Integration of GIS and Remote Sensing

by Mesev, Victor

• ISBN13: 9780470864098
• ISBN10: 0470864095
• eBook ISBN(s): 9780470864111
• Edition: 1st
• Format: Hardcover
• Copyright: 2007-12-26
• Publisher: Wiley
• More Book Details
• Purchase Benefits

Summary

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.

Author Biography

Dr. Victor Mesev. Chair, Department of Geography, Florida State University, USA

Table of Contents

Series Foreword	p. xi
Preface	p. xiii
List of Contributors	p. xv
GIS and remote sensing integration: in search of a definition	p. 1
Introduction	p. 1
In search of a definition	p. 2
Evolutionary integration	p. 4
Methodological integration	p. 5
Outline of the book	p. 8
Conclusions	p. 13
Integration taxonomy and uncertainty	p. 17
Introduction	p. 17
Taxonomy issues	p. 19
Taxonomy of GIS operators	p. 19
Taxonomy of image analysis operators in remote sensing	p. 20
An integrated taxonomy	p. 20
Uncertainty issues	p. 22
Uncertainty in geographic information	p. 22
Uncertainty in the integration of GIS and remote sensing	p. 23
Modelling positional and thematic error in the integration of remote sensing and GIS	p. 27
Positional and thematic uncertainties	p. 27
Problem formulation	p. 28
Modelling positional uncertainty	p. 29
Thematic uncertainties of a classified image	p. 34
Modelling the combined positional and thematic uncertainties	p. 35
Conclusions	p. 38
Data fusion related to GIS and remote sensing	p. 43
Introduction	p. 43
Why do we need GIS-remote sensing fusion?	p. 43
Remote sensing output to GIS	p. 44
GIS input to remote sensing interpretation algorithms	p. 45
Example: urban planning check and update	p. 46
Problems in GIS-remote sensing data fusion	p. 47
Lack of consistent standards	p. 48
Inconsistency of GIS-remote sensing accuracy, legends and scales	p. 49
Different nature of the two sources	p. 51
Need for information rather than data fusion	p. 53
Example: population mapping through remote sensing	p. 54
Present and future solutions	p. 55
Multiscale analysis	p. 55
Fusion techniques	p. 57
Conclusions	p. 60
Integration of remote sensing and GIS into a change detection module	p. 61
The importance of scale in remote sensing and GIS and its implications for data integration	p. 69
Introduction	p. 69
Data models and scales of measurement	p. 70
Raster imagery	p. 70
Vector data	p. 74
Scales of spatial variation	p. 75
Spatial variation in raster data	p. 75
Scales of variation in vector data	p. 79
Processes in the environment	p. 79
Remote sensing and GIS data integration	p. 80
Overlay and regression	p. 80
Remote sensing classification of land cover	p. 84
Conclusion	p. 87
Of patterns and processes: spatial metrics and geostatistics in urban analysis	p. 93
Introduction	p. 93
Geostatistics	p. 95
Spatial metrics	p. 96
Examples	p. 100
Data preparation	p. 100
Linkage from land cover to land use	p. 103
Linking urban form to population density	p. 107
Linking characteristics of spatial patterns and processes	p. 109
Conclusion	p. 112
Using remote sensing and GIS integration to identify spatial characteristics of sprawl at the building-unit level	p. 117
Introduction	p. 117
Sprawl in the remote sensing and GIS literature	p. 118
Definitions of sprawl	p. 119
Spatial characteristics of sprawl at a metropolitan level	p. 122
Spatial characteristics of sprawl at a submetropolitan level	p. 125
Integrating remote sensing and GIS for sprawl research	p. 127
Spatial characteristics of sprawl at a building-unit level	p. 133
A practical building-unit level model for analysing sprawl	p. 135
Urban density	p. 138
Leapfrog	p. 138
Segregated land use	p. 140
Highway strip	p. 141
Community node inaccessibility	p. 141
Normalizing municipal sprawl indicator measures	p. 142
Future benefits of integrating remote sensing and GIS in sprawl research	p. 143
Remote sensing applications in urban socio-economic analysis	p. 149
Introduction	p. 149
Principles of urban socio-economic studies using remote sensing technologies	p. 150
Socio-economic information estimation	p. 153
Population estimation	p. 153
Employment estimation	p. 155
GDP estimation	p. 155
Electrical power consumption estimation	p. 156
Socio-economic activity modelling	p. 157
Population interpolation	p. 157
Socio-economic index generation	p. 158
Understanding and modelling socio-economic phenomena	p. 159
Advantages and limitations of remote sensing technologies in socioeconomic applications	p. 167
Socio-economic information estimation	p. 167
Socio-economic information modelling	p. 168
Conclusions	p. 168
Integrating remote sensing, GIS and spatial modelling for sustainable urban growth management	p. 173
Introduction	p. 173
Research methodology	p. 175
Study area	p. 176
Data acquisition and collection	p. 176
Satellite image processing	p. 178
Change analysis	p. 180
Spatial statistical analysis	p. 181
Dynamic spatial modelling	p. 182
Results and discussion	p. 184
Urban growth	p. 184
Driving force	p. 187
Future growth scenario simulation	p. 191
Conclusions	p. 193
An integrative GIS and remote sensing model for place-based urban vulnerability analysis	p. 199
Introduction	p. 199
Analysis of urban vulnerability: what is it all about?	p. 201
A conceptual framework for place-based analysis of urban vulnerability	p. 202
Integrating GIS and remote sensing into vulnerability analysis	p. 205
A GIS-remote sensing place-based model for urban vulnerability analysis	p. 206
An illustrative example of model application	p. 208
Study area	p. 209
Data	p. 209
Identifying vulnerability hot spots	p. 210
Deriving remote sensing measures of urban morphology in Los Angeles	p. 212
Deriving an index of wealth for Los Angeles County	p. 216
Spatial filtering of variables	p. 217
Generating place-based knowledge of urban vulnerability in Los Angeles	p. 218
To what extent do model results conform to universal knowledge of vulnerability?	p. 222
Conclusions	p. 224
Using GIS and remote sensing for ecological mapping and monitoring	p. 233
Introduction	p. 233
Integration of GIS and remote sensing in ecological research	p. 237
GIS data used in ecological applications	p. 237
Gradient analysis	p. 238
Climate	p. 240
Topography	p. 241
Remotely sensed data for ecological applications	p. 242
Spectral enhancements	p. 243
Land cover	p. 244
Habitat structure	p. 245
Biophysical processes	p. 246
Species distribution models	p. 247
Biodiversity mapping	p. 251
Change detection	p. 253
Case study: using GIS and remote sensing for large-area change detection and efficient map updating	p. 253
Conclusions	p. 260
Remote sensing and GIS for ephemeral wetland monitoring and sustainability in southern Mauritania	p. 269
Introduction	p. 269
Ephemeral wetlands	p. 269
Remote sensing of ephemeral wetlands	p. 270
Ephemeral wetlands in Mauritania	p. 272
Data and processing	p. 274
Results	p. 279
Implications for management	p. 283
Conclusions	p. 284
Index	p. 291
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