integrating remote sensing and gis for urban fire disaster

UNIVERSITY OF NEW SOUTH WALES Thesis/Project Report Sheet

Surname or Family name: Bhaskaran First name: Sunil Other name/s: -----Abbreviation for degree as given in the University calendar: PhD School: Surveying and Spatial Information Systems Faculty: Engineering Title: Integrating Remote Sensing and GIS for Urban Fire Disaster Management

Abstract 350 words maximum:

This research is aimed at developing an urban disaster management system, which will improve the existing method of hazard

assessment and dynamic resource distribution. Disaster management deals with the risk of natural disaster and managing the

events following disasters. Although prevention of disasters is an impossible task, careful risk analysis can help save lives and

reduce monetary damages. The study integrates high spatial resolution remote sensor data with GIS data for developing a

methodology to model urban disaster risk, which will assist in providing decision support systems for emergency operations

and redistribution of dynamic resources (material and human). Since a major part of the research was carried out in

collaboration with the New South Wales Fire Brigades, Sydney, it attempts to provide systems, which aim to improve their

existing methodology of risk assessment and provide decision support systems for dynamic resource distribution.

Research result demonstrates a unique methodology, that integrates appropriate remote sensor and existing GIS data to

semantically model hazard in urban areas. The use of the model will have implications for the existing distribution of dynamic

resources in geographically unique service areas, which can lead to the reassessment of hazard and redistribution of dynamic

resources.A framework for integrating remote sensing and GIS data is also demonstrated as an alternative to the existing

method of distributing dynamic resources. The research results can contribute to an overall improvement in the way urban

disasters are managed and will assist multiple users in the decision making process. Since this research was carried out using

real data and off and on-field collaboration with the Corporate Strategy Division, New South Wales Fire Brigades, Sydney it has

the potential to be implemented immediately.

Declaration relating to disposition of project report/thesis

I am fully aware of the policy of the University relating to the retention and use of higher degree project reports and theses, namely that the University retains the copies submitted for examination and is free to allow them to be consulted or borrowed. Subject to the provisions of the Copyright Act 1968, the University may issue a project report or thesis in whole or in part, in photostat or microfilm or other copying medium.

The University recognises that there may be exceptional circumstances requiring restrictions on copying or conditions on use. Requests for restriction for a period of up to 2 years must be made in writing to the Registrar. Requests for a longer period of restriction may be considered in exceptional circumstances if accompanied by a letter of support from the Supervisor or Head of School. Such requests must be submitted with the thesis/project report.

FOR OFFICE USE ONLY Date of completion of requirements for Award

1-' \· 07 !Registrar a !lid De(!u!Y Princi(!al

I

CERTIFICATE OF ORIGINALITY

I hereby declare that this ·:.ubmission is my own work and to the best of my knowledge it contains no nMerials previously published or written by another person, nor material which to a s~bstantinl extent has been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, b explicitly acknowledged in the thesis.

I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the projccrs design and conception or in style, presentation and linguistic expression is acknowledged

(Signed) L_ ____________________ ~

INT GRA T NG REMOTE E Sl G A D GIS FOR

U BAN Fl E DISASTER MANAGEME T

By

Sunil Bhaskaran

A thesis submitted to

The School of Surveying and Spatial Information Systems

University of New South Wales in partial fulfilment of the requirements for the degree of

Doctor of Philosophy

In School of Surveying and Spatial Information Systems

(Was earlier known as Geomatic Engineering)

March 2002

"I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of a university or other institute of higher learning, except where due acknowledgment is made in the text. "

UN 8 vV

1 6 JAN 2003

LIBRARY

TABLE OF CONTENTS

Table of Contents-----------------------------------------------------------------------------------------------1

References & Append ices--------------------------------------------------------------------------------------VII

Abstract ---------------------------------------------------------------------------------------------------------IX

List of Figures------------------------------------------------------------------------------------------------------XII

List of Tables-------------------------------------------------------------------------------------------------------XV

Acknowledgments-----------------------------------------------------------------------------------------------XVI

Chapter 1: lntroduction-····························································1

1-1 Purpose of the Study--------------------------------------------------------------------- -1

1-1.1 Reasons for Undertaking Research----------------------------------------------------------3

1-1.2 Background and Justification of Study------------------------------------------------------4

1-2 Research Objectives----------------------------------------------------------------------------------------5

1-3 Research Questions----------------------------------------------------------------7

1-4 Thesis Organization -----------------------------------------------------------------------------------------7

Chapter 2: Hazard Monitoring- Review of Remote Sensing and GIS et!; lrC>C>Is.··································································-··············1~

2-1 lntroduction-----------------------------------------------------------------------------------------------------12

2-1.1 Conceptual Issues Definition of Hazard, Vulnerability, Risk and Disaster--------13

2-2 Need for Modelling Hazards-------------------------------------------------------------------------------16

2-3 Hazard Monitoring, Assessment and Analysis--------------------------------------------------------1 9

I

2-4 Remote Sensing and GIS in Disaster Management- ------------------------------------------------22

2-4.1 Weather Hazards-----------------------------------------------------------------------------25

2-4.1.1 Hurricanes, Cyclones and Storms----------------------------------------------25

2-4.2 Geologic Hazards --------------------------------------------------------------------------27

2-4.2.1 Landslide Hazard Mitigation------------------------------------------------------27

2-4 .2 .2 Earthquakes--------------------------------------------------------------------------29

2-4.2.3 Volcanoes ----------------------------------------------------------------------------30

2-4.3 Technological and lnd us trial H azards--------------------------------------------------31

2-4.4 En vi ron men tal H azard s------------------------------------------------------------------------32

2-4.5 Fire Hazards-----------------------------------------------------------------------------33

2-4.5.1 Forest and Grass Fires------------------------------------------------------------33

2-4.6 Risk Management in Urban areas----------------------------------------------------------38

2-4.7 Remote Sensing and GIS for Other Studies----------------------------------------------40

2-5 Su mmary---------------------------------------------------------------------------------------------------------42

Chapter 3: Urban Fire Hazard Categorization and Monitoring--····45

3-1 I ntrod u ctio n------------------------------------------------------------------------------------------------------45

3-2 Case studies of RS/GIS applied to Hazard Management-------------------------------------------48

3-2 .1 National-------------------------------------------------------------------------------------------51

3-3 Risk Assessment by the New South Wales Fire Srigades-------------------------------------------55

3-3.1 Issues with Current Methodology------------------------------------------------------------56

3-3.1.1 Geographicallnsensitivity---------------------------------------------------------59

3-3.1.2 Community Vulnerability-----------------------------------------------------------60

3-3.1.3 Non-Currency of Spatio-temporallnformation------------------------- --- -61

3-3.1.4 Allocation of Dynamic Resources-----------------------------------------------61

II

3-4 Sources of Data for Required Resolutions--------------------------------------------------------------62

3-4 .1 In trod u ction---------------------------------------------------------------------------------------62

3-4.2 Building/Cadastre/lnfrastructure------------------------------------------------------------63

3-4.3 Socio-Economic Variables-------------------------------------------------------------------65

3-4.4 DEMs----------------------------------------------------------------------------------------------65

3-4.5 Utility /lnfrastructure---------------------------------------------------------------------------65

3-5 Benefits and Disadvantages of Airborne Remote Sensing for Hazard Monitoring-----------67

3-6 Temporal Resolution Requirements for Risk Assessment-----------------------------------------68

3-7 Spectral Resolution----------------------------------------------------------------------------------------70

3-8 Spatial Resolution Requirements for Hazard Assessment-----------------------------------------71

3-9 Evaluation of Hazard Related Variables----------------------------------------------------------------71

3-1 0 Restrictions and other F actors-----------------------------------------------------------------------75

3-11 Other Factors------------------------------------------------------------------------------------------75

3-12 Summary --------------------------------------------------------------------------------------------------7 5

Chapter 4: Data Management and Methodology---······················-77

4-1 I ntrod u ctio n-----------------------------------------------------------------------------------------------------77

4-2 Site Requirements---------------------------------------------------------------------------------------------78

4-2.1 Bathurst City -------------------------------------------------------------------------------------79

4-2.2 Hornsby-------------------------------------------------------------------------------------------79

4-2.3 Fire Incident History--------------------------------------------------------------------------- 85

4-3 Information Management Needs---------------------------------------------------------------------------87

4-4 Data Description----------------------------------------------------------------------------------------------88

4-4 .1 Co nto u rs------------------------------------------------------------------------------------------89

4-4.2 Land-use------------------------------------------------------------------------------------------89

Ill

4-4.3 Orthorectified Aerial Photo lmages---------------------------------------------------------90

4-4.4 Digital Elevation Model--------------------------------------------------------------91

4-4.5 Population Density-----------------------------------------------------------------------------92

4-4.6 State Wide Chemical Data Base------------------------------------------------------------92

4-4.7 Special Hazard Locations---------------------------------------------------------------------96

4-4.8 Digital Topographic and Cadastral Database--------------------------------------------97

4-5 Software and Hardware Requirements------------------------------------------------------------------98

4-6 Methodology----------------------------------------------------------------------------------------------------99

4-7 Data Resolution Characteristics--------------------------------------------------------------------------1 DO

4-7.1 Temporal Resolution--------------------------------------------------------------------------1 DO

4-7.2 Spectral Resolution----------------------------------------------------------------------------1 DO

4-7.3 Spatial Resolution------------------------------------------------------------------------------1 01

4-8 Availability of Cartographic Data-----------------------------------------------------------------------1 02

4-9 Methodology in Detail--------------------------------------------------------------------------------------1 02

4-9.1 Processing of Aerial Photo Graphs--------------------------------------------------------1 02

4-9.2 Map to Image Registration-------------------------------------------------------------------1 05

4-1 0 Spatial Overlay of disparate data----------------------------------------------------------------------1 05

4-11 Selection of Spatial Units--------------------------------------------------------------------------------1 06

4-12 Mas ki ng------------------------------------------------------------------------------------------------------1 08

4-13 Geometric lntersection-----------------------------------------------------------------------------------11 0

4-13.1 Results of Geometric lntersection------------------------------------------------------111

4-13.2 Update Operation and Creation of Master Attribute Table-----------------------114

4-14 Justification for the Choice and use of Weighted Overlay---------------------------------------118

4-15 Modelling risk with Weighted Overlay----------------------------------------------------------------122

4-15.1 Data Considerations-------------------------------------------------------------------------125

IV

4-16 Assigning Influences and Biases to Layers----------------------------------------------------126

4-17 Hazard Maps and lnterpretation-----------------------------------------------------------------------127

4-18 Verification of Model Results---------------------------------------------------------------------------129

4-19 Model Capabilities--------------------------------------------------------------------------------------130

4-19.1 Advantages of using Model Builder -----------------------------------------------------134

4-19.2 Limitations of using Model Builder -------------------------------------------------------135

4-20 Summary-----------------------------------------------------------------------------------------------------137

Chapter 5: Potential of applying FRM to the Resource Planning Process ···············································································140

5-1 In trod uction-----------------------------------------------------------------------------------140

5-2 Need for Balanced Resource Allocation and Distribution-----------------------------------------141

5-3 Typical Fire (Material) Resources-----------------------------------------------------------------------142

5-3. 1 Aerial Pu m pers-----------------------------------------------------------------------------------14 2

5-3.2 Water T ankers-----------------------------------------------------------------------------------143

5-3.3 Rescue Veh icles-----------------------------------------------------------------------------143

5-3.4 Aerial Appliances-----------------------------------------------------------------------------144

5-4 New South Wales Fire Brigades -An Overview of the Resource Planning Process--------------------------------------------------------------------------------------------145

5-4.1 The Government Level-------------------------------------------------------------------------146

5-4.2 The Organisational Level-------------------------------------------------------------------146

5-4.3 The Regional Leve l------------------------------------------------------------------------------148

5-4.4 The Local Level-----------------------------------------------------------------------------148

5-5 Issues with the Existing Planning Process-----------------------------------------------------------151

5-5.1 Existing System Variables and Hazard Assessment------------------------------------152

v

5-5.2 Urban Growth Assessment--------------------------------------------------------------------153

5-5.3 Vulnerability Assessment: Population and Demographic Characteristics---------156

5-5.4 Incident Data and Council LEP lnformation-----------------------------------------------156

5-5.5 Multiple Scenario Analysis---------------------------------------------------------------------157

5-6 Summary-----------------------------------------------------------------------------------------------------157

Chapter 6: Fire Risk Model: Interpretation of Risk in Bathurst and Hornsby ·············································································160

6-1 lntroduction--------------------------------------------------------------------------------------------------160

6-2 Semantic Hazard Model of Bathurst City-------------------------------------------------------------162

6-2.1 Distribution of Hazard in Bathurst-------------------------------------------------------------164

6-2.2 Implications of Hazard Model Output--------------------------------------------------------164

6-3 Semantic Risk Model of Hornsby Shire---------------------------------------------------------------17 4

6-3.1 Interpretation of Hazard in Hornsby Shire---------------------------------------------------175

6-4 Summary-----------------------------------------------------------------------------------------------------179

Chapter 7: Results: Advantages of the proposed FRM and comparison with the Existing Methodology followed by ~~\n#F=B ·······•······································································· 180

7-1 lntroduction--------------------------------------------------------------------------------------------------180

7-2 Model capabilities------------------------------------------------------------------------------------------184

7-3 Comparative hazard analysis--------------------- -----------------------------------------------------188

7-4 Management of Resources------------------------------------------------------------------------------ 197

7-5 Summary---------------------------------------------------------------------------------------------------197'

VI

Chapter 8: Summary and Conclusions·-·································200

8-1 Summary-----------------------------------------------------------------------------------------------------200

8-2 Conclusions-------------------------------------------------------------------------------------------------202

8-2.1 Model will have applicability in the NSWFB context--------------------------------------202

8-2.2 By using more variables the model will have more universal application

to hazard analysis in general not only fire hazard-----------------------------------------203

8-2.3 Better data will become increasingly available with new satellite remote sensing

system-----------------------------------------------------------------------------------------------204

8-2.4 The current study has used a limited number of variables to show the

methodology, but in many operational applications more variables may be required---204

8-2.5 Future research should directed to new GIS and RS methods and more

rapid acquisition of data to more effectively model hazards in real time--------------------204

FtE:i=E:FtE:~CE:S~·····································································:ZtJ6

~flflE:~[)ICE:S ·······································································:!:Zt5

Appendix I

Technical report. Integrating Imaging Spectroscopy & GIS for Spatial Emergency Decision Support Systems (SEDSS). A Case of Hail Storm Damage and Post Disaster Management, Sydney, NSW, Australia----------------------------------------------------------------- -226

Appendix II

Description of layers in master table in (Bathurst And Hornsby-Shire) master attribute table Bathurst, NSW, Australia----------------------------------------------------------------- 273

Appendix Ill

Update operations, Options Available and Weighted Average Index Method--------------- 283

vn

Appendix IV

New South Wales Incident Reports----------------------------------------------------------------------288

AppendixV

USGS Classification codes------------------------------------------------------------------------------290

Appendix VI

Extract from NSW Fire Brigades 'Fire News'. Winter Edition 2001 Vol2 No 3 pp1 0 -------------292

Appendix VII

Softwares used------------------------------------------------------------------------------------------------295

vm

ABSTRACT

This research is aimed at developing an urban disaster management system, which may

improve the existing method of hazard assessment and dynamic resource distribution. Disaster

management deals with the risk of natural disaster and managing the events following disasters.

Although prevention of disasters is an impossible task, careful risk analysis can help save lives

and reduce monetary damages. The ability to forecast the probability of hazards can assist state

and local governments in identifying zones particularly susceptible to disaster. This knowledge

aids in planning for local development, and the allocation of emergency resources. Additionally,

timely data on the geography of affected regions can assist in the deployment of disaster

response.

The study integrates high spatial resolution remote sensor data with GIS data for developing a

methodology to model urban disaster risk, which may assist in providing decision support

systems for emergency operations and redistribution of dynamic resources (material and

human). Disasters occurs in all parts of the world, but statistics have shown that those which

occur in urban areas have higher incidence to loss of lives and property due to the close

proximity of manmade, natural features and dense population. Since most of the research was

carried out in collaboration with the New South Wales Fire Brigades, it attempts to provide

systems, which may improve their existing methodology of hazard assessment and analysis and

resource distribution.

Much of the focus of this research is directed towards the hazard of fire (structural, property,

vegetation fires) in urban areas. Fires in urban areas, are very random and irregular. This is

mainly due to the nature of fire hazard in urban areas, which is very unpredictable and can occur

in any place despite the best methods of precaution and protection. This character of urban fire

hazard is in sharp contrast to bush fires where it is possible to make rough predictions about the

time and occurrence of the next bush fire. This is also due to the fact that bush fires are aided by

natural factors such as topography, wind direction and other atmospheric conditions.

Urban fire hazards may have different impacts on a community, due to the uneven spatial

distribution of factors which may influence these hazards such as land-use, structural density,

type of dwelling, construction materials used, location of fire stations, hydrants, location of

IX

special hazards, natural features such as topography, location, presence of water facilities, and

population density and other related demographic characteristics. At any given time the

assessment of hazard and the provision of fire protection must depend on an overall

consideration of these spatial factors. These factors are dynamic by nature, as they undergo

changes (both physical and human), leading to an increase in the level of hazard. It is important

from a disaster management perspective that these changes in hazard be regularly monitored

and hazards reassessed, which may have implications for the existing distribution of dynamic

resources leading to their re-distribution. Fire resources are distributed to provide fire protection

to the citizens in every service areas. In many instances the distribution is based on legislative

criterion and not on an overall understanding of the hazard, which is explained by both physical

and human features. Since hazards are characterized by physical as well as human factors, the

distribution of dynamic resources (material and human) must ideally be based on a combined

assessment of these factors.

Managing disasters in urban areas therefore requires current information on the spatial

distribution of risk and their spatia-temporal changes. The factors which influence risk from fire,

are varied and related with each other in a complex manner. Since, these factors are themselves

subject to frequent irregular changes over a period of space and time there is a need to model

such relationships in near real time by using integrated remote sensing and GIS technologies.

In the first phase of this study, a methodology is demonstrated to develop a semantic Fire Risk

Model (FRM), which enables the assessment and analysis of hazard in near real time. High

spatial resolution aerial photo images are combined with available GIS data to develop a

semantic risk model. Hazard for every spatial unit is analyzed by performing the geometric

intersection operation and weighted overlay process. By a system of assigning biases and

influences, this model may be used for generating hazard profiles in near real time.

In the second phase of the study, the potential benefit of the hazard model in the resource

planning process is discussed. The resource planning process which leads to the integrated

assessment of the decision-making process, is detailed after a brief explanation of the different

hierarchies through which the spatial information is disseminated. Issues related to data

consistency, currency and collective analysis of hazard related variables is documented, and the

benefit of using the hazard model is discussed.

X

The study results are summarized in the following paragraphs

};> Research result demonstrates a unique methodology, that integrates appropriate remote

sensor and existing GIS data to semantically model hazard in urban areas. The use of

the model may have implications for the existing distribution of dynamic resources in

geographically unique service areas, which may lead to the reassessment of hazard and

redistribution of dynamic resources.

};> A framework for integrating remote sensing and GIS data is also demonstrated as an

alternative to the existing method of distributing dynamic resources.

};> The research results can contribute to an overall improvement in the way urban

disasters are managed and may assist multiple users in the decision making process.

Since this research was carried out by using real data and off and on~ field collaboration with the

Corporate Strategy Division, New South Wales Fire Brigades, Sydney it has the potential to be

implemented immediately.

XI

LIST OF FIGURES

Fig 1 Layout of Chapters and Contents ........................................................................ 11

Fig 2 Hazard categorisation by NSWFB in Bathurst City, NSW ......................................... 57

Fig 3 Hazard categorisation by NSWFB in Hornsby Shire, NSW ....................................... 58

Fig 4 Property info layer (Small scale. Source LPI Bathurst, NSW) .................................... 64

Fig 5 Property information layer (Large scale map: Source LPI Bathurst, NSW) ................... 65

Fig 6 Location map of the study areas. Bathurst and Hornsby .......................................... 80

Fig 7 Ortho-rectified aerial photoimage of central business district, Bathurst. ....................... 81

Fig 8 Study area Bathurst City, NSW ........................................................................... 82

Fig 9 Study area CBD Hornsby Shire, NSW .................................................................. 83

Fig 1~ Orthorectified aerial photoimage of CBD Hornsby Shire, NSW ................................. 84

Fig 11 Spatial distribution of fire incident history in Bathurst City: Source NSWFB ................. 85

Fig 12 Spatial distribution of fire incident history in Hornsby Shire Source: NSWFB) ..................................................................................................... 86

Fig 13 Information management cycle (Granger K, 2000) ................................................. 87

Fig 14 Contours showing the general elevation in Hornsby ............................................... 91

Fig 15 Chemical storage locations within consent levels in Bathurst. .................................. 93

Fig 16 Spatial distribution of sensitive locations in Bathurst City, NSW (Source: NSWFB) .................................................................................................... 94

Fig 17 The New South Wales Feature Data Base .......................................................... 95

Fig 18 Legend: New South Wales Feature Data Base ..................................................... 95

Fig 19 Location of special hazard sites: above consent level sites, Bathurst.. ....................... 96

Fig 20 Digital topographic database: Source: Land and Property Information (LPI) Bathurst .................................................................................................................. 98

Fig 21 Methodology to semantically model hazard in urban environments ........................... 104

xn

Fig 22 Spatial unit selection (large yellow box: 500m by 500m, medium 250m by 250m and small 100m by 100m ........................................................ 107

Fig 23 Small area analysis issues: By using orthophotos the area of hazard can be precisely demarcated. Structures are shown by dots within the collector districts .............................................................................................. 1 09

Fig 24 Location of hazard within collector districts: Masking operations in GIS ................... 110

Fig 25 The Geometric intersection process ................................................................ 112

Fig 26 Graphical results of geometric intersection between land-use and spatial units .......................................................................................................... 114

Fig 27 The weighted overlay process ......................................................................... 123

Fig 28 The Weighted Overlay Process ....................................................................... 124

Fig 29 Assigning Influences and Biases ..................................................................... 127

Fig 30 Semantic Hazard Model of Bathurst City and Proximity Analysis ............................ 128

Fig 31 Spatial overlay of vector layers: model verification .............................................. 130

Fig 32 Proximity analysis ........................................................................................ 133

Fig 33 Ability of the model to provide forecast information .............................................. 134

Fig 34 The New South Wales Fire Brigades Resource Planning Process .......................... 150

Fig 35 Use of hazard model in the resource planning process for detecting urban growth ................................................................................................................. 155

Fig 36 Fire hazard model Bathurst.. ........................................................................... 163

Fig 37 Hazard level by commerciallanduse (service business and general business) in Bathurst .............................................................................................. 165

Fig 38 Hazard level by lndustriallanduse in Bathurst.. .................................................. 166

Fig 39 Hazard level by structural density in Bathurst ..................................................... 167

Fig 40 Hazard level by degree of congestion in Bathurst ............................................... 168

Fig 41 Hazard level by population density in Bathurst.. ................................................. 169

Fig 42 Hazard by residential density .......................................................................... 170

Fig 43 Hazard categories by income level ofpeople ...................................................... 171

Xlll

Fig 44 Hazard level by less mobile people ................................................................. 172

Fig 45 Fire hazard model, Bathurst. ........................................................................ 17 4

Fig 46 Hazard categorization in CBD Hornsby Shire .................................................... 175

Fig 47 Hazard categories and locations of special hazards ........................................... 176

Fig 48 Vacant Spaces for Evacuation Strategies ......................................................... 177

Fig 49 Hazard model for resource allocation: spatial distribution of high-rise structures for allocation of aerial pumpers ................................................................. 178

Fig 50 Fire districts in New South Wales (Source NSWFB) .......................................... 186

Fig 51 Model Overlay on Fire districts New South Wales ............................................. 187

Fig 52 Hazard categorization by NSWFB in Bathurst City ............................................. 189

Fig 53 Hazard categorisation by this study ................................................................ 190

Fig 54 Area serviced by existing Fire Stations in Bathurst ............................................ 192

XIV

LIST OF TABLES

1. Hazard Descriptors (Adapted from the NSWFB Project Report, 1996) .............................. 56

2. Urban/Suburban Attributes desired for hazard assessment and the minimum remote sensing resolutions required to provide such information ..................................... 69

3. Attribute Table created after the Geometric Intersection and the Update operation ............................................................................................................ 113

4. Master Attribute Table (MAT) of attributes showing the final fields and columns. For detailed description of fields ............................................................. 115

5. Master Attribute Table (MAT) of attributes showing the final fields and columns. For detailed description of fields .......................................................... 116

6. Master Attribute Table (MAT) of attributes showing the final fields and columns ...................................................................................................... 117

7. Comparison of travel distance to special hazard sites by the existing fire stations of Bathurst and Kelso (Bathurst) .................................................................. 193

8. Directions required to be taken by the fire service to a specific special hazard site 'Ampol and Mobil fuels' ................................................... 194

9. Directions required to be taken by the fire service to a specific special hazard site 'Am pol and Mobil fuels' .................................................... 194

XV

Acknowledgments

My doctoral program had all the ingredients that characterizes a PhD, therefore in the end,

it 2vas a very fit!filling and satisfying achievement. Since I decided to investigate an issue in

remote sensing and GIS, which was of an applied nature, I had to interact with many

organizations and personneL There were numerous people from different organizations and

backgrounds, 2vho were cooperative and helpful in many 2Vq)IS.

First and foremost, I would like to express my gratitude to Professor Bruce Forster 2vho

supervised my progress and guided me in a wqy on!J he can. Professor Forster, provided

ongoing encouragement and maintained prompt communication with me, be it discussing a

project, topic or planning a conference paper. Professor .Forster provided me 1vith a platform

which helped me to maintain my enthusiasm for research.

Some of the chapters in this thesis depended on input from the New South Wales Fire

Brigades (NSWFB). I am grateful to Trevor Neal from the NSW'FB, who cooperated

with me despite his busy schedule during the two mqjor projects, 1vhich I carried out for the

Corporate Strategy Division, NSWFB, Sydnry, Australia. His support and feedback

1vere an invaluable asset to me.

Other thanks are extended to Carmel Donnel!J, Director - Corporate Strategy Division,

New South Wales Fire Brigades, 2vho sanctioned scholarships for the projects, John Nee!J

who 1vas a very reassuring presence at every meeting, Mark Br02vn who cooperated in field

work and validation operations.

A special thanks to my fami!J, particular!J my brotherS uresh 1vho was a source of constant

inspiration at all times and sister Smitha, who was very understanding. Special thanks to

my friends and colleagues; Addie Mercer, Bisun Datt, Anthea Mitchell, Masashi 1vith

1vhom I shared intelligent, thoughtful and humorous moments, 1vhich were important to

keep me going during long hours of research.

XVI

Thanks are also due to Professor Ian Burnlry, Professor Colin Sutherland, Professor Totry

Milne, Professor Bill Kearslry, Professor Barry Garner, Dr Ewan Masters, Yanni

Zakaria, Chris Ryan, John Craag, , John Claque, Chris Dorman, George Antomvich,

Dr Es1varappa and all others, who have played a part in my achievements directjy or

indirectjy.

I would like to dedicate the thesis to my parents, 1vho have been a great source of strength,

inspiration and wisdom.

XVII

Integrating RS & GIS for Urban Fire Disaster Management. Sunil, 1999-2002

Chapter 1: General Introduction.

Purpose of the Study

The purpose of this research is to develop a hazard1 model in an urban environment,

which integrates remote sensing data and cartographic data (GIS) in a manner, that it

embraces the concept of hazard holistically, and provides a decision support system for

the strategic distribution of fire resources (material and human) in near real time. The

occurrence of a disaster triggers a sequence of actions by emergency organizations,

most of which are based on logical and intuitive theories. However, every disaster has

some spatial ramifications, which is embodied in the physical and or human attributes

that are distributed in space and over a period oftime. Besides, the proper management

of a disaster must take into account other factors, which may influence them. However,

this is not an easy task, since the factors which influence hazard are dynamic and need

to be constantly monitored and analysed, leading to the need for risk assessment in all

regions. The identification and assessment of hazard is vital for the distribution of

dynamic resources. Any effective strategy to manage disaster risk must begin with an

identification of the hazards and what is vulnerable to them. This involves information

on the nature and extent of risk that characterizes a particular location, including

information on the nature of particular physical hazards obtained through their risk

assessment, as well as information and data on the degree of exposure of a population

and its built environment to such hazards.

Achieving the above tasks in near real time is important since emergency operations are

based on decisions, which have to be taken immediately.

1 Fire hazard those caused by structural, property and vegetative fires

1

Integrating RS &. GIS for Urban Fire Disaster Management. Sunil, 1999-2002

Emergency services and similar organizations need to regularly assess urban hazard

and associated vulnerability, for the management of urban disasters. However,

developing management systems for urban disasters is a complex task. Remote sensing

data and GIS techniques may be used in the development of such systems, but their use

involves the systematic appraisal of sensor characteristics such as spatial, spectral and

temporal resolutions. Furthermore, GIS data availability may limit the integration of

several attributes, which are vital for developing a risk model. Modelling an urban

disaster management system is therefore a very challenging and complex task.

Existing methods pursued by many emergency services suffer from lack of conceptual

understanding of hazard, and therefore do not take into account several spatia-temporal

variables which influence hazard and are dynamic by nature. There is a need to address

some of these issues and develop an operational model that is capable of detecting,

recording and analysing urban hazards in near real time. Given the unpredictable and

frequent nature of hazards and disaster risks, and the pressure to make quick decisions

during emergency operations, the demand for improved and efficient management has

never been more acute. Since the relationship between factors which cause hazard are

many and complex, there is a need to develop a methodology to model urban hazards

which can make a positive contribution to the management of urban disasters.

The main focus of the study is to demonstrate a methodology, by integrating high

spatial resolution airborne remote sensing data with GIS data, for the purpose of

modelling urban hazard and vulnerability. The study uses an example of hazard caused

by fire to demonstrate the development of an urban disaster management system.

2

Integrating RS a GIS for Urban Fire Disaster Management. Sunil, 1999-2002

Urban disasters caused by fire and other hazards may have different impacts in a given

area due to the spatia-temporal distribution of physical and socio-economic features

such as land-use, structural density, location of hazardous facilities, population density

and demographic characteristics. These factors undergo frequent changes in space and

over a period oftime, and are interrelated in a complex manner.

Modelling these complex relationships is important in order to understand the existing

hazard and vulnerability, which is then important for managing a disaster, which in tum

hinges on an understanding of spatia-temporal factors. The development of an

operational model largely depends on current data, which in many cases can be only be

derived from remotely sensed images. The use of remote sensing data for hazard and

vulnerability assessment depends on a systematic appraisal of sensor resolution

characteristics (spatial, spectral, temporal and radiometric). This study discusses the

applications and suitability of remote sensing images that come in different spatial,

spectral and temporal resolutions, for the purpose of detecting, extracting and recording

hazard and vulnerability related variables and their integration with cartographic (GIS)

data for developing a hazard model in urban environments. The study attempts to

approach the concept of hazard holistically by studying and analysing physical

attributes of hazard such as land-use, structural density and types, with the human

component (population density, demographic and socio-economic characteristics etc).

1-1.1 Reasons for undertaking research

Managing urban disaster demands information on hazard, risk and the spatial analysis

of several factors, which influence disaster risks. Hazards are multi-dimensional, multi

disciplinary and intrinsically complex phenomena caused by a large set of factors,

3

Integrating RS B: GIS for Urban Fire Disaster Management. Sunil, 1999-2002

which have a strong spatial component (Coppock, 1995). The distribution of latent and

realized demand of fire hazards (structural, property and vegetation) clearly indicate a

spatia-temporal irregularity that needs to be understood in order to improve emergency

preparedness, redistribution of dynamic resources and overall disaster mitigation.

Managing risks arising from these hazards is a key issue. Pre-disaster management is

risk management. Disaster risks arise from combinations of hazardousness and

vulnerability that vary over seasonal to decadal time scales as well as geographically.

There is a need to model the complex relationships between factors which influence

and cause hazards and which make certain regions more vulnerable than others. The

use of remote sensing technology and GIS have the potential to provide the ingredients

for an operational model. However, the use of remote sensing data and GIS for the

management of urban hazards presents numerous challenges of which, modelling and

dynamic resource distribution, are two of the more demanding issues which have to be

dealt with in near real time.

1-1.2 Background and justification of study

The proposed research is directed to improving the existing methodology of hazard

categorization as practiced in New South Wales, Australia. It is therefore necessary to

provide a brief summary of the current methodology and related issues. The existing

method for generating hazard categories is undertaken using in-situ field survey

methods, which are time consuming, expensive and requires extensive coordination.

One of the major drawbacks of this methodology is its lack of currency. Decisions

made during emergency situations have to be taken quickly and should be based on the

most recent data. Processes such as urbanization, industrialization, often result in

4


frequent changes in the land-use resulting in a restructuring of spatial patterns. This

will alter the potential degree/scale of hazard either by increasing or decreasing it.

Current information on land-use, population density and changes in structural patterns

is vital in order to draw up appropriate plans and make appropriate decisions.

The in-situ field survey methodology consumes a lot of time in order to develop a

hazard categorization at a regional scale (in this case for the entire state ofNSW). Any

major disaster that may occur in the interim period will have to be addressed by using

existing available information and local knowledge. Besides, it is dependent on

extensive coordination amongst staff, some of who are appointed on a voluntary basis.

Record keeping and information management in this situation can give rise to an

erroneous database. There is a strong reason to use a technology that can improve this

methodology by providing accurate spatia-temporal data in real time.

The allocation of dynamic resources (staff, equipment and vehicles) is to be based on

the level of hazard present in the response area. The hazard categorization process is

yet to be completed in NSW and its completion is some time away. There is therefore

an immediate need to use a technology that acquires spatia-temporal data in real time,

which can then be analyzed in a GIS environment for hazard and vulnerability

assessment modeling. GIS/RS are twin technologies that may answer most of the above

questions.

Research Objectives

The major goal of the study is to develop a fully operational spatial fire hazard and

vulnerability assessment model by using available remotely sensed data that contributes

5

Integrating RS a: GIS for Urban Fire Disaster Management. Sunil, 1999-2002

to an enhanced conceptual understanding of hazard/disasters and attempts to bridge the

gap between physical assessment and community vulnerability.

• To evaluate the existing method adopted by the New South Wales Fire Brigades,

Australia to assess and monitor urban fire hazards and associated risks, and to

address issues arising out of their existing approach.

• To model hazard and vulnerability assessment holistically by integrating image data

With suitable resolution characteristics acquired from both airborne and space

borne sensors and to integrate available sources of cartographic data in order to

understand and map the hazard and vulnerability in a certain region.

• To attempt a systematic appraisal of spatial and temporal resolution capabilities of

available airborne and space-borne sensors with respect to their suitability for

hazard categorization and monitoring and to document the advantages and

disadvantages of each.

• To understand the rationale behind the distribution of dynamic resources (staff and

fire equipment within each fire districts) in NSW and critically evaluate their spatial

distribution by/and using the integrated hazard and vulnerability model for

addressing any imbalances in the distribution of such resources.

One of the major overall goals of the study is to develop a fully operational spatial fire

hazard and vulnerability assessment model that contributes to an enhanced conceptual

understanding of hazard/disasters and attempts to bridge the gap between physical

assessment and community vulnerability

6


Research Questions

Some of the research questions that this research is expected to answer is as follows

.> What is the methodology for developing urban disaster management

systems by integrating remote sensing and GIS data?

.> How can the existing resource planning process be improved by using the hazard model?

.> How can the identification and assessment of hazards and development of

emergency spatial decision support systems assist in urban disaster

management?

Thesis Organisation

Chapter 1 gives a general introduction to the research and describes the background

and justification for undertaking this research. The research objectives and the research

questions are also discussed in this chapter.

Chapter 2 presents a description of some interesting attempts made in the past to

integrate remotely sensed data and GIS. It emphasizes the importance of understanding

the concepts of hazard, risk, vulnerability and disaster, and begins with a brief

explanation between apparently similar, but at times confusing terms of hazard, risk

and disaster. It also describes the importance of fire hazard categorization and the

potential role ofGIS/RS for the study of hazard. The chapter reviews some past studies,

which were carried out using remote sensing and/or GIS in the field of hazard and

disaster management. Practical models developed in this research, which are unique to

this field of risk assessment and analysis is cited along with the original contributions

made by the current research.

7


Chapter 3 presents selected studies in urban fire hazard monitoring which includes

current national and international approaches to hazard management. The methodology

adopted by the New South Wales Fire Brigades is described, along with a discussion of

the major issues that pose a major challenge to the emergency services in the future.

Spatial and temporal resolution requirements of space borne and airborne data are

discussed with particular reference to the analysis of urban hazard. Data needs for the

research are also discussed in this chapter.

Chapter 4 deals with the management of data and the methodology adopted in this

study. The study areas of Bathurst and Hornsby are introduced along with a discussion

on the data management issues. The main content of this chapter is the methodology for

the fire risk model. The various steps and processes involved in the methodology are

explained in detail along with other important spatial operations such as geometric

intersection and update. The ability of the model to depict different risk scenarios in

different regions by using influences and biases is also explained.

Chapter 5 The potential application of the fire risk model to the existing process of

resource planning and risk assessment followed by the NSWFB, Sydney. The various

types of resources (particularly material resources) are briefly discussed and an

overview of the entire planning process is described. Issues with the existing planning

and decision-making process are discussed and the importance and benefits of applying

the fire risk model to the planning process is discussed in this chapter.

8

Integrating RS ft GIS for Urban Fire Disaster Management. Sunil, 1999-2002

Chapter 6 explains the interpretation of the type of hazards and risks as shown by

various individual hazard related indices. Different hazard maps in Bathurst and

Hornsby, which are generated by the FRM are shown and discussed in this chapter.

Chapter 7 presents the results of the various GIS operations and data analysis and

summarizes the overall achievements of the research. The performance of the models

and their potential application during emergency management is discussed in this

chapter. Advantages of the proposed model and comparisons with the existing

methodology followed by the NSWFB is the main content of this chapter.

Chapter 8 summarizes the achievements of research and describes the scope for using

the FRM, and the need to use it for distributing dynamic resources. The potential

benefits of integrating remote sensing data with GIS for assessing and modeling

hazards in urban environment, is also discussed in this chapter. Since the developed

model depends on available data and data which may be extracted from remote sensing

(airborne or spaceborne) sources, their choice and quality are crucial to the success of

model implementation. The need for further research into the extraction of socio

economic information from remote sensing data is emphasized in this chapter.

Limitations of available data and the potential of future remote sensing systems are also

discussed in this chapter.

The methodology developed in the research has been submitted for publication in

international journals and has also been presented and published in various

international conferences, which focused on GIS/RS applications. A major part of the

thesis and research was derived from two projects, which were carried out, jointly by

9


the University of New South Wales and the Corporate Strategy Division, New South

Wales Fire Brigades. Thus the research was of an applied nature where the main

emphasis was to review existing methodology, and to develop systems to improve the

existing methodology of risk assessment and resource distribution. A quick-look layout

of the thesis is illustrated in shown in Figure 1.

10

Integrating RS 8: GIS for Urban Fire Disaster Management. Sunil, 1999-2002

Chapter 1: General Introduction

Chapter 2: Hazard Monitoring- Review of GISIRS as Tools

Chapter 3: Urban Fire Hazard Categorization and Monitoring

Chapter 4: Data Management and Methodology

Chapter 5: Potential of applying FRM to the Resource Planning Process

I Chapter 6: Fire Risk Model: Interpretation of Risk in Bathurst and Hornsby I

Chapter 7: Results: Advantages of the proposed FRM and comparison with the Existing Methodology followed by NSWFB

I Chapter 8: Summary and Conclusions I

Figure 1 Layout of chapters and contents

11

lntegratfng RS 8: GIS for Urban Ffre Dfsaster Management. SunH, 1999-2002

Chapter 2: Hazard Monitoring - Review of GIS/RS as Tools.

2-1 Introduction

An important source of data, available to emergency services during the management

of disasters is remote sensing of the disaster environment. During emergencies and

disasters, detailed description of natural hazards may not exist and there may be a

limited knowledge on the susceptibility of a certain area to natural hazards. Remote

sensing provides synoptic details about the spatial relationships that exist between earth

features, and by using such information in near real time the effect and impact of a

potential disaster may be reduced. Studies (Verstappen, 1995) addressing the role of

remotely sensed geographic information in mitigating rrinstantaneousrr disasters, such as

floods, have resulted in the following list of potential applications:

)>- To establish the susceptibility ofthe land and vulnerability ofthe society.

)>- To construct maps of potential hazard areas for use in physical planning

(hazard zoning maps).

)>- To monitor potentially hazardous situations and processes, providing

advanced warning.

)>- To improve management of emergency situations following a disaster.

Remote sensing technology has often been used with GIS for many applications.

GIS/RS have over the years exhibited two principal advantages for natural hazard

mitigation and research. First, the technology allows long-term time-series studies and

storage ofthe information, which may prove invaluable in future situations. Secondly,

GIS/RS improves information accessibility. Remote sensing platforms can provide

12


large amounts of data quickly and inexpensively, relative to other means of collection,

and GIS can bring together vast amounts of information from a wide variety of sources

and make the information quickly visible and applicable in emergency situations. Their

combined use in the field of hazard and disaster management should be based on a

clear conceptual understanding of hazard and related terms. A clear and concise

understanding of hazard and related terms of risk and disaster is important before

embarking on hazard related studies and analysis.

2-1.1 Conceptual Issues: Definition of hazard, vulnerability, risk and disaster

The definition of hazard and disaster has been the subject of much debate. Hazard is

seen as a situation, that in particular circumstances, can lead to harm (Royal Society,

1983). This defmition states that hazards are omnipresent phenomena that may occur

anytime and anywhere leading to harmful consequences. Statements about the

probability of occurrence of a harmful event are usually referred to as statements about

'risk' (Handmer and Penning-Rowsell, 1990). Hewitt (1983) states that 'hazard' refers

to the potential for damage that exists only in the presence of a vulnerable human

community. This concept usefully embodies the idea the harmful events, which arise in

hazardous environments, may be reduced by altering the vulnerability of the exposed

population. Most of the above definitions have implied that hazard is a harmful event to

which humans are exposed. However, hazards may also affect the flora and fauna in a

harmful manner. For instance, the leaking of oil from a gas tanker in the ocean can pose

a great hazard to the marine life sometimes endangering near extinct species. Most of

the vulnerability assessment studies are underpinned by a poor appreciation and

understanding of the concept of hazard and disaster, resulting in serious shortcomings

in the emergency preparedness plans. Invariably the spatial context is missing in most

13

Integrating RS a GIS for Urban Fire Disaster Management. Sunil, 1999-ZOOZ

of them, since they are guided by legislative rules and are too hazard focused. This has

been highlighted in a number of articles including (Walker et al. 2000, Bull, 1991,

Farmer, 1997).

A hazard is defined as a potential or existing condition that may cause harm to people,

or damage to property or the environment (Guidelines for Hazardous Activity, 1992).

Hazards are inevitable and normal consequences of human interaction with our natural

and social environments and from our development and use oftechnology. From that

point of view, hazards and economic development go hand-in-hand. With each

progressive step taken by mankind there will be an accompanying component of hazard

and risk. For instance, a nuclear installation will carry a high hazard label due to the

inherent potential of harm that it may lead to in the event of a mishap. Similarly, a

chemical industry or dense commercial center characterized by a high density of

population and structural density will also carry an element of hazard. In other words

hazards are omnipresent phenomena that are an unavoidable part and parcel of our

lives. A hazard is any situation, condition or thing that has the potential to disrupt,

damage or bring loss to things that people value (Boughton, 1998). A 'Hazard' is seen

as a 'situation that in particular circumstances could lead to harm' (Royal Society,

1983). 'Hazard Management' embraces everything from hazard evaluation and

identification through to recovery from a disaster and post-event evaluation or

feedback, and includes emergency planning and management and disaster management.

A certain installation can be seen as a potential hazard if it were to be located in the

middle of a thickly populated town or city as was the case with the Bhopal gas disaster

in India, or Chernobyl nuclear disaster in Russia. On the other hand the same site may

not qualify to be hazardous, if it were to be located far away from habitation. (Hewitt,

14


1983) states that hazard refers to the potential for damage that exists only in the

presence of a vulnerable human community. There is a relative shift in the intensity of

the effect a hazard can lead ranging from high to low, depending on the number of

people exposed to it.

'Disaster' is often the term given to the harmful event referred to in the definitions of

hazard. Thus, according to Whittow (1980), whereas "a hazard is a perceived natural

event which threatens both life and property - a disaster is the realization of this

hazard". However, Whittow, whose focus is on environmental hazards, excludes man

made events and those events caused by the failure of technological systems, each of

which can cause disaster. Cohen and Ahearn (1980), whose focus on the human victims

of disasters, refer to disasters as extraordinary events that cause great destruction of

property and may result in death, physical injury, and human suffering'. Once again

this defmition excludes environmental disasters. Keller et al. (1990) state that a disaster

is "an event which afflicts a community the consequences of which are beyond the

immediate fmancial, material or emotional resources of the community". Disasters

have also been defined as overwhelming events and circumstances that test the

adaptation responses of communities or individuals beyond their capability, and lead, at

least temporarily, to massive disruption of function for these communities or

individuals. This defmition is useful because it implies that communities have

differential resilience or capacity to respond to disasters. Disasters can be defmed as an

unusual natural or man-made event, including an event caused by failure of

technological systems, which temporarily overwhelms the response capacity of human

communities, groups of individuals, or natural environments, and which causes massive

damage, economic loss, disruption, injury and/or loss of life. This definition

15

Integrating RS ·& GIS for Urban Fire Disaster Management. Sunil, 1999-2002

encompasses medical accidents and disasters such as those, which have arisen from the

negative affects of whooping cough, vaccine, and HIV I AIDS haemophiliac cases.

Risk on the other hand is a concept used to give meaning to 'things, forces or

circumstances' that pose a danger, whereas 'hazard' is 'something' with the potential to

produce harm. 'Risk' is sometimes taken to be the same as 'hazard' but risk has the

additional implication of the chance of a particular hazard actually occurring. A hazard

may be defined as 'a potential threat to humans and their welfare' and risk as 'the

probability of hazard occurrence'. The distinction can be drawn by the following

example - two people are crossing an ocean, one in a liner and the other in a rowing

boat. The hazard (death by drowning) is the same in both cases but the risk (probability

of drowning) is very different. If the drowning actually occurred, it could be called a

disaster. So a disaster may be seen as 'the realization of a hazard'. Disasters results

when the interaction of a hazard and vulnerability is such that the community cannot

reasonably cope with the results using its societal and materials resources (Stenchion,

1997).

2-2 Need for Modelling Hazards

Hazards are multi-dimensional, multi-disciplinary and intrinsically complex

phenomena caused by a large set of factors, which have a strong spatial component

(Coppock, 1995). The distribution of latent and realized demand of fire hazards

(structural, property and vegetation) clearly indicate a spatio-temporal irregularity that

needs to be understood in order to improve emergency preparedness for disaster

mitigation. Fire hazard categorization is a system of measuring and recording hazards,

which leads to the understanding of these spatio-temporal variations. However, current

16


in-situ methods, to categorize hazards, are beset with problems. Some critical issues

are, for example, being too hazard focused, lacking currency, and most importantly

lacking an adequate theoretical base. There is a need to develop a semantic model that

provides a solution to the above issues, which at first acquires spatial data in real time,

and then is input to a GIS for hazard and vulnerability assessment. A model can

simplify complex events by helping to distinguish between critical elements and noise.

This is particularly useful in a high-pressure disaster response environment, when there

is little time to think about issues or to identify critical issues. A model can lead to

better understanding of the current situation and the processes, which are responsible

for the evolution of a disaster. A model of the disaster process will enable

quantification of disaster events, which in turn is a key element in the reduction of the

complexity of disasters (Kelly, 1999).

However, a major component of such a model will be heavily reliant on data that may

not be available in digital form or have not been spatially referenced. Given the

dynamic and complex nature of hazards, modeling such a database in real time is

currently the greatest need.

The synergism of GIS/RS technology can result in faster and synoptic acquisition of

spatia-temporal data and its subsequent analysis, in order to model hazard and

vulnerability assessment, since they both involve spatially-referenced digital data. At

the wider regional scale, remote sensing can reduce the manpower requirements of

mapping projects by several orders of magnitude, with corresponding increases in the

speed of map compilation (Alexander, 1995). GIS has potential for use in emergencies,

when "real time" - generated maps can show the way to points of major impact and

17


indicate the viability of route ways and other lifelines for relief and aid (Dymon, 1994).

It has excellent potential in modeling, or simulation (ANU, 1994), emergency planning

(Watson, 1992), and logistic research (Hodgson and Palm, 1992). Due to its versatility

and ability to integrate diverse types of information, a GIS can be a valuable tool in

development, monitoring and update of a hazard mitigation plan (USAID/OAS

Caribbean disaster mitigation project, 1993).

The current study examines the potential benefits of integrating high spatial high

resolution remotely sensed data in the form of colour aerial-photo-images and the

recently launched commercial satellite IKONOS, which has a spatial resolution of 1

metre in the visible range and 4 metres in the in the visible and near infrared (multi

spectral), with cartographic GIS data, for developing a hazard and vulnerability

assessment model. It evaluates the existing methodology of categorizing fire hazards in

NSW, Australia and argues for a better understanding of the concept of hazard and

vulnerability and disaster. Going further, the study demonstrates the construction of a

model, which integrates image data, and available cartographic GIS data, and by

employing an expert classifier approach, attempts to explain the spatio-temporal

distribution of hazard and vulnerability in diverse landscapes. The model is developed

using high spatial resolution airborne remotely sensed data with GIS leading to a

critical and systematic appraisal of both sources of data and their suitability for hazard

categorization and monitoring, as part of the model development process.

An immediate benefit from such a model is the systematic and automatic generation of

hazard categories in diverse landscapes, whereas long term benefits will assist the

18


decision making process, particularly for improving the allocation and distribution of

dynamic resources within fire districts.

2-3 Hazard Monitoring, Assessment and Analysis

Hazards are complex and vary greatly in their frequency, speed of onset, duration and

area affected (Coppock, 1995). Throughout history, urban hazards and disasters have

included a mix of natural, technological and social events. Although scientists and

professionals might wish to classify different hazardous phenomena into one of these

categories for purposes of analysis and management, the reality is that city dwellers do

not make such fine distinctions (Mitchell, 1999). Understanding the very complex

processes that lead to hazards and disasters requires a multi-disciplinary approach, and

coping with them require access to spatially referenced data from a wide variety of

sources, at different scales, resolutions and reliability, and often over time, and analyses

of such data in four dimensions (Coppock, 1995). It is vital to model these events in

order to systematically understand and unravel the underlying complex physical and

social processes. In a joint project carried out in the Caribbean (which is a region

subject to multiple hazards), by the Organization of American States (OAS) and the US

Agency for International Development (USAID) a simple but effective approach was

demonstrated to model hazard and vulnerability. The main aim of the project was to

reduce vulnerability to natural hazards throughout the region. A planning process was

evolved through which the risk and susceptibility associated with areas hazards were

analysed and the information was used to mitigate hazard and vulnerability in the

region (USAID/OAS-Caribbean disaster mitigation project, 1993).

19


Another simple example of a modelling process is given by (Ahearn and Chladil,

1999). They examined the relationship between the edge of the bush and house losses

using data from two large fires: the Hobart bushfire of 1967 and the Otways in 1983.

Black and white aerial photographs were used to determine the vegetation boundary.

The photographs were first registered in the mapping package. The distance from each

destroyed house to the vegetation boundary was then measured along the appropriate

wind direction for each location. The study showed the relationships between houses

burnt in bushfires and the distance they stand from vegetation boundaries, which could

be used to reduce risk in other hazard prone areas.

A place becomes prone to hazard over a period of time mainly due to economic

progress, which alters existing spatial arrangements in a manner that adversely affects

the provision of equitable emergency service, and demands more resources, or

maximization of resources than before. In order to understand the increase in hazardous

regions it is essential to monitor these spatio-temporal changes at regular intervals. Key

observations made will provide much needed information to disaster managers and

planners who can unravel the complexities associated with the risk planning.

Monitoring the spatio-temporal changes can be achieved by several, means such as in

situ field survey methods, where trained staff examine the structures on the earth

surface and assess their relative degree of hazard. Recently due to technological

advancements made in the field of GIS/RS, it is easier and faster to detect and measure

such changes by using remotely sensed images either from space borne or airborne

sensors. This is particularly relevant to hazard categorization and its spatial distribution

which are explained as follows.

20


At least four basic types of scientific information are necessary to construct an effective

disaster reduction program: Information about risks, exposure, vulnerability and

responses. Hazard identification is the first and most important step in any hazard

analysis and involves the identification of all possible conditions that could lead to a

hazardous incident. The comprehensive and systematic identification of all hazards is

critical to the success of the hazard analysis (Guidelines for Hazard Activity, 1992).

Hazard identification is the essential foundation upon which all risk assessment is

based. Vulnerability assessment combines the information from hazard identification

with an inventory of the existing or (planned) property and population at risk. It

provides information on who and what are vulnerable to a hazard within the

geographical areas defined by hazard identification and can estimate damages and

casualties that will result from various intensities of the hazard (Burby et al. 2000). Fire

hazard categorization helps in identifying and assessing the vulnerable and susceptible

regions that have a potential for disaster in varying degrees. Hazards differ from place

to place due to variations in the land-use, structural density patterns, population density

and demographic indices to name a few contributing factors. Land-use is one of the

fundamental indicators of this variability of fire hazards. For instance a dense

commercial region will pose a different level of hazard, when compared to a residential

or industrial region.

One of the main objectives for generating fire hazard categories is to understand this

spatia-temporal variation. Hazard categorization permits the measurement, recording

and meaningful translation of these geographic variations for each spatial unit and

enables an overall understanding of prevalent hazard in a region. Hazard categorization

enables the comparison of hazard in one region to that of another and consequently

21


assists in the allocation of dynamic resources. It also helps in visualizing the spatia

temporal patterns of hazards, which would otherwise be difficult to comprehend. When

combined with demographic data and other derived hazard related information, hazard

categories can be used for complex analysis, which will be a major input to disaster

management. One of the key outcomes of hazard categorization is risk assessment for

emergency management.

Hazards may be categorised into different levels according to the spatial distribution of

factors, which influence them. Since an understanding of risk includes the physical and

human attributes, therefore categories of hazard must be based on a combined analysis

of these factors/attributes. Hazard categories may also vary depending on the landscape

and region. For instance, in an urban environment major hazard categories may be

determined by variables such as land-use, structural density, population density, type of

structures and other factors whereas in a region covered by bushes and vegetation the

categorisation will be based on other factors.

2-4 Remote Sensing and GIS in Disaster Management

Remote sensing technology and geographic information systems are both tools for

managing spatially distributed information in large quantities and at a variety of scales.

Both provide a systematic view of spatial information. Both increase the capabilities of

human decision-makers and planners to grasp relationships at larger scales and in more

complex settings than was ever possible (Ehlers, 1993). The use of GIS/RS can play an

important role since they can provide information on the extent of disaster within a

relatively short period of time and they are of extreme importance in obtaining the

22


necessary data to make an evaluation of the hazard, vulnerability and risk, if combined

with other types of cartographic data (Westen van, 1993-4).

Remotely sensed data are inherently suited to provide information on urban land cover

characteristics related to ecological, demographic, socioeconomic, and dynamic aspects

of developed regions at various spatial and temporal scales (Ridd, 1995). Remotely

sensed data is in most cases the only data source, which may make a real time

information system possible (Zhou, 1995). Remotely sensed data, can be put to their

best use if they are incorporated to a GIS which is designed to accept large volumes of

spatial data, derived from a variety of sources, including remote sensing, and to

efficiently store, retrieve, manipulate, analyze, and display these data according to user

defined specifications. All GIS are dependent on the availability of spatially referenced

data whose use, for purposes other than those for which they were collected, will be

enhanced by the systematic appraisal of such sources in respect of both locational

precision and the characteristics of the attribute data, and by the adoption of standards

for data transfer.

Land is a dynamically changing entity. For example, rivers change course, forests are

cut, roads and houses are built. Consequently, our information stored in a GIS is only a

static model of the real world at a particular instant in time that has to be updated on a

regular basis. Satellite data, on the other hand, offer repetitive, synoptic, and accurate

information of the earth's surface, and, as such, offer the potential to monitor these

dynamic changes within a GIS. A GIS, therefore, when combined with up-to-date data

from a remote sensing system, can assist in the automation of interpretation, change

detection, map compilation, and map revision functions. One major part of a GIS is the

23


ability to overlay various layers of spatially referenced data. This allows the user to

determine graphically and analytically, how structures and objects (for example roads,

water distribution, community zoning) interact with one another (Ehlers, 1993).

If disaster reduction efforts are to succeed, urban leaders and managers need to have a

better understanding of the potential for these kinds of intersecting issues to emerge,

and the probability that some kind of combinations may be especially felicitous for the

adoption of innovative disaster reduction measures. The sustainability of cities in the

face of disaster is as much a function of enhancing institutional behavioral capacities to

deal with uncertainty as it is an outcome of material investment in hazard management

technologies and physical infrastructure (Mitchell, 1999).

Many applications using remote sensing imagery (airborne and space borne) have been

carried out to study different types of natural hazards, in different geographic regions.

Applications of remote sensing in natural hazard studies are characterized by different

objectives and therefore were unique to each other with respect to the resolution

characteristics required, and the scale of details. Therefore the choice of remote sensing

data was guided by the objectives and purpose of each study. Applications using

remote sensing data and GIS range from assessment of atmospheric, earth,

technological and many other types of hazards. Some selected studies which have used

remote sensing and GIS data are discussed briefly, as follows, to illustrate the benefits

of these tools, and to underpin the argument for their beneficial application in urban

and near urban fire hazard monitoring and management.

24


2-4.1 Weather hazards

Of all the natural hazards, which threaten human society, those caused or facilitated by

weather extremes are the most common (Smith et al. 1996). On a worldwide basis,

relatively few people are directly exposed to geologic hazards (e.g. earthquakes or

mass movement ofthe earth's surface) everyone however is exposed to the variability

ofweather and climate.

2-4.1.1 Hurricanes, cyclones and storms

One of the earliest uses of satellite remote sensing was for locating and tracking

hurricanes, because of the ease by which these large systems could be observed from

space and because of the importance of providing advance warning on their potential

landfalls. A combination of Landsat Thematic Mapper, SPOT, aerial photography,

existing maps, and field surveys were incorporated to evaluate the damage to renewable

resources in Thailand by Typhoon (Hurricane) Gay in November 1989. This successful

study estimated that over 250,000 hectares of agricultural lands were damaged, and

over 38,000 homes were destroyed. The results of this damage evaluation were used in

post -disaster resource allocation (Omakupt, 1992).

In 1989 Hurricane Hugo caused extensive damage to forest cover when it came ashore

in southeastern U.S.A. A change detection analysis based on 1987 and 1989 Landsat

Thematic Mapper imagery determined that over 90,000 hectares of forest were

seriously affected near Charleston. In the entire state of South Carolina, it was

estimated, using remote sensing and field surveys, that 36% of the forested lands,

1,800,000, hectares, worth over a billion dollars, were damaged (Remion, 1990).

25


Zerger (1998) developed a methodology to model risk arising out of storms, by using

geographic information systems. The study examined the potential of uncertainty

modelling methodology for using digital elevation models for inundation modelling.

Results show that a relatively simple risk modelling methodology may support the

decision making process.

(Sunil et al. 2001) demonstrated the use of high spatial resolution ortho rectified broad

band aerial photo images sensors for risk assessment and vulnerability mapping in a

section of the Central Business District of Hornsby Shire, NSW, in Australia. By

performing a supervised classification and spatial analysis in a GIS environment an

ortho rectified colour RGB composite image was used to map roof types with varying

degree of resistance, which may be susceptible to future hail storm damage. A

methodology to design such a model in near real time and the potential of this database

for dynamic resource allocation was described in this paper.

In another study carried out for the New South Wales Fire Brigades and linked to the

current research, (Bhaskaran and Datt, 2000) used airborne hyper-spectral and GIS data

for developing a spatial emergency decision support system. The hyperspectral data

was found to be suitable for urban area analysis where the main emphasis was to detect

and distinguish spectrally dissimilar roof types on a large scale in an image pixel. A

spectral library from field samples as well as laboratory samples were analysed using a

FR (full range) field spectra-radiometer. A vulnerability map was generated by a

supervised classification method, which was integrated with census and available GIS

data to develop a spatial emergency decision support system (SEDSS) for the NSWFB

(see appendix I for full technical report). Such information can be used with the current

fire hazard research to develop total urban hazard models.

26


The post disaster problems associated with the hazard of rainstorms in 1998 were

assessed by a geo-technical team in the city of Wollongong, Australia. The team

employed Hazard-Consequence Matrix based Risk Assessment approach, in

accordance with the AS/NZS 4360:(1995) Risk Management Standard, which was

adopted for the qualitative risk assessment work (Flentje et al. 1999). A description of

urban hail damage modeling carried out at the Natural Hazards Research Centre,

Macquarie University was summarized in the paper titled 'Hailstorm risk assessment

in New South Wales' (McMaster, 1999). The NHRC hail data set for Sydney, one of

the few hail data sets in the country was used with the hailstorm climatology for the

Sydney region, to model urban hail damage. A reasonably reliable, regional risk

assessment of losses due to this natural peril was developed. The results of these

investigations created an ideal starting point for a more quantitative risk assessment and

insurance-industry-oriented, loss modeling of these phenomena Leigh and Kuhnel

(1999). In yet another study on hailstorm risk assessment in rural New South Wales

three data sets (severe thunderstorm, phenomena and crop insurance datasets) were

used to determine the relative risk of hail damage in the various weather forecasting

districts of New South Wales. However, data inconsistency posed some limitations to

this study (McMaster, 1999).

2-4.2 Geologic hazards

2-4.2.1 Landslide hazard mitigation

Landslides are mass movements of rock and unconsolidated materials such as soil,

mud, and volcanic debris. Landslides are caused by mass movement of bedrock and

unconsolidated materials, which result in different types of slides, magnitudes, and

27


rates of movement. Some large landslides may be detected by remote sensing imagery

but smaller landslides, which are more :frequent, cannot always be detected. Sinkholes,

which are a form of circular collapse landslide, may be detected in Karst topography by

satellite imagery such as Landsat MSS, Landsat TM, and SPOT, due to their pitted

appearance and evidence of internal drainage. The spatial resolution required for the

recognition of most landslide features is about 10m (Richards, 1986). Detection of

landslide features is more easily achieved using airborne sensors because of their better

spatial resolution. Aerial photographs can normally be used to detect both small and

large landslide features. The more recent launch of high resolution satellite remote

sensing systems (better than 1 metre spatial resolution) will now allow satellite data to

be more widely used, even for small landslides.

The rapid growth of many cities in the developing world, especially in the tropics, has

resulted in the increased incidence and severity of urban environmental hazards, such

as slope failure and flooding. This problem was investigated by Smyth and Royle

(2000) and they concluded that the use of GIS can facilitate the study of

physicaVenvironmental factors and human land-use activities, which may all, lead to

the mitigation ofurban landslide hazards.

In a paper entitled "Embedding a Geographic Information System in a Decision

Support System for Landslide Hazard Monitoring", Lazzari and Salvaneschi (1999)

presented an application that exploits a geographic information system as a :front end of

a complex information system supporting the management of landslide hazard in

Valtellina, an alpine valley in Northern Italy. (Barreda et al. 2000) carried out a GIS

based study to evaluate the mass movement hazard in Spain's Canaria Islands. This

study used two different types of knowledge driven approaches: a direct method and an

28


indirect method. In the direct method very detailed geomorphologic mapping was

carried out, using uniquely coded polygons, which were evaluated one-by-one by an

expert to assess the type and degree of hazard. The indirect method followed an

indexing approach. Parameters including slope angle, landslide activity, landslide

phases, material, proximity to drainage channels and reservoirs, and land use change

were combined using multi-criteria evaluation techniques.

2-4.2.2 Earthquakes

The application of remote sensing to the hazard of earthquakes is limited in the sense

that earthquakes cannot generally be predicted. However the faults associated with the

tectonic activity, which is the major cause of earthquakes, can be frequently identified

on satellite imagery. However, in order to identify earthquakes it is necessary to have

the expertise to recognize them and then determine the correct remote sensing tools to

best delimit them. Landsat imagery has been widely used for detecting faults. Airborne

radar mosaics, multispectral imagery obtained from the Landsat TM and I or MSS or

SPOT HR.V sensors have also been successfully used for delineation of fault zones.

In their article titled "Photogrammetry and geographic information systems for quick

assessment, documentation and analysis of earthquakes", (Altan et al. 2001) combined

remote sensing data with GIS data in order to carry out a quick assessment of disaster

caused by earthquakes. The aim of this study was to make an improvement, using a

combination of GIS as a management and data analysis tool, and photogrammetry as a

documentation method. It was demonstrated that by combining rapid data acquisition

and GIS a quick and holistic assessment of a post disaster situation could be made.

29


2-4.2.3 Volcanoes

Many hazards are linked to a volcanic explosion, which may have long term as well as

short term affects. For instance, the immediate destruction of structures which may lie

in the vicinity of the eruption, the pollution caused by the smoke and soot released

during an eruption, break out of diseases, and the breakdown in infrastructure and

normal life. Predicting an earthquake is almost impossible due to the limited knowledge

of tectonic activity so that satellite imagery may have only limited application in the

identification of volcanic eruptions.

Volcanic eruptions can present unpredictable hazards to populations living within

regions containing potentially active volcanoes and for people travelling in jet aircraft

that intersect ash-laden eruption clouds. Methods of monitoring volcanic activity

include searching for variations in the thermal signal from active fumaroles, lava

domes, lava lakes, flows, and other features. Over many active volcanoes in the

Western Hemisphere, low spatial resolution ( 4 km/pixel) weather satellite data acquired

every 15 minutes are used to identify changes in eruptive activity, but are of

insufficient spatial resolution to map active volcanic features.

In a study of volcanoes by (Luke et al. 2001) used the Enhanced Thematic Mapper Plus

(ETM+) on Landsat 7 to monitor active volcanoes at a higher spatial resolution (15- to

60-m pixels). They were able to map lava flow fields, trace very high temperature lava

channels, and, at Lascar volcano, identify an arcuate feature associated with a collapsed

crater floor, a phenomenon that may precede explosive activity. With improved spatial

resolution in the thermal IR, they were able to map the bifurcation and braiding of

underground lava tubes at Kilauea. Identifying these tube locations and tracking their

30


extension were considered important because, for a given volumetric lava production

(effusion) rate, tube-fed flows can extend a much greater distance than surface flows.

(Pareschi et al. 2000) in their paper "GIS and Volcanic Risk Management" presented a

methodology to link GIS with remote sensing technology and

telecommunications/warning systems, for a decision making process. In their paper the

role of GIS in risk management is discussed, keeping in mind the different volcanic

scenarios of effusive and explosive phenomena. The GIS data base included satellite

images, digital elevation models, volcanic hazard maps and vector data on natural and

artificial features (energy supply lines, strategic buildings, roads, railways, etc). The

GIS was planned for

a) Volcanic risk mitigation (hazard, value, vulnerability and risk mapping),

b) to provide suitable tools during an impending crisis, and

c) to provide a basis for emergency plans.

A GIS was also used to assess risk to people and property from lava flow hazard in the

Vesuvian area of Italy. Lirer and Vitelli (1998) used a lava flow hazard map overlain

on a land use map, and to extract spatial and numerical information on a land-use map

to extract spatial and numerical information on risk.

2-4.3 Technological and industrial hazards

The analysis of technological and industrial hazards has received a considerable

amount of attention during the past two decades, with researchers focusing on specific

problems such as airborne toxic releases (Cutter, 1987), the emergency, management of

chemical spills (Gould et al. 1988), regional evacuation analysis (Cova and Church,

31


1997), and the assessment of community vulnerability to hazardous contaminants

(McMaster 1990; Chakraborty and Armstrong 1999; Lowry et al. 1995).

Weinstein et al. 1995, in a paper titled "Fire Growth Modelling in an Integrated GIS

Environment" developed a fire simulation application FIRE!, which integrates state of

the art fire behaviour modelling into the Arclnfo GIS environment. The model's user

interface was designed so that advanced computer and GIS skills are not required for

model execution. The model puts the power of comprehensive fire behaviour prediction

into the hands of qualified, on-the-ground resource managers, where it can be most

effectively applied.

2-4.4 Environmental hazards

There have been a few studies that integrated RS/GIS directly in the area of

hazard/disaster management such as that by (Tappan et al. 1991) where grasshopper

and locusts habitats were monitored in Sahelian Africa using GIS and remote sensing

technology. An environmental sensitivity index (ESI) mapping for oil spills using

remote sensing and geographic information system technology was carried out by

Ferrier (1999) who performed hyperspectral analysis to identify ferruginous materials,

which are left out after mining, These ferruginous materials often contain trace

elements that are environmentally harmful and possibly toxic. The distribution of the

ferruginous materials identified from the imaging spectrometer data supported the

results of laboratory experiments on the dependence of the formation of iron species on

their geochemical and physical situation.

32


2-4.5 Fire hazards

2-4.5.1 GIS/RS for forest/grass fires

The diversity of factors affecting the occurrence and spreading of wildfire necessitates

an integrated analysis approach. Some of these factors also have importance for urban

fires. Modern space technology together with Geographic Information System (GIS)

application can provide a very useful support for the fire control and analysis.

Garner (1989) demonstrated that geospatial technologies such as satellite imagery,

global positioning systems, and geographic information systems have the potential to

identify, inventory, and map the risk posed by wildfire to residences in the wildland

urban interface. In his project the following spatial models were developed which

generated: threat matrix, suppression matrix, and risk matrix. When combined, these

matrixes produced a predictive model of wildfire risk to rural residences in northwest

Arkansas. Four interim computer models for risk assessment were developed for this

study: access potential, ignition potential, fuel potential, and response potential. These

models have the potential to assist local volunteer firefighters and natural resource

managers to better assess the risks and hazards threatening rural landowner's property.

These computer models could also be quickly updated as more detailed and timely

information became available (Garner, 1989). This was also the major thrust of a paper

by De Fusco et al. (1992) which dealt with the applications of both GIS and satellite

remote sensing techniques, developed within the framework of Italian Space Agency -

Telespazio project for the implementation of a prototype system for forest fire

management.

33


In a study carried out by Cartalis and Chrysoulakis, (2000) the potential of

NOAA/A VHRR was demonstrated in the detection of fires caused by industrial

accidents in Lyon, France and Kalohori, Greece.

Epp and Lanoville (1996) developed a method of analysing remotely sensed data, a

geographic information system, and an intelligent fire management system to provide

integrated resource data for fire and other resource management. Natural and cultural

features were digitised from 1:50,000 topographic maps using a geographic information

system (GIS) to cover the 29 communities below the tree line in the Western Canadian

Arctic. Landsat Thematic Mapper data covering the same area were classified into

landcover or fuel types. Detailed information on each fire such as location, area burned,

date of discovery, fire number, fire zone, fire class and source of ignition was obtained

and added to each map sheet as attribute data. A generalized vegetation cover map

using NOAA AVHRR data was used in this study. All these data were combined to

develop an intelligent Fire Management Information System (IFMIS) that integrates

relational data-base, geographic information display, and expert systems. (Smith et al.

1999) also demonstrated the use of NOAA-AVHRR remote sensing data for fire

management in Australia's tropical savannas. Monitoring of fires on a regional scale is

undertaken using the NOAA-A VHRR satellite data to develop sustainable management

methods. The areas burned by forest fires are launched and posted on the world-wide

web. Upon the completion of the fire season, this information is put into a GIS, which

has fire area information from regular periods. By providing near real time information

to land managers the utility of satellite data on a regional scale was highlighted.

In yet another study carried out by Siegert and Hoffi.nann (2000), which used high

resolution, multitemporal ERS-2 SAR images and NOAA-A VHRR Hotspot Data were

34


used for a quantitative evaluation of forest fires in East Kalimantan, Indonesia; This

paper describes the results of the combined synergistic use ofNOAA-AVHRR hotspot

data and multi-temporal ERS-2 SAR images for burned scar mapping in the province of

East Kalimantan. Burned areas detected by ERS were verified using A VHRR data and

extensive field surveys during the fire season in April 1998. Furthermore, a vegetation

classification discerning five classes was derived from the ERS-2 SAR images and

compared to the mapped burned scars. The total burned area of the test site was

estimated to be 1.3 mil ha out of 1.85 mil ha (71 %).

The most common approach used in risk assessment is to classify vegetation into fuel

classes then combining this information through a GIS with collateral information such

as slope, aspect, elevation and fire history to assess hazard or fire spread . Some

examples of research using this approach can be found in Chuveico and Congalton

(1989); Cosentino et al. 1981; Burgan and Shasby, 1984; Yool et al. 1985; Chuvieco

and Salas, (1996).

Kushla and Ripple (1998) evaluated the utility of Landsat Thematic Mapper imagery to

map forest survival after a wildfire using a single-date and multi-date TM imagery.

regressions of TM band transformations were used to estimate forest survival. Single

date TM 4/5 accounted for 73% (P<0.0001). The pre-fire landscape had a matrix of

closed mature/old growth stands comprising 77% of the area. The study found that

overall habitat diversity and edge diversity increased after the bum, but interior habitat

decreased. By using Landsat TM data foresters can assess the effects of larger wildfires

more quickly and more cheaply than the interpretation of aerial photography.

35


The relationship between fuel conditions and fire ignition and spread can be understood

by considering meteo-climatic parameters. Weather variables, such as precipitation,

temperature, wind speed and direction, are used to determine fire risk by means of

various indices, which take into account the actual meteorological conditions as well as

recent climatic history affecting fuel moisture (Martellaci et al. 1993). The application

of satellite technology to dynamic risk evaluation is still in the pre-operational stage,

while static fire risk maps are already commonly used in fire defence activities. These

products allow the estimate of fire occurrence probability, combining natural and

human parameters, in order to classify the region of interest in terms of different fire

hazard levels (Chou et al. 1993)

Nancy et al. (1999) found that Radarsat imagery collected at incidence angles similar

to ERS-2 result in a similar, relatively bright backscatter from the fires scar, while

backscatter from Radarsat image collected at a higher incidence angle is relatively dark.

Model results confirm that incidence angle is critical in determining the relative

brightness of the backscatter from fire scars. These results have implications when

looking at broad-coverage SAR images, which span a large range of incidence angles

from near to far range.

Research was carried out by Riafio et al. (200 1) a spectral mixture analysis (SMA),

from Airborne Visible/Infrared Imaging Spectrometer (A VIRIS), was used to

understand regeneration patterns after fire in two semiarid shrub communities of the

Santa Monica Mountains, California: northern mixed chaparral and coastal sage scrub.

Two fires were analysed: the Malibu Topanga fire (3 November 1993) and the

Calabasas fire (21 October 1996). SMA was compared to the results ofthe Normalized

Difference Vegetation Index (NDVI) to assess vegetation recovery. An unburned

36


control plot (within the past 20 years), having similar environmental features, was used

to generate two relative fire regeneration indices, Regeneration Index (Rl) and

Normalized Regeneration Index (NRI). Indices were calculated using the Green

Vegetation (GV) endmember and the NDVI. These indices were determined to be

largely independent of A VIRIS radiometric calibration uncertainty, minor errors in the

atmospheric correction, topographic distortions, and differences in the phenological

state of the vegetation because of interannual or seasonal differences. The temporal

evolution of the two fires were combined to produce a longer observation period and

used to fit a logarithmic regression model for each Mediterranean shrub community.

The NRI developed from the GV endmember (NRIGv) produced the closest estimate

for the time of recovery in both communities based on recovery times in the literature.

The use of NDVI worked very well for recovery in the northern mixed chaparral, but

was less successful in the coastal sage scrub, mainly because of extensive herbaceous

cover during the first years of the regeneration process. Endmembers generated from

hyperspectral images were more accurate because they are tuned to capture the

greenness of the shrub type of vegetation. Use of matching plots having similar

environmental features, but which were burned in different years were demonstrated to

improve estimates of the recovery within each community.

In a study carried out by Giri and Shrestha (2000) Landsat Thematic Mapper TM was

found to be extremely useful for accurate delineation and demarcation of burnt areas in

the Huay Kha Kheang Wildlife Sanctuary of Thailand. A high overall accuracy was

achieved which underlines the importance of remote sensing in determining forest fire

boundaries on a large scale.

37


2-4.6 Risk management in urban areas

Risk is the outcome of the interaction between hazard phenomena and the vulnerable

elements at risk (the people, buildings and infrastructure) within the community

(Granger et al. 1999). In this project extensive use of GIS was made to carry out

analysis and assessment. Risk GIS as it has been christened in the cities project, is a

fusion of the decision support capabilities of GIS and the philosophy of risk

management. Total risk assessments for each of the hazards, earthquake landslide,

flood, destructive cyclonic wind and storm tide, were constructed to give total risk

profiles for Cairns, taking into account the vulnerability of the whole community as

well as the suburb-by-suburb risk for that hazard.

In the paper entitled "Risk management in the Fire and Emergency Services" (Smith et

al. 1996) the responses of the County Fire Authority (CF A) are examined, and a

broader agenda for risk management within the fire and emergency services sector in

Australia is proposed. It was argued that recognizing incident response represents only

part of the process of managing community risks and combined with significant

changes to the operating environment and fire and emergency service provides, poses

considerable pressures for change and creativity within the fire and emergency services

sector.

Remotely sensed data are inherently suited to the provision of information on land

cover characteristics related to ecological, demographic, socio-economic, and dynamic

aspects of developed regions at various spatial and temporal scales (Ridd, 1995). Many

studies, which integrated GIS/RS, especially in the field of natural resource

management/urban studies (which is quite similar to the field of hazard and disaster, if

38


one leaves out the aspect of urgency in hazards), have been undertaken successfully

(Forster, 1980, 1983; Gong and Howarth 1990a,b; Jensen, 1993; Kam, 1995). Some

further examples are provided as follows.

Uta Hieden et al. (2001) used airborne hyperspectral sensor data for an automated

material-oriented identification of materials. A combined classification and unmixing

approach is applied which was developed for the analysis ofhyperspectral DAIS 7915

data. The result shows the great potential of hyperspectral HyMap data for automated

material-oriented identification of urban surfaces.

In his article entitled "Monitoring of hazards and urban growth in Villavicencio,

Columbia, using scanned air photos and satellite imagery" Nossin (1999) presents a

method to monitor the development of geomorphic hazard affecting urban areas by

overlaying time series of airphotos in a digital image processing system. The paper

describes a methodology where natural hazards affecting an urban area can be

identified and monitored by the use of remote sensing techniques and digitised air

photos. He also demonstrated that a quantification of the urban areas affected by

hazards, and of the growth of the urban area, is possible. Altan et al. (2001) looks at the

important problem of providing shelter and housing for the homeless. Focus on

improving the information systems using Geographic Information Systems (GIS) and

photogrammetry are described in this article.

Gao. J and Skillcom (1998) demonstrated the capability of SPOT XS data in producing

detailed land cover maps along the urban rural periphery. Two images, one recorded in

summer and another in winter, were critically evaluated for generating detailed land

cover maps. A supervised classification using the maximum likelihood classifier

39


approach was employed to generate 1 0 categories of land use by using the Anderson

classification. Foresman and Millete (1997) described the use of Landsat, which was

integrated to derive land-use and other spatial data for regional planning of 25 towns in

Vermont. More recently Ambrosia et al. (1998), carried out real time data transfer of

thermal line scanner data from an airborne platform via a cellular data phone

transmission, and near-real time map integration and development was demonstrated

using portable uplink/downlink systems to move data and asset information (such as

vehicle and personnel location) to a fire camp and a disaster control center. Kam (1995)

also undertook an urban land-use study in Brunei, using an image analysis filtering

spatial model to integrate image analysis with the GIS.

2-4.7 Remote Sensing and GIS for other studies

Wiesmann et al. (2001) demonstrated the potential of synthetic aperture radar (SAR)

and InSAR for forest storm, flood and avalanche mapping. In their methodology,

process models were used to describe the targets before, during or after the hazard

event. Results from the study showed good potential of multi-temporal SAR and

InSAR for hazard mapping.

The importance of suitable data and appropriate handling were highlighted in the study

by Thomson and Hardin (2000) titled Remote sensing I GIS integration to identifY

potential low-income housing sites. The study reviewed the development of remote

sensing and geographic information system (GIS) techniques for urban analysis. It then

applies these techniques to evaluate several types of planning related information in a

raster based (GIS) to identifY potential low income housing sites in the eastern portion

of Bangkok Metropolitan Area, Thailand.

40


Chakraborty and Armstrong (1999) developed a methodology and demonstrated an

approach for assessing the population at risk to airborne releases of extremely

hazardous substances. This methodology was based on the recent USEP A" guidelines

(USEPA, 1996) for risk assessment and modeling worst-case releases. They also

examined the relationship between potential sources of chemical hazards and the

special needs of population in a medium sized metropolitan area and determined

whether the distribution of environmental risks disproportionately impacted the special

needs population. Coulter et al. (1999) addressed aspect of currency for metropolitan

transportation planning through integration ofRS/GIS data.

In a study carried out by Ulbricht and Heckendorff (1998), satellite images from 1985,

1988 and 1996 were used for a visual interpretation of the coastal area around Joao

Pessoa, situated in the northeast Brazilian state of Paraiba. The interpretation resulted

in the recognition of several changes and geographic relationships: changes in the

course of the river Paraiba over a distance of several kilometres, coastline changes

resulting from the S-N ocean current induced by the southeastern trade winds, growth

of the city of Joao Pessoa and increase in density of the built-up areas, and constancy of

the borders between sugar-cane fields, pasture land, and the remnants of the original

coastal forests, the 'Matas Atlanticas'.

Abdullah Al-Garni (1996) demonstrated a methodology to develop a conceptual model

based prototype urban Geographic Information System (UGIS). The base maps used in

developing the system and acquiring visual attributes were obtained from aerial

photographs. A multi-purpose parcel-based system, one that can serve many urban

• U.S. Environmental Protection Agency

41


applications such as public utilities, health centres, schools, population estimation, road

engineering and maintenance, and many others was developed. A modem region in the

capital city of Saudi Arabia is used for the study. The developed model was operational

for one urban application (population estimation) and was tested for that particular

application. The results showed that the system has a satisfactory accuracy and that it

may well be promising for other similar urban applications in countries with similar

demographic and social characteristics.

2-5 Summary

Almost all reported studies deal with the use of GIS/RS for studying hazards, but very

few addressed urban fire hazard and management. However, statistics show that urban

fires have resulted in huge losses of lives and property damage worth billions of

dollars. Unfortunately, due to many obvious reasons, one being the spatial resolution of

space borne remote sensing data, attention may not have focused on urban disaster

applications. Yet another common feature about all these studies is that remote sensing

was largely concerned with extracting generalized information and error detection,

while GIS applications tend to focus on database management issues, cartographic

modeling and presentation of data. A truly operational GIS for planning purposes

requires the integration and automation of at least some of the most routine procedures

in both approaches.

Applications of remote sensing data and GIS, to the study of natural hazards are

generally unique, to each other since different natural hazards are caused by different

phenomena and are characterized by different attributes. These studies therefore use

different types of remote sensing data with unique sensor resolution characteristics,

42


which are influenced by the different attributes, which have to be detected by the

sensor. However, most of the studies used broadband remote sensing. For each

phenomenon there is a unique methodology and sensor resolution characteristics, and

all studies generally are case dependent. Monitoring different types of hazards, both

natural and manmade require a systematic appraisal of sensor resolution characteristics,

an understanding of the type of hazard and the need for assessment of the hazard.

Urban hazards may have different impacts due to the distribution of spatial features,

which in turn may be found in different shapes, sizes and patterns.

Very few studies have used satellite data for assessing hazards in an urban environment

since the spatial resolution of space-borne sensors until recently was not adequate for

analysis in urban areas. Most applications of remote sensing to urban areas still fall

short of the required spatial resolution (Jensen and Cowen, 1999). Inadequacy of spatial

resolution of satellite data was one of the main reasons in the current research for

choosing high resolution aerial photo images for assessing and modeling hazards. In a

few instances, high spatial and spectral resolution is needed for the material-oriented

detection of urban surface features often found in close proximity, such as roofing

materials and their material composition, while this information may be required it

cannot be provided by broadband remote sensing data that are mainly used in the

studies of urban environment (Sunil et al. 2001). The use ofhyperspectral image data

can open up new opportunities for an area-wide inventory of urban surfaces. The high

spectral and spatial resolution of these data yields the potential for identification of

urban surface cover types based on their material-specific spectral reflectance

characteristics (Roessner et al. 1998). Although some recent advancement in the field

of remote sensing have delivered high spatial resolution data such as from IKON OS,

43


the availability of such data is currently affected by long delays attributed to their high

costs, accuracy and coverage. Comparatively, aerial photographs, which are scale

independent, offer potential benefits since they are not affected by any of the factors,

which influence the purchase, and use of high spatial resolution space-borne data.

Managing urban disasters must begin with the identification of hazard. Significant

work has been carried out in the past on disaster management, few of which have

focused on urban areas. A run down of selected attempts to study urban disaster is

described in the following chapter. These hazards and disasters provides insight into

the nature of urban fire hazards, which are different to other natural hazards such as

earthquakes, volcanoes, hail storms, and highlights the importance of identification of

hazard be it natural or manmade.

44


Chapter 3: Urban Fire Hazard Categorization and Monitoring.

3-1 Introduction

The reasons for increasing vulnerability are many, and one of the main factors is the

accelerated growth of cities and increasing scale of urban industrial activity. They raise

the vulnerability of urban areas to both natural and technological hazards. It is

commonly recognized that the characteristics of "urban natural disasters" have become

increasingly complex because of the high density of population and the strong

concentration of social capital Elo et al. (1996). Hazards therefore are omnipresent

phenomena, which cannot be detached from urban life. To reduce the impact of

disasters a complete strategy for disaster management is required which involves the

following aspects:

Disaster prevention

:> Hazard analysis: assessing the probability of occurrence of potentially

damaging phenomena.

:> Vulnerability analysis: assessing the degree of loss expected to population,

infrastructure, economic activities, as the consequence of an event of a certain

magnitude

:> Risk assessment: assessing the numbers of lives likely to be lost, the persons

injured, damage to property and disruption of economic activities caused by a

particular natural phenomena.

:> Landuse planning and legislation: implementation of the risk map in the form of

building codes and restrictions.

Disaster preparedness

45


)> Forecasts/warning/prediction of disasters (for example Hail storm warning)

)> Monitoring: evaluating the development through time of disasters (for example

floods)

Disaster relief

)> Damage assessment shortly after the occurrence of a disaster

)> Defming safe areas, to indicate possible escape areas

)> Infrastructural monitoring, to ensure an undisturbed supply of aid. (OAS, 1990;

UNDRO, 1991).

Therefore identification of hazards is an important issue and is the first and the most

important step in any hazard analysis. It involves the identification of all possible

conditions that could lead to a hazardous incident. The comprehensive and systematic

identification of all hazards is critical to the success of hazard analysis. The Department

of Planning, NSW, Australia has recognized the need for an integrated approach to the

control of hazardous industry, incorporating environmental risk impact assessment.

This approach essentially requires that

a) All hazards associated with the operations of potentially hazardous installation

are identified.

b) Such hazards are analysed in terms of their consequences to people, property

and the biophysical environment and their likelihood of occurrence; and

c) Risks from the operations are quantified and assessed in terms of their location

and land use planning implications.

Fire hazard categorization helps in identifying and assessing the vulnerable and

susceptible regions that have a potential for disaster in varying degrees. Hazards differ

from place to place due to variations in land-use, structural density patterns, population

density and demographic indices, to name a few contributing factors. The areas most

46


vulnerable to extreme natural events are those where there are concentrations of

elements at risk: people, buildings and infrastructure, i.e. human settlements. (Elo et al.

1996). In one of the technical sessions at the world conference on natural disaster

reduction in 1994, Yokohama, Japan, emphasis was laid on effects of disasters on

urban areas. The panel of experts recognized that urban areas are extremely vulnerable

to disasters and likely to become even more so, because of the concentration of

population, resources and activities. Land-use patterns often increase the level of risk.

Disaster reduction should become an integral part of the development process of human

settlements, and IDNDR"', aims to facilitate the activities associated with that. Land-use

is one of the fundamental indicators of this variability of fire hazards. For instance a

dense commercial region will pose a different level of hazard, when compared to a

residential or industrial region. One of the main objectives behind the generation of fire

hazard categories is to understand these spatial variations. Hazard categorization

measures, records and translates these geographic variations for each spatial unit and

enables an overall understanding of the prevalent hazard in a region. Hazard

categorization enables the comparison of hazard in one region to that of another, and

consequently assists in the allocation of dynamic resources. These categories help in

visualizing the spatia-temporal patterns of hazards, which would otherwise be difficult

to comprehend. When combined with demographic data and other derived hazard

related information, hazard categories can be used for complex analysis which will be a

major input to disaster management.

• International Decade for Natural Disaster Reduction (IDNDR, 1990-2000)

47


3-2 Case Studies of RS/GIS Applied to Hazard Management

Most studies that have used GIS/RS to study urban hazards have been carried out on

brush fires and their hazard to residential areas and local topography. Broadband

sensors were used to study the outbreak of fire and the adjacent land-use in order to

develop decision support systems. Brush fires have the potential to instigate hazard,

years after the fire has been extinguished. The removal of vegetation in these scar

regions may facilitate rapid flood run-off and soil erosion over the next several

monsoon seasons. In an effort to understand these dangers in light of the rapid increase

in urbanization and population in cities throughout the southwest USA and in similar

environments around the world, remote sensing data was analysed by group of

scientists from USA (Ramsey and Arrowsmith, 2001).

A pilot study is now underway examining historic data from the Landsat TM, SIR-C

radar, and airborne thermal infrared scanners of existing brush fire scars surrounding

Phoenix, USA. This study investigates the linkage between the fire scar age, vegetation

type/recovery, soil type and local topography. It is proposed to use this information to

model surface response to heavy rainfall and assess the potential for future flood and

fire hazards. These data will be valuable because ofthe high spatial resolution (15-90

m/pixel), the multi-spectral coverage (visible-thermal wavelengths), and the ability to

generate along-track moderate resolution digital elevation models (DEMs) critical for

urban topographic analysis. Remote sensing work will expand to surrounding cities

with different growth rates, vegetation species, and fire/flood potential. These cities

may include San Diego and Los Angeles, Albuquerque, and Las Vegas. Airborne

MASTER data have already been acquired over several of these targets, and will be

used in conjunction with aerial photography to validate detection and classification

48


algorithms. ASTER data from the UEM project will also be examined for similar fire

scar evidence globally.

Many studies found that the accuracy and utility of most GIS fire behaviour models

was compromised when the process of generalizing vegetation components into fuel

models takes place. Twenty-three different vegetation communities recorded in this

study were placed into three generalized fuel models, experience has shown that some

of the vegetation types lumped together have quite different flammability potential.

GIS was found to be a fast and effective link between census information and wildland

urban hazards reduction and fire prevention efforts particularly in USA. GIS lets land

and fire managers overlay rural population trends and forecast, fire behaviour factors,

and past fire occurrence records. New fire simulation models such as the National

Interagency Wildfire Laboratory's Farsite use local GIS data sets to project fire

problems years into the future.

At California's Cuyamaca Rancho State Park, GIS was used to develop long range

management plans from detailed information about climate, weather, topography,

geology, soils, vegetation, fauna, and modem cultural features. Realizing the

importance of fire's role in preserving native wildlands, prescribed burn offs becomes

an important management tool, (Wells and Mckinsey, 1991). They based their fuel

model on those described by the National Fire-Danger Rating System, National

Wildlife Coordination Group in 1981. They concluded that the GIS program was

limited by the categories of fuel, the 23 vegetation classes being placed into three

generalized fuel models.

49


The Nicaragua Land Resources (Fire) Monitoring Project is an environmental

monitoring project carried out by the Ministerio del Ambiente y los Recursos Naturales

(MARENA) and the Natural Resources Institute (NRI). The Project was supported by

the UK Government Department For International Development (DFID) until June

1998. A PC-based NOAA satellite receiver, installed at MARENA headquarters in

Managua since its start in June 1995, has enabled daily observations to be made on

vegetation fires in Nicaragua and Central America. These observations are used

particularly to assist and support operational forest management activities in Nicaragua.

Spatial and time distributions of hot spots were examined and assessed with regard to

forest/land use type coverage and to population density and rural poverty levels.

In a study in California, related to firestorms which have devastated many acres of

land, a spatial decision support system was developed to provide planners with tools to

assist in managing disasters caused by firestorms. This paper models and assesses the

risk of frrestorms in the East Bay Hills and produces a spatial support system that could

help manage and reduce the risk of future firestorms. A GIS was used as a framework

for quantifying fire hazard in this heterogeneous landscape. Two models, one to assess

the wildland fire hazard and the other to assess the urban/residential fire hazard, are

integrated and embedded within the GIS to map both regional and neighbourhood risk.

Risk assessment maps were generated which represented the results of quantitative

multivariate analysis and clearly identified the hazardous neighbourhoods. The system

produced and provided an ideal tool for managing and reducing the risk of a firestorm.

It allowed for future adjustment of one or all of the parameters, as fire prevention

policy and techniques are implemented and hazardous conditions are mitigated. This

50


feature provided a mechanism for accounting and feedback, allowing for the appraisal

of the success of mitigation.

3-2.1 National

Most of the studies related to mitigation of hazards and disaster in Australia have been

carried out on bush fires, community participation and related risk management issues.

For instance, in January 1994, almost all of coastal NSW experienced an extended

period of extreme fire weather. In excess of 800 fires started between 27 December

1993 and 16 January 1994 burning approximately 800,000 hectares. The areas

primarily affected included the coastal plains and nearby ranges. Intrusion of fire into

the Sydney and nearby metropolitan area occurred on a scale never before documented.

Over two hundred houses mostly in urban areas were destroyed. Many others sustained

severe damage. Two fire fighters and two other people were killed.

Of these, many have focused on community participation and awareness for mitigation

of disasters. As such very few attempts have been made at studying fire hazards and

risk and surprisingly not many attempts have been made to study urban disaster risk by

using GIS/RS data.

The AGSO's Cities Project was established in 1996 with the objective of determining

the vulnerability of Australian urban communities to the effects of geological hazards

and to provide emergency managers and planners with information and decision

support tools that will aid in the mitigation of the hazards. The mission of this project

was to conduct research that would lead to safer, more sustainable and more prosperous

communities. This effort formed a major part of Australia's contribution to the United

51


Nations International Decade for Natural Disaster Reduction (IDNDR) and the

development of national risk mitigation strategies.

A study carried out by O'Neill et al. (1993), integrated remote sensing and spatial

analysis techniques to compare Aboriginal and pastoral fire patterns in the East

Kimberley region in the Northern Territory of Australia. Remote sensing and spatial

analysis techniques were used to compare the different uses of fire and provide some

quantitative assessment of the ecological impacts. Multi-temporal Landsat Thematic

Mapper imagery was used to delineate fire patterns over a three-year period. A

geographic information system was then used to integrate the resultant landcover maps,

land system maps and other spatial data. Cross tabulations, proximity analysis and the

production of a unique criteria map provided quantified data on the extent and spatial

distribution of different fire types, fire frequency and the relationships between fire

type, land systems and areas of most intensive Aboriginal land-use.

Ahearn and Chladil (1999) discussed effective targeting of bush fire prevention,

management and suppression activities, and argued about the distance or the limits to

which the bush fires may impact. A distance distribution was obtained for each fire

event. A combined distribution was obtained for each fire event from a base data and

tested against the 1994 data. Results showed some consistency between all fires and a

relationship between houses burnt in bushfires and the distance they stand from

vegetation boundaries.

The increased pressure on tropical cyclone risk management resulting from

demographic growth and climate change requires the best possible information,

52


preferably quantitative, about both the hazard levels and the vulnerability of the coastal

communities. An understanding to this effect lead to the launch of the "The Tropical

Cyclone Coastal Impacts Program" (TCCIP): a collaborative Australian IDNDR, to

help focus attention and resources on this problem. The most significant achievement

of this program was the development in Australia of a significant multi-disciplinary

community, interested in cyclone hazard mitigation. An article by Falls et al. (1999)

highlighted the approach and achievements of the TCCIP. Henderson et al. (1999)

investigated the impact of cyclone Vance in Western Australia and the strength of

houses to withstand the hazard. Insurance losses in excess of 12 million dollars were

reported drawing attention to the need for addressing this hazard. Berry and King

(1999) discussed the catastrophic impact of cyclones in Northern Australia to

communities in the short term as well as long term. In the paper, community

vulnerability studies indicated that residents are increasingly vulnerable and

decreasingly resilient to the cyclone hazard. The paper highlighted the need to improve

early warning response and levels of preparation throughout the community.

The Australian International Decade for Natural Disaster Reduction (IDNDR) have

facilitated earthquake mitigation studies through earthquake zonation mapping, GIS

based approaches and disaster management aspects for several areas in Australia. The

IDNDR launched the Radius (Risk Assessment Tools for Diagnosis of Urban Areas

against Seismic Disasters) initiative with financial assistance from the Japanese

government. The aim of this program was to promote worldwide activities to reduce

seismic disasters in urban areas, particularly in developing countries, Rynn and

Okazaki (1999).

53


Rhodes et al. (1999) in their paper titled " A framework for understanding and

monitoring levels of preparedness for wildfire" provided a framework that could be

utilized as a basis to develop a measurement tool to assess preparedness levels to deal

with risks. This paper seeks to explore some of the issues surrounding preparedness for

wildfire. It examines how household preparedness is described both within the

community and the emergency services and how preparedness might be described.

A GIS-based assessment of landslide risk to the Cairns community has been carried out

to provide information to the Cairns City Council for planning and emergency

management purposes. These studies were undertaken in the Cairns area as a part of an

AGSO Cities Project multi-hazard risk assessment. GIS polygons were used to

delineate and characterize the areas that could be affected by landslides. The nature,

number and geographic distribution of the elements at risk were obtained by

interrogating the GIS, and their vulnerability to destruction by the two main landslide

slope processes were assessed. From this information, specific risk maps were

produced for: people living in houses and flats; for buildings (houses and flats only)

and for roads. Maps, which quantitatively depict the total risks per square kilometer,

per 100 years for residential people and buildings in each GIS polygon in the currently

developed parts of Cairns, was constructed by this study. However, the study had some

limitations with respect to mapping, which was carried out only at a reconnaissance

level. Therefore detail site specific assessments could only be confirmed by geo

technical specialists.

Most of the above studies relied on existing data and therefore had to contend with

issues related to data inconsistencies and quality. Noticeably very few studies have

54


applied the technologies of GIS/RS together to manage disasters in urban

environments. Some of the issues raised by these applications relate to problems

associated with data collection, which may be solved by GIS/RS.

3-3 Risk assessment by the New South Wales Fire Brigades

The New South Wales Fire Brigades are responsible for providing fire protection in

urban environments, to a major part ofNew South Wales. The rural fue services are

responsible for the brush fires and other fire hazards and disaster management in the

rural areas of Australia. The NSFWB are in the process of collecting hazard related

data by in-situ field survey based methods to manage disasters and to provide decision

support systems. A brief insight into the existing methodology and some related issues

is given in the following paragraphs below.

The New South Wales Fire Brigades are responsible for providing emergency services

to a major part of the state of New South Wales making it arguably the largest

emergency organization in the world, with respect to the area over which it has

jurisdiction. The management of any disaster demands spatial and temporal data. The

NSWFB understands the importance of collecting data pertaining to hazards, which

will enable them to carry out risk assessment and resource planning. The existing

method of collecting hazard related variables by the NSWFB involves extensive in-situ

surveying and inspection of individual sites for assessment against a predetermined

descriptive hazard summary as illustrated in (Tablel). In this methodology regions are

divided into unit survey squares (USS derived from lkm Australian Map Grid) for

enabling a systematic survey of built structures. Each USS is assessed in the field from

a vehicle by a team oftwo survey staff.

55


GENERAL SPECIFIC MAP BRIEF DESCRIPTION OF HAZARD HAZARD HAZARD COLOUR CATEGORY

TYPE CATEGORY CODE A site that poses extreme hazards for people, property or environment, e.g. some hospitals

Special Hazard Category 1 Red and aged hostels, major LPG depots chemical plants, oil refinery, etc.

May include some high hazard residential-but basically high hazard industrial I commercial

Category 2 Orange occupancies with a high level of structural density within the USS

Intermediate Hazard May include some high hazard residential-

but basically high hazard industrial I Category 3 Yellow commercial occupancies with low to moderate

level of structural density in USS A fully developed area of residential hazard

and low hazard industrial I commercial Low Category 4 Dark occupancies

Green Or Base-Level)

Hazard Category 5 Light A partially developed area of residential and Green low hazard industrial I commercial

occuR_ancies

Table 1 Hazard descriptors (Adapted from the Hazard Categorization project report, 1996)

Descriptors for each hazard category provide a reference for surveyors who compare

these to the patterns of structures, which predominate within the USS. In this way each

USS is classified into one of the five hazard categories (Hazard Categorization Project,

1996). Figures 2 and 3 show two examples of hazard categorization maps over the

urban regions ofthe city ofBathurst and Hornsby, a suburb on the edge of Sydney.

56


FIRE HAZARD CATEGORISATION BY NEW SOUTH WALES FIRE BRIGADES

, __ =• • .I a ---- r-- -

• I -· kE Ls, 01 IIR rr~ ITi lN

-~ ..... ,...... f-r-- -

_I ~~ ~ f- . 1-

• ,,_

l.S 3

llU"'" ... '

Figure 2 Hazard categorization by NSWFB in Bathurst City, NSW

(Red: Special hazard Category 1; Orange and Yellow: Intermediate hazard Category 2 and 3; Green and light green; Low hazard Category 4 and 5)

57


HAZARD CATI!GOReS IN HORNSBY NSWP15 PICTURE ALSO SHOWS SPECIAL HAZARDS

Figure 3 Hazard categorization by NSWFB in Hornsby Shire, NSW (Red: Special hazard Category 1; Orange and Yellow: Intermediate hazard Category 2 and 3; Green and light green; Low hazard Category 4 and 5)

3-3.1 Issues with current methodology of risk assessment

The methodology followed by the NSWFB to assess hazards is a high budget, time

consuming exercise that requires extensive coordination and man power. Further more

such data lacks currency and is not generated in a digital form leading to further delay

of analysis. Due to these limitations the assessment and spatio-temporal analysis of fire

hazards on a regional scale may not be possible which may affect overall hazard

management and resource allocation. The current methodology raises number of

important issues such as geographical insensitivity, non-currency etc, which may pose

serious concerns for emergency planners, and resource allocation and redistribution.

58


3-3.1.1 Geographical insensitivity

Existing methodology is hazard focused and the allocation of dynamic resources (fire

fighting staff and equipment) made on the basis of a single criterion of structural

assessment alone will result in an inefficient distribution of resources, which will in

tum affect disaster mitigation efforts. A place becomes increasingly hazardous when

the forces of urbanization, industrialization, population increase and new housing,

result in changes to the existing spatial patterns. These spatial changes, more often than

not, increase the element of hazard in a harmful way leading to a disaster at some point

in time. In other words there is geographic disorder and a breakdown in the balance of

spatial arrangement. The spatial disorganization of hazards is manifest in the disasters,

in the sense that disaster is a disruption and unraveling of urban order. Harm and

survival, human impairment and adaptive response, exhibit strong spatial variations

(Hewitt, 1997). Issues that confront the fire services, or any other emergency

organization, must extend from hazard categorization to overall planning. For instance,

in the eventuality of a gas leak from a chemical plant that is located in the vicinity of a

residential area, the limits of the hazard will extend beyond the bounds of the site from

where it originated to the area where the toxic gas can spread randomly. The study of

hazard is therefore not restricted to the location of the chemical plant. Therefore it is

essential to understand the demography, relative locations of features like buildings,

hydrants, water bodies, heritage areas, population, street network, travel impedances

and so on. A single criterion for identifying major accident hazards based on inventory

level alone has, in its simplicity, a legislative attraction, which in part explains its wide

adoption (Rayner, 1992). Spatially linked data has to be understood and studied in

totality and not exclusively of each another (Walker et al. 2000).

59


3-3.1.2 Community vulnerability

The existing method of hazard categorization does not take into account the community

vulnerability. The concept of community vulnerability has been particularly important

in understanding the physical relationship between hazards and communities at risk,

accident preparedness and mitigation and the social geography of potentially affected

populations (Blaikie et al. 1994). A hazard only becomes a disaster when it affects a

human population that is exposed and vulnerable (Ditto, 1998). This is particularly true

of developing nations where a combination of high population density and hazard

potential can lead to disasters. Hazards are important only in so far as they threaten or

harm human activities or assets or those (such as the environment) on which we place

some value. Understanding the spatial variation of population in relation to hazard

related indices is essential in order to plan for an emergency. In its strategic sense,

emergency management is not just about understanding the full range of consequences

of hazard impact, but is also about understanding the relationships of environmental,

political, social and economic forces that influence, shape and determine the frequency,

nature and location of emergencies. Comprehensive knowledge about the type of

people, age, socioeconomic indices and education are all essential prerequisites in an

emergency operation. In some cases the local knowledge of people, who are long term

residents in a certain area, may provide valuable information to emergency services.

Their local knowledge may be used in the location of high hazard sites, structures and

their use functions, and for identification of built features during scientific visualization

of remotely sensed data. The goal of emergency management is the effective delivery

of services to a target population (Buckle, 1999). To achieve effective services

emergency managers therefore need a clear understanding of the phenomena with

60


which they have to deal. These include the hazards themselves as well as the people at

risk from these hazards.

3-3.1.3 Non-currency of spatio-temporal information

Urbanization, industrialization, and new developments all act together to cause an

increase in population, and a change in the landscape. These create spatial disorder

bringing about variations in the distribution of existing and new hazards. In order to

make appropriate decisions in emergency planning, these spatia-temporal changes of

hazard will have to be detected and recorded regularly. These hazard changes occur in

an unplanned or unforeseen way in most urban areas, and are difficult to detect, record

and update immediately. For instance new development in the form of a built feature

representing an industrial activity or residential land-use may change the overall degree

of hazard and a pose a new level of risk. Detailed and regular monitoring and recording

of these changes will provide the emergency services with timely information which

will enable assessment and redress of issues related to planning such as resource

redistribution or allocation. Given the dynamic nature of changes in land-use in urban

areas, the existing in-situ field survey method will prove inadequate for detecting,

recording and monitoring these changes regularly.

3-3.1.4 Allocation of dynamic resources.

The allocation of dynamic resources assumes a high level of importance since it

determines the weight of attack in the event of a disaster. For instance, in a particular

fire district where there are many welfare homes and aged care facilities, rescue

operations will have to be performed by skilled staff that is adept in handling less

mobile people. Similarly in the event of an industrial disaster skilled or trained staff

61


will have to be dispatched to combat this type of emergency. Specialized fire

equipment will also have to be appropriately located for this type of hazard.

Availability and accessibility of these resources in the respective designated areas (fire

districts) have to be based on the spatial distribution ofhazards.

3-4 Sources of Data for Required Resolution.

3-4.1 Introduction

Disasters occur when the combined forces of hazard and vulnerability are more than a

community can cope with. Amongst the numerous indicators of hazard and

vulnerability that can be detected from remotely sensed imagery, structural density and

location of special hazards is one of the most important. An appreciation of urban

attributes temporal, spectral and spatial resolution characteristics is essential to

remotely sense these phenomena. A description of spatia-temporal requirements for

fire hazard categorization and monitoring are given in table 1. Evaluation of hazard

related variables (Urban/Suburban Attributes), spatial and temporal requirements and

the availability of air/space-borne sensors to satisfy the information. Hazard categories

given in the Table 1 are discussed in the following paragraphs.

Hazard category 1, hazard category 2, hazard category 3 and hazard category 4, which

are assessed against a hazard description, are in essence a land-use classification. For

instance hazard category!, consisting of high-density residential/commercial/industrial,

can be determined by a sensor that has a spatial resolution of 1 0-20m (Jensen and

Cowen, 1999). In the case of hazard categories 1, 2, 3 & 4 the spatial pattern of

structural density has to be determined, whereas to identify special hazards sites, which

could be in the form of a large industrial/chemical activity or structure, a sensor with a

62


spatial resolution of ::::;0.25 to lm would be preferable. Even though the spectral

resolution is not very important for urban-suburban attributes there must be sufficient

spectral contrast between the object of interest (a building) and its background (e.g.

surrounding landscape) in order to detect, distinguish between, and identify the object

from its background (Jensen and Cowen, 1999). Depending on the classification and

level of detail required, high resolution data can be re-sampled to a lower resolution for

example to understand the spatial patterns of structural density, and if details on

identifying respective structures is the focus, then the high resolution image can be

used. In other words the sensor resolution should be such that it gives the user the

flexibility to detect features and phenomena at different levels of details.

3-4.2 Building, cadastral (property line) infrastructure

In addition to the nominal scale and the land-use and land-cover information, detailed

information of property, buildings and their boundaries may be required to plan

emergency operations for example see Figures 4 & 5. Location of special hazards is

important since have greatest influence. Their detection at a large scale is essential in

order to plan for emergency operations and location of dynamic resources. A spatial

resolution of;::: 0.25 to lm and a spectral resolution of at least panchromatic visible are

necessary or a colour image derived from the three visible wavelengths related to blue,

green and red to better distinguish built and natural features. Similarly many other

sensitive site locations such as schools, hospitals, airports have to be detected at a large

scale for identification, but as well at a small scale for appropriate emergency planning

measures.

63


N

A.

~. Kilometers '\]

Figure 4 Property info layer (Small scale. Source: LPI Bathurst, NSW)

64


[[; c

Kllo,..lerw

Figure 5 Property information layer (Large scale map. Source: LPI Bathurst, NSW)

3-4.3 Socio-economic variables

The vulnerability of a place to hazard/disaster is explained by the density of people

who live in conjunction with the relative location of physical features (structures).

Density can be estimated by using a spatial resolution of ~0 .25 to I m a panchromatic

image spectral range. An attempt to extract population density by using low spatial

resolution data has been carried out by some scientists, when single dwellings were

counted from an image and multiplied with the size of the family unit per dwelling.

Remotely sensed data are inherently suited to provide information on urban land cover

characteristics related to ecological, demographic, socioeconomic, and dynamic aspects

65


of developed regions at various spatial and temporal scales (Ridd, 1995). However,

they are not without their limitations. For example, the spatial resolutions of satellite

data, which are currently available, do not provide the accuracy or specificity required

for many urban applications. The importance of ancillary data to improve satellite data

classifications and analysis is acknowledged everywhere. On the other hand the cost of

high resolution satellite images (HRSI) and aerial remote sensing data have prevented

the frequent use of aerial remote sensing data. Data accuracy especially where several

types and resolutions of remote sensing data is used is another major issue that

demands further research and attention.

3-4.4 Digital Elevation Model (DEM)

Visualization and height determination can provide useful information about the

topography, slope, number of multi-storied buildings, all of which can assist in the

allocation of dynamic resources. A spatial resolution of ~0.25 to 1m will again be

required in the panchromatic range. The DEM data can be modeled to compute slope

and aspect of surfaces for a variety of applications. It can be used to identify the

optimum location for placing various utilities such as aerial pumpers, which are widely

used for emergency operations involving high-rise structures.

3-4.5 Utility infrastructure

Planning rescue operations often depend on the knowledge oflocation of water sources,

above ground pipelines, fire stations and so on. Extracting some of these individual

features can be done using a high spatial resolution sensor ranging from ~1 to 5m in the

panchromatic range, however very small features may require much higher resolution

66


imagery or site visits to identify and locate them. Temporal resolutions of a few days to

a few weeks can be delivered by existing and future sensors.

3-5 Benefits and Disadvantages of Airborne Remote Sensing and Spaceborne Remote Sensing Data for Hazard Categorization and Monitoring.

Both forms of remotely sensor data (airborne and spacebome) have advantages and

disadvantages for hazard and vulnerability assessment. A careful and systematic

appraisal of these data sources, and their inherent strengths and demerits will need to be

made before arriving at any decision to use them for hazard categorization and

vulnerability assessment. Fundamental aspects of spatial, spectral and temporal

resolutions required will have to be assessed prior to their use. Table 2 lists the

requirements for the hazard and vulnerability assessment and monitoring. Since most of

the factors that influence hazards in an urban-suburban spatial setting are composed of

attributes such as structural density and strategic location of sensitive structures the

spectral resolution does not play an important role compared to the spatial resolution.

However, with the advent of high spatial resolution remote sensing data the spatial

resolution is not as important an issue as in the past. Nevertheless to ensure that the

most appropriate and cost effective acquisition systems are used, resolution must still

be considered, particularly where there is a requirement for better than lm resolution.

The selection and use of remotely sensed data differs from one study to another. To

remotely sense the required urban-suburban attributes, it is first necessary to appreciate

the "temporal, spectral and spatial resolution characteristics" ofthese attributes.

67


3-6 Temporal resolution requirements for risk assessment

Three types of temporal resolution should be considered when monitoring hazard and

vulnerability in an urban-suburban setting. These are related to cyclic change, sensor

return period, and user needs. First urban/suburban phenomena progress through an

identifiable developmental cycle much like vegetation progresses through a

phenological cycle. Urban agricultural land is subdivided into urban allotments, and

houses, shops, factories are built and gardens and trees are planted and then mature. In

older areas densification of land-use occurs or existing use is converted to a higher and

better use, as a function of the value of land. Understanding these changes additions to

the existing spatial environment is crucial because they bring in an imbalance between

available resources and demand for more or sometimes less dynamic resources for fire

service protection. It is important therefore that the image analyst understands the

temporal development cycle of the urban phenomena to detect and monitor hazards and

vulnerabilities (Jensen and Toll, 1982). The second type oftemporal resolution relates

to the revisit or potential revisit schedule of the sensor, whether on a project basis as for

most airborne systems, or on a repetitive basis for space borne sensors. Finally temporal

resolution may refer to how often land/managers/emergency planners require a specific

type of information. In the context of this study vulnerability related indices such as

structural density would need to be monitored at an interval ranging between 6-12

months, since urbanization often results in an increase or change in built features within

a relatively short time frame. However, the temporal requirements are met by most of

the available sensors as explained in Table 2.

68


Table No 2 Urban/suburban attributes desired for risk assessment and the minimum remote sensing resolutions required providing such information (adapted and modified from Jensen and Cowen, 1999)

Attributes

Land-use/Land Cover*(USGS) L1-USGS Level I L2-USGS Level II L3-USGS Level III L4-USGS Level IV Building and Property Infrastructure B 1-building perimeter, area, height and cadastral information (property lines) Transportation Infrastructure T1-general road centerline T2-precise road width T3-traffic count studies (cars, airplanes, etc) T4-parking studies

Digital Elevation Model (DEM) creation D !-large scale DEM, visualization D2- Large scale slope maps Socio-economic Characteristics S !-local population estimation 82- regionaVnational population estimation 83-quality oflife indicators Meteorological Data Wind Direction Hail Storms

Disaster Emergency Response DE !-pre-emergency imagery DE2-post-emergency imagery DE3-damaged housing stock DE4-damaged transportation DE5-damaged utilities, services DE6-damaged roofs, constructions

• See appendix V

Minimum Resolution Requirements

Temporal

5-10 years 5-10 years 3-5 years 1-3 years

1-5 years

years

years

5-10 min 10-60 min

1-5 years 1-2 years

0-1 year

1-2 years 1-2 years

5 min 5 min

1-5 years 12hrs-2 days days

1-2 days 1-2 days 1-3 days

69

Spatial

20-100 m 5-20m 1-5m 0.25-1 m

0.25-0.5 m

1-30m 0.25-0.5 m

0.25-0.5 m 0.25-0.5 m

0.25-0.5 m 0.25-0.5 m

0.25-5m

5-20m 0.25-30 m

1-8 km 1 km

1-5m 0.25-2 m 0.25-1 m 0.25-1 m 0.25-lm 0.25-0.5m

Spectral

V -NIR-MIR-Radar V -NIR-MIR-Radar Pan-V -NIR-MIR Panchromatic

Pan-Visible

Pan-V-NIR Pan-V

Pan-V Pan-V

Pan-Visible Pan-Visible

Pan-V-NIR

Pan-V-NIR Pan-V-NIR

V-NIR-TIR TIR -Hyperspectral

Pan-V-NIR Pan-V-NIR-Radar Pan-V-NIR Pan-V-NIR Pan-V-NIR Hyperspectral


3-7 Spectral resolution

Spatial resolution is usually more important than spectral resolution for extracting

urban-suburban information from remotely sensed data. It is more important to have

high spatial resolution (small resolution element) than high spectral resolution (a large

number of spectral bands). For example local population estimates based on building

unit counts usually require a minimum spatial resolution of from 0.25 to 5m to detect,

distinguish between and or identify the type of individual buildings. However, there

must be sufficient spectral contrast between the object of interest (e.g. a building) and

its background (e.g., the surrounding landscape) in order to distinguish the object from

its background. While this can usually be obtained with a single band covering the

visible spectrum, often smaller or different wavelengths band intervals can be used. For

example in the visible blue, green and red (for true colour) may be required for

identification or wavelengths in the visible and near infrared used for easier distinction

between built and vegetated surfaces, while microwave radar sensors may be used to

penetrate cloud where essential immediate information is required, or for urban

structural form. Furthermore, in some cases the spectral resolution is very important to

distinguish between several urban features such as roofing materials, which may be

found in close proximity. For example, in a study carried out by (Bhaskaran et al. 2001,

2001a) for the New South Wales Fire Brigades in Sydney, it was necessary to

spectrally differentiate roofing materials on the basis of their material composition.

This type of analysis is important to determine the regions having more vulnerable

roofmg materials that may be damaged by natural perils such as hail storms, which are

responsible for some of the largest disaster bills in Australia.

70


3-8 Spatial resolution requirements for hazard assessment

For the current study high spatial resolution is vital in order to distinguish between

individual buildings. Aerial photographs can be flown on request at the required height

in order to deliver the capture the data at the required resolution or aerial photos can be

secured from the archives often at appropriate scales. In spite of the successful use of

satellite data as an essential data for the appraisal of urban environments which is

shown in previous studies (Forster, 1985; Welch, 1982; Jensen, 1983; Lo and Welch,

1977, Jensen and Toll, (1982), Murai and Mustra (1988) and more recently Sutton

(1997) the urban environment continues to challenge the demands of remote sensing

users as the coarse spatial resolution of remotely sensed data has not been as suitable

for urban surface analysis as researchers had hoped (Thomson and Hardin, 2000).

While the spatial resolution of satellite products has not yet matched the very high

spatial resolution provided by airborne sensors, most of the needs of urban hazard

monitoring can be met by the recently launched high spatial resolution satellite system,

with spatial resolutions of better than 1 metre as shown in Table 2.

3-9 Evaluation of hazard related variables found in urban/suburban environments and the availability of remote sensing systems to provide such information

Some of the important factors, which influence hazard may be summarized as follows

land-use, special hazards, infrastructure, street network, population density and

demographic characteristics, structural density, topographic features (slope, elevation,

aspect), weather phenomena (wind direction, conditions).

Land-use/landcover is one of the most fundamental variables required for assessing

hazard. Landcover refers to the physical cover found in different areas and as such

71


consists of natural and built features, whereas, land-use refers to the predominant

activity carried out with in that landcover. The land-use is more important for assessing

hazards since hazard in urban environments are a function of the activity and its

potential for hazard. For instance, a tiled roof area (land cover) by itself is not a hazard,

but when it covers an activity such as a workshop or chemical factory (land-use), it

may carry a certain hazard to the people working in it or exposed nearby. However it is

important to have a landcover map before constructing a land-use map since in ideal

conditions the land cover map may provide details about the land-use. A

comprehensive land-use map may require ancillary information about the activity

found in that land cover. Such data may be integrated into the land cover map in order

to determine or confirm the land-use. A moderate resolution remote sensing data such

as Landsat and SPOT may be used to develop landcover maps but high resolution data

such as IKONOS will be required to distinguish separate urban features that aid in the

determination of land-use. In some special cases where detection of small features is

crucial for planning a very high resolution will be required, where scale independent

remote sensing data such as those exposed by airborne platforms, will be required

(Jensen and Cowen, 1999).

Mapping urban sprawl is very important component of risk assessment. Such stages of

urban phenology can be effectively studied by using moderate remote sensing data such

as SPOT and Landsat, which can monitor the urban growth. Image data from these

systems will function like a flagging mechanism, highlighting where change has taken

place but the detailed description of the structures or urban features will have to

ascertained by high spatial resolution data such as those provided by IKONOS or

airborne sensor systems, or from field visits.

72


High spatial resolution space/airborne data will have to use for locating individual

features, transportation centres, storage locations, and special hazard locations. In many

instances even the highest resolution of satellite data may fall short of the required

resolution for detecting urban landuse. Airborne data (aerial photo images) will

therefore always be required until satellite can be provided at a very high spatial

resolution (<0.5).

Determining the height of structures is also important in assessing the hazard and

specifically for allocating materials such as aerial pumpers. High resolution DEMs may

be required to identify such structures. Ridley et al. (1998) conducted a feasibility study

and found that simulated lm by lm satellite stereoscopic data could have potential for

creating a nationwide three dimensional model if the processes were automated.

However, the use of such synthetic imagery may not provide the detailed planimetric

(perimeter area) and topographic detail and accuracy (building height and volume) that

can be extracted from aerial photography. Nevertheless such resolution may be

sufficient enough for assessing hazards and locating high-rise buildings, and for

creating a database, which can be used for visualization.

Atmospheric data from weather satellites may be used in conjunction with high spatial

resolution data in order to prepare for an emergency rescue operation. For instance in

the case of hail storms in Australia, meteorological data may be used in conjunction

with hyperspectral and airborne aerial photo images to locate vulnerable areas. Such

data-base may be linked with other available ground based spatial data, and analyzed in

a GIS environment for managing disasters.

73


Table 2 generally shows that high spatial resolution images would be required to

identify large features in urban areas. For the purposes of risk assessment in urban

environments a very high spatial resolution is required which is also true for most

urban/suburban areas (Jensen and Cowen, 1999). This is due to the need to identify

each structure and other relative features such as street network, drainage lines, special

hazard sites, water-hydrants, fire stations, emergency sites. At any point during a

disaster these features will have to monitored and studied in their relative locations,

which may provide valuable input to resource planning. The range and sizes of features

in urban areas can be very high, with a smallest structure measuring 15 metres to 20

metres or considerably lesser (e.g. water hydrant), therefore risk assessment generally

requires very high spatial resolution data.

However, in some instances a high spectral resolution image may also be required, by

the study as was shown by Bhaskaran and Neal (2002) in a study where there was a

need to identify roofmg materials in Sydney. The study demanded a high spatial

resolution as well as spectral resolution. Hyperspectral data may be used in urban areas

to distinguish between built features and the materials used in their construction.

Roofing materials were distinguished in an attempt to create a vulnerability assessment

map for identifying hail-storm prone areas in Sydney, Australia (Bhaskaran et al. 2001

a,b). A high spatial and spectral resolution image HyMap was used for analysis and

integration into a GIS environment for identifying exposed population for every

collector district level.

74


3-10 Restrictions and other factors

The sourcing of aerial photographs can be hampered by political factors. For instance

in some developing countries the use of aerial photographs is restricted, while satellite

products are generally available. The policy of the government also influences the

choice of remote sensing data. Similarly low level flying can be prohibited in some

sensitive areas such as the city centres (CBD).

3-11 Other Factors

The gestation time involved in exposing a fresh scene on an airborne sensor involves

two factors. The topography of the region may be such that safe flight of the aircraft

may not be possible. These regions can be better exposed by space borne sensors.

Satellite data can be secured even in poor weather conditions provided the cloud cover

is within acceptable limits, while data acquisition from airborne sensors may not be

possible in such situations.

3-12 Summary

From the above discussion it is clear that the study of hazard cannot be based on a

single criterion, such as the hazard focused approach followed by the NSWFB, since

they are influenced by many spatia-temporal factors. On the one hand is the issue of

combining hazard and vulnerability related variables and on the other is the issue of

providing composite information on the existing hazard to assist in emergency

operations. There is a need to develop a semantic fire risk model in order to assist

emergency operations during urban fires. In order to model hazards numerous types of

data which may influence hazard in an urban environment may have to be sourced and

managed.

75


While it can be seen from the discussion of hazard that a wide range of urban variables

can have an impact, it was decided that structural density, land-use, demographic

variables (age, income levels, education) and roof materials, could be considered as

surrogates for most other variables, thus reducing the data sets to manageable size

without compromising the integrity of the study. Management of data is one of the

main issues when developing and modeling hazards. Data management begins by

identifying the type of data required, sourcing it and finally analyzing it to derive new

additional information. The identification of data will be influenced by the objective of

the study, level of details required, and general availability of data. Data management

and choice of sites and the methodology developed for the current study are discussed

in the following chapter.

The main contribution of this study was to present a methodology which can

incorporate various types of spatial data such as remote sensing, cartographic and field

data, all of which when combined systematically and analyzed, will assist in better

decision making and improve the existing methodology of assessing risk and managing

resources. A significant portion of the following chapter explains the step by step

process involved in developing the model and the various analyses.

76


Chapter 4: Data Management and Methodology.

4-1 Introduction

The current research is based on using data, which is extracted from remote sensing

sources and other available GIS cartographic data. The first step in developing a model

is to identify and acquire the raw data and their sources by using the latest technology

such as remote sensing and available cartographic data. The data is generally

determined by the problem to be studied, which will influence the scale, type and

details in the data. The type of details which have to be studied and analyzed will vary

from one region to another depending on the local landscape and geography. Data

management is therefore a complex aspect of hazard modeling which includes the

identification, sourcing and management of data. Once the data has been identified then

details pertaining to accuracy, content, projection and datum need to be recorded and

stored for future uses. Data management is also based on the study area, its geography

and other socio-economic characteristics. The sites chosen for this study were the

developing city of Bathurst and a section of the densely populated Shire of Hornsby, on

the edge of Sydney.

A detailed description of the data management aspects and the methodology adopted in

this research is the main content of this chapter. The methodology for developing a

semantic model is a complex and time consuming process, involving the systematic

appraisal of spatial data, which is sourced from high spatial remote sensing data and

available cartographic data, that has been validated and has or is being used by other

and different organizations. A detailed step-by-step account of the methodology will

77


enable users to repeat these processes for their own requirements and replicate this

process with minor modifications.

4-2 Site Requirements

The rationale behind the selection of study areas was to use contrasting landscapes for

assessing the hazard and vulnerability. The hazard and vulnerability presented by one

region should be distinct to that of the other in terms of land-use, structural density,

population density, and other spatial patterns. A study of hazard and vulnerability

assessment must be carried out in these diverse settings in order to develop a

representative model. These two study areas chosed have the necessary features and

variations that can then be extrapolated to other parts of New South Wales.

Descriptions ofthe study areas chosen are as follows.

4-2.1 Bathurst City, NSW, Australia

Bathurst (see Figure 6,7 and 8) is approximately 210 Ians to the west of Sydney. The

city is located at the junction of the Great Western, Mid Western and Mitchell

Highways. Other major towns and cities in the vicinity of Bathurst include Orange,

Blayney, Oberon and Lithgow. The Bathurst Local Government Area is approximately

240 Km2 in area and forms part of the Evans Shire. Within this area elevations vary

from 635m (AHD), adjacent to the Mcquarie River at the north-western boundary of

Bathurst City, to 879m (AHD) directly to the south of Mount Panorama. The majority

of Bathurst City is situated on undulating to gently rolling terrain (slopes ranging from

2.5% to 7.5%) mostly around 700m (AHD).

78


The estimated resident population of the Bathurst Local Government Area (LOA) at

30th June, 1998 was 29,683 (Source: Australian Bureau of Statistics, 1998). Over recent

decades Bathurst has undergone a sustained period of growth. Within this period

Bathurst has grown at a rate of between 2% and 2.5% per annum. This consistently

places Bathurst among the fastest growing inland cities in New South Wales.

Agriculture is the dominant land use, but urban and associated rural residential

development has occurred near Bathurst. As both cities continue to grow, urban

expansion is competing for agricultural land. Rural residential lots continue to be

popular throughout the region, as are weekend retreats near Bathurst and Oberon due to

their proximity to Sydney. Extensive areas of Crown Land including national parks,

nature reserves and state forests are located on steep terrain. Commercial radiata pine

forests have increased in extent on adjacent agricultural land. Rural residential

development is increasing around Orange, Bathurst, Oberon and Cowra, with Bathurst

and Oberon attracting many absentee landlords Industrial development is rurally based.

Complexes include the Blayney Abattoir and Edgells' vegetable canning operations at

Bathurst.

4-2.2 Hornsby-Shire, NSW, Australia

Hornsby (Figure 6, 9 & 1 0) on the other hand, is undulating topography with a

residential-bushland interface. Hornsby-Shire is a unique region with its contrasting

environments reflecting urban, rural, bushland and riverine settings. The urban areas of

Hornsby-Shire cover some 65 Km2 or 12% of the total area. The location and zoning of

land reflects cadastral and historical patterns, rather than being a response to

environmental/land capability analysis and planning. Non-urban lands within Hornsby

Shire continue to diminish, as this remaining land is comes under extreme pressure for

79


urban development. The natural environment includes varying geology, topography,

drainage, soil, landscapes, flora and fauna, and bushfire hazard. Land-use and zoning

within Hornsby Shire is controlled at the local level.

Territory

Western Australia South

• Bathurst

Figure 6 Location map of the study areas. Bathurst and Hornsby

80


Section of(CBD) Bathurst, NSW, Australia N

A ., .....

Figure 7 Orthorectified aerial photoimage of central business district, Bathurst

81


Study Area of Bathurst City, NSW, Australia (Orthorectifled Aerial Photo Image)

Figure 8 Study area ofBathurst City, NSW

82


li270600 +

8270000 +

62till600 +

+

+

62E8000 +

8287600 +

8287000 "L--"'"""*'-82SOOO

Study area of Hornsby Shire, NSW (Orthorectified Aerial Photo Image)

Figure 9 Study area CBD Hornsby Shire, NSW

83


Section of (CBD) Hornsby Shire, NSW, Australia N

A

Figure 10 Orthorectified aerial photoimage ofCBD Hornsby Shire, NSW

84


Spatial distribution of Incident history in Bathurst City.

(Each point shows incident number)

* * ~46,104

•

. 7' 86,177 .... '1111 46,079

~69,135 '*486,133 86,016

~8,968

i.86,219-f669 on 10,700 A

~69,121

*>56,326

10,WO i410,686 * . 86,093

*f·206 : 6a,sil • 10,743

l i486,229 'J\486,013 *'56,244 10,663

-Ati56,m ~46,143 '~745•24~69,064 10,758 ill10,7lf

,1846,430

ls~f:A01 2 * •45,2~45,2sr 1 0,673 -le46,097

~10 630 *45·146 6 ·1.~ 'lilt 820 * ' fr 'i669,011 11!1116,111 ' 86,169 i845943 * i.t10,684 '*-86,008 'J\410,714 *45·339 • '\10,698 '

# 86•110 .10,701 'l\569,02 :tl10,~5 86,139

*-"§!1,015 * o'i!!!!!liiioii!.4~~o.o 'i(;56,1B1 a&,12&\ec5e9'9 10,645

Kilometers * '5 26 86•052 ' il10,761

. 10,749 ~

ie46,125 'l\s!a.s2a'656,352

'J\410,751 *'56,344

* oM69,030 ~68,925 i m,222

* 86,211

'i668,942

1!186,000

-le56,465

+;e5e,2sc

*'56,319

~5,518

Figure 11 Spatial distribution of fire incident history in Bathurst City. Each point location shows the incident ID number which are used by the NSWFB, during dispatch operations (Source: NSWFB)

4-2.3 Fire incident history

The fire incident history is a layer, which shows the number of incidents, which have

occurred in the past in both Bathurst and Hornsby (see Figure 11 & 12). These point

layers are geocoded and created for estimating the trends and statistical analysis in the

incidents of fire by the NSWFB. This database describes the number of incidents as

85


well as the percentage increase in population, for each fire station in NSW by postcodes

and available resources found at the fire station. The incidents, which have occurred

from 1991 to 1996 according to local government area are also shown. This data may

be overlain on the hazard map, which would reveal new trends in the occurrences of

hazards.

1e9o,m

Spatial distribution of Incident history in Hornbsy Shire (Each point shows incident number)

~14.281

'*!33,162

~3.113 0 0.3

I<Jiometm

~44. 560 'IIMI,796

~63,1.f

~.017

11896,492

'11119,116 '111190,634

0.0

•s..055 lieU,553 1e90,190

llJJ,

33,158

ie33,322

..... ... 63,915

*I 3,017 ... 18,951

. 64.207

• n.l\9 ~0.319

1614,1 . 20.951

111161,056

io9o,656

111720,915

'111549,525

Figure 12 Spatial distribution of fire incident history in Hornsby Shire Each point location shows the incident ID numbers, which are used by the NSWFB, during dispatch operations. (Source: NSWFB)

86


4-3 Information Management Needs

Managing the information is the first important step towards creating a database for risk

assessment. Good information is needed not only to help fire service managers make

decisions, but also to back up those decisions with hard data that can stand the scrutiny

of city managers, budget analysts, the press, and others (Schaenman, 1988). The most

important step is a data users needs assessment that attempts to identify the data that is

required, its specifications, accuracies etc. Subsequent important steps in the

information management process are collection, processing of data and finally

dissemination. The objective or the direction of the study necessitates a detailed search

for and collection of data (Figure 13). A considerable proportion of the time for this

project was therefore spent on collecting and organizing the data.

Issue

DIRECTION

Dissemioa tion Collection

Processing

Figure 13 Information management cycle (Granger K, 2000)

The major part of the vector data required by the study was procured in digital format

from the Bathurst City council and Land and Property Information (L.P.I Bathurst,

NSW, Australia). A process of spatial overlays and geometric intersections generated

87


the attribute data for each areal unit (250m by 250m). A contact print of an aerial

photograph (1:16,000) exposed in 1998 was scanned to a 1000 dpi and registered using

several ground control points from a digital elevation model (DEM) to accuracy of ±5

meters: This image was re-sampled to a 1 by 1m spatial resolution and saved as a tag

imported file format. These types of digital image maps (or image maps) are becoming

more common than conventional cartographic line maps for many land-related

mapping applications, especially in relation to hybrid GIS (Parent, 1991). Hybrid in this

context means the capability of a GIS to capture, manage and analyze, and present

vector and raster data efficiently (Grenzdorffer and Bill, 1994). These digital images

can be geometrically processed to achieve high accuracy, which would be comparable

to basic maps of similar scale (Thorpe et al. 1994; Doyle, 1996). Where new data is

required, remote sensing technology, on satellites or aircraft, holds great potential in a

disaster situation (Granger, 2000).

4-4 Data Description

Several types of data were sourced for this study particularly those which were related

to risk. Modelling urban fire risk requires a systematic appraisal of such varied types of

data and their utility in explaining the spread of urban hazard. The accuracy and quality

of the database varies for each study, however those used in this research were found to

be suitable with respect to accuracy and details. A brief description and utility of some

of the important datasets used for the research is explained in the following

subsections. (see appendix V for complete description of data used in current research

and Metadata.)

88


4~4.1 Contours (Source: Hornsby Shire Council) land and property information (LPI)

This information may be used to assess the elevation of the service area mainly for

visualization. The topography of a service area may combine with other existing factors

which influence hazard in presenting additional difficulties during emergency

operations. For instance, if the general elevation is mapped then the degree of difficulty

of routing an appliance such as the aerial pumper can be determined and strategically

located. The elevation details may be input to the fire hazard model to draw and create

picture of the existing hazard. In the current study the contours were not used due to

some technical problems with the data. However see Figure 14 for an example of

contour data over Hornsby.

4~4.2 Land~use (LEP) (Source: Bathurst city council and Hornsby Shire council)

These data were fundamental to the assessment of hazard since land~use is one of the

main factors which, influences the hazard in a particular region. The database was

secured from the Bathurst City Council who created it. New developments are updated

and validated them at regular intervals. This data therefore has high accuracy. The land~

use data consists of three major land~use types such as residential, commercial and

Industrial. Commercial land~use was further sub~divided; for example service business

and general business were combined to create the commercial land~use. The other

major categories of land~use were market gardening and general residential. The data

was provided as a polygon layers in multiple formats such as e.OO (Arcinfo Interchange

file format) and TAB (Map Info format) for the sake of software compatibility.

89


The land-use map was not available from the Hornsby council in a digital format since

they were encountering problems with the export of data from a specialized GIS

software to Map Info. The hardcopy map showing the land-use in the central business

district of Hornsby was converted from analogue to digital form using an AO size

digitizing table and Arclnfo software. Data editing was followed by the construction of

topology and the coverage was projected and transformed to the UTM Zone 56 wgs84

geodetic model. Digitizing still proves to be a very useful method of creating spatial

data especially when no other sources of acquiring such data is possible.

4-4.3 Orthorectified aerial photoimages (Source: Land and Property Information (LPI), Bathurst and Hornsby Shire)

Airborne data were preferred over space borne data mainly because of the inability of

space borne data to detect small details about some features in the urban environment.

Study areas in Bathurst and Hornsby were exposed at a scale of 1:16,000 and the

contact prints of these high resolution aerial photographs were secured from the LPI,

Bathurst and Hornsby Shire council. These were then scanned by using a digital

elevation model (DEM) and ground control points (GCPs) which had accuracies of

±5m and ±1m respectively. These products were orthorectified at the LPI, Bathurst

City. The digital images were then registered with the DCDB by a process of image to

map registration. Many types of vector data were overlain and analyzed with the aerial

photo images, to reveal many hidden patterns and spatial information which were

particularly significant in the research.

90


4-4.4 Digital elevation model (Source: LPI, Bathurst)

In some undulating regions of Bathurst and Hornsby, the topography, especially the

slope and gradient may present some obstacles to emergency operations and overall

disaster management. Visualizing the hazards in three dimension will render additional

information on areas, which are not accessible or that may take a longer time to reach.

While there is enough potential for the use of DEM in the current study but they were

not used because of some inherent problems with the data and limitations of time.

Contours Hornsby Shire Council Source: Hornsby Shire Council Environment Division

Figure 14 Contours showing elevation in a section ofHomsby Shire. Contour Interval-! 0 Metres. Elevation of peak heights shown. Coordinates are AMG, UTM Zone 56

91


4-4.5 Population density and demographic characteristics. Source: Australian Bureau of Statistics. (Source: Australian Bureau of Statistics)

Risk assessment must involve the assessment of the hazard (physical factor) as well as

the population who may be exposed to such hazards. Night-time population density and

demographic characteristics such as population density, the income level, distribution

of less mobile people, were extracted from the census data which was procured from

the ABS. The data from the census was inadequate in some ways. For instance, the

demographic data is a reflection of the night-time population and there is no

information to show the day-time population, and the data provided was given for the

entire collector district, whereas in reality only certain portions of the collector district

was populated. Even though this information, in many ways, is not very adequate for

the study, it was the best available data source about population and their

characteristics. The layers were provided in polygon map feature and had to be

processed before inputting them into the model. The data was secured in TAB format

and the data type was in the form of polygons. Most of the data used in this research

was extracted at the collector district level, which is the lowest level of detail provided

by the ABS.

4-4.6 Statewide chemical database

There are many sites in NSW, which present some hazard since they contain stored

chemicals. Most of these sites were under the acceptable standards of normal consent

levels stipulated by the NSWFB (Figure 15). Although most of the sites were strictly

monitored and were found to be under the permissible levels of hazardous

contaminants they had the potential to influence nearby regions and increase hazard

due to the material store and their nature and purpose of use. These sites have the

92


potential to significantly influence the overall hazard in any region. The location of

such sites is therefore vital for the overall assessment and analysis of urban hazard in

the study regions. These sites were shown by point layers and were created by the

works cover authority in Sydney. Some of the data-base were erroneous which resulted

due to the inefficiency of the geo-coding process.

Chemical Storage locations within consent levels and *N Spatial Distribution of Hazard in Bathurst w E

' s

A Flrutadons D 1Km butrorft"om tho exlatlnt Flrutulons, Bathurst

• Chemical Storaeo Sitos • Extreme Huard Sites

1\1 StrootNotwor11 D Spatial Unit 250 by 2GO m Huard In Bathurat

Hazard

Figure 15 Chemical storage locations within consent levels in Bathurst

93


Spatial distribution of sensitive locations in Bathurst, NSW

7S4000 780000 7:10000 740000 742000 744000

118011000 + + + + + +

6!04000 +-

11:102000 +

6100000 +

62111000 + 000

s

• • . . SALEYARDS OV~ • BATHURST SALEYARDS

• Sensitive locations (points) D Spatial Unit 260 by 260 m Hazard Categories In Bathurst

Very Low Hazard

7S4000 780000 788000 74 2000 744000

3 3 Kilometers ~~--~~~~

740000

0

748000 -Low Hazard D Moderate Hazard

High Hazard c::J No Data

Figure 16 Spatial distribution of sensitive locations in Bathurst City, NSW. (Source: NSWFB)

Knowledge of locations such as hospitals, educational institutions, places of worship,

all of which are places where people congregate is important to posses prior to

undertaking any emergency planning operations. This data is available in the digital

database of NSW which was obtained from the NSWFB. This database shows the

locations of educational institutions, hospitals, places of congregations such as religious

institutions, clubs, associations and important buildings (Figures 16 & 17). A detailed

legend is also shown in Figure 18. Prior knowledge of such locations will assist in

planning for emergency operations before a potential disaster, during a disaster and

94


• . '· • •• • }II

•

Maplnfo Australia Feature File Legend

+ Accommodation + Airports + Ambulance

* Armed Forces • Bus Depot A Business Building + Cemetery

' Church .a. College

* Embassy • Factory, Power Station

" Fire Station • Geographic Point

Golf Course 0 High School o Hospital J. lighthouse, Jetty

• Parte:, Reserve

• Parl<ing * Po~ce Station, Court

" Post Office Pre-School, Kindergarten

0 Primary School o Prison 0 Private/Religious School 0 Radio Tower X Railway Station A Reservoir ~ Space Centre 0 Special School 6 Sports Centre

• Suburb 0 Technical School A. Tourist .&. University

Figure 17 The New South Wales feature data base map (north is to the top and symbol edge represents the border ofNSW, Australia, approx. 1 OOOkms east-west). Figure 18 Legend

4-4.7 Special (Extreme) hazard locations: above consent level sites (Source: New South Wales Fire Brigades, Sydney)

95


Some sites were identified as hazardous due to the excess storage of harmful chemical

contaminants (Figure19). These sites have the potential to affect other nearby areas

adversely which are within close proximity to them. Emergency preparedness measures

must include the locations of these sites as weU as the hazard level of the nearby areas,

which may be effected in the event of a gas leak or any other unforeseen disaster. This

database is similar to the NSW digital database in that it shows the locations of

sensitive installations which have been earmarked and categorized by the NSWFB.

734000

302000

300000

298000

Population distribution and location of Special hazard sites

Bathurst, NSW

736000 '738000 740000 '742000 744000

3040 00

6302000

300000

296000

• Extreme Hazard Sites

s

D Spatial Units 250m by 250m Population Density

734T00~0--73=so":":oo:---~73~B~00~--=74-:0'!":oo~o ---:7~42~0o~o--':"":74":'!:4 ~oo:---~ 0 V low hazard CV low popn density) low hazard (low popn density) Moderate hazard (Moderate popn density) Hgh hazard (High popn denslcy)

Figure 19 Population distribution and location of special hazard sites, Bathurst, NSW

96


4-4.8 Digital topographic and cadastral database (DTDB & DCDB; Source LPI)

These data-base were secured from the LPI, Bathurst in digital form and consisted of

basic information on the street network, land parcels, locations of important sites and

most importantly is prepared for the entire state of NSW. The DTDB and DCDB

(Figure 20) are therefore consistent for the entire state ofNSW province and has been

validated. The DTDB comprises data derived from the existing hardcopy topographic

maps series. The captured data is available as natural drainage, contours 2, I 0 and 20

meter interval data, and transport/cultural data. The DTDB is designed as a state-wide

coverage suitable for the majority of users. Although the dataset is sensibly

independent of scale, and some data such as urban road centerlines are sourced from

large scale cadastral maps, the content, accuracy and representation of features is

related to the source documents from which the data is obtained. Therefore the

currency of the topographical digital data is not always maintained. The digital

topographical database shows the location of important features.

The source data for the DCDB was I: 1000, 1 :2000 and 1:4000 scale mapping over

urban areas, and 1:25,000, 1:50,000 and 1:100,000 scale mapping over rural areas. The

Sydney Water Board collected data throughout its coverage area from its I :500

cadastral sheets and selected Councils also provided data. This data was polygonised,

tagged and incorporated into the DCDB.The DCDB contains up to fourteen layers of

data depending in the occurrence of various features within any specified area. The

DCDB consists of details about the property, ownership, plot number which may

provide indirect details about the nature of land-use. The DCDB was used to register

the orthorectified image in the current research.

97


Figure 20 Digital topographic database: Source: Land and Property Information (LPI), Bathurst

4-5 Software & hardware requirements

Modelling urban hazard demands the systematic and accurate analysis of spatial

information and data which may range over various resolution, scales and projections.

Modem day softwares have different abilities, which are unique to each one. Different

types of softwares were used for spatial analysis and extraction of information from

remote sensing data. Due to the nature of analysis, different GIS and image analysis

softwares were used. For instance, the Master Attribute Table (MAT) was created after

performing the 'geometric intersection' and 'update operation' in Mapinfo, while the

image to map registration was performed by using the image analysis software

ENVIJIDL. The weighted overlay was performed by using the model builder module

98


provided with the Arc View software. These softwares and the purposes of their use are

listed in appendix vn.

The accuracy ofthe data used is as important as the objective of the study. In this study

the relative locations of features was important for mapping the spatial distribution of

risk in urban environment. Even though some inaccuracies were detected during the use

of some of the data, it was within acceptable limits, and therefore a decision was made

to use them anyway. But this is another aspect of data integration and modeling, which

needs appraisal since GIS data deteriorates with an increased information content and

sophistication. Different data types may lead to various issues such as data

incompatibility which is attributed to the individual and self oriented attitude of

organizations, who are involved in preparing data-base to suit their own personalized

needs and softwares. This can be resolved by using multiple softwares which may

accept all commonly used data formats.

4-6 Methodology

The methodology to generate hazard categories in Bathurst involved the integration of

orthorectified aerial photo images with available cartographic data, which was sourced

from different agencies. These cartographic data were found to exist at different scales,

projections and formats which were spatially referenced to the ortho-image by a

process of map to image registration. High spatial resolution (lm by lm) remotely

sensed data :from airborne platforms were used to visualize and derive spatial data in

the study and test areas of Bathurst and a section of the central business district of

Hornsby Shire. The methodology to develop hazard categories using remotely sensed

airborne images and cartographic GIS data was designed after a careful appraisal of

99


spatia-temporal resolution characteristics of available sensors (both space borne and

airborne) and available cartographic data. Whilst, there were also advantages with

space borne data there were also some disadvantages. These advantages and

disadvantages were considered and the advantages optimized, depending on the hazard

related variable to be derived.

4-7 Data resolution characteristics

The following resolution characteristics were considered before deciding between

airborne and space borne sensors. Resolution (or resolving power) is a measure of the

ability of a sensor to distinguish between signals that are spatially near or spectrally

similar.

4-7.1 Temporal resolution

The temporal resolution of a sensor system refers to how often it records imagery of a

particular area. Choice of the temporal resolution will ideally depend on the phenomena

to be studied. In this study acquiring fundamental data on Urban/suburban attributes

changes was crucial, so a temporal resolution of 6-12 months was deemed appropriate

for the study. Air-borne sensors could provide this since they could be flown whenever

required subject to restrictions imposed on flying low altitude, restricted areas, weather

and unfriendly terrain.

4-7.2 Spectral resolution

Spectral resolution refers to the number and dimension of specific wavelength intervals

in the electromagnetic spectrum to which a sensor is sensitive. Since most of the study

was to be carried out in an urban/suburban setting the spectral resolution was not as

100


important compared to the spatial resolution, because urban features are identified more

by shape and size and not their colour. However, it is important to have sufficient

spectral resolution to make out the features to be identified from their background

setting. For example, a building from surrounding vegetation.

4-7.3 Spatial resolution

Spatial resolution is a measure of the smallest angular or linear separation between two

objects that can be resolved by the sensor. Evaluation of spatial resolutions offered by

many sensors (from both airborne and space borne platforms) were carried out before

deciding on the most appropriate option. In this study it was necessary to determine the

object being studied individually as well as in a group of objects. The spatial resolution

had to be sufficient enough to provide those details. Satellite products were not able to

provide a spatial resolution of ~lm, necessary for most urban identification, and also

high spatial resolution aerial colour photo images were used for the study. The

adequacy of satellite data with respect to the spatial resolution may be fulfilled with

improved sensors in the future, but there are other aspects related to the satellite data

that may act as an impediment. For instance the nadir/off nadir viewing angle (vantage

point from which the sensor detects the object). If the sensor detects at nadir the spatial

resolution will be much better than when the sensor detects at off nadir. Thus the

viewing ability of the sensor can offset the high spatial resolution of the satellite

product. Therefore, the adequacy of satellite data has to be systematically appraised

with respect to all of its features and characteristics spatial resolution including

temporal resolution and viewing abilities, before the decision is made to use such data

in an urban hazard context.

101


4-8 Availability of cartographic data

Since most of the study is data driven and would need to be extrapolated later in an

operational mode to the entire state of NSW, it was essential to identify and collect

available data that could be found in other parts of the state for e.g. the Local

Environment Plan or LEP prepared by all local authorities. Another reason to use

available and common data was to standardize the methodology over the entire state of

New South Wales in an attempt to replicate the methodology without many difficulties.

4-9 Methodology in detail (See Figure 21)

The overall methodology for data modelling is shown in Figure 21. This procedure

commences with the digitising of ortho rectified aerial photos, and ends with an output

hazard model that can be used for Impact Analysis and Forecasting. Each of the steps

will be discussed in detail as follows:

4-9.1 Processing of aerial photograph

An aerial photograph of scale 1:16,000 contact print exposed in early September, 1998

was scanned at 1000 dots per inch (DPI). An aerial photo will have distortions from a

uniform scale due to, topographic variations, and terrain and image displacement

resulting from relief and tilt, which must be corrected in order to make it planimetric

(scale correct). Spatial analysis or data derivation from these map bases will result in

erroneous calculations and analysis if they are not ortho rectified. The process of ortho

rectification requires accurate ground control points and terrain elevation. The process

of ortho-rectification is effective in undulating terrain and least effective on relatively

flat terrain due to the minimum scale distortions, which are attributed to uneven relief.

102


The ortho-rectification process results in photos with a single scale in undulating

terrain, (region that is not flat) which is the same as a map.

The distortions and relief displacements were eliminated and the image was made

planimetric and scale correct by the process of orthorectification. A DEM with an

accuracy of ± 5m and GCPs with an accuracy of ±1m were used to orthorectify the

image using a stereoplotter. The image was formatted to the Tagg Imported File format

(Tiff). Other data parameters such as projection, scale of the ortho rectified image are

listed in the Metadata table shown in appendix V.

103


1:16000 contact print

Scanned at 1000

dpi

Overlay of disparate data (Vector and Raster)

Structural Density

Land-use j----~

Demographic Variables

Built-up Roof Types

Material Composition

ASSIGNING WEIGHTS, BIASES

UPDATABLE DATABASE

ORTHORECTIFIED with

DEMIGCPs

Map to image registration Consistent and Continuous

coordinate system

GEOMETRIC INTERSECTION

Update Operation And Creation of Master Attribute

Table

DERIVED KNOWLEDGE BASED VECTOR DATA

SETT1NG MODEL DEFAULTS Cell size, Map Extent, Map Units

Processing of Aerial Photograph

GIS Analysis

Welghtad Overlay

Hazard Maps

VERIFICATION

PROXIMITY ANALYSIS

Figure 21 Methodology to semantically model hazard in urban environments

104


4-9.2 Map to image registration and transformation to a common coordinate system

Data from various sources, as from digitising and field surveying, may be used together

only if they are referenced to a common coordinate system. An image to map

registration was performed where the ortho rectified source image pixels were

geometrically rectified to the coordinates of the georeferenced land-use vector map

provided by the Bathurst City Council. The two layers (image and the map) were

displayed simultaneously in the Maplnfo GIS software environment and common

GCPs were selected in order to perform the registration. The GCPs were spread out

over the entire image so that scale errors about were kept to the minimum. The Root

Mean Square Error (RMSE), which is a simple method to measure distortions, was kept

within acceptable limits of ±1m. The map was projected to the Australian Map Grid

(AMG84) projection system. Having done this the other disparate (layers with different

projections, themes, datums, scales) vector layers could now be spatially overlaid onto

the image.

4-10 Spatial overlay of disparate data

Overlay is a spatial operation in which a thematic layer containing polygons is

superimposed on another to form a new thematic layer with new polygons. This new or

derived layer will have composite information about the variables and will provide

additional knowledge of various spatial phenomena that would have been otherwise

difficult to observe. In the study several layers were observed by the spatial overlay

command. The overlay process enables the visualization of a composite of different

themes that have different purposes individually. By themselves these themes may not

hold much value, but when viewed in conjunction they form a powerful piece of value-

105


added spatial information that can be directly used as decision support systems. For

instance, the overlay of structural density and land-use can help emergency planners in

deriving value added information about the type of structure and therefore in

determining the degree of hazard.

4-11 Selection of Spatial Unit

Quantification of hazard scales is possible if there were to be some geometrically

uniform spatial unit that can be used for comparisons, calculations and analysis. A

spatial unit is a polygon layer, which is a geometrically shaped cell (measuring 250m

by 250m in this study).

In order to quantify the relative degree of hazard that prevails in a certain unit space

over a certain period of time it is necessary to have some form of basic spatial unit

(BSU) to which these hazard degrees can be input. Using these spatial units can allow

comparisons between hazards. The hazard potential in a certain place and time is

explained by the location of various variables such as the structural density and

population density. These variables are not uniform geographically, and have to be

quantified in order to understand the potential of hazard. BSUs consisting of unique

cells measuring 250 by 250m in area were used in the study. The selection of spatial

units depends on the phenomena to be observed and studied and therefore will change

from one study to another (Visvalingam, 1991). One of the main criteria behind the

selection of the 250 by 250m spatial units was to enable the detection of special site

hazards for example jails and special hazards (LPG, chemical Industries). On testing

some different cell sizes for e.g. the 500m by 500m, 250m by 250m and the lOOm by

106


1OOm it was found that the amount of details on the land-use that they recorded was

either very excessive or inadequate for the information required for hazard analysis.

Selection of Spatial Units D 250 by 250 m Spllllll units N

W*E

0 .. 1!!3 !!ll!!!!!!!liiiiiiiiiiiiiiiiiiiiiiiii!O!!II!!!!!!II!!!!!!II!!!!!!!!!!!!i!Oiiii;.3iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii~O. I Kllometo,..

Figure 22 Spatial unit selection (large yellow box: 500m by 500m, medium 250m by 250m and small lOOm by lOOm)

The amount of details, which will be stored and spatially analyzed in the case of the

500m by 500m spatial units (large yellow box, see Figure 22) may contain too many

different land-uses, while the lOOm by lOOm spatial unit (smallest yellow box) may not

sufficiently characterize the area. For these reasons it was decided that a spatial unit of

250m by 250m would be appropriate for this study. This spatial unit is smaller than the

size adopted by the NSWFB, which is 500m by 500m. The choice of spatial unit will

be unique to each study and will vary with the objectives and purpose of the study

(Visvalingam, 1991).

107

s


4-12 Masking Operations

The census data was used for inputting the population density and other demographic

variables. The nature of this data created an immediate problem in assessing the

hazard/vulnerability. The population density which is an estimate of the night-time

residential population is aggregated to the collector district (CD), but does not indicate

the precise location of the structure i.e. the residential dwelling. Since the information

presented is not calibrated with the location of these residential structures the database

to a large degree is generalized. From the hazard/vulnerability assessment point of view

this lack of specific location does not assist in the analysis of hazard/vulnerability

particularly in mapping and resource allocation, as certain parts of the CD can be

densely populated and others not. Therefore by a process of visualization of the high

spatial resolution airborne data, new vector polygons (shown in blue colour around the

dots) were created around these residential structures (Figure 23). The data provided by

census could then be clearly associated with the residential dwellings by a process of

geometric intersection. This process of masking improves the positional accuracy of the

census to a considerable degree, and pinpoints high population areas and therefore the

potential of hazard/vulnerability (see Figure 24). Another method of reducing the

effects of CD data aggregation is by overlaying other land-use layers such as vacant

spaces, parks, bushland and so on, but in this case the layers must be available and in

the right map projection.

108


MASKED DWELLING CONCENTRATION WITHIN COllECTORS DISTRICT N

BATHURST CITY, NSW, AUS RAUA i\

------ -----

Figure 23 Small area analysis issues: By using orthophotos the area of hazard can be precisely demarcated. Structures are shown by dots within the collector districts. The legend shows the total number of flats (range) found in each polygon. The number of polygons is indicated in brackets. Ranges are 3-4 (9), coloured gray; 4-17 (9)coloured pink, 17-48 (9)coloured red, 48-131(8),coloured dark red

109

---.--. __


.. . .. :·:: .: . . . . . ... . . :· .. ::·.: . . . . . .. · ..... . . . . . . . . .. . . . . . . . . . . . .

Before masking

I S1nmtw-al density; 1500 struct.un:s I 200ha 7.5 structures /Ira

Figure 24 Location of hazard within collector districts: Masking operations in GIS

4-13 Geometric Intersection

The determination of hazard involves the combined study of more than one variable.

For instance it may be useful to map the population density and the land-use in a region

in order to understand the type of hazard, which may arise in this location. Spatial

analysis which combines one or more variables in order to determine the total hazard is

vital to disaster management. This was achieved in this research by using the operation

"Geometric Intersection".

Geometric intersection is a process where two layers that have the same map feature

(polygon, line, or point) are intersected to derive new, additional information. The

110


procedure adopted for the geometric intersection process is shown in Figure 25. For

instance, the geometric intersection between the spatial unit (polygon layer) and the

land-use will result in new information that will explain the type of land-use within

each spatial unit. This new layer that shows the land-use for each spatial unit can be

intersected further to derive new information depending on the need to generate

additional information.

4-13.1 Results of geometric intersection

The results of the geometric intersection operation performed in this study are

described below. The residential units were intersected with each spatial unit (250m by

250m). Advanced sequel (SQL) programs available with Map Info were used to

perform the geometric intersection and update operations. This resulted in a new layer

where the residential land-use for each spatial unit was derived. This procedure was

undertaken for the entire city as shown. Figure 26 shows the results of the geometric

intersection process and Table 3 shows the updated attribute table after the geometric

intersection process for the city of Bathurst. Similar operations were performed for

several other layers in order to generate additional knowledge based maps.

111


Residential hldustrial

Roof Area Built Area Percentage of Built area

Land-use DEMOGRAPHIC DATA

Population Density Less Mobile People Dwelling Density Dwelling Type Average h1come

Service Business General Business

LAYERS GEOMETRICALLY INTERSECTED WITH SPATIAL UNITS

250M BY 250M

UPDATE BY WEIGHTED AVERAGE INDEX METHOD

UNIQUE RECORDS SHOWING COMPOSITE HAZARD INFLUENCING VALUES IN MASTER ATTRIBUTE TABLE (See Table No 3)

Figure 25 The Geometric intersection process

112

Integrating RS &: GIS for Urban Fire Disaster Management. Sunil, 1999-2002

!AREA I PERIMETER I SULUCOV I SULUCOV 10 I PR 10 - I PARCEL NO I STREET NUM I - -12,629.3 527.234 2 2 10,902 12,930

.4,296.47 270.894 3 1 9,790 12,943 27,000 719.18 4 3 9,790 12,943

33,790.84 752.283 5 14 9,790 12,943

494.031 122.521 6 16 4,323 11,188 2,893.38 233.276 7 50 6;051 11,411

3,115.78 245.733 8 49 6,050 11,412 3,306.41 257.641 9 48 6,049 11,413

3,745.78 273.702 10 47 6,048 11,414 33,547.69 769.461 11 109 9,615 12,939

38,242.19 810.539 12 110 9,615 12,939

60,668.5 976.792 13 112 7,361 12,852 49,648.44 900.848 14 113 7,361 12,852

47,401.33 876.811 15 114 7,315 12,851

408.859 149.97 16 116 7,315 12,851 59,656.25 971.413 17 117 8,571 12,863

46,250 873.908 18 118 8,571 12,863

21,335.72 638.54 19 120 3,617 13,711

37,625 805.757 20 122 3,617 13,711

48,815 885.627 21 125 3,617 13,711 1,060 233.87 22 124 2,369 13,597

10,915.08 591.973 23 127 2,369 13,597 23,693.53 699.758 24 130 2,369 13,597

Table 3 Attribute table created after the geometric intersection and the update operation

113

92

44 44

44

20 58

54 50

46 456

456 384 384

47

47 310

310 772

772 772

62

62 62


Geometric intersection between spatial units and landuse in Bathurst.

Land.uso Of B illllunt ,_._ - flo l,atlal Unitt 1 (a)Ceneralltw"al 1(b) MlttttO...., 1(c) llUnlll•l<l-aiZo 1(d) llunl ., .. ,al ,..., •• 2(a) Roll-11 Zono (lo 3(a) o-nal Business (lo 3(b) l•rvlce BUlin•• Zon 4(o) lnaullr1a1Zono G(a) lpoclal Usos - ,ubll

Loc:ll Recrution Zon DaUs

Figure 26 Graphical results of geometric intersection between land-use and spatial units

4-13.2 Update operation and creation of "Master Attribute Table"

The derived data from the geometric intersection was updated to create a master

attribute table (MAT, see appendix II). This table stored all the individual spatial units

with their respective details (area, perimeter etc) for land-use, population density,

dwelling units. This information was updated to the table by using the "'weighted

average index method' (which assigns derived values from the "Geometric Intersection

Operation" to each record proportionally). For instance if a spatial unit layer was

intersected with the land-use layer (polygon map features), then the value of the

maximum land-use was assigned to the spatial unit on the basis of the area the land-use

occupies in the spatial unit area. In the MAT, the record will show the maximum land-

use for that spatial unit. With weighting average index, Maplnfo adjusts the calculation

114


of averages, so that the values from each selected object are weighted more or less

heavily (see appendix ill for various other options and methods of updating, which

were considered on census data before selecting the weighted average index method).

This method of updating the intersected derived information to each record reduces

subjectivity to a minimum. The Maplnfo program was used to update the derived

values to the newly created records of information. The total number of records which

consisted of various spatial information for every spatial unit was stored in a table

called the 'Master Attribute Table' (Table 4, 5 and 6)

IAmgrd IPermtr IAmcv .. IAmcv id lstructr I Rfarea - IResa hnda lsevbz IGenbz

62,!m 1,000 470 470 D [] 0 0 0 62,!m 1,000 471 471 0 0 0 0 0 62,!m 1,000 472 472 0 0 0 0 0 62,!m 1,000 473 473 29 8,843 23,620 0 0 62,500 1,000 474 474 45 11,500 34,710 0 0 62,500 1,000 475 475 50· 13,14[] 42,900 0 0 62,500 1,000 476 476 39 9,388 44,400 0 0 62,!m 1,000 477 477 35 8,324 39,760 0 0 62,500 1,000 478 478 31 9,553 32,530 0 0 62,500 1,000 479 479 0 0 4,507 0 0 62,!m 1,000 480 480 9 384 0 0 0 62,!m 1,000 481 481 9 4,939 0 0 (]

62,500 1,000 482 482 24 3,309 19,970 0 0 62,500 1,000 483 483 33 4,375 17,700 0 0 62,500 1,000 484 484 0 0 0 D D 62,!m 1,000 485 485 3 942 0 0 0 62,!m 1,000 486 486 (] D D 0 0 62,500 1,000 487 487 0 0 0 0 0 62,500 1,000 488 468 0 0 0 0 0 62,500 1 ,DOD 489 489 2 0 0 0 0 62,500 1,000 490 490 0 0 0 0 0 62,500 1,000 491 491 D D 0 0 0 62,!m 1,000 492 492 7 1,001 0 0 0 62,500 1,000 493 493 7 2,969 0 D D 62,500 1,000 494 494 45 12,870 33,750 0 D 62,500 1,000 495 495 31 9,728 22,110 0 0

62,500 1,000 496 496 13 6,445 0 [J 0

Table 4 Master Attribute Table (MAT) of attributes showing the fmal fields and columns. For detailed description of fields (see appendix II for more details).

115

0

0 (]

0 0 0

0 0 0 0

0

0 0

0

0 0

0 0 0

0 D 0 0

0

0 0

0


IResa linda lsevbz IGenbz IPerbu led IWIAvStctmsk IWIAvPopmsliWIAvlncmsk lwtAvDwlmskl

0 0 0 0 0 1,141,912 0 0 0 0 0 0 0 1,141,912 ll 0 0 0 0 0 0 1,141,912 0 0

23,620 0 0 0 14 1,141,912 285 669 34,710 0 0 0 19 1,141,911 248 704 42,000 0 0 0 21 1,141,911 213 738 44,480 0 0 0 15 1,141,009 2!i5 677 39,760 0 0 0 13 1,141,909 187 738 32,!UI 0 0 0 15 1,141,909 132 800 4,ffJ7 0 0 0 0 1,141,901 0 []

0 0 0 0 1 1,141,913 124 1,001 0 0 0 0 8 1,141,913 124 1,001

19,970 0 0 0 5 1,141,612 284 620 17,700 0 0 0 7 1,141,812 284 620

0 0 0 0 0 1,141,812 0 0 0 0 0 0 2 1,141,812 0 0 0 0 0 0 0 1,141,801 0 0 0 0 0 0 [] 1,141,601 0 0 0 0 [] [] ll 1,141,601 ll 0 0 0 0 0 0 1,141,601 0 []

0 0 0 0 0 1,141,601 0 []

0 0 0 0 0 1,141,601 0 0 0 0 0 0 3 1,141,601 0 0 0 0 0 0 5 1,141,601 0 0

33,7ffJ 0 0 0 21 1,141,611 0 0 22,110 0 0 0 16 1,141,611 0 0

0 0 0 0 10 1,141,610 ll 0

Table 5 Master Attribute Table (MAT) showing the fmal fields and columns. for detailed descriP.tion of :fields (See appendix II for more details)

116

0 0 0 0 0 0

498 [J

401 1 309 2 229 3 381 3

568 3 0 0

710 0 710 0 462 12 462 12

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


IResa linda ISilYbz IGenbz IPeibu led IWtAvStctmsk IWtAYPopms!IWIAvfncmsk IWtAvDwlnml .... 0 0, ·o ~ 0 !,141'~12 0 0: 0 0 Q Qli ,(j: 0 o. i.t~t~14 b a Q < • D. 0 0. 0 0 0 1,j41·~1~ 0 0 '0 •... p

23;620. a fi .0 14 1,141~12 m 669 400 ii 34;710 Q, 0 0 19 1,141.91t 248" 704 401 1 42)JJJ 0 0 0 21 1,141 ,911' 213 7ll l:a 2

~ p 0 0. 15 1,141.!m. .. 2!!5• str 229 3

0 ::. 0 p: 13 1,14hro9. 187; 738 sat,; 3

~~ 9 .o. jj 15 LH.i~· 137 •.. .. 800 $' ~ 4$1 il 0 0 0 t.Hi.Wr 0 0 0 il

0 0 0 o· 1 1,141:,913 1i4 iJ)OI 710 iJ 0 Q, 0 0 B 1,14(913 124 l,lllf' 710 0

19,970 0 .,0 .0 5 1,14.1;812 284 62o 462· 12 17;7~ .. 0": .. .0 :o: 7 1,141jlt~ . ·i!fl4· @ 46.7 lt

0 0 0 .0 0 1,141'.$1~ 0 0 tr (j

d 0 0 ll 2 1.141,812 0 0 0 0 .0 0 0. 0 0 1,141~1 0 Q 0 0 0 0 .n 0 0 1,14t.601 0 a 0 0 o; Q Q 0 0 1,141\601 0. 0 0 0

•(I: o: :·q .o:·· d Q4h60J, 0: fi tr 0 :o o· ·o 0 ·o 1,141-:S~il o: 0 0 0 0 0 0 0 0 1,141,601 0 0 0 0 0 0 0 0 3 1,141#)1 0 0 0 0 0 0 0 0 5 1,141.6QI 0 0 0. 0

.33;7!:1:1 r.i iJ 0 21 l,14fptl 0 0 il 0 ~22:r1o: .Q .: : :. :'_. :·o . o· 16 t.t4f.6f1· p p 0 0

:o D .. o: :o: 10 i;14l.S10 0 a o' 0

Table 6 Master Attribute Table (MAT) showing the final fields and columns. For a detailed description of fields see appendix II

The master attribute table (table 6) consisted of all the records created for the city of

Bathurst. Many records did not have any data since many spatial units had no activity

or population in them. The compatibility of computer programs was an issue because

some of the functions, such as weighted overlay, could be performed in one particular

software but could not be carried out in another software. Therefore different softwares

were used to perform multiple analysis and final map outputs. Once the database was

created the table was exported to the Arc View software environment, where the

weighted overlay process was undertaken.

117


4-14 Justification for the choice and use of Weighted Overlay and Model Builder for study.

Planning for risk mitigation involves critical decisions, which demands the use of

existing spatial data and performing multi-criteria modelling approach. Using available

data, inherent trends and patterns in data related to risk assessment could be located. By

combining a set of risk related variables and weighting these criteria in different ways,

risk assessment scenarios for different regions can be created. These alternatives can

then be visualized and used in planning the distribution of existing dynamic resources.

In the study there was a need to integrate and perform combined analysis of multiple

variables all of which influenced the risk at a certain geographical region.

Furthermore, there was a need to spatially analyse multiple variables and communicate

different scenarios to stations and between stations, which may lead to fresh demand

for requirements for resources. For instance, a local service area may undergo changes

in the land-use due to an economic boom or industrialisation resulting in increase in the

number of infrastructure, dwellings, all of which may lead to a revaluation of the

existing hazard and vulnerability. These developments can be input from each station

and provided to the head office in the planning process where appropriate measures

may be taken to redistribute the dynamic resources. After the variables are collected,

recorded and input to the model the model can be re-run using different function

parameters, thus enabling them to calibrate their models or examine how they perform

using different set of values. Users can also copy portions oftheir models and smaller

models can be used to build larger models.

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The new risk map will then show the increase in risk areas than before, which may

have implications to the existing distribution of resources. On the one hand there is a

need to update the records and input to the model, on the other there is a need to keep

this process simple and straightforward so that fire staff may be trained in the use of the

system and input spatial and non-spatial data to the model. There was a need for a

system, which can communicate with different operators (fire stations) produce spatial

data, manage spatial data, update spatial data. Coordinated strategy to combat hazard

demands the combined use of available spatial data. There is a need for a system which

will enable the fire brigades to perform this task, and at the same time allow the

different stations to input data which may be typical to their service area. In this

manner a well co-ordinated strategy can be devised to support the decision making

process.

The use of Model builder will allow fire stations to develop their own models and share

it with different stations that may benefit from the model. This means that

organizations can develop model "templates" for processing specific types of data and

then distribute those templates to their users. New users can then add their own data to

their model and run it using a consistent or prescribed modelling strategy. The model

builder can therefore play a very important role in the development of standard

modelling strategy and in the distribution of spatial data across the planning process. Of

the many challenges, one of the most pressing is to build an infrastructure that can

mould complex data and withstand intricate analysis from an easy-to-use readily

accessible environment (Gonzales, 2002).

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All this could be achieved by using the weighted overlay approach provided in the

Arc View Model builder, which helps in multi-criteria analysis and generating 'What If

scenarios, which will help the emergency service in better planning strategies. Complex

relationships between variables and disasters can be designed and built into a single

model. For example, what if a fire broke out in an industrial area consisting of chemical

factories, and which lies in the proximity of landuse which had residential areas,

welfare homes, schools, petrol stations, multi-storeyed dwelling, commercial areas and

so on. In the context of the present study, this spatial analyst tool is invaluable for

simulating spatial data transfer in the NSWFB planning process. In fact the corporate

office have already started experimenting with the effectiveness of introducing the

results of the Model Builder by combining it with ArciMS (Internet Map Server) which

will enable local stations to input data from their service areas, so that such data may be

analysed in conjunction with other service areas. A common strategy plan for

distributing resources and planning can be drawn by this system. Although, the

scientific treatment of selected variables and fresh variables will be crucial, this

approach has the potential to resolve multi criteria issues such as the creation of a

system that can be used by all personnel effectively and the use of complex

programmes that may be written and read into the model, which upon running can yield

the desired results.

Using existing data sets and a multi-criteria modelling approach, hazard zones can be

mapped based on the physical properties and cultural values. Alternatives can then be

visualized which will assist emergency planners and decision-making process. Weights

rank individual elements in order of influence, for example if the number of multi

storeyed constructions are higher, then these constructions would hold more weight,

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thereby having a greater bearing on the overall hazard categories. The decisions to

assign weights have to be developed after consultations, discussions and experimenting

with the local fire stations and the head office.

The weighted overlay reclassifies the cell values of two or more input themes to a

common scale, then multiplies the reclassified values by influence factors and adds the

values to produce an output grid. Model builder is a relatively new extension from

ESRI that extends the capabilities of Spatial Analyst to allow a model to be developed

ran quickly and distributed to others. This model is constructed of individual processes

from project data, spatial functions, and derived data, which are then linked together.

The creation of the model takes place in a user-friendly graphical user interface (GUI)

with the help of drag-and-drop options and I or instructional wizards. Model builder

uses several spatial analyst operations for manipulation of data.

There are two ways that the user can enter information into Model Builder - wizards

and property sheets. The wizard is an automatic method, which will suit new users or

those who are unsure of the information they wish to use. After entering the data by

either method (wizard or by using the various drag and drop methods), the check model

status button can be used to check if all the pertinent information is entered.

Determination of Weightage: of each class is the most crucial in integrated analysis,

as the output is largely dependent on the assignment of appropriate weightage. In this

study the determination of input variables was done by holding discussions with

officials from the fire brigades. However these were not subjected to any rigorous

scientific treatment, mainly because the decision to select the most appropriate

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variables for assessing risk has to be done after detailed discussions with the fire

stations in the respective service areas and the Corporate Strategy Division, NSWFB

and this entails a substantial period of time, which can only be developed over a period

of time. The study used some variables to show that different variables can be input to

the model and visualized which will assist in the decision making process. If the

inclusion of fresh and existing· variables could be done according to mutual

understanding between the various fire services and liasing agencies then the concept

of risk can be better understood and implemented. Further work needs to be done in

order to refine the model particularly with respect to the selection of more types of

variables and refining the existing choices. This will consume considerable time and

collaboration between many agencies including the NSWFB.

4-15 Modelling risk with the "Weighted Overlay Process".

The "weighted overlay process" creates a discrete grid theme that combines multiple

input themes (See Figure 27 & 28). The output theme represents the weighted

influence of multiple features in a geographic area. Each of the input themes is assigned

a percent influence based on its importance. Values within the themes are reassigned to

a common evaluation scale. In order to build the composite hazard model it was

essential to extract individual themes, which had to be input uniquely to the model

builder process.

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Fire Hazard Variables ... -. 1. Structural Density

2. Built-Up Area ,,. 3. Residential Land use

Map Extent & Cell Size I 4. Commercial Land-use 5. Industrial Land-use 6. Less Mobile persons

,r 7. Multi-storeyed dwellings

Classification by 8. Population

Natural Break

Vector to Grid Map Display & Analysis

Conversion .a.

Spatial Fire Hazard Model

ir ..4 ~

Reclassification of Variables

Overlay Special Hazard Sites

.4~

, Setting the Evaluation Scale,

Biases & Influences

, .. Model Execution

I

Figure 27 The weighted overlay process

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file fdot ~- Model Add Process l:felp

D l~l liil l ~ .w.l~l -1 •:IEBI ~-1~1 ~~ ..::• J..::I ...J.:J

Figure 28 The Weighted Overlay Process

The vector themes were converted into a grid structure by using the vector to grid

conversion process in Arcview' s model builder. The vector conversion process creates

a discrete or continuous grid theme from a vector theme. A grid is a matrix of square

cells representing geographic features. Each cell in a discrete grid theme has an integer

value which represents a feature for example 1 =land-use, 2=special hazard location. On

the other hand a continuous grid theme is a matrix of square cells representing a

surface. Each cell in a continuous grid has a floating point value (contains a decimal

point). Unlike a discrete grid, a continuous grid does not have definable boundaries for

the features.

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All the themes, ranging from land-use, population density to, structural density, were

extracted from the master table into separate themes in order to perform the weighted

overlay operation. These themes were classified by the natural break method into 4

class intervals. Before modelling the composite hazard by the weighted overlay

process, it was necessary to ensure data transformations and set the model defaults.

4-15.1 Data considerations and image environment for the weighted overlay process

The weighted overlay process requires all input themes/layers to be a grid file.

Conversion to a grid format enables uniform calculations and analysis, unlike the

vector formats where calculations and analysis can lead to erroneous results.

)> Cell size and map units

A grid theme is composed of square cells with values. The size of the cells determines

the resolution of the grid theme. The smaller the size of the cells the more precise will

be the outline of the geographic feature but will consume more time and storage space

because of the increased number of cells. For the study a cell size corresponding to the

size of the spatial unit 250 by 250m was chosen. All the output hazard/vulnerability

categories were assessed to this cells size. The map units were set to meters.

)> Map extent

The extent of a theme is the area on the earth's surface covered by the theme. An extent

is always rectangular and it has coordinates that locate its position. The map extent for

this study was set to the extent of the structural density theme. All the output composite

hazard categories were generated to this map extent.

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)> Classification method

Classification methods are calculations that are applied to the data to define the class

ranges. The purpose of classification is twofold: to make the process of reading and

understanding a map easier and to show something about the area being mapped which

is not clear and self evident. The study used the natural breaks method of classification

which identifies breakpoints between classes using a statistical formula called Jenk's

optimisation. Natural breaks help in finding groupings and patterns inherent in the data.

The reclassification process is used in the study to group the values of an input grid

theme to new values in a discrete grid theme. For example, the land costs in a theme

could be grouped into high, medium and low price ranges (Using Model Builder,

2000).

4-16 Assigning influences and biases to layers

The major advantage of using the weighted overlay process for arriving at the

composite hazard categories is its flexibility in allowing the input and exclusion of

variables that may/may not be important, depending on the geographic region. For

instance, in a region dominated by dense structural density and commercial land-use

the most important influencing factor would be the variables that depict these, while

other variables such as open space might be excluded, or have little weight. Thus the

most important influence will be assigned to these variables in comparison to the others

(Figure 29). These influences will vary from region to region depending on the

geography and arrangement of spatial features. The "Evaluation Scale" is set to

weight the values of the input themes for the weighted overlay. For instance, raw

values have to be converted to a common evaluation scale in order for these to be

processed and analysed. Hazard categories are generated to 4 distinct categories (i.e.

126


2,3,4 and 5) while the special hazard site locations were given the highest ranking of

one (1). It is therefore necessary to convert the other variables such as structural density

and residential land-use into similar scales.

Ev!WetionScele

Deline llle -ighted overlay table Specify !he Percent ("/o) lnftuence lor e8Ch theme ll!ld 11 Scele Vlllue lor e8Ch inpul Foeld Vlllue. Scele Vlllues will be multiplied by the % lntluence value before lhey are added to o1het1hemet. To edit 11% Influence Vlllue. dick on

type11 new one 11 SCilla Velue. dick on It 1hen use the dropdown list or type a velue. Cells Willa Aestrided velue are not added to olherlhemes end retain the Aeltricled lllllue in the output theme. To add 11 new input tbama. dick the Add Theme button. To dalate an lnpullhelnll. dick on its n11111e.lhan dick lhe Oehat& Th11m1 buttt:Jn.

lr,pul I twrnf' ' lrtf lrtpul t t1•ld l11ptJII tlH·I

I I INOOATA INoODia 2

11 I0-91775 12 19177.5 - 18355 13 i 18355 - 27532.5 14 i 27532.5- 36710 INOOATA -NoODia

0 11 0- 8272.6 12 8272.6 · 16545 13 16545 - 24817,5 14 24817 5- 33090

' I 1h \/oaiiJ>

.

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5 Aes1ridlld

2 3 4 5

El

I ;I

-1NOOATA No0Dia

I 1 I0-13 2' 2 113 25 - ' !6.5 3 3 126 5-3 175 4 4 139 75 - 'i3 5 NOOATA INoODia Aemaed

0 I I II ! 0-29 2

Su• ollnlluencee poo (muet equall OO"l .. I

Cencel Run

Figure 29 Assigning influences and biases

4-17 Hazard Maps and Interpretation

Once these weights are assigned to respective variables the weighted overlay process is

run to generate the composite fire hazard profile in the study areas of Bathurst and

Hornsby. The model will create a semantic hazard map (Figure 30), which may be

manipulated and re-run to create an altogether new profile, for example by assigning

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new influences and bias values the model can show the different types of hazard

profiles in various regions.

)'> (80% bias/influence) on structural density

The structural density plays a crucial role in determining the weight of attack during a

rescue or emergency operation. A map output can be generated which reflects the

major influence or bias to structural density in Bathurst and Hornsby Shire respectively,

as against very low influences assigned to other variables.

Fire Risk Model of Bathurst City and Proximity Analysis. N

W*E

Hazvclllt•

t-'--'-'-'-.Ji-1'-'-'~~-' ~ l,alal Unit zeo by :zeo m Huvcl c-.ort• In Bllltlurst

Vory Low Hazard L-Hazvcl MocMnll>l H az arc1

t ' IIIID.,tt•• lfltlh Hazaril ~~~~----~~~~~~~ ~om

Figure 30 semantic fire risk models ofBathurst and Proximity Analysis

(80% bias/influence) on population density

s

Similarly another map output can be generated which reflects the distribution of hazard

based on the population density. In this case more influence is assigned to the variable

128


population density, m other words more importance IS given to the community

vulnerability.

~ (50% bias/influence) each on population density and structural density.

This hazard profile can give an altogether different results as compared to the earlier

outputs since the biases are equally divided between population density and structural

density. The potential of the model to show the changes and simulate them indicates the

flexibility of the model with respect to the free input of hazard related variables and the

concentration of resources that may be required during emergencies. Depending on the

fire district and the geography of the region hazard categorization will vary, but the

model can be given biases and influences to give the best available option for

deploying dynamic resources and emergency management.

4-18 Verification of model results

The model was created by using several hazard related input layers. These layers were

sourced from different agencies and therefore had mixed accuracies. Some of the layers

such as the local environment plan and land-use were validated on the ground so they

had a high level of accuracy, but the same could not be said about the others. Since the

weighted overlay is a data driven process, the best way to check the reliability of the

model was to overlay the original vector layers on the model and check them visually

against the model attribute values (Figure 31) The verification process can also be

undertaken by ground truthing but would have to be carried out after updating the

vector data using more recent remotely sensed data such as Quick Bird (spatial

resolution 0.61m by 0.61m)

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The model achieved its objective of generating hazard categories in Bathurst, NSW,

Australia. The semantic model (Figure 30) offers a great amount of flexibility. For

instance all urban regions do not have the same spatial patterns and therefore have

different scales of hazard and vulnerabilities. These differences are explained by

variables, which are input to the model in numerical form such as population density.

63 0~0 00

6303000

63 02 000

6301000

6300000

6299000

6298000

6297000

Bathurst hazard model verification by spatial overlay

3 0

s

Special uses Rurspcpur

N RuraJres N Resident!• landuse N Recrelltlonallanduse N Marketgarden land use

Local recreatlonallanduse lndustriallanduse General rurallanduse Devappcov Commerc:lallanduse

• Flrestatlons D Splltlal Unit 260 by 260 m

oo Hazard Categories in Bllthurst D Very Low Hazard -Low Hazard CJ Moderllte Hazard c:::::J High Hazard CJ No Data

Figure 31 Bathurst hazard model verification by spatial overlay of vector layers

4-19 ModeUing Capabilities

Hazard is a dynamic phenomenon and therefore the levels of hazard can change over

time. Any attempt to model hazards should depict these changes so that the

organizations which are dependent on informed decision support systems are provided

with timely and accurate information. The model has several capabilities, such as its

130


ability to input fresh data which may be processed at regular intervals, perform impact

analysis and forecasting. Some of these capabilities are briefly described as follows

)> Updating data-base

Emergency management is heavily reliant on the most recent spatia-temporal data. For

instance in the rural urban fringe unplanned development takes place leading to

increase or decrease in the level of hazard. This is an acute problem, particularly in

developing countries. Unhindered development can be detected by new high resolution

space borne and airborne sensors. The data can then be input to the model and the risk

assessment can be undertaken by combining the new and current data with the other

variables. By exposing the study area with new space borne and airborne sensors, the

model can be updated. For instance, by using high resolution ortho rectified images and

simple methods such as 'On-screen digitising' newly developed regions can be

regularly monitored and updated before analysis and modelling. The frequency of

acquiring a new image will be influenced by an understanding of the phenological

cycles and changes that occur in an urban area.

)> Free input of data

The model can input fresh data that may explain hazard in a certain region. This would

facilitate the incorporation of spatial data that may not have been available in the initial

stages of the development of the model. For instance, in a recent study which

investigated the mapping of vulnerable areas to the hazard of hail storm in Sydney,

hyperspectral images were analysed and classified to produce end user maps. The

classified information was imported to a GIS environment (See Appendix I for a full

description of the report). The results of such analysis may be input to the hazard model

for further analysis. This ability to input data will mean that hazards can be assessed

comprehensively and information may be input as and when available, and can be

131


crucial to the derivation of the latest information for emergency management. Another

important capability of the model is the input of data in any format, which may be a an

issue since data is available in different forms, scales and resolutions.

);>- Impact Analysis

New developments in the form of a chemical industry or any other hazardous activity

can redefine the scale of hazard which may have implications for the distribution of

dynamic resources mostly leading to an increase in number of such resources. Similarly

the siting of an industrial plant may alter the degree of hazard in an undesirable

manner. For instance, if the location of these sites were in the vicinity of residential

areas, or commercial areas, then necessary precaution may have to be taken in order to

avoid major affects. The impact analysis may contribute to land-use and decision

making processes by influencing the location of these sites on the basis of the

information provided by the hazard model. These facts can be ascertained

geographically by overlaying the site (point feature) on the model.

);>- Proximity analysis (See Figure 32)

Proximity analysis is a powerful tool which may be performed around the site to assess

the number and type of people who may be affected by the location of the proposed

site. Knowledge about the number of people who live near a hazardous location is

important in order to provide fire protection. Proximity analysis not only provides a

comprehensive scenario on the area that could be affected by the hazardous location,

but can also generate a report about the number, category and type of people who may

be affected by a disaster. Proximity analysis can be carried out on the model, since the

model depicts a comprehensive picture of hazard by including two main hazard

determinants- Physical structure and Population.

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Proximity Analysis in Bathurst

A Flrestadons

• Storage Sitos

3~!!!!!'!!!!!!!!!!!1iiiiiiiiiiiiiiiiiiiiiiiilo!!!!!!!!!!!!!'!!!!!!'!!!!!!'!!!!!!'!~3 Kilometers

Figure 32 Proximity analysis. Figure shows .5km as weiJ as lkm buffer from fire stations in Bathurst

);> Forecasting

The model can predict future hazard scenarios if it can be input with variables such as

projected population, and urban development, for example This assessment will depend

on the reliability of the data in terms of estimating growth patterns. By incorporating

such data the model can assist in drawing futuristic hazard scenarios, which will aid in

emergency planning. A simple, example is shown in Figure 33 where two new

chemical plants (shown within red circles) are planned to be located in Bathurst. The

model can assess the effect such planned locations may have since it shows the

composite hazard in Bathurst.

133

s


Ability of the model to provide visualization tools for forecasting and planning purposes.

5ed chemical factorie5

s

around propo5ed chemical factories

Figure 33 Ability of the model to provide visualization tools for forecasting and planning purposes

4-19.1 Advantages of using the Model Builder

One of the advantages of Model Builder is the ability to easily re-run a model with

slight changes or modifications, and easily exported to other users. When a model is re-

run Model Builder' s first step is to delete the old derived data theme. Weighted overlay

analysis is a simple and straightforward method for a combined analysis of multi-class

maps. The efficacy of this method lies in that human judgment can be incorporated in

the analysis. A weight represents the relative importance of a parameter vis-a-vis the

objective. Weighted index overlay method takes into consideration the relative

importance of the parameters and the classes belonging to each parameter. There is no

standard scale for a simple weighted overlay method (Saraf and Choudhury, 1997;

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Saraf and Choudhury, 1998). For larger models, which would involve many map

calculations, Model Builder would be beneficial in the creation, running and exporting

a model.

Emergency operations with agencies such as the NSWFB demand multi-criteria

analysis and visualization. Risks are dynamic by nature and planning for the mitigation

of risks require the generation of multiple scenarios and communicating the results of

the analysis between different fire stations is vital for managing resources by any

emergency service, such as the NSWFB. Currently the NSWFB is testing the model by

using ArciMS, which is a web based GIS system that assists in the graphic

dissemination and free flow of spatial data between several fire stations. The system

will promote the input of data from the fire stations which can then be sent to the

corporate office where most of this information is further integrated and analysed in

order to plan better and mobilize existing and fresh resources. The flow diagrams are

not only a convenient way to build and modify spatial models but are also an excellent

way to document your work. The program eases the process of creating and working

with multiple what-if scenarios.

4-19.2 Limitations of using the Model Builder

There are some disadvantages of using the Model Builder, but they did not have much

effect on the existing study. Evaluation scales can only be set for single digit and not

finer scales. Other limitations such as grid cell numbers can cause problems in the use

of Model Builder for risk assessment. A brief explanation about the disadvantages is

provided in the following paragraphs.

135


Evaluation Scale limitations: There are predefined evaluation scales in the weighted

overlay function and a particular model may require a different evaluation scale. For

instance there are set evaluation scales in the Model Builder, which prevents the user

from using their own customized evaluation scales. This aspect of Model Builder did

not pose many problems in the study since the numbers ofhazards were limited to four,

the fifth and most hazardous being the point locations, consisting of extreme hazard

sites. But in a different type of analysis where there is a need for customizing and

setting multiple evaluation scales the model builder does not have the provision, due to

the fact that the program written for Model Builder was coded to accept only single

digits.

Grid Cell Limitations: There are some limitations on the grid cells and the derived

theme being an integer theme. The Model builder allows only for 1,000,000 cells,

which may not be enough for analyzing a large area. For the study this was not a

problem, but may become a problem if a very large area may be involved and if many

areas were to be analyzed together in combination. However, this shortcoming can be

reduced or altered to some extent by manipulating the size of the cell, when the model

environment is set initially, even though this will have implications on the scale ofthe

final risk map.

The results from the weighted overlay are an integer grid. The values from the

computation are rounded to obtain integers. This generalizes the results, but the grids

load faster and the table for the theme can be viewed. This is particularly useful in a

network situation such as the planning process of the NSWFB, where information may

have to be viewed and sent quickly back and forth.

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ReclassifYing 'No Data' areas: Another flaw was observed while reclassifying the

'No Data Areas'. For example, in many of the data layers there are areas with no value

or data, for e.g. vacant land areas particularly in Bathurst city posed a problem. When

the model is run the 'vacant land', which presents no hazard or risk is also reclassed,

instead of assigning a value of zero to such areas. This brings in local errors in the final

risk assessment map leading to a certain amount of subjectivity to the final risk map

results. The extent of this subjectivity in the present study was considerable which has

affected the quality of the final results to some extent. As long as these areas of 'No

Data' lie outside the study area, it will not have any implications to the hazard mapping

process, but subjectivity will increase considerably, if such areas of 'No Data' were to

lie within the study areas. The solution to resolving this problem may be to undertake

ground surveys and mask these vacant lands before the analysis.

Another minor issue with Model Builder is that when it is re-run, it deletes the old

derived data theme, therefore preventing the comparison between the new derived

theme and the older one. The solution is to rename the entire model (Save as) andre

running the model. This will ensure that the older model is not affected or overwritten

by the changes in the new model.

4-20 Summary

Modelling hazards requires the combined assessment of many factors, which influence

hazard. An attempt has been made in this study to model urban hazard where some

relevant data (surrogates) were sourced and spatially analysed. The entire list of hazard

relevant data may be much more than what is included in this chapter, but the

acquisition of relevant data is a difficult and costly process. All GIS are dependent on

137


the availability of spatially referenced data whose use for purposes other than those for

which they were collected will be enhanced by the systematic appraisal of such sources

in respect of both locational precision and the characteristics of the attribute data, and

by the adoption of standards for data transfer. Modelling the physical and human

processes requires not only the availability of data on the factors thought responsible,

but also an understanding ofthe processes involved (Coppock, 1995). Remote sensing

data can play a significant role with respect to the acquisition of current data. New

satellites, especially those capable of providing data of higher resolution and with

sensors that are independent of both cloud and daylight, will help to provide some of

the missing information (Coppock, 1995). Some of the information related to hazard

may be available within organizations and planning bodies whilst most of it may not

exist at all. The model discussed in this chapter integrates information from remote

sensing data as well as available GIS cartographic information. There is a need to

acquire more relevant and accurate data from wider sources. There is also a need to

establish reliable data centres, which can gather hazard relevant data and provide them

to all organizations that may have to use them. This will facilitate the modeling process

and assist in producing improved decision support systems. Assessment and analysis of

fire hazard levels is vital to plan resources such as fire equipment and human resources.

The model may be also used in the existing resource planning process to determine

urban growth, vulnerability and other hazard related indices.

The approach for modeling presented in this chapter may be relatively simple but has

scope to improve if the data acquisition process is streamlined and relevant data is

constantly acquired and analysed at regular intervals. The model uses physical

variables, as well as introduces the human component by including variables such as

138


density of population and demographic indicators such as distribution of senior

citizens, and income levels of people. The hazard maps produced by running the model

show that there is scope for including more data, which explains the hazard more

completely. For instance, the daytime population is important to determine the density

ofpeople who may be exposed in the event of a disaster.

The study has substantial potential to be integrated into the existing planning process of

the NSWFB. There is a need to highlight the implications and advantages the model

may have by describing the existing planning process of the NSWFB. The wide

network of the fire stations and other offices must be linked not only conceptually and

philosophically but the free flow of spatio-temporal data is one of the main ingredients

to sustain a unified approach to understanding the distribution of risk and consequently

a planned management to risk.

A thorough understanding of the existing planning process is a prerequisite to

understanding the potential advantages of the risk model developed in the study. There

are many stages in the planning process where remote sensing and GIS and a combined

analysis of spatial data can assist in the betterment of risk management, which will

result in a much improved way of assessing, analyzing and mapping spatial

information.

The following chapter describes in detail some of the dynamic resources (mainly

materials) used during emergency operations and the various stages of the NSWFB

planning process.

139


Chapter 5: Potential of applying FRM to the Resource Planning Process

5-l Introduction

Managing resources for the emergency services involves a broad spectrum of

management concepts and activities. Resource management encompasses human

resources (people doing a job), material resources (things people need to do the job),

and fiscal resources (money to pay the people and purchase the equipment). It also

involves the effective use of time (priorities and schedules for both human and material

resources). Determining the level of resources required for fire, emergency medical,

and related services is a complex task. A community must determine its needs in

relation to the existing hazards, the level of risk that is politically acceptable, and the

level of service that is financially obtainable (Managing Fire Services, 1988).

Most fire protection agencies are experiencing escalating demands for fire suppression

and fire prevention services, fire safety education, emergency medical services, and

hazardous materials control. However, the resources required to provide these services

are limited or diminishing. To adequately meet these demands, a community- with the

guidance of the fire department- must take the following steps

1. Identify the nature and extent of the risks its faces

2. Establish the levels of service desired

3. Identify the most efficient and effective use of public and private resources to

provide the established service levels

140


4. Implement a management and evaluation system that ensures the attainment and

timely revision of service-levels standards

It is clear from the above paragraphs that the identification of the nature and extent of

risks a community may face, in the eventuality of a disaster, is one of the most

important tasks faced by emergency services. Risk identification has to be performed

by using a model, which integrates most of the variables that may influence fire risk.

Such a model may be further used for regional and local distribution of dynamic

resources.

5-2 Need for Balanced Resource Allocation and Distribution

Most fire protection agencies are experiencing escalating demands while resources

required providing these services are limited or diminishing. Planning is therefore a

basic step in fire protection management (Managing Fire Services, 1988). Dynamic

resources (fire Fighting equipment and staff) are provided to designated geographic

regions to ensure maximum protection from any hazard. The existing degree of hazard

and vulnerability may vary from one region to another depending upon the local sets of

physical and human conditions, such as land-use, economic activity, population

density, local geography, presence of hazardous installations, and many other related

factors. This means that resources cannot be distributed in a uniform manner to all

places, they have to be distributed intelligently so that maximum fire protection may be

provided. Urban landscapes may undergo rapid change, which results in a

corresponding change in the level of risk giving rise to an increase in demand for

additional dynamic resources. Therefore, these resources must be redistributed

according to the current factors.

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5-3 Typical Fire Service (material) Resources

The location of material fire service resources is a key factor behind the provision of

fire protection services. Different types of fire service resources have varying functions

and capabilities, therefore their prime location is vital for planning for community fire

protection. The distribution of such resources may vary from one area to another

depending on various factors. Since these fire service resources perform different

functions it is important to describe them briefly.

5-3.1 Aerial pumpers

Modern fire engines used in urban areas are called pumpers. These are truck based

vehicles which must fulfil the following criteria:

)> Must be self propelled

)> Be capable of carrying the crew ( 4-6 people) to the incident,

)> Be equipped with an efficient pump of sufficient capacity,

)> Be equipped with a water carrying capacity (usually at least 1,800 litres), and

)> Be able to accommodate the necessary hose and equipment.

A pumper, its crew and equipment, are the primary resources available to the fire

service to extinguish structure fires and to provide fire protection during bushfires or

other emergency situations. Pumpers are often classified into a range of types, with the

main difference between them being in the pump capacity and the durability of the

vehicle chassis.

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5-3.2 Water tankers

Water tankers are truck based vehicles the primary role of which is to transport large

quantities of water to the scene of a fire (usually but not exclusively, a bushfire ). These

vehicles have 4WD for traversing rough roads and hilly terrain. Although carrying only

basic hose and equipment, Water tankers are fitted with a tank, which carries between

3500 and 4000 litres of water. Water tankers have a "pump and roll" capacity, which

allows water to be delivered onto the fire while the vehicle is in motion. Apart from

bushfrre situations, a Water tanker is a very useful fire fighting resource in other

situations such as structure fires where the water pressure from street mains is poor, or

for vehicle fires Hazmat (hazardous materials) incidents on freeways/highways where

mains water is unavailable.

5-3.3 Rescue vehicles

A rescue appliance may take a number of forms, for example a truck based appliance, a

van based appliance or rescue pumper where a single vehicle has multiple functions.

However it is now more common to find specialised vehicles being put into service to

carry out the role of rescue appliance. It is more usually a vehicle with storage and

transport capacity for a large quantity of equipment that may be needed at an

emergency incident. Essentially, it is a major support vehicle carrying not only bulky

equipment that cannot be carried on a pumper, but provides a range of additional

equipment such as:

~ Water-proof sheeting

~ Large range of both power and hand tools for entry purposes,

~ Generating and lighting equipment for night-time operations,

~ Mops, buckets, shovels and other clean-up equipment,

143


~ Hydraulic, electric and hand-held rescue equipment for entry and extrication

work,

~ Basic medical equipment, and

~ A quantity of miscellaneous equipment.

These vehicles are specifically designed to provide land based rescue services and

damage mitigation services at incidents.

5-3.4 Aerial appliances

Aerial appliances are major rescue or fire-fighting vehicles, which consist of a series of

extendable ladders, or an hydraulic cherry-picker type mechanism, mounted on an

appropriately sized and powered truck chassis. The main functions of Aerial

Appliances include:

~ Rescue from heights in conjunction with ropes, harnesses, stretchers, etc

Persons can be rescued from above or below ground level.

~ Water tower- water can be pumped through a pipe which extends or retracts in

line with the ladder/cherry-picker mechanism to be delivered down onto a fire

from an appropriate height,

~ External staircase- the extending ladder of some aerials can be positioned to

provide a means of escape for persons otherwise trapped in above-ground levels

of a building,

~ Observation tower incident commanders can use these appliances to obtain an

elevated view of the prevailing situation.

~ Gear lift transporting equipment to upper levels of a structure

Minor aerials, commonly referred to as Rescue Monitors, are usually fitted with a

large capacity pump to enable independent operations and have a ladder mechanism

144


that extends to about 15 metres. Major aerials, commonly referred to as turntable

ladders or hydraulic platforms, may have to rely upon water being pumped to them by a

number of Pumpers. Turntable ladders have an extendable ladder mechanism, while

hydraulic platforms are fitted with an hydraulic cherry-picker mechanism. Depending

on the model, major aerials can have a vertical reach of about 30-40 metres.

5-4 New South Wales Fire Brigades, Sydney- An overview of the resource planning process.

Application of the hazard model has some potential benefits to the existing resource

planning and management of disasters. However in order to highlight the advantages it

is vital to gain an understanding of the existing resource planning process currently

employed by the New South Wales Fire Brigades.

The New South Wales Fire Brigades have an elaborate planning process leading to

decision making and the final resource allocation (Figure 34). Planning takes place at

four major levels namely, the organizational level, regional level and local level,

through which the entire process of decision-making and information is disseminated

and exchanged. However, all plans must comply with the standards and be consistent in

its application and recommendations. The organization looks after the consistency,

expertise and methodology which contribute to the creation of a final decision making

process and plans. Each level have their responsibilities and tasks in the planning

process which may be unique or similar to what other levels perform in order to realize

an integrated and prioritized final plan. The government level lies at the top of the

hierarchy and they are responsible for making the ultimate decisions to be implemented

145


based on the plans outcome, costs and benefit analysis. The four different levels are

briefly described below

5-4.1 The government level

All the other levels have to adhere to the directions laid out by the government for

planning and formulating strategies for resource allocation. Policy directives for

resource allocation and planning are charted by the government and submitted to the

next level, which is the organisational level. Overall planning takes place at this level

where original plans are created and implemented. Initial plans for planning

information and data requirements for developing decision support systems are

identified at this level. The entire planning process, dealing with the type of data

required and the sources and methodology required to collect such data for resource

allocation is drafted at the organisational level. There is a high level of interdependency

between the local, regional and organisational levels due to the nature of spatial

information involved for assessing hazard and consequently resource allocation.

Therefore, information required is sourced from the local as well as the regional level

to create the plans and develop standards for decision support systems, which may

assist in resource management.

5-4.2 The organisational level

At the organisational level all plans are drafted in a manner that they adhere and

comply with the policy directives set by the government. This leads to the

standardisation of plans and consistency in the planning process and community

outcomes. Although the initial plans were created at the organisational levels, there are

instances where the planning process is decentralised and data gathering is initiated at

146


the regional and local levels. This leads to some autonomy with in the regions and local

levels. For instance, data on urban growth is an important determinant of resource

planning. Such data is sought from the regions and local levels by the organisational

level. Another instance is the incident data which gives details on the type and number

of incidents in the region and local service area which has to be input and generated

from the local and regional level. This is done to increase efficiency and

comprehensiveness of the plans. Due to the sheer magnitude of data pertaining to

resource allocation and the nature and forms of such data these data are secured directly

from the regional and local levels. At present information sought at this level comes

from data on urban growth and development, Australian Incident Reporting System

(AIRS), service delivery framework, population and demographic characteristics,

hazard and risk assessments. Once the information is gathered and integrated, at this

level, it is used in the assessment and decision making process.

Therefore the fmal assessment and decision-making process is determined by a

combination of spatia-temporal data which is sourced from all levels and analysed at

the organisational level. Another important function which is managed at the

organisational level is the economic appraisal of the plan for additional resource

allocation. This is achieved by carrying out cost/benefit analysis, which leads to a final

budget submission to the government. A decision is made by the government and

resources are allocated or re-distributed after due consideration of the resource

allocation. A major input is derived from the regions who actually deal with the hazard

in the field and therefore have first hand knowledge about the hazards in their

respective service areas. They have a very important role to play in the decision making

process.

147


5-4.3 The regional level

The regional level constitutes of 3 major regions i.e. North Region, South Region and

West Region. Each of these regions consists of seven zones, which have in total 336

stations equipped with dynamic resources, which provide fire protection to individual

service areas. One of the key functions carried out at the regional levels is the

formulation of plans for new proposals and additional resources. New development in

the form of physical and human attributes leads to the reassessment of existing hazard.

This in tum leads to a demand for additional resources which are discussed at a

management committee, where the need for additional resources by all regions are

taken and finalised by priority and finally put forward to the government for

consideration. Therefore fresh demand for resources is presented at the regional level.

The regions also interact at the local levels with respective service areas and fire

stations whose feedback forms a vital indicator of the demand for additional resources.

At the regional level plans for resource allocation and distribution are worked out after

consultations between the region and local areas under the respective regions.

Consultations and assistance are provided to the region and service areas at the local

level.

5-4.4 The local level

The local level comprise of individual service areas and the static resources (fire

stations) as well as the dynamic resources, which are stationed there. Each service area

is responsible for providing vital information on urban growth, incident data, local

environment planning land-use, population estimates and demographic characteristics

and most importantly hazard and risk assessment information. All these databases from

different service areas will be formulated into one major regional plan. Thus the request

148

Integrating RS 8: GIS for Urban Fire Disaster Management. Sunil, 1999-ZOOZ

for additional resources is indeed a reflection of the existing hazard and risk at the local

level. The station identifies specific issues related to local service delivery. These are

then forwarded to the region level, which in tum confirm the compliance ofthe service

delivery standards at the organisational level.

In essence all the information process involved in the formulation of plans for resource

management is a two-way process, often called top-down bottom-up planning, starting

from either the local level to the organisational level and from the organisational level

to the local level. The system is designed in a manner that it facilitates the distribution

of information from one level to another and the assessment of disaster risk is carried in

many ways by studying different variables, which may influence hazards. Finally the

results of each assessment are integrated to generate an integrated assessment for the

decision-making process. In this manner available GIS data, which are collected either

manually or by indirect means, are assessed together to map the spatial distribution of

hazards.

149


Government Level

Organisational Level

INFORMATION FLOW IN THE DECISION MAKING PROCESS

~;~ .. ·'?~.;.:;.:~~~·.~_ .;, ' Assessment of :~-'Y~ ·?~ ,:_; ·. J.·. Decision Process

Government Policy

Directions 1--~... Urban Growth ,.. Urban GrOJr!~ Urban

Assessmentlr"' .. Assessmen~ Growth Monitoring (Stn)

AIRS Incident data analysis ........ ~----+-------+-!

Incident data Recorded

Service,4....,1----4___.

Delivery • Framework

Specific service «~~....,1---+

delivery

Objectives

Identification by S n of specific issues pertaining to -+---1

the service deliver: ~

Socio-econ.,.~._i_c._ ______ -+-Data and ""

Demographic data from counciuil--+--l

Population Projections

Hazard/Risk Assessments

.....

.....

Consultation &

.. .. Plans for

population ,. updates

Council LEP info. Stn based Hazard info

Consultation

-+--

Assislance resource

~ Allocation/ I L Distribution

• Economic Appraisal Cost/Benefit, Analysis, budget submission

Government • Approval and Provision of Funds & Allocation of Decisions Resources

Figure 34 The New South Wales Fire Brigades Resource Planning Process

150

Integrated Assessments for Decision Making Process


5-5 Issues with the existing planning process.

Efficient and balanced distribution of dynamic fire resources is required for managing

disasters, which in turn will depend on risk assessment. Risk assessment has to be

carried out in a uniform and consistent manner at all levels across the state of NSW.

The methodology, whilst laying emphasis on current data is mostly a manual approach

with emphasis on information provided by all fire stations. This information is used at

the organization level where most of the decisions are taken in compliance with the

rules set at the government level. Some of the major drawbacks which result from the

present system may be summarized by these statements.

)- The process of collecting and gathering spatial and temporal data is carried out

by a manual process which may be inconsistent, time consuming and demands

extensive coordination amongst departments and levels.

)- The methodology process does not gather current data which may render the

risk assessment and planning process inadequate

)- The methodology is not an effective method of showing increasing hazard since

it does not show the changes visually. As such it cannot be used at meetings at

the government level, where decisions are often made more with a political

rather than scientific bias.

)- In its present state the methodology may not permit combined analysis by using

available cartographic data, since most of the data is collected manually which

prevents the modeling of hazard and vulnerability, that is an essential

prerequisite to the assessment of hazard.

On of the major drawbacks of the present methodology of planning adopted by the

NSWFB (Bhaskaran, 2001) in the planning process is its incapability to develop a

151


comprehensive spatia-temporal model for hazard and vulnerability assessment which is

required to allocated or redistribute existing resources. A detailed and critical

discussion of the present system of risk assessment in the planning process is given in

the following paragraphs.

5-5.1 Existing system variables and risk assessment

Risk assessment is performed at many service areas in an effort to create informed

understanding of the spatial distribution of the prevalent hazards. However the

methodology may be improved and made more efficient by the use of a semantic

hazard model. For instance, a major part of the spatial data, which contributes to the

overall risk assessment, is collected by the in-situ field survey method, which may take

a considerable amount of time. The level of hazards is always increasing with

increasing urban development and population increase. There is a need for rapid data

collection, so that the data may be integrated in the planning process in near real time.

The concept of hazard includes the assessment of physical as well as human attributes,

which are embodied in the form and shape of built structures, patterns of land-use and

other invisible features such as population and demographic characteristics. These

factors/variables have to be assessed in combination and not in exclusion. The hazard

model provides information on the level of existing hazard as a function of physical

and human variables. This information may be used at the local and regional level to

assess hazards and disasters risks in a consistent and uniform manner.

There must be a consistent standard of assessing disaster risks based on predetermined

spatial criteria; otherwise the planning process will be biased to some regions against

others. The organization takes care of this aspect and ensures that the initiatives taken

152


at the regional and local level comply with policies ofthe government and standards for

risk assessment. However data still needs to be collected at these levels and there may

be instances where the data is not consistent since the entire process is based on manual

feedback and local knowledge. By using the hazard model, this approach can be made

more efficient, timely and consistent since remote sensing and other data may be

customized to the objective of the study. Risk assessment can be carried out by using a

seamless layer/s of remotely sensed data, and data can be selected and organized

consistently throughout the entire resource process. There are significant advantages in

having a resource allocation model based on consistent spatial data in order to make

appraisals and recommendations.

5-5.2 Urban growth assessments

Urban growth results in an increasing population, new housing and accompanying

community infrastructure all of which have implications to the existing level ofhazard,

most of the time leading to an increase in the degree of hazard. The data on urban

growth is sourced at the organizational level from the government, regional and local

levels. Sourcing information on such developments is based on local knowledge and

input from the local level to the regional level. Once again such input of data can be

efficiently detected and recorded automatically by using spatia-temporal remote

sensing data on an appropriate scale. The hazard model can perform the task of

monitoring the increased number of built features and analyze them collectively with

other variables. For instance, urban development leads to an increase in structural

density, which may be integrated into the model. The model may be run in order to get

a new picture of the revised hazard level (Figure 35). Since remote sensing data is

advancing with respect to sensor resolution characteristics (spatial and spectral

153


resolution) their suitability to study and analyze urban environments are ever growing.

It may be useful to use these new sensors and source more data pertaining to hazard.

The hazard model can incorporate new sensor data and integrate it with other spatial

data to assess hazards and risks.

154


Adequate understanding of urban phenology

Appropriate Space borne or Airborne Remote Sensing

Data

.,,.

Monitoring of the urban areas

.,,.

Urban sprawl, New Developments

~

Census data and extraction of Socio-economic data

,, Use of hazard model for

spatial analysis and risk assessment at regional and local Level

,, Validated spatio-temporal Information provided to

the organization level

Figure 35 Use of hazard model in the resource planning process for detecting urban growth. The above methodology ensures data acquisition, consistency and analysis in near real time from the local and regional levels to organization and policy levels

155


5-5.3 Vulnerability assessment: population and demographic characteristics

Risk assessment will be incomplete if the data on the number and type of people who

may be exposed to a hazard is not calculated and incorporated in the planning process.

Data relating to population density and other demographic characteristics such as

income level, ethnic composition are secured at a local level which is then compiled at

a regional level and provided to the organization to make plans for resource allocation.

Data from the latest census is also generated at the organization level, which may be

integrated into the model for multiple analyses.

5-5.4 Incident data and council LEP information

The incident data is extracted from an extensive database, which comprehensively

records information about all emergency incidents attended since 1987. Such data may

be used with the other spatia-temporal data in order to compile a comprehensive hazard

scenario. The ability of the hazard model to input additional data may be used here to

find out the areas with the highest call rates.

Local Environment Plans are land-use plans produced by almost all councils in

Australia. They show the boundaries of different land-uses, which are found in the

region. Land developments are restricted to those activities within the zones, except for

those activities, which have been there for a long time. The LEPs therefore are a

planning control, which prevent haphazard development and use of land. The land-use

is a critical factor which influences hazard since it shows the distribution of residential,

commercial, industrial and recreational activity in a region. The land-use information is

secured at the local level and one of the available methods of providing such

information is through the Local Environment Plans or LEPs, which are available

156


throughout Australia with some minor inconsistencies. They may be input to the hazard

model in the form of numerical values. For instance the total area of the respective

land-use can be categorised into 4 major classes. Once this information is intersected

with the spatial unit, the total area of each land-use within each spatial unit can be

determined. In this manner the predominant land-use for each spatial unit can be

depicted visually, which will help in the assessment and distribution of existing hazard.

Once again this database can be input to the hazard model in order to assess hazard

comprehensively.

5-5.5 Multiple scenario analysis

The ability of the model to perform multi-scenario analysis may assist in the existing

planning process. Resource allocation may be done on the basis of existing hazards but

sometimes there is a need to plan for an event and find out the number of features,

which may be affected by a potential disaster. This in tum calls for the management of

multiple data from different sources and with different resolutions, scales, details, and

information. Analysis of such data has to be performed in a computer environment

since it demands complex, comprehensive analysis, rapid results and multiple outputs.

5-6 Summary

Hazard analysis is a data driven process and therefore needs flexibility in addressing

issues such as incorporation of new data, automation of data exchanges in a consistent

manner between different agencies which are involved in the emergency planning and

strategic resource allocation. For instance, urban developments redefme the level of

hazard, which calls for additional fire fighting resources. Such changes can be

represented by variables which best explain them. There is a need to incorporate new

157


data to model these changes. The methodology developed has the potential to address

several issues, which may be required during emergency planning and operations.

Some of the advantages of using such an approach may lead to benefits, which are

described below:

> Automation of spatio-temporal information

A greater degree of automation can be injected into the existing system of resource

planning by using the hazard model. This may lead to improvements in the planning

mechanism by upgrading the comprehensiveness and complexity of the information

and finally placing a request for additional resources. Hazards are dynamic

phenomenon and therefore by using the hazard model, assessments can be carried out

in a more consistent and timely manner.

> Urban growth monitoring

Changes in the landscape are reflected in new developments, and structures and

infrastructure. The methodology can record and analyze these new structures and their

land-use. Fresh input to the model can be made from updated information which is

extracted from space borne and airborne images. In this manner urban growth can be

recorded and analyzed with the other hazard related variables in near real time. The

model can be used at the local, regional and organizational levels for recording urban

growth.

> Consistency of data collected at all levels and implementability

The organization provides the framework for the methodology of assessing hazards and

decision support, with assistance from the local and regional levels who provide them

with information on various aspects ofhazard, such as population density and changes,

land-use and so on. The implementation of any decision based on this information will

have to be validated at every level if they were manually collected and analyzed, which

158


may lead to inconsistency and wrong analysis of data. The use of the hazard model will

avoid data inconsistency by maintaining the methodology since it uses the same type,

format and accurate databases. Sound planning decisions must be based on spatial

information which is based on a continuous and consistent coordinate system. This will

enable the plans to be incorporated with other planning organizations on a regional and

national scale.

)- Visualization and reporting

Visualization of integrated remote sensing and GIS databases can provide additional

information on the topography, aspect and weather conditions of a region. This is

especially important in countries where fire hazard is particularly severe. Simulation of

the real world is important in the planning process.

The FRM can extract variables separately and show the different types of hazards and

vulnerabilities, which may be present in a region from time to time. Mapping these

variables to show the different types of hazards and vulnerability will show the

distribution of existing hazard and vulnerabilities and can lead to important strategic

decisions especially with respect to the allocation of dynamic resources. The

emergency services can use these risk maps to determine the extent and level of

hazard/vulnerability and plan for mobilizing their limited resources. Combining these

maps can give a composite idea about the overall risk in the region. An interpretation of

risk, which is possible by using the FRM model for both Bathurst and Hornsby, is the

main content of the next chapter.

159


Chapter 6: Fire Risk Model-- Interpretation of Risk in Bathurst and Hornsby.

6.1 Introduction

Any effective strategy to manage disaster risk must begin with an identification of the

hazards and what is vulnerable to them. This involves information on the nature and

extent of risk that characterizes a particular location, including information on the

nature of particular physical hazards obtained through risk assessments, as well as

information and data on the degree of exposure of a population and its built

environment to such hazards. In this way informed decisions can be made on where to

invest and how to design sustainable projects that will withstand the impacts of

potential users. This research has attempted to develop improved tools and systems for

managing an urban disaster. It has highlighted some of the drawbacks found in the

existing methodology used for assessing hazard in the state of New South Wales. The

research attempts to improve upon the existing methodology by integrating remote

sensing data with available GIS data.

The spatial distribution of risk (combination of physical attributes and human factors)

in urban areas is not uniform. The distribution of dynamic resources will have to be

based on the type and level of risk found in an urban environment. This will lead to

maximisation of the use of dynamic resources and improved management of urban

disasters. The factors which have to be taken into account are many and varied.

Modelling hazard and vulnerability in an urban environment is important to keep the

effects of any disaster to a minimum and to tackle the worst possible accident in an

efficient manner. At the same time, it is important to create information decision

160


support systems in near real time so that emergency services are equipped with the

latest information on risk.

The study has demonstrated the potential use of high spatial resolution remote sensing

data with GIS for assessing hazards and vulnerability. This is made possible by using

and analysing a wide range of data that come in different scales, resolutions, and

structures. Modem day space-borne and airborne sensors may be used to extract vital

risk related information. However, their use will depend on a systematic appraisal and

selection of such data sources, which consist of information that is vital for the

successful implementation of this model.

The study has also demonstrated that airborne remote sensing data are important to

assess hazards in urban areas and vital to build an information system. One of the major

outcomes of this study will be to compare and assess the disadvantages and advantages

of airborne and space borne remote sensing data with respect to the study of hazard and

vulnerability. The choice of remotely sensed data has to undergo several considerations

mainly dealing with spatial, temporal and spectral resolutions that will have to be

optimised in order to come up with the best selection of data.

The distribution of dynamic resources must be based on the gross assessment of the

physical as well as the human components that explain hazard in urban areas. The

methodology for developing a semantic model to assess risk comprehensively in near

real time is demonstrated in this research. The Fire Risk Model may be used to ensure

appropriate use of existing dynamic resources.

161


The research has produced the following results:

6-2 A Semantic Model - Bathurst City (See Figure 36)

Two sites with different land-uses and population densities were selected for

developing the methodology. Identification of data and data sources was followed by

collection. Vector data from various organizations and sources were collected. The data

was formatted and processed before the analysis. A geometric intersection was

performed between different layers in order to create new derived layers. A master

attribute table was created by the 'Update' operation, which used the weighted average

index method to assign variables to each respective spatial unit. This process was

carried out for all the records in both the sites ofBathurst and Hornsby. Finally hazard

attribute input layers were used in a weighted overlay process, where each layer was

converted from a vector layer to a grid layer, reclassified and input to a predetermined

evaluation scale consisting of four classes of hazard. The highest hazards were assigned

to a point layer, which shows the special hazard sites in the area.

A semantic hazard model for the city of Bathurst developed in this research uses data

from various sources, scales and resolutions for spatial analysis. One of the major

advantages of this model is that, according to the changes in the land-use and other

hazard related variables influences and biases could be assigned in order to simulate the

changes in the real world. Hazard maps can be generated which will show the varying

hazard in different regions. Since the distribution of hazard in urban areas is

characterised by abrupt variations, the model generates hazard for each spatial unit

therefore enabling comparisons between different regions at various scales. It provides

an overall scenario analysis of hazard in the region by classifYing the hazards into 4

162


major categories. Since the special hazard category, which is in the form of hazardous

site locations is the first category they are shown as overlain on the hazard categories.

Fire hazard model Bathurst, NSW, Australia

6304000

6303000

6302000

6301000

6300000

6299000

6298000

6297000

73sooo 73&ooo 737000 738ooo 739ooo 7• oooo 741ooo 742000 7Hooo 1••ooo 7•sooo

+

+

s

A Flreatattona

000 CJ .llkm blll'rer from ulatlng Flr'Htatlona

• Eldreme Harvel SltlH

N 111:r.-N-or1t

-i,..~~+~~~~~~~'-+~~~~~~----"'~.....-.t.._.J.b<~ 000 D Bpallal Unlt2110 by 2110 m Hazard CatOfOriM In BathUnt

3 3 Kilometers ~~--~~~~

0

D Vert Low Hazard Low Hazard Moclorate Hazard High Hazard

c:::J NoDIIta

Figure 36 Fire hazard model in the City ofBathurst. Map also shows area (by circle)

within 0.5km from existing fire stations. The following input variables

and influences (in%) have been assigned to derive the hazard map.

Structures - 40% influence, Residentiallanduse - 3%, Service Business -

4%, Industriallanduse - 2%, Percent built-up area - 1%, Population Density

- 50%

163


6-2.1 Distribution of hazard in Bathurst

In the following section a number of examples will be shown and described, for both

Bathurst and Hornsby. The example results given are illustrative and do not encompass

the full range of output results.

The city of Bathurst does not show very high overall hazard. The locations of sites

which are classified as highly hazardous are also very limited and restricted to the

south. However these sites, which consist of some fuel deposits and chemical storages

are located within the proximity of residential areas towards the south of the city. In the

eventuality of a gas leak, these houses may be affected adversely, and the model

recommendation would then be that appropriate resources should be stationed to carry

out rescue operations. The model can develop reports about the number of houses,

which may be located in the vicinity of the hazardous sites (category no.l). The

characteristics of the population with respect to the age of people, their socio-economic

conditions, ethnic background etc can be mapped from the model, which will aid in

emergency preparedness measures.

6-2.2 Implications of hazard model output to the emergency preparation in Bathurst

The area serviced by fire stations is limited in the case of the hazard model prepared by

the NSWFB methodology, whereas the integrated hazard model shows more area

which is not serviced by the fire stations and which may be hazardous to the residents

in Bathurst and Hornsby. This is due to the increase in economic development and

activity and resulting increase in the number of infra-structural facilities.

164


Commercial land-use (Figure 3 7) is represented by service and general business for

Bathurst City. This information is validated by the Bathurst city council at regular

intervals. Bathurst has very little commercial activity, the most prominent of these are

found to the south of the city. Industrial activity is limited to the few major food and

beverage industries. They are found to the south and south east (Figure 38).

10~ 000

102000

100000

!98000

Spatial distribution of commercial hazard in Bathurst. 7360 00 73000 0 740000 7~ 200 0

I I / 1---

lf-

--... .\ ,\

I'C'

_-I ~if ll '-------.\

\ 73600 0 738000 7~0bOO 7~ 2 bO O

74~000

\ 7~~000

~

~

1/ 1-s

---

302000

.& Flrest.tl om 300000 1\/ StreetNetwork

D Spatial Unlt260 by260 m Sfivlce BualrMn

V.Low Hazard Low Hazard

D Moder.te Hazard Hgh Hazard

~I-s 2980 00 No Data eneral Bu1lne1a

V.Low Hazard Low Hazard

c:::J Moderatt Hazard Hgh Hazard No Data

Figure 37 Hazard level by commerciallanduse (service business and general business) in Bathurst

165

104000

102000

100000

!98000


Hazard level by Industrial Density in Bathurst.

736000 738000 740000 742000

I / r--.

lJ-

--..

v ~ :t>~l! D '--------\ flfll1-736000 738000 740000 742boo

744000

~

~

IV ~

• --___...,.,~

\_ 744000

s 304000

302000

300000

A. Firutatlons 29Boolll\/ StreetNetwork

c:J Spatial Unit 260 by 260 m indu&tr1al D V.Low Hazan:!

Low Hazard D Modtratt Hazard

3~~~§iiiiiiiiiiil0~~~~~~3iiiiiiiiiiiiiiiiiiiiiiiiiiiiii6 Kilometers D High Hann:t <= c:::J No Data

Figure 38 Hazard level by Industriallanduse in Bathurst

Bathurst is growing in terms of new developments, mainly residential, commercial and

industrial . This has led to the construction of new buildings, mainly residential. The

density of structures is shown to be high in the central business district (Figure 39).

Towards the east, prominent residential areas are highlighted which is an expanding

area. The built area between the east and west regions is the market gardening land-use

which is carried out on the floodplains, whenever the Macquarie river overflows its

banks. The region around the floodplain is also susceptible to floods, which can be

mapped in order to determine the number of houses and the demographic

characteristics of the people who may be vulnerable to a disaster.

166


Hazard level by Structural Density in Bathurst

mpoo 73spoo 74opoo 74 2000 744 ~ 00

I I / .....___

1040 00 J 10 2000

:~ _... ~

::( ~ !--.._ qt-- • II - ~~ ~ ......._

10 0000

~ v .../ 'T

!98000

__; .....,.- ., ~v n '- - } ~

736000 738000 74 0000 74 2000 744 000

Figure 39 Hazard level by structural density in Bathurst

-S3 04 000

~302000

~300000

..6298000

s

.a. Flreltatlons /\1 stre etNetwork CJ Spatial Unlt260 by2150 m structural O.nslty c:::J V Low Hazard

Low Hazard D Moderate Hazard 0 Hgh Hazard CJ NoData

Some of the structures present challenges to fire fighters during disaster mainly due to

the lack of operational space, which is necessary to put out fires. Such spaces were

determined by polygonizing rooftops of large structures. The CBD has some structures,

which are located in close proximity and may add to the difficulty of fire containment.

These structures are shown by the orange colour and yellow colours in Figure 40.

Careful planning is required for these areas in case of fire hazard, since the spacing of

structures in this area may make fire containment very difficult.

167

104000

102000

100000

!98000


Hazard level by Degree of Congestion in Bathurst

s

Stroo!N4ttw orlt Sp;otial Unlt2GO by 2GO 111

Built up AroiiiD•••• of Congowon 0 V. Low Hanrd

Low Huard 0 Moderato Huard 0 HllhHazanl

~!!!'!'!!!!!'!!!!'!'!!!!!'!!liiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiilo!!!'!'!!!!!'!!!!'!'!!!!!'!!!!'!'!!!!!'!!!!'!'!!!!!'!!!!'!'!!!!!'!!!!'!'!!!!!'!!lli3iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiil6 Kilometers 0 No Data

Figure 40 Hazard level by degree of congestion in Bathurst City

Determining the number of people who may be exposed to a hazard enables efficient

management of resources with respect to their strategic location. Additionally the

demographic structure of the population will aid in the location of specialized

equipment and improve management of a disaster. The City of Bathurst has a

population of approximately 30,000. Most of these people are found to be located near

the CBD and in pockets to the west and east of the city (Figure 41 ). During the day

however, there will be a shift in the pattern since a major part of the population will

either go to work in the CBD or to schools or other activities. Data from the Australian

Bureau of Statistics, which was used to create this model, shows the night-time

residential population, but does not show the day time population, which is crucial for

allocating resources during the day. The Figure 42 clearly shows that there are several

168


residential pockets in Bathurst, which are found mainly to the north east, west and

south-west. If this pattern were to be combined with the location of chemical sites in

Bathurst, it is possible to get a different perspective to the distribution ofhazard.

Hazard Level by Density of Population in Bathurst

736000 738000 7400 00

l040 00

102000

lOOOOO

!98000

3

Figure 41 Hazard level by population density in Bathurst

169

s

.a. Flrestatlons streetNetwor1( Spatial Unit 250 by 250 m aaon Density V.LowHazwd LowHaz.-d Moderate Hazard .. {11Hazard

c::J NoData


Hazard Level by Residential Density in Bathurst

m QOO 738~00 740900 742000 744 000

I / r---000 U- f-630 4000

l02 000 ~302000

v •• _.,_ _,..,_

000 c.-..... Ji300000 JOO

• -~ • I -• 000· .../ f-62980 00

_____./ ~ 0 '---- \ \

!98

73&boo 738000 7400 00 742000 744 000

3!!!!!!~~!'1iiiiiiiiiiiiiiiiiiiiiiiiiiilo~~~~~~!!i;3iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiliiiii6 Kllometera

Figure 42 Hazard by residential density in Bathurst

s

"" Flr .. tatlons 1\/ streetNetwork 0 Spllllsl Unit 260 by260 m Resldentlll Oenllty

V.lowHaz•d low Hazard Moderllte Hazard HghHuard No Ollta

The income level of the population helps in understanding the impact of major hazards

on low income people, who may not be as weU equipped to escape potential hazards.

This is a typical scenario in third world countries, where invariably underprivileged and

poor people are affected by hazards. The fact that their educational levels may not very

high also makes them more prone to hazards. The Figure 43 shows the hazard levels on

the basis of the income level of the people.

170


Income Groups of People in Bathurst 00

104000

10 2000

100000

!980 00

Figure 43 Hazard categories by income level of people

s

.& Flrest.tlons /\1 streetNetwori( D Spatial Unit 250 by 260 m Income Group D V low Income Group -low Income Group D Moderate Income Group D High Income Group CJNo Data

The location of less mobile people (children, disabled citizens, old people in welfare

homes) will face difficulties when they are confronted with hazards. The rescue

operations and staff have to be adept in handling such peoples since they may succumb

to shock. Disasters would be better managed if their locations were known beforehand.

This will enable the emergency services to prepare and station resources (material and

staff) which may be trained to handle this section of community. The dimension of

disaster may increase if the location of such people were found to be in close proximity

to chemical plants or any other hazardous installation. Bathurst has a few welfare

homes where less mobile people are located (Figure 44).

171


Distribution of less mobile people in Bathurst 736~00 738900 740000 742000

I I / f'-.....

lUOOO lJ l02000

1)1

-----... :X

lOOOOO

!98000 (\j

v lA

~ ~ n '-----\

736boo 738bOO 740boo 742boo

Figure 44 Hazard categories by less mobile people

744000

!V

---'\

744boo

N

i-&304000 W*E

s

i-&302000

1-&300000

.6 F I """""' • AI

lrestatlons strMINetwork Spalla! UnR 250 by 250 m

Citizens - ~0~ D V.Low Hazard

L ow Hazard D Moder.ta Hazard D High Hazard O NoDIIa

The ability of GIS to map different layers of hazards allows us to spatially depict the

hazard caused by each influencing factor. However it is essential to determine the

composite of hazard in order to manage disasters in a better way. There is a need to

combine these layers, which was done by using the weighted overlay process. After

performing the weighted overlay process which essentially combines data related to

risk assessment, a comprehensive hazard distribution of Bathurst may be spatially and

temporally depicted at regular intervals.

By assigning influences and biases to the model a composite of hazards can be shown

and overlain by point features which show the location of highly hazardous sites and

172


other chemical sites. In this way the model can give a clear picture of the existing

hazards in the city of Bathurst and any other city. By combining various hazards related

variables from remote sensing data as well as cartographic (GIS) data (Figure 45). The

hazard map of Bathurst can be used to perform proximity analysis where the number

and type of people who may be exposed to a hazardous event may be estimated. Based

on this data, disasters maybe managed better and efficiently. In the map given in Figure

45 below the residences within 500 metres of category 1 or special hazard sites are

shown. A report may be generated to determine the vulnerable population, and the

equipment required for managing disasters may be planned in the future. For instance,

by working out the proximity of the people living near a chemical factory, rescue

operations can be planned for in the event of a fire, gas leak or explosion.

173


Fire hazard model, Bathurst

6304000 s

6303000

6302000 +

6301000 +

6300000

6299000 + .6. Flrestatlons

6298000 + • CIM111Ical St0f'11ga Sit ..

000 • !Extrema H112ard Situ

StreecNatwork Spatl al UnIt 2!50 by 2110 m

-1-....P..--"'-"""'-op;::=-.......,:;;;;:..l.l.lolji~.U...+---L......_-......,_..J....t---~--+-~-~>~~ ooo rd Catagorlaaln Bathurst Vary Low Huard

6297 000

4 0 - Low H112an:t D Moderate H112ard

Hllh Hazard CJ NoOata

Figure 45 Fire hazard model, Bathurst. Spatial distribution of overall hazard shown in Bathurst City, NSW by the weighted overlay process. Other layers shown are chemical storage locations within consent levels, and extreme hazard sites, which are categorized as the highest hazard category

6-3 Semantic risk model of Hornsby Shire

The hazard model developed for Bathurst was applied and tested in the Hornsby Shire,

NSW. A small section of the Central Business District was selected because of the high

density of structures, which included residential, commercial, high-rise buildings and

many special hazard locations in the form of educational institutions and places of

worship. A number of examples are provided to illustrate the range of results that can

be generated by the hazard model. A semantic fire hazard model of the CBD area in

Hornsby Shire is shown in Figure 46.

174


Hazard Categorization in Hornsby, NSW

1\1 Street Network • Extreme Hazard Sites

D Spatial Units 250m by 250m Hazard Categories

V Low Hazard Low Hazard Moderate Hazard High Hazard No Dma

Figure 46 Hazard Categorization in CBD, Hornsby Shire, NSW

6-3.1 Interpretation of hazard in Hornsby Shire

N

The central business district of Hornsby-Shire clearly has a high level of hazard due to

the high density of structures. This is reflected by the number of orange and yellow

colours, which represent a high density of structures. The central business district of

175


Hornsby-Shire is juxtaposed to forest and other dense vegetation making it vulnerable

to bush fire hazard. Special hazard sites are located throughout the central business

district of Hornsby Shire (Figure 4 7). Therefore the emergency resources should also

be planned in such a manner that they are in a position to rapidly respond to any

disaster associated with these sites.

Hazard categories and location of Special Hazard Sites in a section of Hornsby Shire

NSW, Australia

6271000

6270000

6269000

6268000

s

N Street Networit

6267000

• Extreme Hazard Sltea Spatial Units 260m by 260rr

Loz n.:w ....... Categories Vlow Hazard Low Hazard Moderate Hazard Hgh Hazard No Dllta

Figure 47 Hazard categories and locations of special hazards in a section of Hornsby Shire, NSW, Australia

One common means of protection is evacuation. Evacuation sites are extremely useful

during disaster operations where inhabitants in a densely structured locality may

needed to be evacuated to a safer place quickly (Figure 48). Prior knowledge of such

places will enable emergency services to plan an unforeseen event appropriately. In the

176


case of fire, an immediate evacuation to a predetermined area away from the facility

may be necessary.

69000

68000

67000

Vacant Spaces for Evacuation during disaster in Hornsby


D Spatial Units 250m by 250m Open Spaces G R

V Less Area Less Area Moderate Area Large area No Data

Figure 48 Vacant Spaces for Evacuation Strategies

The location of fire equipment, such as aerial pumpers, is vital for containing fire in

high-rise structures (Figure 49). Aerial pumpers are bulky vehicles, which carry ladders

and gallons of water. Therefore these equipment will take more time than any other fire

177


equipment to reach the disaster. If a city can be classified on the basis of high-rise

structures then this information can be mapped and the location of aerial appliances can

be planned in a suitable manner. The model can generate the locations of high-rise

structures automatically and therefore provides valuable information for emergency

planning.

71000

70000

69000

69000

67000

Spatial Distribution of High-rise Buildings

Sh

0 2 t<llometers

~!liiiiiiiiiiiii~~~iiiiiiiiiiiiiiiiiiiiiiiii


D Spatial Units 250m by 250m Four Storeyed Buildings GR

0-37.5 - 37.5-75 D 75-112.5

112.5 -150 D No Data

Figure 49 Hazard model for resource allocation: spatial distribution of high-rise structures for allocation of aerial pumpers. (There are 3 and two Storied buildings in Hornsby Shire but 4 Storied buildings are only shown in this figure)

178


6-4 Summary

Selective attributes may be visualized together for planning purposes, for instance the

location of special hazards in relation to the distribution of population may give an idea

of the number of people who may be exposed to a gas leak or explosion. In another

example, the location of high-rise buildings and distribution of less mobile people may

assist in the location of skilled staff that is adept in handling these people. If the

allocation of resources is based on accurate spatio-temporal data then the resource

mobilization will be maximized and disaster mitigation can be achieved. In urban areas

hazards are defined by a combination of built features in their natural settings and the

number of people who may be exposed to hazard.

One of the major results of the study was to develop a methodology and finally a

model, which could assess multiple variables, all of which influence risk in a certain

place. The model has also many advantages, which can be clearly understood by

attempting a comparison of the existing model over the existing the methodology

employed by the NSWFB. A comparison of the existing methodology and the specific

advantages of the model are described in detail in the following next chapter.

179


Chapter 7: Results --Advantages of the proposed FRM and comparison with the Existing Methodology followed by NSWFB.

7-1 Introduction

Hazards are multi-faceted and multi-dimensional phenomena that are complex and

difficult to understand. The structural, property and other types of fires that occur in

regions that are densely populated result in human casualties and destruction of

property worth millions of dollars. It is not possible to put a stop to this hazard but it is

possible to mitigate the effects of such potential disasters by combining the various

current sources of technology, such as GIS/RS.

Patterns of hazard and disaster are likely to vary widely among cities. Although some

trends appear to be widespread (e.g., reappearance of 'extinct' hazards; marked spatial

shifts in the locus of hazards; social polarization and spatial segregation of hazard-

susceptible populations), future hazard profiles of megacities are likely to resemble a

mosiac composed of complex blends of risk, exposure, vulnerability and response. In

addition, for hazards management to be effective it can no longer remain a separate

aspect of urban management it must become a component of integrated programs that

are designed to address broader goals ofurban sustainability (Mitchell, 1999).

Monitoring spatia-temporal changes assumes crucial importance if efforts to mitigate

fire hazards are to be consistently undertaken. Vulnerability assessments have to be

carried out by strategic planning for people living in high-risk areas. Both these issues

can be addressed by using new resolution satellite data such as IKONOS, which have a

180


high resolution of0.81 by 0.81 m and Quick Bird 0.60 by 0.60 m, comparable to aerial

photography. Urban development has to be closely monitored at regular intervals in

order to capture the most recent additions to the spatial environment, be it people or

structures. The intensity of a potential disaster, which can occur in a certain place, is

attributed to the constant spatia-temporal build-up, which increases risk. Once the

current developments are recorded, then necessary planning can be carried out to

provide equitable service levels to all the citizens. This is a very important issue in

developing countries where there is a limited resource for addressing disasters that may

involve thousands ofhuman casualties.

There are many available databases in Australia, which can be used in a consistent

manner to generate derived knowledge based information that can assist in disaster

mitigation. However they cannot be directly used in the way they are released. The

census database explains night-time residential population for collectors district but

they do not exactly indicate the location of dwellings. Calibrating such data-base to the

residential dwellings can be a useful exercise to reduce the randomness, especially from

the point of view of hazard related studies where accurate location of people and

dwelling is important. To explain the vulnerability a thorough understanding of the

dynamics of population is important. It is also important to model the various processes

prior to the unfolding of a hazard. It is essential therefore to breakdown the links and

spatial intricacies that hazards are made up of, in order to plan for emergency services

and resource allocation. This has shown to be possible by the various examples given in

the results chapter.

181


~ Potential of the model to be applied in different regions

Even though the study has used local variables to assess and model hazard in an urban

environment most of these data are available or can be extracted from remote sensing

and available sources in the form of cartographic data. Whilst in developing nations

such data bases may not be available at all, developing countries may have such data

readily available. In developing countries the emphasis must be placed on using high

spatial and airborne remote sensing data for systematically extracting data about

landcover and by using the census information to begin modeling hazards and

managing resources efficiently. Intensive use of space borne and airborne data can

counter the lack of data in such nations. Once the data has been obtained it can be

managed in a GIS environment and modeled by using the approach developed in this

study.

~ A multi-sensor approach

A multi-sensor approach to model hazards inn urban areas will be very useful since an

urban area is dominated by numerous features, which are found in different sizes,

shapes and patterns. The use of remote sensing data can be used for estimating this

variety of features and patterns. Multiple remote sensing data in different spatial and

spectral resolutions may be used to determine land-use, individual features and for

differentiating different urban materials (as is shown in another study of an urban

environment in Appendix I, which uses hyperspectral data)

~ Data limitations

The study of hazard in an urban environment demands the collection of a variety of

data and must be followed by their systematic appraisal before being used in

emergency operations. This study has not taken all data which is required to

comprehensively assess and model hazards, but has attempted to demonstrate that it is

182


possible to incorporate a multitude of data which may come in different scales and

resolutions and from different sources over a period of time. The increasing use of

remote sensing data and the fact that remote sensing data is constantly improving in

terms of its resolution and spectral qualities augurs well for the study of urban hazards.

On the one hand remote sensing of the environment will assist in monitoring the

changes on the land surface and on the other in rapid data acquisition.

~ Extraction of socio-economic characteristics from remote sensing data

The density of population and the characteristics of the people who may be exposed to

a hazard are of considerable importance in resource planning. However, the sources of

data are limited to the Census. This means that the population during the day-time is

not accounted for which may cause serious shortcomings in resource planning. In

Australia a major drawback is that the census data is collected by collector districts

level, which is the lowest level for which data is collected. It is random by nature in the

sense that the data for a collector's district is highly generalized since it does not tag the

data to a respective structure or dwelling unit. So the population for a given collectors

district may be 6000 for the entire district, but in reality the number of dwelling units

may only occupy a certain percentage of the collector district. In this study an attempt

was made to mask the collectors district and population data by overlaying the census

district with aerial photographs and the number of structures. This resulted in a more

accurate means of depicting hazard indicators like structures and their population. It is

interesting to note that the Australian Bureau of Statistics have already embarked on a

regional study to incorporate aerial photos and to link demographic data.

The long period of time between census data collection also does not assist in

emergency planning, since the population undergoes rapid changes in numbers and

183


demographic structure. Remote sensing data may be used for estimating population

density but these studies underlined the limitations and the manual process involved in

using remote senor data. This is attributed to the fact that remote sensing data is useful

for estimating the physical attributes of the earth and any indirect assessment will need

to make use of ancillary data.

)> Generalization of data in the hazard model

The operations and processes involved in developing the model have generalized the

spatial data to some extent. For example when the geometric intersection is performed

between the spatial unit layer and the landuse layer both of which are polygon map

features, there is some subjectivity. The landuse which has the highest landuse in terms

of the area is retained and updated to the master attribute table. The other landuses are

left out. However this has allowed data from a wide range of sources to be incorporated

into the model.

7-2 Model Capabilities

The model developed in the current research has capabilities, which are briefly

summarized below

)> Proximity analysis (See Figure 32)

The model can perform proximity analysis which is an important tool for emergency

planning. For instance a chemical factory which may be located in the vicinity of a

residential or commercial area. The proximity analysis will lead to an estimation of the

area which may be affected in the event of a gas leak, the number of residences which

may be affected, the type of population who reside within that vicinity. This

information is vital for emergency departments who have to plan and locate their

limited resources strategically (Bhaskaran, 2001).

184


~ Forecasting (See Figure 33)

Whilst updating takes us from past to present, forecasting is directed at examining how

changes may take place in the future. Projected population Figures can be input to the

model to arrive at the potential number of people who may be affected in the event of a

disaster. The non-availability of such information can jeopardize appropriate planning

and affect emergency operations. Planning and locating new activities and sites which

may be involved in harmful elements (chemical factories, petroleum based industries)

demands comprehensive knowledge about the existing areas. In other words an idea

about the land-use, the population type, demographic features, infrastructures and

structures is essential for planning and locating new installations. This also means

taking into account future residential, industrial and commercial plans and projections

before the location of any activity, which involves harmful elements. The model will be

able to forecast the trend and effects a future plan or investment might have on the

existing hazard and therefore will provide a basis for allocating fresh resources or

redistribute dynamic resources, or advising against the location of such installations.

~ Basis for allocation of resources

Equitable fire services can be provided to all citizens, if the allocation of limited

dynamic resources to fire districts (geographical boundaries) is done according to a

comprehensive hazard and vulnerability assessment model. Spatial distribution of key

hazard indicators of population, structural density and sensitive sites has to be

considered before allocating such resources. The model, which is a composite of

important variables that influence hazard and vulnerability, can be used as a guide for

allocating dynamic resources efficiently. If the fire districts (Figure 50) are categorised

by using the model (Figure 51) developed in the current research, it may assist in

reassessment and distribution of the existing dynamic resources in a more optimal way.

185


STATEWIDE Fire districts of NSW. Only a ~ection of the entire fire districts

are sbown

Figure 50 Fire districts in New South Wales. Zone numbers are shown. (Source:

NSWFB)

186


Fire hazard model A basis for allocating Dynamic Resources

0 Fire dl.crtct boundery a ElltnlnWlt)f hllz:tudoue .. _

0 251 by' 2Sim ••tiel unit Fire hazard categortzetlon CJ R...trlcted CJ V Low hllzerd

L- hnerd Modonte hllzerd High hezerd llo Oete

2 o 2 Kllom eters

~--~~

Figure 51 Fire hazard model. A basis for allocating dynamic resources

187


)> Impact analysis

Once the decision has been made to install a high-risk plant, which may be hazardous

to a particular area the model can simulate the impact that installation may have in the

event of a disaster. This will be shown by the number of people who may be affected,

the number of structures, which may be damaged, and the activities, which may be

jeopardized in the event of a disaster. Since the model is created by using a

combination of variables, which comprise both physical and human attributes, it will be

useful in taking precautions and advanced safety measures.

In the following section a comparative analysis of the results of the model developed in

the current research and the methodology adopted by the NSWFB is undertaken to

bring the results together in a single analysis and to highlight the benefits of the model

vis-a-vis the current fire methodology.

7-3 Comparative Hazard Analysis of Bathurst by the NSWFB Methodology and the Methodology Developed by the Current Research.

The current methodology adopted by the NSWFB makes an interesting comparison

with the approach followed in the current research (Figure 52 & 53). Both

methodologies are interested in depicting the fire hazard in urban environments. A

visual comparison between the hazard maps produced by both methodologies show that

the cl.rrrent research has considered the concept of hazard as one not involving the

physical agent alone but also the other important factors which ideally influence hazard

of any given region. The specific points of comparisons are described in the following

paragraphs.

188


FIRE HAZARD CATEGORISATION BY NEW SOUTH WALES FIRE BRIGADES

l.l 1/ • - 1-fa~

• rtiiJ

I ,--II -<E _s :J 'IR: -f-' -

• -· • • 1- -I-1-

0 lt.5 3

·- ;- ..... - - - 1- r- 1- f-1- 1- -

Figure 52 Hazard categorization by NSWFB in Bathurst City, NSW. (Special hazard Category 1: Red; Yellow: Intermediate hazard Category 2 and 3; Green and light green; Low hazard Category 4 and 5)

189


Fire Hazard Model, Bathurst, NSW, Australia

•

• Area not shown in the NSWFB methodology .

Additional area, which needs to be served by existing fire

stations

St. Vincents Hospital

s

• Special Hazard (Category 1) CJ Spatial Unit (250m by 250m)

• Chemical Storages Fire Hazard Categories CJ Restricted

V Low Hazard - Low Hazard CJ Moderate Hazard

High Hazard CJ NoData

3~~~5iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiio~~~~~~~3iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiliiiii6 Kilometers

Figure 53 Hazard categorisation by this study

);> Evaluating service area accessibility: fire service area will be increased

The current research methodology enables network analysis, which clearly shows the

advantages of using the hazard model developed by the current study over the NSWFB

approach. Service areas identify the region within a certain traveling time or distance

from a site. In this example the cost field was the length of the street. The area to be

serviced by the NSWFB as shown by the NSWFB map clearly illustrates lesser area to

be serviced as compared to the model developed in the current research. Since the

190


current research has taken into account more variables, which include physical as well

as human components of hazard, it shows a larger area consisting of considerable

hazard level, which will have to be serviced by the emergency organization. This can

be illustrated by a simple, example where the service area of the fire station can be

estimated on the basis of travel distance from the fire station (Figure 54). A service area

(polygon layer) is created which shows the service area that the existing fire stations

cover. This program uses the travel distance from the fire station to estimate the total

service area that the fire stations will be able to service. As can be clearly seen the

service area has to be increased considerably, in order to cover the entire city of

Bathurst. Service networks are line themes that identifY the streets within a certain

distance or travel time of a site via the road network. The Figure 54 also shows the

service networks, which are used by the existing fire stations. Certain areas are not

serviced which may increase the existing hazard level, if we assume that the

hazardousness of a place is a function of a) The total inherent risk and b) The total

distance ofthe hazard area from the fire station.

This type of simple analysis can easily show that the resources are not strategically

distributed in the city of Bathurst. When compared to the NSWFB methodology of risk

assessment, the methodology demonstrated in the current research reveals more areas,

which are left unserviced.

191


Area serviced by existing fire stations in Bathurst

s

• Special HazMd (Category 1) A. Fire Stations

Streets serviced by the Fire Staltlons / 1.76 Area serviced by existing Fire Stations 01.76 D Spalla! Unit (250m by 250m I Population Density GR

V.low density Low Density

D Moderalte Density High Density No Ollila

V Low Hazard Low Hazard Moderlllte Hazard High Hazard No Data

Figure 54 Area serviced by existing Fire Stations in Bathurst. Hazard model developed by current research. The total service area is 10.82 sq km and the total distance traversed is 86.26 sq km. The cost field is the line length in the above example. 1. 76 is the travel cost

}> Estimation of accessibility to special (extreme) hazard sites

In the event of a disaster, it is vital for the fire services or any other allied emergency

services to quickly reach sites, which have high hazard levels. Further more it ts

important to have an overall idea of the type of hazard, which may be impacted in the

event of, for example a gas leak, from a chemical gas depot. For example there are 6

special hazard sites and two fire stations in Bathurst. The special hazard sites may have

to be reached in the event of a disaster. Estimating the existing travel times, and at a

certain cutoff cost, is crucial in determining the time required to access all the special

hazard sites by the existing fire stations. Tables 7, 8 and 9 show the distance and

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directions required to travel from the facilities (i.e. Bathurst and Kelso) fire stations to

the special hazard sites. It can be deduced that most of the special hazard sites are

closer to the Bathurst fire station rather than the Kelso fire station. In this example the

travel distance by default is the travel cost. In the real world however there may be

impedances such as high volume of traffic at different times in a day, one-way traffic,

and other obstacles. More and more variables and values may be added to simulate the

real world conditions in order to estimate the travel distance. The research model show

more developed areas, which need fire protection and resources due to their existing

level ofhazard.

Facility #1: Bathurst fire station

Special hazard sites Travel Distance _!.Km~

Bathurst base hospital 2.28 Correctional centre 1.85 El~as 1.79 ~obilfuelsupply 1.65 ~cQuarie care centre 3.06 Ampol and mobil fuels 1.73 St Vincents hospital 2.94

Facility# 2: Kelso fire station

Special hazard sites Travel Distance (Kms)

Bathurst base hospital 5.04 Correctional centre 6.63 El~as 4.17 ~obilfuelsupply 4.03 ~cQuarie care centre 5.44 Ampol and mobil fuels 4.60 St Vincents hospital 5.32

Table 7: Comparison of travel distance to special hazard sites the existing fire stations ofBathurst and Kelso (Bathurst)

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Starting from Facility #1 Turn left onto GEORGE ST

Travel on GEORGE ST for 0.13 km Turn left onto LAMBERT ST

Travel on LAMBERT ST for 0.93 km Turn right onto HA V ANNAH S

Travel on HA V ANNAH S for 0.12 km Turn left onto OAKES ST

Travel on OAKES ST for 0.19 km Turn right onto BANT ST

Travel on BANT ST for 0.09 km Turn left onto RAILWAY PD

Travel on RAILWAY PD for 0.26 km Turn left into Ampol & Mobil Fuels

Total distance traveled is 1.73 km

Table 8

Starting from Facility #2 Turn left onto GREAT WEST

Travel on GREAT WEST for 1.25 km Continue straight onto SYDNEY RD

Travel on SYDNEY RD for 1.24 km Continue straight onto BRIDGE ST

Travel on BRIDGE ST for 0.31 km Turn left onto KENDALL A V

Travel on KENDALL A V for 0.16 km Turn right onto HA V ANNAH S

Travel on HA V ANNAH S for 1.12 km Turn left onto RAILWAY PD

Travel on RAILWAY PD for 0.50 km Turn right into Ampol & Mobil Fuels

Total distance traveled is 4.60 km

Table 9

Table 8 & 9 shows the directions, which are required to be taken by the fire service to a specific special hazard site 'Ampol and Mobil fuels'

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);;> NSWFB method lacks currency of information and is not cost-effective

Information is out-of-date the moment after it has been collected. Fire hazard and

vulnerability assessment and management requires current information at regular

intervals. The NSWFB approach is based on field survey methods and if the database

has to be developed for a large area on a regional scale, it will be very difficult to

accomplish the task within a short period of time. Most importantly, the changes in

hazard in urban areas are very frequent and these changes may not be recorded or

registered by the NSWFB approach. By imaging the study area with high resolution

aerial photos and orthorectifying them, the current research methodology can monitor

and flag the changes in the land-use by efficiently detecting them on a consistent scale

and continuous coordinate system throughout the study area, leading to a seamless

standalone database. The current research methodology in comparison will provide a

synoptic view and can detect changes in the land-use and other new developments, all

of which may affect the levels of existing hazard.

);;> Current research methodology is more cost effective and accurate

By using othorectified aerial photoimages multiple hazard causing factors, which are

shown as numerical variables, can be collected, extracted and consistently merged to

develop a model, which will show the changing hazard accurately. This is shown by the

current research. The NSWFB approach however may require high levels of

coordination and may take a long time to be created, since it is based on an in-situ

survey method. The hazardousness of a place is indicated by many factors, which may

jointly influence the total hazard. The NSWFB methodology focuses more on structural

assessment to categorize hazards, which may not give sufficient input information

about the total hazard. The current research, even though it uses a limited number of

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variables, has shown a methodology that can incorporate a wide range of variables,

which may influence hazard in urban areas.

~ Current research methodology can be introduced into the 'Resource Planning Process'.

The planning process explained in chapter 5 clearly describes that there is a need for

consistent and reliable information. A manual method of collecting details and

maintaining files may not be a very efficient way of storing and disseminating spatia-

temporal information to different agencies of the organization. These shortcomings

may be avoided by using the current research methodology since it stores

spatiotemporal information on a uniform scale and can register changes in the level of

hazard very easily. By pursuing appropriate R&D (research and development)

techniques the current methodology can be used as a reliable method for assessing

hazards and preparing subsequent reports, which are essential for the management of

dynamic and static resources.

~ Need for providing spatio-temporal information for allocation of resources or redistributing existing resources

Allocation of fresh resources (Static or Dynamic) will depend on the timely

identification and assessment of fire hazards, which may not be possible using the

approach adopted by the NSWFB methodology, for reasons, which were explained in

the earlier paragraphs. The current methodology may be used on a regional basis for the

entire state of NSW to assess hazards uniformly, which will in turn provide a broad

basis for the allocation of dynamic resources in particular. The service area can also be

calculated vis-a-vis the existing location of fire stations and resources on the one hand

and the ever changing hazard on the other.

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7-4 Management of Resources (Distance and hazard for optimization of resource location)

Efficient management of resources is a complex task that is key to the success of an

organization. The proper use and development of people, things, and money will, to a

great extent, determine the success of a fire department in achieving its goals and

objectives (Forsman, 1988). Resources must be managed according to a number of

variables, which influence hazard and community vulnerability. The current research

has underlined the importance of data such as land-use, population density, socio-

economic data, all of which have to be jointly assessed and evaluated for each region in

a consistent and systematic manner, for the allocation of fresh resources and

redistribution of existing resources.

The hazard model developed in the study may be used to estimate hazardousness of a

place on the basis of travel distance and the level of hazard. The cells in the hazard

model which represent geometrically regular spatial units have a distance and hazard

level value attached to it, for instance each cell represents a value of 0.25 km and the

level of hazard is indicated by the hazard value. The total hazard risk of a certain

location can then be calculated as a factor of the hazard level and the travel distance

from a fire station to that location. The closer the location is to a fire station the lesser

the risk it will carry.

7-5 Summary

Hazard modeling demands spatial and temporal data, which have to be updated at

frequent intervals. Remote sensing technology and GIS can provide such information.

Hazard related variables appear on the surface of the earth in many different shapes,

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sizes, scales and level of details, particularly in urban environments, which may have to

be detected by multiple sensors. For example a large industrial plant may have to be

detected at a large scale as well as a medium scale. Regular patterns such as residential

land-use may have to be detected at a medium scale. The use of different sensor

systems will speed up the process of variable data acquisition on a consistent scale.

Spatiotemporal data is vital for emergency services since their decisions are based on

systems, which provide them with up-to-date and reliable information. The currency of

spatia-temporal information may not be maintained by the present methodology

adopted by the NSWFB, while the current research provides a methodology, which can

be used to rapidly acquire, assess and analyse those spatio-temporal data, which

influence risk.

Socioeconomic data is essential for estimating the prevailing risk in different places.

However there is limited data on some aspects of risk, such as daytime population,

which is very important to estimate the number of people who may be exposed to a

hazard. The census data is based on night-time residential population estimates, which

may not be sufficient to adequately assess risk. Remote sensing data provides us with

details on the physical objects found on the earth, but are incapable of providing direct

estimates of variables, such as population, unless they are combined with ancillary data

such as dwelling size, land-use or other related data-base.

Management of fire resources (staff and equipment) is vital since it has a bearing on the

disaster operations and consequences. It may not be possible to provide one hundred

percent fire protection to all citizens since fire fighting resources are always less than

198


the number of people and property for which they are responsible to provide protection.

The hazard model developed in the current research is useful for providing a basis for

the allocation of resources since the model reflects the combination ofvariables, which

influence risk. By using this model over the entire state ofNSW a broad but consistent

allocation of resources, can be made. In-depth studies of certain areas with high risk

may be made by using the model with data derived from limited in-situ field survey

method.

The hazard model developed in the current research may be used by the NSWFB in

their planning process since it provides a consistent yet reliable methodology for

combining and analyzing data. With the advent ofthe World Wide Web and Internet

based GIS, such as ArciMS the NSWFB have already put this into use albeit in a slow

and steady manner. By using the model, at all the levels of the planning process, a

consistent mechanism of storing and assessing risk can be achieved. The planning

process entails a lot of discussion, reporting and critical assessment of decisions made,

particularly about acquiring new resources and other kinds of investments, all of which

have to be confirmed and supported by hardcore facts at a high level. A methodology

based on GIS/RS, supported by field surveys, will lend authenticity and reliability to

the entire process of planning and decision making.

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Chapter 8: Summary and Conclusions.

8-1 Summary

At the outset the research focused on addressing the issue of urban fire hazard

modeling and attempted to base this research on the current methodology adopted by

the NSWFB to model hazards. One of the main objectives was to improve the existing

methodology of assessing and analyzing hazards particularly in urban environments by

the use of GIS/RS.

High resolution aerial photo images were exposed over the study sites of Bathurst and

Hornsby at a scale of 1:16,000 respectively. These images were then orthorectified

using digital elevation models and ground control points. Available cartographic GIS

data were identified for modeling purposes and also to study hazards as a combined

influence both physical and human agents. Data were gathered from different sources

and the digital cadastral data base was registered to the orthorectified image in order to

bring all the layers into one consistent coordinate system. The image was used for

visualization and extraction of additional layers such as roof area, and total built up

area, which assisted in explaining areas with a high degree of congestion which could

in tum lead to a high degree of difficulty in fire fighting. Many other layers were

combined and analysed by using various spatial operations such as Geometric

Intersection, Update and a master attribute table was created for both the study areas.

The weighted average index method was used for updating newly created values to the

master attribute table after the geometric intersection process was completed. By using

this method the subjectivity was kept to a minimum and the values were partitioned

according to the total area they occupied in the spatial unit. This master attribute table

200


consisted of raw data, derived and spatially processed data, all ofwhich were stored to

a 250m by 250m spatial unit.

A weighted overlay process was performed to analyse the total hazard for each study

site. The image environment was set, which involved setting the cell size, map extent,

evaluation scale and converting the vector layers from the master attribute table to a

grid, which is a format that allows consistent analysis of hazards. An evaluation scale

was decided and four hazard categories were finalized. The ability of the model to

simulate real world situations was made possible by assigning influences and biases to

each of the input layers. The model is semantic in that it can be adjusted and updated to

show the current level of combined hazard in any study site. The data chosen for

modeling is available from most of the local government councils in NSW, and thus the

model has wide applicability.

The project delivered:

• a broad based land use classification system compatible with the current risk

assessment method that allows quantification of hazard levels and

comparisons to be drawn between different areas,

• a systematic methodology and design for integrating aerospace data with

cartographic (thematic) information,

• the development of a spatial data model that can assist in strategic resource

planning,

• the development of a GIS database that provides for the integration of

disparate data through a continuous and consistent co-ordinate system.

201


• a potential future link for specific site based information to assist the

management of emergency resources at incidents,

• a method which is repeatable and allows for periodic updates, and

• a method, which is applicable in any and all urban centres across NSW.

(Report to NSWFB, 2001 and NSW Fire News, 2002. See appendix VI for

summary of report)

8-2 Conclusions

8-2.1 Applicability of model in the NSWFB context

The current methodology adopted by the NSWFB is based on in-situ field surveys,

which involve a lot of time and may not be cost effective. Besides the methodology

does not depict the current level of hazard. The integrated model uses an orthorectified

image base to model hazards, thus providing an accurate planimetric database upon

which available vector and raster data may be overlain and analysed in combination.

This aspect of the model will be very useful for the NSWFB, as it will enable the

creation and flow of consistent spatia-temporal information from one level to another.

This will reduce any possibility of confusion in the data gathering process since all the

levels will use the same database. Additional input at the local level can be

incorporated into the database and used by officials in other levels. All this will lead to

a database system, that will have unique input from different levels, but can be related

and used at all the levels due to the fact that they are all in a single coordinate system.

The database will reduce duplication and repetition of GIS data and will also lead to a

cost effective way of developing decision support systems. By systematic validation of

the database, clarity can be lent to the database thus making it more reliable. New data

collected in the field can be incorporated into the model which can then be accessed at

202


all fire stations and agency branches by worldwide web I internet access. This would

mean that the model would become increasingly informed about hazard. The model

therefore increases the possibility of data sharing and will be reliable sufficiently to be

implemented across all levels ofNSWFB.

The integrated model developed in the current research was planned with regular

interactions and feedback from the NSWFB officials. The methodology shows a major

improvement in data collection, analyses and mapping over the current NSWFB

methodology. The ability ofthe model to be extrapolated and used anywhere in NSW

is useful for the NSWFB since they require consistent data on hazard on a regional,

zonal and local level.

8-2.2 Universal applications

The choice of variables selected for modeling in the current research was limited by the

non-availability of some data. However an attempt was made to develop a methodology

to model hazard by combining variables which best explain the concept of hazard. If

more data is available they can be input to the model and spatially analysed. At the

moment data from Australia is used but the approach can be duplicated on a global

basis if the input layers are provided. Some common layers such as land-use can be

found in most countries or can be created by using high resolution airborne or

spacebome remote sensing data.

8-2.3 Data from new satellite remote sensing systems

New satellite data such as the Quick Bird II launched in 2001, that has a resolution of

0.61m by 0.61m and has a consistent accurate accuracy throughout the scene. Such data

203


bases can be used for an entire region and the methodology shown by the current study

may be used to develop semantic models of hazards. This will lead to a reliable and

consistent database, which will result in a uniform method of assessing hazards. Such

information will then be useful for allocating strategic resources and redistributing

existing resources. The use of satellite data will also provide currency to the entire

model since they can be secured at planned, regular intervals.

8-2.4 More variables may be required

Many other operational applications such as ambulance, bush fire, medical services,

and police, require additional variables apart from those included in the current

research where the objective was to model urban fire hazard. The current research

however does not include all these variables. The methodology may be extended to

develop systems for emergency organizations, as well as allied agencies, which may

seek information of the same type. With additional and related data, such systems can

solve problems related to data sharing, and will promote geographic data sharing.

8-2.5 Future research

Hazards are dynamic phenomenon as they change at regular intervals either resulting in

an increase in the level of existing hazard or a decrease. This is a very common trend in

developing countries where unplanned growth may lead to a fluctuation in the levels of

hazards, which in turn will lead to demand for additional resources (static and

dynamic). Emergency organizations have limited resources but are responsible for

providing services of all kinds, at all times, to an increasingly growing population and

complex infrastructures. This problem has an additional dimension in the rural-urban

interface, where new developments and activities occur rapidly and often go unnoticed,

204


and change the existing land-use, leading to a new type of hazard and vulnerability.

Timely detection of such a trend is vital to provide and plan for emergency services in

an optimal manner. Remote sensing is probably the only method for assessing hazards

particularly in near real time. Future research must focus on the systematic appraisal of

such remote sensing systems, which are constantly improving in their abilities, in order

to detect features more accurately, both in space and time. Issues dealing with the

appropriate resolution (spatial, spectral, and temporal) for assessing and modeling

hazards must be key areas of future research.

205


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225


Appendix I

INTEGRATING IMAGING SPECTROSCOPY & GIS FOR SPATIAL EMERGENCY DECISION SUPPORT SYSTEMS

(SEDSS)

A Case of Hail Storm Damage and Post Disaster Management, Sydney, NSW, Australia

By

*Sunil Bhaskaran, *Bruce Forster & **Trevor Neal

*SCHOOL OF SURVEYING & SPATIAL INFORMATION SYSTEMS (Formerly Geomatic Engineering)

Faculty of Engineering, University ofNew South Wales Sydney,2052,NSVV

**CORPORATE STRATEGY DIVISION, New South Wales Fire Brigades

Sydney,2000 Australia

Technical Report Prepared for the Corporate Strategy Division, New South Wales Fire Brigades, Sydney, Australia.

December 2001

226

The following article has been removed from the digital copy of this thesis. Please see the print copy of the thesis for a complete manuscript.

Title: INTEGRATING IMAGING SPECTROSCOPY & GIS FORSPATIAL EMERGENCY DECISION SUPPORT SYSTEMS (SEDSS)： A Case of Hail Storm Damage and Post Disaster Management, Sydney，NSW, Australia Authors: Sunil Bhaskaran, Bruce Forster & Trevor Neal


Appendix II

Description of layers in master table in (Bathurst And Hornsby-Shire) master attribute

table - Bathurst, NSW, Australia.

The MAT has fifteen (15) fields and one thousand eight (1 008) records of data, which

contain detailed hazard related information for each spatial unit. The Master Attribute

table was created after various spatial analysis operations in multiple GIS/RS

softwares.

1.Amgrd: This field shows the basic spatial units used in this study. The area of each

unit is given in metres (62,500 sq metres) which is equal to 250m by 250. The hazard

distribution is shown for each spatial unit, which enables comparison of different levels

of hazards.

2.Permtr (Perimeter): This field shows the total perimeter area of each spatial unit

3.Amcv _: The identification of each spatial unit is issued by the GIS system

automatically. These ID numbers are unique and were used to perform analyses.

4.Amcv _id: The identification issued by the user. This is also unique and can be

manipulated according to the users requirements. This id was also used in several

analyses.

5.Structr (Structures): The number of structures were calculated for the entire region of

Bathurst. Advanced sequel (SQL) programmes were used to intersect the number of

structures for each spatial units. This field shows the number of structures for each

spatial unit.

273


6.Rfarea (Roof Area): The built area in the region was calculated by drawing polygons

on the roofs of the structures from the orthorecti:fied aerial photo images. This field

gives an estimate of overall built area and the degree of congestion.

7. Resa (Residential area): Land-uses were estimated for each spatial unit by geometric

intersection. The field shows the area of residential land in each spatial unit

8.1nda (Industrial land-use): The industrial land-use for each spatial unit was estimated

by geometric intersection. The industrial land-use area covered by each spatial gives an

idea of the hazard posed by industries in that spatial unit

9. Sevbz (Service business): Commercial land-use was estimated for the entire region.

Commercial land-use was subdivided into service business and general business area.

This field shows the service business area for each spatial unit.

10. Genbz (General Business): Commercial land-use was estimated for the entire

region. Commercial land-use was subdivided into service business and general business

area. This field shows the general business area for each spatial unit.

274


Amgrd Permtr Amcv Amcv id Structr Rfarea Resa lnda Sevbz Genbz

62,500 1,000 470 470 0 0 0 0 0 0 62,500 1,001 471 471 0 0 0 0 0 0 62,500 1,000 472 472 0 0 0 0 0 0 62,600 1,000 473 473 29 8,843 23,620 0 0 0 62,600 1,000 474 474 45 11,580 34,710 0 0 0 62,600 1,001 475 475 50 13,140 42,!XXI 0 0 0 62,500 1,000 476 476 39 9,388 44,480 0 0 0 62,500 1,000 477 477 35 8,324 39,760 0 0 0 62,500 1,000 478 478 31 9,553 32,530 0 0 0 62,500 1,000 479 479 0 0 4,507 0 0 0 62,500 1,000 480 480 9 364 0 0 0 0 62,500 1,000 481 481 9 4,939 0 0 0 0 62,500 1,000 482 482 24 3,313 19,970 0 0 0 62,500 1,00J 483 483 33 4,375 17,790 0 0 0 62,500 1,000 484 484 0 0 0 0 0 0 62,600 1,000 485 485 3 942 0 0 0 0 62,500 1,000 486 486 0 0 0 0 0 0 62,500 1,000 487 487 0 0 0 0 0 0 62,500 1,000 488 488 0 0 0 0 0 0 62,600 1,000 489 489 2 0 0 0 0 0 62,500 1,000 490 490 0 0 0 0 0 0 62,500 1,000 491 491 0 0 0 0 0 0 62,600 1,000 492 492 7 1,801 0 0 0 0 62,500 1,001 493 493 7 2,969 0 0 0 0 62,500 1,000 494 494 46 12,870 33,750 0 0 0 62,600 1,000 495 495 31 9,728 22,110 0 0 0 62,500 1,000 496 496 13 6,445 0 0 0· 0

275


Resa lnda Sevbz Genbz Perbu cd IWtAvStctmsk WIAvPopmsl WIAvlncmsk WtAvDwlmsl<

0 0 0 0 0 1,141,912 0 0 0 0

0 0 0 0 0 1,141,912 0 0 0 0 0 0 0 0 0 1,141,912 0 0 0 0

23,62) 0 0 0 14 1,141,912 285 669 498 0 34,710 0 0 0 19 1,141,911 248 704 401 1 42,001 0 0 0 21 1,141,911 213 733 309 2 44,400 0 0 0 15 1,141,009 255 677 229 3 39,760 0 0 0 13 1,141 ,rog 187 733 381 3 32,5ll 0 0 0 15 1,141,909 132 880 588 3 4,507 0 0 0 0 1,141,901 0 0 0 0

0 0 0 0 1 1,141,913 124 1.001 710 0

0 0 0 0 8 1,141,913 124 1.001 710 0 19,970 0 0 0 5 1,141,812 284 62) 462 12

17.790 0 0 0 7 1,141,812 284 620 462 12

0 0 0 0 0 1,141,812 0 0 0 0 0 0 0 0 2 1,141,812 0 0 0 0

0 0 0 0 0 1,141,801 0 0 0 0 0 0 0 0 0 1,141,601 0 0 0 0

0 0 0 0 0 1,141,601 0 0 0 0

0 0 0 0 0 1,141,601 0 0 0 0 0 0 0 0 0 1,141,601 0 0 0 0

0 0 0 0 0 1,141,601 0 0 0 0 0 0 0 0 3 1,141,601 0 0 0 0

0 0 0 0 5 1,141,601 0 0 0 0

33,750 0 0 0 21 1,141,611 0 0 0 0 22,110 0 0 0 16 1,141,611 0 0 0 0

0 0 0 0 10 1,141,610 0 0 0 0

11 Perbu (Percentage ofBuilt area): The percentage ofbuilt area was estimated by

using columns Roofarea and Spatial unit. The total built area within each spatial unit

was calculated by dividing the total area of roofs within each spatial unit by the area of

the spatial unit.

12 CD (Collector District): The field shows the collector district to which each record

belongs. The codes of collector districts are shown in this field.

13 WtAvStctmsk (Weighted Average Index Senior Citizens Mask): The distribution of

senior citizens was derived by geometric intersection between census layers and spatial

units. The intersection was performed after masking the census layers with respect to

the exact locations of structures as shown in aerial photo images.

276


14. WtAvPopmsk (Weighted Average Index Population Mask): The distribution of

population was derived by geometric intersection between census layers and spatial

units. The intersection was performed after masking the census layers with respect to

the exact locations of structures as shown in aerial photo images.

15. WtAvDwlmsk (Weighted Average Index Dwelling Mask): The distribution of

dwellings and types (multi storied or detached /semidetached) was derived by

geometric intersection between census layers and spatial units. The intersection was

performed after masking the census layers with respect to the exact locations of

structures as shown in aerial photo images.

277


MASTER ATTRIBUTE TABLE- HORNSBY, NSW, AUSTRALIA

AREA PERIMRER HORH250 HORII250 _ID Structure _I_Roofarea Residential Business Industrial Specuses

62,500 1,000 2 273 0 0 8,424 0 0 0 62,500 1,000 3 274 0 0 30,314 0 0 0 62,500 1,000 4 275 0 0 54,988 0 0 0 62,500 1,000 5 276 0 0 49,143 0 0 0 62,500 1,000 6 277 0 0 52,825 0 0 0 62,500 1,000 7 278 0 0 57,002 0 0 0 62,500 1,000 B 279 0 0 58,611 0 0 0 62,500 1,000 9 280 0 0 46,539 0 0 0 62,500 1,000 10 281 0 0 31 ,oll3 0 0 1,149 62,500 1,000 11 282 0 0 33,540 0 0 18,213 62,500 1,000 12 283 0 0 51,783 0 0 2,828 62,500 1,000 13 284 0 0 10,5$ 0 0 1,209 62,500 1,000 14 285 0 0 0 0 0 0 62,500 1,000 15 286 0 0 0 0 0 0 62,500 1,000 16 287 0 0 0 0 0 0 62,500 1,000 17 288 0 0 0 0 0 0 62,500 1,000 18 289 0 0 0 0 0 0 62,500 1,000 19 256 0 0 0 0 0 0 62,500 1,000 20 257 0 0 18,892 0 0 0 62,500 1,000 21 25B 0 311 48,716 0 0 0 62,500 1,000 22 259 0 479 50,637 0 0 0 62,500 1,000 23 260 0 0 49,193 0 0 0 62,500 1,000 24 261 0 9 56,484 0 0 0

62,500 1,000 25 262 0 205 50,323 0 0 0 62,500 1,000 26 263 0 190 25,ff!il 13,oo6 0 3,472 62,500 1,000 27 264 0 360 26,919 2,094 0 9,262 62,500 1,000 28 265 0 0 48,995 0 0 3,695

278


Open IEnvprotzn Nparksw TOTPOP IWlAvgOfTOTPOI WtAvgOfTOTdwl WtAvgOfAggrcgated_ Inc WlAvlncmsk

0 0 0 416 619.044 0 153.055 0 0 0 0 0 416 645.703 0 51.1027 416 416 0 0 0 416 771.852 0 51.1027 416 416 0 0 0 809 813.564 0 82.0213 416 416 0 0 0 869 825.826 0 134.343 869 869 0 0 0 869 811.266 0 134.343 869 869

165 0 0 869 826.714 0 136.077 759 814 2,123 0 0 759 692.098 0 108.068 759 657

0 0 0 635 639.283 0 117.831 759 624 0 0 0 635 626..435 0 115.726 556 556 0 0 0 635 546.68 0 115.726 556 556

49,560 0 0 635 550.372 0 115.726 556 556 27,614 0 0 635 635 0 118 556 556

0 0 0 635 635 0 118 0 0 0 0 0 635 635 0 118 0 0 0 0 0 635 635 0 118 0 0 0 0 0 635 1,047.45 0 95.0957 0 0 0 0 0 416 622.126 0 153.055 0 0 0 0 0 416 480.587 0 153.055 416 416 0 0 0 416 428.83 0 56.607 416 416 0 0 0 486 633.95 0 104.005 416 529

0 0 0 869 663.519 0 104.005 416 669 358 0 0 869 869 0 188 521 695

1,868 0 0 869 862.302 0 166.83 759 709 0 0 0 759 766.076 0 151.994 759 653 0 0 0 525 834.503 0 96.6987 759 643

0 0 0 525 543.576 0 75.7814 556 556

279


Open Emtprotzn NparksJSV TOTPOP WtAvgOfTOTPOP WtAvgOfTOTdwll WtAvgOfAggregaled_age Inc

0 0 0 416 619.044 0 153.055 0

0 0 0 416 645.703 0 51.1027 416 0 0 0 416 771.852 0 51.1027 416

0 0 0 809 813.564 0 82.0213 416 0 0 0 869 825.826 0 134.343 869 0 0 0 869 811.266 0 134.343 869

165 0 0 869 826.714 0 136.077 759 2,123 0 0 759 692.098 0 108.11i8 759

0 0 0 635 639.283 0 117.831 759 0 0 0 635 626.435 0 115.726 556

0 0 0 635 546.68 0 115.726 556 49,560 0 0 635 550.372 0 115.726 556 27,614 0 0 635 635 0 118 556

0 0 0 635 635 0 118 0

0 0 0 635 635 0 118 0 0 0 0 635 635 0 118 0

0 0 0 635 1,047.45 0 95.0957 0 0 0 0 416 622.126 0 153.055 0

0 0 0 416 480.587 0 153.055 416

0 0 0 416 428.83 0 56.607 416 0 0 0 486 633.95 0 104.005 416

0 0 0 869 663.519 0 104.005 416 358 0 0 869 869 0 188 521

1,868 0 0 869 862.302 0 1E6.83 759 0 0 0 759 766.076 0 151.994 759

280

Integrating RS &: GIS for Urban Fire Disaster Management. Sunil, 1999-2002

W!AvgOfTOTdwll WfAvgOfAggregaled_age Inc W!Avlncmsk W!Avpopmsk WfAvScmsk Wavfls Wavf3s Wavf4:

0 153.055 0 0 0 0 0 0 a 0 51.1027 416 416 344 46 0 0 a 0 51.1027 416 416 344 46 a 0 0 0 82.0213 416 416 344 46 0 0 0 0 134.343 009 869 690 188 34 0 0 0 134.343 869 869 690 188 34 0 0 0 136.077 759 814 653 164 21 0 0 0 108.068 759 657 537 106 4 0 0 0 117.831 759 624 510 95 3 0 0 0 115.726 556 556 457 72 0 0 0 0 115.726 556 556 457 72 0 0 0 0 115.726 556 556 457 72 0 0 0 0 118 556 556 457 72 0 0 0

0 118 0 0 0 0 0 0 0

a 118 0 0 0 0 a 0 0 0 118 0 0 0 0 0 0 0 0 95.0957 0 0 0 0 0 0 0 0 153.055 0 0 0 0 0 0 0 0 153.055 416 416 344 46 0 0 0 0 56.607 416 416 344 46 0 0 a 0 104.005 416 529 431 82 9 0 0 0 104.005 416 669 540 182 24 0 0 0 188 521 695 563 247 ::0 0 0 0 166.83 759 709 579 214 26 0 0 0 151.994 759 653 536 144 14 0 0

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282


Appendix III

Update operations, options available and weighted average index method

The update operation and options considered before using the weighted average index

method. Many techniques of updating the geometrically intersected values to the

records in the Master Attribute Table. Of all the available methods ofupdating values

to the records the 'Weighted Average Index method was selected since the

generalization in this method was minimal.

An example of what each ofthe available methods does is described and shown below.

In this case the variable is population. For each grid the values will be updated after the

intersection process. The different CD codes are given in the first column. All these

CDs intersect the spatial unit.

The 'Sum' Method

1. Grid no 142

CDid Area Pop

1251 510 25 180m2 342 1251 505 2 337m2 931 1250 609 8 740m2 406 1250 608 25 920m2 413

Overall Area Pop Assigned is 342

2.Grid no 125

CDid Area Pop

1251512 12020 246 1251510 3143 342 1250608 6801 413 1250607 11900 700 1250606 28650 499

283


Overall Pop Assigned is 246

The 'Average' Method

1. Grid no 142

CDid

1251 510 1251 505 1250 609 1250 608

Area Pop

25 180m2 342 2 337m2 931 8 740m2 406

25 920m2 413

Overall Area Pop Assigned is 504.052

2.Grid no 125

CDid

1251512 1251510 1250608 1250607 1250606

Area

12020 3143 6801 11900 28650

Pop

246 342 413 700 499

Overall Pop Assigned is 465.3333333

The 'Weighted Average' Method

1. Grid no 142

CDid

1251 510 1251 505 1250 609 1250 608

Area Pop

25 180m2 342 2 337m2 931 8 740m2 406

25 920m2 413

Overall Area Pop Assigned is 504.052

2.Grid no 125

CDid Area Pop

1251512 12020 246

284


1251510 3143 342 1250608 6801 413 1250607 11900 700 1250606 28650 499

Overall Pop Assigned is 692.884

The 'Maximum' Method

1. Grid no 142

CDid Area Pop 1251 510 25 180m2 342 1251 505 2 337m2 931 1250 609 8 740m2 406 1250 608 25 920m2 413


2.Grid no 125

Cdld Area Pop

1251512 12020 246 1251510 3143 342 1250608 6801 413 1250607 11900 700 1250606 28650 499


The 'Proportional Sum' Method

1. Grid no 142

Cdld Area Pop

1251 510 25 180m2 342 1251 505 2 337m2 931 1250 609 8 740m2 406 1250 608 25 920m2 413


2.Grid no 125

Cdld Area Pop

285


1251512 1251510 1250608 1250607 1250606

12020 3143 6801 11900 28650


246 342 413 700 499

Among the above the weighted average was used. Maplnfo adjusts the calculation of averages so that the values from each selected object are weighted more or less heavily.

286


287


Appendix IV

New South Wales Incident Reports

This database describes the number of incidents as well as the percentage increase in

population, for each fire station in NSW by postcodes and available resources found at

the fire station. The incidents, which have occurred since 1991 till 1996 according to

local govermnent area are also shown. This data may be overlain on the hazard map

which would reveal new trends in the occurrences ofhazards

288


Intentionally left blank ••• •.•••••••..••••••.•••••••.•••••••••••••••••

289


AppendixV

USGS Classification Codes

Classification Codes-first and second level categories

1 Urban or Built-Up Land

11 Residential 12 Commercial Services 13 Industrial 14 Transportation, Communications 15 Industrial and Commercial 16 Mixed Urban or Built-Up Land 17 Other Urban or Built-Up Land

2 Agricultural Land

21 Cropland and Pasture 22 Orchards, Groves, Vineyards, Nurseries 23 Confined Feeding Operations 24 Other Agricultural Land

3 Rangeland

31 Herbaceous Rangeland 32 Shrub and Brush Rangeland 33 Mixed Rangeland

4 Forest Land

41 Deciduous Forest Land 42 Evergreen Forest Land 43 Mixed Forest Land

5 Water

51 Streams and Canals 52 Lakes 53 Reservoirs 54 Bays and Estuaries

6 Wetland

61 Forested Wetlands 62 Nonforested Wetlands

7 Barren Land

290


71 Dry Salt Flats 72 Beaches 73 Sandy Areas Other than Beaches 7 4 Bare Exposed Rock 75 Strip Mines, Quarries, and Gravel Pits 76 Transitional Areas 77 Mixed Barren Land

8 Tundra

81 Shrub and Brush Tundra 82 Herbaceous Tundra 83 Bare Ground 84 Wet Tundra 85 Mixed Tundra

9 Perennial Snow and Ice

91 Perennial Snowfields 92 Glaciers

291


Appendix VI

Extract from: NSW Fire Brigades 'Fire News'. Winter Edition 2001 Vol2 No 3 pplO.

REMOTE SENSING OF HAZARDS

Project Background

For a number of years now the Operations Research Unit (ORU) of the NSW Fire Brigades (NSWFB) has been involved with the development and use of risk assessment techniques, which assist in the strategic planning of fire-fighting resources across the State.

While very effective in analysing and justifYing the need for more fire-fighters and new stations, the methodology is currently based on extensive field studies which can be very time consuming and unsustainable, in terms of the frequency of updates. As such, there has long been recognition of the need for a more automated method, which provides more comprehensive data and is more easily updated.

In April 1999, the NSWFB was approached by Sunil Bhaskaran, a university student from India, who was working with Dr. Bruno Parolin from the University of NSW (UNSW). The University, through Sunil, were keen to establish a working relationship with the NSWFB.

The UNSW advanced the concept of developing a proposal for a joint research project between the two organizations that would meet both the academic needs of Sunil's university studies and the needs of the NSWFB in regard to an improved methodology for categorising structural fire hazards.

A series of formal consultations and informal discussions between ORU and the UNSW Geography Department took place until the middle of September, when a formal draft of the project proposal was presented to the NSWFB. The Brigades accepted the proposal and in December 1999, Sunil began the research project in consultation with Trevor Neal from the ORU.

Project Information

The Regional Centre of Bathurst was chosen as the prime study area as it has a substantial population base and a good mix of residential, commercial, industrial and rural interface areas.

The project was based upon the utilisation of "Remote Sensing" technology to assist in the development of a methodology that would reverse the 80/20 field survey to computer analysis ratio, which makes the current method so time consuming. Remote Sensing is the capture of digital information from either airborne platforms, as in the case of aerial photography and space borne platforms, such as satellite imagery. Remote Sensing has been used extensively in analysing the characteristics of rural

292


areas for agricultural, mining and forestry pursuits, but little research exists into the use of this digital data for analysing the characteristics of urban environments.

The remotely sensed data was then linked to road, council planning and land parcel information within a computer mapping environment called a Geographic Information System (GIS). This allowed Sunil to analyse the relationship of several layers of information and from this analysis, develop a layer of "new" information. For example, by analysing the relationship between land size (taken from the land parcel layer), roof area (taken from the aerial photo) and land use zoning (taken from Council planning information), Sunil was able to create a data layer of information relating to a range of structural fire hazards. Variable characteristics of population groupings in regard to various fire risk levels can be extracted from Census data and then be linked to the hazard information layer, which improves our understanding of vulnerability in the community.

The methodology was then validated in a study centred around the central business district of Hornsby, to the north of Sydney. Here the density and complexity of structures added another dimension to the method of analysis.

Project Outcomes

Completed in March 2001, the project has been beneficial to the NSWFB in a number of ways. The project delivered:

• a broad based land use classification system compatible with the current risk assessment method that allows quantification of hazard levels and comparisons to be drawn between different areas,

• a systematic methodology and design for integrating aerospace data with cartographic (thematic) information,

• the development of a Spatial Data Model that can assist in strategic resource planning,

• the development of a GIS database that provides for the integration of disparate data through a continuous and consistent co-ordinate system.

• a potential future link for specific site based information to assist the management of emergency resources at incidents,

• a method which is repeatable and allows for periodic updates, and • a method, which is applicable in any and all urban centres across NSW.

The project report is comprehensive in detailing the processes of the methodology and shows the feasibility of using remotely sensed data within a computer mapping system to help the NSWFB understand the characteristics of urban communities and assist in analysing the fire service needs of that community.

Just as importantly for the Brigades, the report addresses not only the extensive benefits of the methodology, but provides a balanced assessment ofthe cost and impediments of applying the methodology across the state. Additionally, the project provided an opportunity for NSWFB staff to improve their knowledge of GIS technology. Professor Bruce Forster, a nationally and internationally recognised expert in Remote Sensing,

293


assisted Sunil in providing skills training to NSWFB staff in remote sensing theory and techniques.

This joint project between the NSWFB and UNSW has been the topic of presentations by Sunil at a number of international conferences and seminars in Sydney, Taiwan, Adelaide, Hawaii and Singapore.

The project could not have been completed without the support and co-operation of Land & Property Information, Bathurst (formerly the Land Information Centre) that provided much of the digital data, together with the valuable input of officers from both Bathurst City Council and Hornsby Shire Council.

294


Appendix VII

Softwares Used

Arcinfo (8.0.2)

Arc View (3.2a)

Map Info (6.0)

Erdas Imagine

ENVIIIDL

Plotter

Full Range Spectroradiometer

Purpose

Basic Spatial Analysis

Basic Spatial Analysis and Modelling

Basic Spatial Analysis Creation of Master Attribute Table

Classification

Registration and Classification and Hyperspectral Analysis

Plotting for Field Work

Hyperspectral Analysis

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