 61-2; vector data and. 45-7

Tourism Opportunity Spectrum, IBB

townships. 37 Triangular Irregular Network

(TIN). 48-9 typography. map. 76--7 Tyrwhi((. Jacqueline. 5

union processes. 170. 175-B. lBO- I universal polar sterographic (ups)

system. 35 Universal Transverse Mercawr

(UTM).33 Universiry Consortium for

Geographic Information

Science (UCGIS). 245 'un-select'. 92 update process, 157-69; reasons

for. 158 Urban and Regional information

Systems Association (URlSA).

246 US Geological Survey (USGS). 6.

39-42.49- 50; 30 meter OEMs. 197; 7.5 Minute Series

278

268 Index

Quadrangle maps, 39-43, 57-B

Valdez, P., and Mehrabian, A., 7B vecto r databases, B9, 202-5 vecto r database struccure, 38,

44-B,49 verification processes. 59-60

vertex, 45-6 viewshed analysis, 205-B

warramies: maps and . 79

watershed delineation , 20B-IO Wide Area Augmenration System

(WMS) , IB Wing, M.G., and Karsky, R., 19

Wing, M.G. , et aI. , 19 workstations, 19-20, 22-3 'World Data Bank', 5

World Geodetic System of 1984 (WDSB4), 29, 30

zonal stat istics, 216

279

268 Index

Quadrangle maps, 39-43, 57-8

Va ldez, P., and Mehrabian, A., 78 vecto r databases, 89, 202-5 vector database struccu re, 38,

44-8,49 verification processes, 59-60

vertex, 45-6 viewshed ana lysis, 205-8

warranries: maps and, 79 warershed delineat ion, 208- 10 Wide Area Augmentarion Sysrem

(WAAS) , 18 Wing, M.G., and Karsky, R., 19

Wing, M.G., er aI. , 19 works rations, 19-20, 22-3 'World Data Bank', 5

World Geodetic System of 1984 (WDS84), 29, 30

zonal sratisrics, 216

geographic information systems

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