Integration of GIS and Remote Sensing (Mastering Gis: Technology, Applications an Management)

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Integration of GIS and Remote Sensing (Mastering Gis: Technology, Applications an Management)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 296 p.
  • 言語 ENG
  • 商品コード 9780470864104
  • DDC分類 910.285

Full Description


In an age of unprecedented proliferation of data from disparate sources the urgency is to create efficient methodologies that can optimise data combinations and at the same time solve increasingly complex application problems. Integration of GIS and Remote Sensing explores the tremendous potential that lies along the interface between GIS and remote sensing for activating interoperable databases and instigating information interchange. It concentrates on the rigorous and meticulous aspects of analytical data matching and thematic compatibility - the true roots of all branches of GIS/remote sensing applications. However closer harmonization is tempered by numerous technical and institutional issues, including scale incompatibility, measurement disparities, and the inescapable notion that data from GIS and remote sensing essentially represent diametrically opposing conceptual views of reality. The first part of the book defines and characterises GIS and remote sensing and presents the reader with an awareness of the many scale, taxonomical and analytical problems when attempting integration.The second part of the book moves on to demonstrate the benefits and costs of integration across a number of human and environmental applications. This book is an invaluable reference for students and professionals dealing not only with GIS and remote sensing, but also computer science, civil engineering, environmental science and urban planning within the academic, governmental and commercial/business sectors.

Table of Contents

Series Foreword                                    xi
Preface xiii
List of Contributors xv
1 GIS and remote sensing integration: in 1
search of a definition
Victor Mesev and Alexandra Walrath
1.1 Introduction 1
1.2 In search of a definition 2
1.2.1 Evolutionary integration 4
1.2.2 Methodological integration 5
1.3 Outline of the book 8
1.4 Conclusions 13
2 Integration taxonomy and uncertainty 17
Manfred Ehlers
2.1 Introduction 17
2.2 Taxonomy issues 19
2.2.1 Taxonomy of GIS operators 19
2.2.2 Taxonomy of image analysis 20
operators in remote sensing
2.2.3 An integrated taxonomy 20
2.3 Uncertainty issues 22
2.3.1 Uncertainty in geographic 22
information
2.3.2 Uncertainty in the integration of 23
GIS and remote sensing
2.4 Modelling positional and thematic 27
error in the integration of remote
sensing and GIS
2.4.1 Positional and thematic 27
uncertainties
2.4.2 Problem formulation 28
2.4.3 Modelling positional uncertainty 29
2.4.4 Thematic uncertainties of a 34
classified image
2.4.5 Modelling the combined positional 35
and thematic uncertainties
2.5 Conclusions 38
3 Data fusion related to GIS and remote 43
sensing
Paolo Gamba and Fabio Dell'Acqua
3.1 Introduction 43
3.2 Why do we need GIS羊emote sensing 43
fusion?
3.2.1 Remote sensing output to GIS 44
3.2.2 GIS input to remote sensing 45
interpretation algorithms
3.2.3 Example: urban planning check and 46
update
3.3 Problems in GIS羊emote sensing data 47
fusion
3.3.1 Lack of consistent standards 48
3.3.2 Inconsistency of GIS羊emote 49
sensing accuracy, legends and scales
3.3.3 Different nature of the two 51
sources
3.3.4 Need for information rather than 53
data fusion
3.3.5 Example: population mapping 54
through remote sensing
3.4 Present and future solutions 55
3.4.1 Multiscale analysis 55
3.4.2 Fusion techniques 57
3.5 Conclusions 60
3.5.1 Integration of remote sensing and 61
GIS into a change detection module
4 The importance of scale in remote sensing 69
and GIS and its implications for data
integration
Peter M. Atkinson
4.1 Introduction 69
4.2 Data models and scales of measurement 70
4.2.1 Raster imagery 70
4.2.2 Vector data 74
4.3 Scales of spatial variation 75
4.3.1 Spatial variation in raster data 75
4.3.2 Scales of variation in vector data 79
4.3.3 Processes in the environment 79
4.4 Remote sensing and GIS data 80
integration
4.4.1 Overlay and regression 80
4.4.2 Remote sensing classification of 84
land cover
4.5 Conclusion 87
5 Of patterns and processes: spatial metrics 93
and geostatistics in urban analysis
XiaoHang Liu and Martin Herold
5.1 Introduction 93
5.2 Geostatistics 95
5.3 Spatial metrics 96
5.4 Examples 100
5.4.1 Data preparation 100
5.4.2 Linkage from land cover to land 103
use
5.4.3 Linking urban form to population 107
density
5.4.5 Linking characteristics of 109
spatial patterns and processes
5.5 Conclusion 112
6 Using remote sensing and GIS integration to 117
identify spatial characteristics of sprawl at
the building-unit level
John Hasse
6.1 Introduction 117
6.2 Sprawl in the remote sensing and GIS 118
literature
6.2.1 Definitions of sprawl 119
6.2.2 Spatial characteristics of sprawl 122
at a metropolitan level
6.2.3 Spatial characteristics of sprawl 125
at a submetropolitan level
6.3 Integrating remote sensing and GIS 127
for sprawl research
6.4 Spatial characteristics of sprawl at 133
a building-unit level
6.5 A practical building-unit level model 135
for analysing sprawl
6.5.1 Urban density 138
6.5.2 Leapfrog 138
6.5.3 Segregated land use 140
6.5.4 Highway strip 141
6.5.5 Community node inaccessibility 141
6.5.6 Normalizing municipal sprawl 142
indicator measures
6.6 Future benefits of integrating remote 143
sensing and GIS in sprawl research
7 Remote sensing applications in urban 149
socio-economic analysis
Changshan Wu
7.1 Introduction 149
7.2 Principles of urban socio-economic 150
studies using remote sensing technologies
7.3 Socio-economic information estimation 153
7.3.1 Population estimation 153
7.3.2 Employment estimation 155
7.3.3 GDP estimation 155
7.3.4 Electrical power consumption 156
estimation
7.4 Socio-economic activity modelling 157
7.4.1 Population interpolation 157
7.4.2 Socio-economic index generation 158
7.4.3 Understanding and modelling 159
socio-economic phenomena
7.5 Advantages and limitations of remote 167
sensing technologies in socio-economic
applications
7.5.1 Socio-economic information 167
estimation
7.5.2 Socio-economic information 168
modelling
7.6 Conclusions 168
8 Integrating remote sensing, GIS and spatial 173
modelling for sustainable urban growth
management
Xiaojun Yang
8.1 Introduction 173
8.2 Research methodology 175
8.2.1 Study area 176
8.2.2 Data acquisition and collection 176
8.2.3 Satellite image processing 178
8.2.4 Change analysis 180
8.2.5 Spatial statistical analysis 181
8.2.6 Dynamic spatial modelling 182
8.3 Results and discussion 184
8.3.1 Urban growth 184
8.3.2 Driving force 187
8.3.3 Future growth scenario simulation 191
8.4 Conclusions 193
9 An integrative GIS and remote sensing model 199
for place-based urban vulnerability analysis
Tarek Rashed, John Weeks, Helen Couclelis
and Martin Herold
9.1 Introduction 199
9.2 Analysis of urban vulnerability: what 201
is it all about?
9.3 A conceptual framework for 202
place-based analysis of urban
vulnerability
9.4 Integrating GIS and remote sensing 205
into vulnerability analysis
9.5 A GIS睦emote sensing place-based 206
model for urban vulnerability analysis
9.6 An illustrative example of model 208
application
9.6.1 Study area 209
9.6.2 Data 209
9.6.3 Identifying vulnerability hot 210
spots
9.6.4 Deriving remote sensing measures 212
of urban morphology in Los Angeles
9.6.5 Deriving an index of wealth for 216
Los Angeles County
9.6.6 Spatial filtering of variables 217
9.6.7 Generating place-based knowledge 218
of urban vulnerability in Los Angeles
9.6.8 To what extent do model results 222
conform to universal knowledge of
vulnerability?
9.7 Conclusions 224
10 Using GIS and remote sensing for ecological 233
mapping and monitoring
Jennifer A. Miller and John Rogan
10.1 Introduction 233
10.2 Integration of GIS and remote 237
sensing in ecological research
10.3 GIS data used in ecological 237
applications
10.3.1 Gradient analysis 238
10.3.2 Climate 240
10.3.3 Topography 241
10.4 Remotely sensed data for ecological 242
applications
10.4.1 Spectral enhancements 243
10.4.2 Land cover 244
10.4.3 Habitat structure 245
10.4.4 Biophysical processes 246
10.5 Species distribution models 247
10.5.1 Biodiversity mapping 251
10.6 Change detection 253
10.6.1 Case study: using GIS and remote 253
sensing for large-area change detection
and efficient map updating
10.7 Conclusions 260
11 Remote sensing and GIS for ephemeral wetland 269
monitoring and sustainability in southern
Mauritania
Tara Shine and Victor Mesev
11.1 Introduction 269
11.1.1 Ephemeral wetlands 269
11.1.2 Remote sensing of ephemeral 270
wetlands
11.2 Ephemeral wetlands in Mauritania 272
11.2.1 Data and processing 274
11.2.2 Results 279
11.2.3 Implications for management 283
11.3 Conclusions 284
Index 291