1 8/16/2015 ron briggs, utdallas gisc 6383 gis management and implementation data sources and...

104/19/23 Ron Briggs, UTDallas GISC 6383 GIS Management and Implementation

Data Sources and Conversion Feeding the GIS.

Like a teenager, a GIS can consume more than data you ever imagined!Discussion here focuses more on projects than organization-wide

implementation. Often, data collection is an end in itself. Almost invariably, it’s the

costliest element of any project and of most organizational implementations-- > 80%.


4. Design A Process for Obtaining and Converting Data

from Source

--identify source (document, map, digital file, etc) for each and every entity and its attributes

--defining the procedures for converting data from source and into the database

RECAP from Implementation Steps/db design

We will talk tonight primarily about sources


IdentifyDatabase

Requirements

IdentifyData to beCreated

IdentifyAppropriate

Data Sources

DevelopConceptualDatabaseDesign

DetermineConversionStrategy

DevelopPhysicalDatabaseDesign

ProcureConversionServices

IdentifyAccuracy

Requirements

Develop DataConversionWork Plan

CommenceSource

Preparationand Scrub

CommenceOther

In-HouseActivities

FinalizeAcceptanceCriteria and

QC Plan

EditDelivered

Data

CommenceDatabase

Maintenance

DevelopDatabase

MaintenanceProcedures

In practice, identifying data sources and developing a conversion strategy is interwoven with the conceptual and physical data design process.

RECAP from db design


….some steps/tasks in the process• Identifying data

– internal and external sources

– checking for completeness and quality

– new data via field or aerial surveys

• Fixing problems in the data source– map scrubbing

– coding source documents with unique IDs

• Converting to digital form– scanning or digitizing

– raster to vector conversion strategy

– entry of attribute data

• Data conversion specifications– horizontal and vertical control

– projection coordinate system

– accuracy requirements

• Document flow control– monitor flow of maps, documents and

digital files thru conversion process

– change control for changes to data that occur during this time period

• Quality control procedures– potentially highly complex

– errors will occur

– generally a combo of automated and manual procedures

– requires comparing digital version to original source and checking internal consistency

– problem resolution process and correction responsibilities need to be defined

• Final acceptance criteria– criteria data must meet before final

loading into database



Steps in creating a topologically correct vector polygon database

FIELD DATA

NON-SPATIALATTRIBUTES

SPATIALDATA

INPUT TO TEXT FILE

MANUAL DIGITIZING

DIGITIZE SCAN AND VECTORIZE

SCANNING

linked by uniqueindentifiers

VISUAL CHECK

CLEAN UP LINES AND JUNCTIONS

WEED OUT EXCESSCOORDINATES

CORRECT FORSCALE AND

WARPING

ADD UNIQUEIDENTIFIERSMANUALLY

CONSTRUCTPOLYGONS

LINK SPATIALTO NON-SPATIAL DATA

TOPOLOGICALLY CORRECTVECTOR DATABASE OFPOLYGONS

ADD UNIQUE

IDENTIFIERS



Where do I get data? & What form is it in?Where?• Secondary: existing data

– already published/available– special tabulation/contract

• Administrative records: data as by-product

– within your organization– other organizations

• Primary data: from scratch– developed in-house (DIY)– contracted out

(field work is always slow and expensive!)

What format?– machine readable (digital)– hardcopy (paper, maps)

Time &Cost Increase

Applicability&suitabilitygenerally decrease.

Spatial data in digital form is the most valuable since this is generally the most expensive to obtain.


Don’t forget to look in-house!

• collected by your organization as data

• by-product of normal agency operations

• acquired for some other project

Don’t forget to look, especially if it’s a large organization. There may already be a GIS project in existence or about to be launched!


Major GIS Data Sources

• Maps• Drawings (sketch or engineering)• Aerial (or other) Photographs• Satellite Imagery• CAD data bases• Government & commercial spatial (GIS) data bases• Government & commercial attribute data bases• Paper records and documents


Pre-processing and Conversion: almost invariably required!

• Maps and Drawings– digitizing, or– scanning than raster to vector conversion

• Aerial Photographs– photogrammetry/photo interpretation to extract

features– digitizing or scanning to convert to digital– rectification and DTM (digital terrain model) to

create digital orthos

• Satellite Imagery– rectification and DTM to create digital orthos (if

desired)

• CAD Data Bases– translator software (pre-existing or custom-

written) needed to convert to required GIS format

• GIS Data Bases– conversion between proprietary

standards (ARC/INFO, Intergraph, AutoCAD, etc.)

– Spatial Data Transfer Standard

• Attribute Databases– geocoding if micro data– conversion between geographic units

(e.g. zip codes and census tracts)– conversion between different

databases

• Records and Documents– OCR (optical character recognition)

scanning– keyboarding– then, same as attribute data bases

Data Conversions: general comments• Paper Maps to Digital

– generally the most complex & expensive– automated extraction of layers problemmatic and error prone

• requires scanning then raster to vector conversion

– digitizing may be freehand with tablet, or “heads-up” on screen

• Digital to Digital Conversions– Safe Software’s Feature Manipulation Engine (FME) product provides translation

between different vendor’s GIS formats (now ESRI’s Data Interoperability Extension)

– spreadsheet software (Excel) is a powerful beginning point for converting to required database format (e.g. to .dbf for ArcView)

– specialized conversion packages for converting between different databases also available e.g. DBMS/Copy Plus, Data Junction

– efforts at standardization, which reduces need for conversions, have had limited success ‘cos of competitive pressures

• FGDC’s, Spatial Data Transfer Standard (SDTS), is a federal standard

• Open GIS Consortium, a vendor and user group, lobbies for standards and non-proprietary approaches to GIS database creation


Data Conversion: hints on the process• NEVER CONVERT ON THE

ORIGINAL FILE ALWAYS A COPY.

• ALWAYS convert in an unrelated sub-directory

• Document each new file that is made in the conversion process.

• Archive the original files on a readily available media

• Automate as many processes as possible– Projections

– Many like files

– Replication of data for output

• Record all your steps while Record all your steps while converting data formats, in converting data formats, in a journal or notebook. a journal or notebook. You WILL use that same You WILL use that same conversion sometime in conversion sometime in the futurethe future


Data Sources: Table of ContentsOverview• Federal Data Sources: Spatial Data• Federal & Non-profit Data Sources: Attribute data• Private Sector Data Resources: Spatial and Attribute

Selected Sources in Detail • DIME• TIGER• USGS: Overview

– DEM detail– DLG Detail– DOQs and DLGs

• Digital Chart of the World• Shuttle Radar Topography Mission (SRTM)• NAVSTAR: gps• Remote Sensing• US Census Bureau Attribute Data• Primary Data Collection: Some Issues

Guides and sources for GIS data include:

cast.uark.edu/local/hunt/index.html

www.geographynetwork.com/

www.geospatial-online.com/0501/0501thrall.html

www.geospatial-online.com/0601/0601thrall.html

www.gisdatadepot.com

For others see: www.utdallas.edu/~briggs/other_gis.html


Federal Data Sources: Spatial DataFederal Data Agencies:

• USGS (Geological Survey, National Mapping Div.--Interior)

– all kinds of mapping, not just geology!

• NGS (National Geodetic Service-- Commerce, part of NOAA)

– geodetic surveying

[Ordnance Survey (in U.K.) combines both functions.]

Federal Mission Agencies• USDA (Agriculture)

– Resource Conservation Service (formerly Soil Conservation Service)

– US Forestry Service

• Interior– US Fish and Wildlife: wetlands– Bureau of Land Management

• Environmental Protection Agency– TRI (toxic release inventory) sites

• DoD (Defense)– National Geospatial-Intelligence Agency

(NIMA)• formerly National Imagery and Mapping

Agency (NIMA)• originally Defense Mapping Agency (DMA) • US and world terrain mappings

– NAVSTAR: gps satellites– US Army Corp. of Eng.: flood control

• NASA (National Aeronautics and Space Administration

– LANDSAT satellites• Commerce

– Census Bureau: DIME & TIGER files– NOAA (National Oceanic and Atmospheric

Administration)• AVHRR (Advanced Very High

Resolution Radiometer) weather satellites


Federal & Non-profit Data Sources: Attribute data

Federal Data Agencies• CB (Census Bureau-- Dept of Commerce)

– population and industry data from surveys

• BEA (Bureau of Economic Analysis-- Dept. of Commerce)

– STAT-US: national accounts

Federal Mission Agencies

Most federal agencies now have a stat. dept– Bureau of Labor Statistics

– National Center for Health Statistics

– National Center for Education Statistics

– National Center for Criminal Justice Statistics

– National Center for Transportation Statistics

– Interstate Commerce Commission

– Internal Revenue Service

Non-profit interest groups:– Urban and Regional Information

Systems Association (URISA)

– National League of Cities

– Population Reference Bureau

– Transportation Assoc. of America

Trade Associations:– American Public Transit Assoc.

– see Encyclopedia of Associations

Trade Publications– Progressive Grocer

– see Business Periodicals Index

University Research Centers– University of Michigan, National

Institute for Social Research


Private Sector Data ResourcesSpatial data• GIS software vendors

– e.g. ArcData Catalog

• Satellite Data Sellers– e.g. Space Imaging Inc.– See Remote Sensing slides for list

• Topological data (street networks and boundaries)

– TeleAtlas (European, bought out Etak)– DeLorme– Geographic Data Technology

(Absorbed and disbanded Wessex. Now owned by RL Polk)

– Navtech: in-vehicle navigation system data

– Maptech: Navigation charts

• Environmental– Earthinfo– Hydrosphere– Meteorlogix

• Aerial Surveying/ Engineers/Consultants – For primary data: legions of them

Attribute DataWide array of companies and services.

– pollsters and market surveyors– remarketeers/updaters of federal gov. data

(census data, TIGER files, etc..)– data aggregators: collect admin. data from

state and local gov. (e.g. building permits)– gap fillers in government offerings

Larger providers include:– Claritas (National Planning Data

Corporation,SMI/Donnelly) – Equifax/National Decision Systems– ESRI BIS (Business Information Solutions)

formerly CACI Marketing Services– Economy.com

Specialized providers include:– Dun and Bradstreet (company finances)– InfoUSA (business yellow pages)– TRW-REDI (property data)


Vector Data Implementations: DIME file (Dual Independent Map Encoding)

• introduced for the 1970 US Census and used again in 1980; replaced by TIGER in 1990• pioneering early example of topological structure• basic record was a line segment• flat file structure with all info in one record (Star and Estes misleading)• segments defined between every intersection for all linear features in landscape (streets,

railroads, etc)• each segment record contained items such as:

– segment ID Segment type

– from node ID to node ID from node x,y to node x,y

– address range left address range right

– city left city right tract left tract right

– other left/right polygon ID info as needed e.g. county, block,

• prepared only for metroplitan areas (278 files covering about 2% of nation) • some cities (very few) maintained and expanded (e.g add zoning) them after Census • inconsistent with Metroplitan Map Series paper maps published for each census • very compute intensive to process into continuous streets or polygons


Vector Data Implementation: TIGER File(Topologically Integrated Geographic Encoding and Referencing file)

• introduced for 1990 Census to eliminate inconsistencies between census products

• cover entire country, and released by county• include hydrography, roads, railroads, etc.• uses relational data base model • data derived from 3 sources:

– scanned USGS 1:100,000 Map Series– addresses ranges from DIME file, originally

updated to 1986/7– geographic area relationship files used by CB

to process 1980 census • problems with TIGER

– accuracy limited by USGS base map and processing (100m horizontal)

– one time only; many segments missing.– many local gov. records better – data only: requires software to process.

• First version was Tiger/1992

• Latest is TIGER/Line 1998, issued July, 1999

• comprises 6 record types (tables)– basic data record (type 1): line segment

records similar to DIME file– shape coordinates (type 2): extra coords

to define curved line segments– area codes (type 3): block records giving

higher order geog (tract, city, etc)– feature name index (type 4): line segment

records with code for alternative names(used when a segment has two or more charateristics (e.g both Main St and US 66)

– feature name list (type 5): names associated with codes n Type 4

– special addresses ranges (type 6): additional address ranges (e.g if zip code boundary splits a line segment

• Minor differences exist in layout of various versions of TIGER which can lead to reading problems


Vector/Raster Data Implementation: USGS(United States Geological Survey Digital Data)

• Digital Elevation Model (DEM) data and new (2000) National Elevation Dataset (NED)– Raster elevation data– available at 30m, 2 arc second, and 3 arc second spacing (1 sec. of lat ~100ft)

• Digital Line Graph Data (DLG) data– digital representations of the cartographic line info. on main USGS map series.

• National Hydrography Dataset– Combines water data from DLG with EPA’s Reach File Version 3– Plans to update both through cooperative projects with local gov. agencies

• National Land Cover Dataset (NLCD)/Land Use and Land Cover (LULC) data– NLCD (release started 2000) updates LULC data of 1970/1980 – NLCD: 30 meter resolution, 21 landuse categories, derived from mid 1990s Landsat-7

• Geographic Name Information System (GNIS) Data– standardized place names and feature classification

• Digital Orthoquads (DOQ) and Digital Raster Graphs (DRG) raster data– DOQ: 1 meter resolution digital orthophotos for entire US (if locals cooperated!)– DRG: scanned USGS 7.5 minute quads

Distribution of digital data by USGS began in the early 1980s. For details on early data see: USGS National Mapping Program USGS Digital Cartographic Data Standards,

Washington, D.C.: Geological Survey Circular 895A thru G, 1983.


USGS: Elevation Data Detail(Digital Elevation Model and National Elevation Dataset)

DEM raster elevation data. • 7.5 minute, 1:24,000 USGS quads

(15 minutes in Alaska)– elevations at 30 meter spacing– UTM coords, NAD27 datum– accuarcy: <15m RMSE (some <7)

(horizontal: 15m)

• 30 minute, 1:100,000 USGS topo sheet – 2 arc second spacing– NAD27 datum– accuracy: 5-25m--1/2 map contour int.

(horizontal: 50m)

• 1 by 2 degree, 1:250,000 USGS sheets– from Defense Mapping Agency (DMA)– 3 arc second spacing– WGS72 datum– variable: 30-75m (horizontal: 100m)

• National Elevation Dataset (late 2000 availability)

– Derived from earlier 7.5 and 30 DEM data sources

– Seamless US coverage with consistent • Datum: NAD83• Projection: geographic (lat/long)• Units: meters• Spacing: 1 arc second (approx. 30 meters or

100 ft) 2-arc second for Alaska (interpolation used if source at lower res.)

Each file has three records:– Record A: descriptive information– Record B: elevation data– Record C: accuracy statistics

Files classified into one of three levels depending on editing, etc– Level 1: raw elevation data; only ‘gross blunders’ corrected. – Level 2: data edited and smoothed for consistency. – Level 3: data modified for consistency with planimetric data such as

hydrography and trans.

Data has gaps, overlaps, holes and artifacts, hence need for NED


USGS DLG Data Detail(Digital Line Graph)

Three products:

• Large Scale (ls) -- generally 1:24,000– 7.5 minutes per file

• Medium Scale (ms) -- 1:100,000 – 30x30 minute files (half a map sheet)

• Small Scale (ss) --1:2,000,000– 21 files for nation (one CD-ROM)

Three formats:• Standard (no longer available)

– internal cartesian coords (saves storage)– limited topological info;

• Optional (DLG-3) (use for GIS):– UTM metric (Albers Equal Area Polyconic for small

scale)– full topological info

• Graphic (small scale only)– GS-CAM compatible; no topological info.– OK for display

• Layers (up to 9)– Hydrography: all flowing and standing

water, and wetlands

– Hypsography: contours and elevation

– Transportation: roads, trails, railroads, pipelines, transmission lines

– Boundaries: political & administrative

– Public Land Survey System (PLSS): township, range, section (not ss)

– Vegetative surfaces (ls only)

– Non-veg surfaces (e.g. sand) (ls)

– survey control and markers (ls)

– manmade features (e.g. buildings)(ls)

• Horizontal Accuracy:– large scale (7.5min.): 12-50m

– medium (1:100,000): 50m

– small : ??


USGS/EPA

National Hydrographic Dataset

• Combines USGS DLG data with EPA Reach File Version 3• The DLG files contribute a national coverage of millions of

features, including water bodies such as lakes and ponds, linear water features such as streams and rivers, and also point features such as springs and wells. – These files provide standardized feature types, delineation, and spatial

accuracy.

• Reach file contributes hydrographic sequencing, upstream and downstream navigation for modeling applications, and reach codes. – The reach codes provide a way to integrate data from organizations at all

levels by linking the data to this nationally consistent hydrographic network.


USGS: National Land Cover Dataset (NLCD) Land Use and Land Cover (LULC)

data detail NLCD• Part of Land Cover Characterization Program which also includes Global Land Cover Characterization

program and Urban Dynamic program tracking change for selected US metro areas (not D/FW)• Cooperate effort of USGS, EPA, NOAA, USFS• Data release began in 2000• Derived from early to mid 1990s Landsat-7 Thematic Mapper ™• 30 meter resolution, NAD 83, Albers Conic Equal area projection• 21 categories of land use divided into 9 major groups• Distributed by State but will mosaic to larger “regional” coverages• Two release levels

– “accuracy assessed” states: GeoTIFF format – “yet to be assessed” states: 8-bit binary with values 0-255

• Uses unsupervised clustering on multi-band TM data supplemented with ground observation, aerial photos, census data, wetland data, land use maps, etc.

• Designed to be compatible with earlier LULC data of 1970’s and 1980s.LULC• Based on 1970s and 1980s information • derived from aerial photographs and based on 1:100,000 and 1:250,000 map sheets• Available as both vector polygons and grid cell rasters with 4 hectare (10 acre, approx. 200m) resolution


USGS DOQs and DRGs: data detailDigital Ortho Quads (still in progress--depends on state/local cooperation)Digital image of an aerial photo in which displacement caused by camera lens, airplane’s

position, and the terrain have been removed-- image characteristics of a photo and geometric properties of a map.

• 1:12,000 scale; UTM coords, NAD83 datum• 1 meter resolution; 33 feet (10m) positional accuracy (national map stand.)• associated DEM (digital elevation model) 7m vertical accuracy • quarter quadrangle coverage: 3.75 by 3.75 minutes• use as base for topo and planimetric maps (if accuracy is sufficient)

Digital Raster GraphicsScanned image of USGS topo map, recast in some cases to UTM.• 1:24,000/7.5 quads; also 1:100,000 & 1:250,000 • 250dpi; 8-bit color; TIFF file; 64 per CD-ROM• use as backdrop/validation for other digital data• Format is new, data is old!


National Spatial Data InfrastructureConcept: local, tribal, regional, state and federal agencies, the private sector, non-profit

organizations, professional societies, academia, and others cooperating to provide spatial data (rather than the feds doing it all).

“Framework”: focus on seven themes of commonly used digital geographic data– Geodetic control– Digital orthoimagery– Elevation data– Transportation– Hydrography– Governmental Units– Cadastral (reference system and public parcels)

plus standardized metadata (data describing data) for each

Federal Geographic Data Committee (FGDC): assigned Federal leadership responsibilities for developing the NSDI by Executive Order 12906 (April 11, 1994)

Examples: Transportation: integration of Census Bureau’s TIGER fileDOT’s National Transportation Atlas Data BaseUSGS’s Digital Line Graph data

Hydrography: integration of EPA’s REACH USGS’s Digital Line Graph


Digital Chart of the World…now primarily of historical interest

• spatial data base of the world.; 1st released cerca 1992

• 1:1 million target mapping scale

• US DoD project in coop. with Canada, Australia, and UK

• 1.7GB of data on 4 CD-ROMs (North America, Europe/Northern Asia, South America/Africa/Antarctica, SouthernAsia/Australia). $200 cost

• derived from DMA's 1:1 million scale Operational Navigational Chart (ONC) base maps

• in Vector Product Format (VPF), but also available in most GIS vendor formats, and ASCII

• The VPFVIEW 1.1 freeware for DOS and SUN OS available to view VPF

• World Geodetic System 84 datum

• Airports, boundaries, coastal, contours, elevation, geographic names, international boundaries, land cover, ports, railroads, roads, surface and manmade features, topography, transmission lines, waterway

• 1,000 ft contours with 250ft supplements

17 layers with 31 feature classes

* Aeronautical Information

* Cultural

* Landmarks

* Data Quality

* Drainage

* Supplemental Drainage

* Utilities

* Vegetation � * Supplemental Hypsography

* Land Cover

* Ocean Features

* Physiography

* Political

* Populated Places

* Railroads

* Roads

* Transportation Structures

worldwide index with 100,000 place name


Shuttle Radar Topography Mission (SRTM)• Goal: accurate topographic model of entire earth’s land surface • Data Characteristics:

– Spacing: 30 by 30 m. for US (same as DEM from USGS 7.5” Quads), 90 m entire world (for public)

– Accuracy: 16 m. absolute vertical accuracy, 20 m. horizontal circular– Coverage: 56°S to 60°N latitude; 80% land area; 90% population

• Dates:– Data acquired by space shuttle Endeavour in Feb 2000– Raw data initially released by NASA in 2003– Edited data from NGA in Digital Terrain Elevation Format (DTED) by end of 2004

• Sponsors: NASA, NGA (formerly NIMA), German Aerospace Center (DLR), Italian Space Agency (ASI)

• Technology: interferometry of two radar signals mounted on each end of 200 foot mast extended from Space Shuttle

• Earlier missions: Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR).

• Web site: http://www.jpl.nasa.gov/srtm/index.html\

NAVSTAR Global Positioning System (gps)

NAVSTAR Satellite Program • 24 (NAVigation Satellite Time and Ranging)

satellites in 11,00 mile orbit provide 24 hour coverage worldwide

• first launched 1978; full system operational December 1993.

• gps receiver computes locations/elevations via signals from simultaneously visible satellites (minimum 3 for 2-D, 4 for 3-D)

• Selective Availability (SA) security system– 100m accuracy with single receiver, if active– 10-15m accuracy if inactive

• SAS turned off May 1st, 2000– Multiple ways to counteract SA– Even USCG broadcasted correction signal!– Europeans threatened to compete– Regional denial of signal possible

• Russia’s 21-satellite GLONASS (Global Navigation Satellite System) also available.

Types of Ground Collection and CorrrectionAutonomous – Hand-held unit provides 10m accuracy (with SA off)– $150-$1,500 per unit

WAAS (wide area augmentation system)– <3 meter accuracy in practice (spec. is 7m vert/horiz)– Base stations (25 across US) monitor satellites– 2 master stations (E & W coast) calculate corrections – upload to two geosynchronous satellites over equator– correction signal broadcast to GPS receivers (no special extra

equipment needed unlike DGPS)– Began operation June, 1998– To be expanded to cover Canada, Mexico, Panama – European EGNO, Asian MSAS under development

Differential (DGPS-predecessor to WAAS)– accuracy 1-5m depending on equipment/exact method– equipment $1,500-$15,000 per receiver– correct for SA and other errors via either

• real time correction signals over FM radio • post process with data from Internet

Kinematic: – high accuracy engineering (within cms); – two receivers (base station and rover– must lock-on to satellites– equipment $15-30K per station

–use to collect ground control for imagery/orthos –or for point/line data (manholes, roads, etc)


Factors Affecting GPS Accuracy• Ionosphere

– worst in evening at low altitudes (but ephemerous best there)

• troposhere– especially water vapor which slows signal

• multipath– reflected signals from buildings, cliffs, etc

• ephemerous– position and number of satellites in sky

– 4 required for 3D (horiz. and vertical), 3 for 2D (no elevation)

– ideallly, 3 every 120° horizon. with 20° elev., 1 directly above

• blockage (of satellite signal)– by foliage, buildings, cliffs, etc.

– WAAS signal espec. subject to blocking by terrain & buildings ‘cos is from geostationary equatorial satellite

Overall, accuracy better at night than during day.


GPS Receiver Characteristics• Irrespective of cost ($150 to $50,000) all have same accuracy in autonomous mode!• processing speed & channel capacity (# of satellite data streams simultaneously

processed)• storage capability: internal & PCM/CIA cards• codes it can process (L1, L2; code, carrier phase, etc.)• antenna type and remote connection support • interface capabilities

– RTCM: standard for input of differential correction signal

– NMEA (National Marine Electronics Association):positions for real-time interface to instruments (also to PC software e.g. for location on a map)

– RINEX (receiver independent exchange): output of raw satellite data for post processing

– other proprietary: for waypoints, routes, position data, etc. upload/ download

• specialized user support features (hiking, marine nav., surveying, civil eng., etc.)

Remote Sensing• remote sensing: info. via systems not in direct contact with objects of interest:

– via cameras recording on film, which may then be scanned (primarily aerial photos)– via sensors, which directly output digital data (primarily satellites, but also planes)

• image processing: manipulating data derived via remote sensing

• photographic film types: – monochrome (black and white) – natural color – infra-red (insensitive to blue, but goes past visible red; good for geology, veg. , heat)

• types of sensors – passive (most common): record natural electromagnetic energy emissions from surface– active (radar): record reflected value of a transmitted signal (e.g. Canada’s RADARSAT, NASA’s SIR-C/X-SAR)

• penetrate clouds; also, some ground penetration possible.

• passive sensors: typically store one byte of info (256 values) per spectral band (a selected wavelength interval in the electromagnetic spectrum);

– panchromatic: single band recorded (e.g. SPOT Panchromatic)– multi-spectral: multiple bands recorded (e.g. LANDSAT MMS-4, TM-6)– hyperspectral: hundreds of bands (TRW’s proposed Lewis satellite has 384)

• spectral signature: the set of values for each band typifying a particular phenomena (e.g. blighted corn, concrete highway) to allow unique identification


First Generation Satellites: GovernmentSatellite Name Main Purpose Accuracy Resolution

LORAN-C Navigation 250 m ARGOS Wildlife tracking 500m NIMBUS-AVHRR 1978 Weather 1000m 1km TRANSIT/Doppler predecessor to GPS NAVSTAR (1993- global positioning 100m to 1cm SPOT Panchromatic (1986-

remote sensing single band (visible)

10-25m 10m

SPOT Multispectral (1986-

remote sensing 3-bands (inc. infra-red)

20-50 20

LANDSAT (1982-) Thematic Mapper (TM)

remote sensing 6-bands

30-70 30

LANDSAT (1972-) Multi-Spectral (MSS)

remote sensing 4-bands

70-150 80 (1:100,000)

LANDSAT (1994- Enhanced TM

remote sensing

15-50 15 (1:50,000)

Next generation (1997>) remote sensing 1

Source: Keating, BLM Tech. Note # 389, 1993

Commercial satellites were planned from 1998 onward with resolutions to 1 meter. After several costly failures, the first (Space Imaging’s Ikonos) became operational in late 1999.

Some Notes on Current Satellites • satellites vary by: orbit, altitude, resolution, revisit frequency, revisit variability (steering)

capability, width of swath, image size, stereo capability, wavelengths collected, other sensors, customer delivery time, etc.

• Primary Commercial Vendors are – Digital Globe (formerly Earthwatch): Longmont, CO

• Quickbird 2 satellite launched October 2001 (Quickbird 1 was lost)– 0.6 meter panchromatic, 2.5 meter multispectral (color)

• Partners include WorldView Imaging Corp ,Ball Aerospace, Hitachi (Japan), Nuova Telespazio (Italy), MacDonald Dettwiler (Canada),

– Space Imaging/EOSAT: Thornton, CO• Ikonos 2 satellite launched Sept 1999 (Ikonos 1 was lost)

– 1m panchromatic, 4m multispectral– First high resolution commercial satellite

• Partners include: Lockheed Martin, Raytheon/E-Systems,Mitsubishi, Kodak, EOSAT (Earth Observation Satellite Company purchased 11/96)

– Orbimage: Dulles, VA Note: Orbimage acquired Space Imaging in January 2006• Orbview 3 launched June 2003 Combined operation called GeoEye

– 1m panchromatic, 4 m multispectral

– Spot: France• Spot 5 launched May 2002

– Stereo images at 2.5m panchromatic• Had the highest resolution commercial imagery (at 10m panchromatic) from its Spot 1-3 satellites

(launched 1986-1993) until Space Imaging’s Ikonos launched in 1999

• The Global Change research project’s Earth Observation System (EOS), which includes NASA’s Mission to Planet Earth (now called Earth Science Enterprise), includes a wide variety of monitors & sensors on multiple satellites from different countries through 2008

• Countries with existing/planned satellites include: Argentine, Brazil, Canada, France, Germany, India, Israel, Japan, Korea (South), Ukraine, US.

Current Operational Satellites (as of 2002)

(1 of 4)

(2 of 4)

(3 of 4)

Source: http://www.planetary.brown.edu/arc/sensor.html

(4 of 4)


Next Generation Satellites• NGA (National Geospatial-Intelligence Agency) has signed NextView

contracts for development of next generation of commercial satellites, with DOD being given priority access in times of need

• Digitalglobe contract in fall 2003, focused on– Higher resolution (perhaps to .25 m by 2008)– Delivery time to customer

• 3 hours now (Iraq war)• Future: 90 minutes standard, 20 minutes “rush jobs”

• Orbimage contract in fall 2004– For OrbView 5 satellite to launch early 2007– 0.41 m panchromatic, 1.64 m multispectral– 3 m. position accuracyNote: the award of this contract to Orbimage resulted in their acquisition of Space

Imaging (which failed to get the contract) in January 2006 and renaming of the combined entities as GeoEye. OrbView 5 now called GeoEye-1


U.S. Census Bureau: Attribute Data(see: Census Catalog and Guide published annually)

• Census of Population and Housing – 10 year cycle (1990)

– two main tabulations

• Full count (STF1 & 2)– geog. detail

– down to block

• Sample (STF3 & 4) – 20% stratified sample

– ‘long form’

– attribute detail

• Economic Census – 5 year cycle (1993)

– agriculture, retail, manufacturing, service, transportation, government, construction

Data Collection Methodologies• Census

– mandatory, entire population– regular but infrequent, as benchmark

• Update surveys– not mandatory, update censuses– limited geog detail, usually annual (some

weekly)

• Special Surveys– not mandatory; cover data not in census– often on contract with other agency (e.g

National Health Survey)

• Non-Survey– admin records from other agencies– update census (e.g. Current Poplation

Reports)– provide additional info (e.g. County Business

Patterns)


Aggregation Issues in Attribute DataDisaggregate (micro) data

• individuals or individual entities– persons, households, firms,

– parcels, housing units, establishments

– trees, poles, wells

• geocoding required

• confidentiality/disclosure a critical issue

• suppresion may be imposed on aggregate data

Aggregate data• groups of individuals or entities

– by geographic area--block, tract

– by time: rainfall/sales by day, month, year

– by characteristic: age group, race, species

• polygons required for mapping

• Cross-sectional: different spatial units at one point in time

• Longitudinal: one spatial unit at different points in time

• Dynamic: continuously produced over time and space (some satellites; CORS program)


Samples, Populations and Spatial PatternsSome Issues for Primary Data Collection

• Population: --all instances of a phenomena

• Sample: subset of population

– random: each pop. member has equal chance of being chosen

– systematic: members chosen based on repetitive rule (every 10th; every 4 feet)

– stratified:; sampling conducted within groups to ensure representation

Especially tricky for spatial data!

random

Spatial sampling methods– point: collect info at one spot– transect: along a line– quadrat: within a square

clustered dispersed

Probability of one point being close to anotherequal high low


Summary of Data Collection IssuesSuitability/Appropriateness for the Task

• horizontal (and vertical) accuracy: – 33 feet USGS DOQ, versus 3 feet for urban needs

• documentation– often bad for administrative records

• currency and frequency of update– is date and/or update cycle appropriate?

• completeness– is undercount/omission a serious problem?– e.g. most ‘lists’ miss the poor (census undercounts); TIGER file once per decade

• aggregation and sampling – are they appropriate?

• cost -- highly associated with accuracy– is cost within budget? – is benefit greater than cost?