List of Contributors xiii
Author's Biography xvi
Preface xix
PART 1 INTRODUCTION 1
1 What is urban remote sensing? 3
Xiaojun Yang
1.1 Introduction 4
1.2 Remote sensing and urban studies 5
1.3 Remote sensing systems for urban areas 6
1.4 Algorithms and techniques for urban attribute extraction 7
1.5 Urban socioeconomic analyses 7
1.6 Urban environmental analyses 8
1.7 Urban growth and landscape change modeling 8
Summary and concluding remarks 9
References 10
PART 2 REMOTE SENSING SYSTEMS FOR URBAN AREAS 13
2 Use of archival Landsat imagery to monitor urban spatial growth 15
Xiaojun Yang
2.1 Introduction 16
2.2 Landsat program and imaging sensors 16
2.3 Mapping urban spatial growth in an American metropolis 18
2.4 Discussion 27
3 Limits and challenges of optical very-high-spatial-resolution satellite remote sensing for urban applications 35
Paolo Gamba, Fabio Dell'Acqua, Mattia Stasolla, Giovanna Trianni and Gianni Lisini
3.1 Introduction 36
3.2 Geometrical problems 36
3.3 Spectral problems 38
3.4 Mapping limits and challenges 38
3.5 Adding the time factor: VHR and change detection 39
3.6 A possible way forward 39
3.7 Building damage assessment 43
Conclusions 46
References 47
4 Potential of hyperspectral remote sensing for analyzing the urban environment 49
Sigrid Roessner, Karl Segl, Mathias Bochow, Uta Heiden, Wieke Heldens and Hermann Kaufmann
4.1 Introduction 50
4.2 Spectral characteristics of urban surface materials 50
4.3 Automated identification of urban surface materials 54
4.4 Results and discussion of their potential for urban analysis 58
References 60
5 Very-high-resolution spaceborne synthetic aperture radar and urban areas: looking into details of a complex environment 63
Fabio Dell'Acqua, Paolo Gamba and Diego Polli
5.1 Introduction 64
5.2 Before spaceborne high-resolution SAR 64
5.3 High-resolution SAR 66
Conclusions 70
Acknowledgments 70
References 70
6 3D building reconstruction from airborne lidar point clouds fused with aerial imagery 75
Jonathan Li and Haiyan Guan
6.1 Lidar-drived building models: related work 76
6.2 Our building reconstruction method 77
6.3 Results and discussion 85
Concluding remarks 89
Acknowledgments 90
References 90
PART 3 ALGORITHMS AND TECHNIQUES FOR URBAN ATTRIBUTE EXTRACTION 93
7 Parameterizing neural network models to improve land classification performance 95
Xiaojun Yang and Libin Zhou
7.1 Introduction 96
7.2 Fundamentals of neural networks 96
7.3 Internal parameters and classification accuracy 100
7.4 Training algorithm performance 105
7.5 Toward a systematic approach to image classification by neural networks 107
8 Characterizing urban subpixel composition using spectral mixture analysis 111
Rebecca Powell
8.1 Introduction 112
8.2 Overview of SMA implementation 112
8.3 Two case studies 118
Conclusions 124
Acknowledgments 126
References 126
9 An object-oriented pattern recognition approach for urban classification 129
Soe W. Myint and Douglas Stow
9.1 Introduction 130
9.2 Object-oriented classification 130
9.3 Data and study area 133
9.4 Methodology 134
9.5 Results and discussion 137
Conclusion 139
References 140
10 Spatial enhancement of multispectral images on urban areas 141
Bruno Aiazzi, Stefano Baronti, Luca Capobianco, Andrea Garzelli and Massimo Selva
10.1 Introduction 142
10.2 Multiresolution fusion scheme 144
10.3 Component substitution fusion scheme 144
10.4 Hybrid MRA - component substitution method 146
10.5 Results 147
Conclusions 152
References 152
11 Exploring the temporal lag between the structure and function of urban areas 155
Victor Mesev
11.1 Introduction 156
11.2 Micro and macro urban remote sensing 156
11.3 The temporal lag challenge 157
11.4 Structural-functional links 157
11.5 Temporal-structural-functional links 159
11.6 Empirical measurement of temporal lags 159
Conclusions 161
References 161
PART 4 URBAN SOCIOECONOMIC ANALYSES 163
12 A pluralistic approach to defining and measuring urban sprawl 165
Amnon Frenkel and Daniel Orenstein
12.1 Introduction 166
12.2 The diversity of definitions of sprawl 166
12.3 Historic forms of ''urban sprawl'' 168
12.4 Qualitative dimensions of sprawl and quantitative variables for measuring them 169
Conclusion 178
References 178
13 Small area population estimation with high-resolution remote sensing and lidar 183
Le Wang and Jose-Silvan Cardenas
13.1 Introduction 184
13.2 Study sites and data 185
13.3 Methodology 186
13.4 Results 187
Discussion and conclusions 192
Acknowledgments 192
References 192
14 Dasymetric mapping for population and sociodemographic data redistribution 195
James B. Holt and Hua Lu
14.1 Introduction 196
14.2 Dasymetric maps, dasymetric mapping, and areal interpolation 196
14.3 Application example: metropolitan Atlanta, Georgia 200
Conclusions 205
Acknowledgments 208
References 208
15 Who's in the dark-satellite based estimates of electrification rates 211
Christopher D.Elvidge, Kimberly E. Baugh, Paul C. Sutton, Budhendra Bhaduri, Benjamin T. Tuttle, Tilotamma Ghosh, Daniel Ziskin and Edward H. Erwin
15.1 Introduction 212
15.2 Methods 212
15.3 Results 213
15.4 Discussion 214
Conclusion 223
Acknowledgments 223
References 223
16 Integrating remote sensing and GIS for environmental justice research 225
Jeremy Mennis
16.1 Introduction 226
16.2 Environmental justice research 226
16.3 Remote sensing for environmental equity analysis 227
16.4 Integrating remotely sensed and other spatial data using GIS 229
16.5 Case study: vegetation and socioeconomic character in Philadelphia, Pennsylvania 230
Conclusion 234
References 235
PART 5 URBAN ENVIRONMENTAL ANALYSES 239
17 Remote sensing of high resolution urban impervious surfaces 241
Changshan Wu and Fei Yuan
17.1 Introduction 242
17.2 Impervious surface estimation 242
17.3 Pixel-based models for estimating high-resolution impervious surface 243
17.4 Object-based models for estimating high-resolution impervious surface 249
Conclusions 252
References 252
18 Use of impervious surface data obtained from remote sensing in distributed hydrological modeling of urban areas 255
Frank Canters, Okke Batelaan, Tim Van de Voorde, Jaros?aw Chorma?ski and Boud Verbeiren
18.1 Introduction 256
18.2 Spatially distributed hydrological modeling 256
18.3 Impervious surface mapping 257
18.4 The WetSpa model 258
18.5 Impact of different approaches for estimating impervious surface cover on runoff calculation and
prediction of peak discharges 261
Conclusions 270
Acknowledgments 270
References 270
19 Impacts of urban growth on vegetation carbon sequestration 275
Tingting Zhao
19.1 Introduction 276
19.2 Vegetation productivities and estimation 276
19.3 Data and analysis 277
19.4 Results 280
19.5 Discussion 283
Conclusions 284
Acknowledgments 284
References 285
20 Characterizing biodiversity in urban areas using remote sensing 287
Marcus Hedblom and Ulla Mortberg
20.1 Introduction 288
20.2 Remote sensing methods in urban biodiversity studies 288
20.3 Hierarchical levels and definitions of urban ecosystems 292
20.4 Using remote sensing to interpret effects of urbanization on species distribution 294
20.5 Long-term monitoring of biodiversity in urban green areas - methodology development 295
20.6 Applications in urban planning and management 296
Conclusions 297
Acknowledgments 300
References 300
21 Urbanweather, climate and air quality modeling: increasing resolution and accuracy using improved urbanmorphology 305
Susanne Grossman-Clarke, William L. Stefanov and Joseph A. Zehnder
21.1 Introduction 306
21.2 Physical approaches for the representation of urban areas in regional atmospheric models 306
21.3 Remotely sensed data as input for regional atmospheric models 307
21.4 Case studies investigating the effects of urbanization on weather, climate and air quality 311
Conclusions 316
Acknowledgments 316
References 316
PART 6 URBAN GROWTH AND LANDSCAPE CHANGE MODELING 321
22 Cellular automata and agent base models for urban studies: from pixels to cells to hexa-dpi's 323
Elisabete A. Silva
22.1 Introduction 324
22.2 Computation: the raster-pixel aproach 324
22.3 Cells: migrating from basic pixels 324
22.4 Agents: joining with cells 327
22.5 Cells and agents in a computer's ''artificial life'' 328
22.6 The hexa-dpi: closing the cycle in the digital age 330
Conclusions 332
References 332
23 Calibrating and validating cellular automata models of urbanization 335
Paul M. Torrens
23.1 Introduction 336
23.2 Calibration 336
23.3 Validating automata models 339
Conclusions 341
Acknowledgments 342
References 342
24 Agent-based urban modeling:simulating urban growth and subsequent landscape change in suzhou, china 347
Yichun Xie and Xining Yang
24.1 Introduction 348
24.2 Design, construction, calibration, and validation of ABM 348
24.3 Case study - desakota development in Suzhou, China 350
24.4 The Suzhou Urban Growth Agent Model 351
Summary and conclusion 354
References 355
25 Ecological modeling in urban environments: predicting changes in biodiversity in response to future urban development 359
Jeffrey Hepinstall-Cymerman
25.1 Introduction 360
25.2 Predicting changes in land cover and avian biodiversity for an area north of Seattle, Washington 362
Conclusions 365
Acknowledgments 367
References 368
26 Rethinking progress in urban analysis and modeling: models, metaphors, and meaning 371
Daniel Z. Sui
26.1 Introduction 372
26.2 Pepper's world hypotheses: the role of root metaphors in understanding reality 373
26.3 Progress in urban analysis and modeling: metaphors urban modelers live by 373
26.4 Models, metaphors, and the meaning of progress: further discussions 377
Summary and concluding remarks 377
Acknowledgments 378
Notes 378
References 378
Index 383
