List of Contributors ......................................... xiii
Author's Biography ............................................ xvi
Preface ....................................................... xix
PART I 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
2.3.1 Research design ................................. 18
2.3.2 Data acquisition and land classification
scheme .......................................... 19
2.3.3 Image processing of remotely sensed data ........ 20
2.3.4 Change detection ................................ 21
2.3.5 Interpretation and analysis ..................... 25
2.3.6 Summary ......................................... 27
2.4 Discussion ............................................. 27
2.4.1 A generic urban growth monitoring workflow ...... 27
2.4.2 Image resolution and land use/cover
classification .................................. 27
2.4.3 Image preprocessing ............................. 28
2.4.4 Change detection methods ........................ 29
Summary and concluding remarks .............................. 30
References .................................................. 30
3 Limitsand 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
4.2.1 Categories of interest for material mapping ..... 50
4.2.2 Establishment of urban spectral libraries ....... 52
4.2.3 Determination of robust spectral features ....... 52
4.3 Automated identification of urban surface materials .... 54
4.3.1 State of the art of automated hyperspectral
image analysis .................................. 54
4.3.2 Processing system for automated material
mapping ......................................... 57
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
5.3.1 Extraction of single buildings .................. 66
5.3.2 Damage assessment with VHR SAR data ............. 67
5.3.3 Vulnerability mapping with VHR SAR data ......... 69
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
6.1.1 Building detection .............................. 76
6.1.2 Building reconstruction ......................... 76
6.2 Our building reconstruction method ..................... 77
6.2.1 Our strategy using fused data ................... 77
6.2.2 Building detection .............................. 78
6.2.3 Building reconstruction ......................... 81
6.3 Results and discussion ................................. 85
6.3.1 Datasets ........................................ 85
6.3.2 Results ......................................... 85
Concluding remarks .......................................... 89
Acknowledgments ............................................. 90
References .................................................. 90
PART 3 ALGORITHMSAND 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
7.2.1 Neural network types ............................ 96
7.2.2 Network topology ................................ 98
7.2.3 Neural training ................................. 98
7.3 Internal parameters and classification accuracy ....... 100
7.3.1 Experimental design ............................ 100
7.3.2 Remotely sensed data and land classification
scheme ......................................... 101
7.3.3 Network configuration and training ............. 101
7.3.4 Image classification and accuracy assessment ... 103
7.3.5 Interpretation and analysis .................... 103
7.3.6 Summary ........................................ 105
7.4 Training algorithm performance ........................ 105
7.4.1 Experimental design ............................ 105
7.4.2 Network training and image classification ...... 105
7.4.3 Performance evaluation ......................... 106
7.5 Toward a systematic approach to image classification
by neural networks .................................... 107
Future research directions ................................. 108
References ................................................. 108
8 Characterizing urban subpixel composition using spectral
mixture analysis ........................................... 111
Rebecca Powell
8.1 Introduction .......................................... 112
8.2 Overview of SMA implementation ........................ 112
8.2.1 SMA background ................................. 112
8.2.2 Endmember selection ............................ 114
8.2.3 SMA models ..................................... 116
8.2.4 Mapping fraction images ........................ 117
8.2.5 Model complexity ............................... 118
8.2.6 Accuracy assessment ............................ 118
8.3 Two case studies ...................................... 118
8.3.1 Evolution of urban morphology on a tropical
forest frontier ................................ 119
8.3.2 Discriminating urban tree and lawn cover in a
western US city ................................ 122
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
9.2.1 Image segmentation ............................. 130
9.2.2 Features ....................................... 131
9.2.3 Classifiers .................................... 132
9.3 Data and study area ................................... 133
9.4 Methodology ........................................... 134
9.4.1 Rule-based detection of swimming pools ......... 134
9.4.2 Nearest neighbor classifier to extract urban
land covers .................................... 136
9.5 Results and discussion ................................ 137
9.5.1 Decision rule set to extract pool .............. 137
9.5.2 Nearest neighbor classifier to extract urban
land covers .................................... 138
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
10.1.1 Component substitution fusion methods .......... 142
10.1.2 Multiresolution analysis fusion methods ........ 142
10.1.3 Injection model of details ..................... 143
10.1.4 Quality assessment ............................. 143
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
12.2.1 Definitions describing an urban spatial
development phenomenon ......................... 167
12.2.2 Definitions based on consequences of sprawl;
sprawl is as sprawl does ....................... 167
12.2.3 Definitions according to the social and/or
economic processes that give rise to particular
urban spatial development patterns ............. 168
12.2.4 Sprawl redux: focusing on the concerns of
remote sensing experts ......................... 168
12.3 Historic forms of "urban sprawl" ...................... 168
12.4 Qualitative dimensions of sprawl and quantitative
variables for measuring them .......................... 169
12.4.1 Criteria for a good sprawl measurement
variable ....................................... 170
12.4.2 What shall we measure? ......................... 170
12.4.3 Choosing among the sprawl measures ............. 173
Conclusion ................................................. 178
References ................................................. 178
13 Small area population estimation with high-resolution
remote sensing and lidar ................................... 18З
Le Wang and Jose-Silvan Cardenas
13.1 Introduction .......................................... 184
13.2 Study sites and data .................................. 185
13.3 Methodology ........................................... 186
13.3.1 Data preprocessing ............................. 186
13.3.2 Building extraction ............................ 186
13.3.3 Land use classification ........................ 186
13.3.4 Population estimation models ................... 187
13.3.5 Accuracy assessment ............................ 187
13.4 Results ............................................... 187
13.4.1 Building detection results ..................... 187
13.4.2 Land use classification results ................ 189
13.4.3 Population estimation results .................. 189
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
14.2.1 Ancillary data ................................. 197
14.2.2 Dasymetric mapping ............................. 197
14.2.3 Origins ........................................ 197
14.2.4 Dasymetric mapping variations .................. 198
14.3 Application example: metropolitan Atlanta, Georgia .... 200
14.3.1 Data ........................................... 200
14.3.2 Dasymetric maps ................................ 201
14.3.3 Areal interpolation ............................ 203
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
15.2.1 Data sources ................................... 212
15.2.2 Data processing ................................ 213
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
17.2.1 Pixel-based models ............................. 242
17.2.2 Object-based models ............................ 243
17.3 Pixel-based models for estimating high-resolution
impervious surface .................................... 243
17.3.1 Introduction ................................... 243
17.3.2 Study area and data ............................ 243
17.3.3 Methodology .................................... 244
17.3.4 Results ........................................ 248
17.4 Object-based models for estimating high-resolution
impervious surface .................................... 249
17.4.1 Study area and data preparation ................ 249
17.4.2 Object-oriented classification ................. 249
17.4.3 Results ........................................ 251
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 Voerde, Jarosław
Chormański 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
18.4.1 Surface runoff ................................. 258
18.4.2 Flow routing ................................... 260
18.4.3 Water balance .................................. 261
18.5 Impact of different approaches for estimating
impervious surface cover on runoff calculation and
prediction of peak discharges ......................... 261
18.5.1 Study area and data ............................ 261
18.5.2 Impervious surface mapping ..................... 262
18.5.3 Impact of land-cover distribution on
estimation of peak discharges .................. 264
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
19.2.1 Vegetation productivities ...................... 276
19.2.2 Estimation of vegetation productivities ........ 276
19.3 Data and analysis ..................................... 277
19.3.1 Identifying urban growth ....................... 277
19.3.2 Preparing vegetation maps and light-use
efficiency parameters .......................... 279
19.3.3 Estimating APAR, GPP and changes in GPP ........ 279
19.4 Results ............................................... 280
19.5 Discussion ............................................ 283
19.5.1 Urban growth in the South Atlantic division .... 283
19.5.2 Impacts of urban growth on vegetation
productivities ................................. 283
Conclusions ................................................ 284
Acknowledgments ............................................ 284
References ................................................. 285
20 Characterizing biodiversity in urban areas using remote
sensing .................................................... 287
Marcus Hedblom and Ulla Mörtberg
20.1 Introduction .......................................... 288
20.2 Remote sensing methods in urban biodiversity
studies ............................................... 288
20.2.1 Direct approaches .............................. 289
20.2.2 Indirect approaches ............................ 289
20.3 Hierarchical levels and definitions of urban
ecosystems ............................................ 292
20.3.1 Flora and fauna along urban gradients .......... 292
20.3.2 Using remote sensing to quantify urbanization
patterns ....................................... 293
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 Urban weather, climate and air quality modeling:
increasing resolution and accuracy using improved urban
morphology ................................................. 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
21.2.1 Roughness approach ............................. 307
21.2.2 Single-layer urban canopy approaches ........... 307
21.2.3 Multilayer urban canopy approaches ............. 307
21.3 Remotely sensed data as input for regional
atmospheric models .................................... 307
21.3.1 Urban land use and land cover data ............. 308
21.3.2 Building data .................................. 310
21.4 Case studies investigating the effects of
urbanization on weather, climate and air quality ...... 311
21.4.1 Studies investigating effects of urban land
use and land cover on meteorology and air
quality ........................................ 311
21.4.2 Case study for Phoenix ......................... 312
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
23.2.1 Conditional transition rules ................... 336
23.2.2 Weighted transition rules ...................... 337
23.2.3 Seeding and initial conditions ................. 337
23.2.4 Specifying the value of calibration
parameters ..................................... 338
23.2.5 Coupling automata and exogenous models ......... 338
23.2.6 Automatic calibration .......................... 339
23.3 Validating automata models ............................ 339
23.3.1 Pixel matching ................................. 339
23.3.2 Feature and pattern recognition ................ 340
23.3.3 Running models exhaustively .................... 341
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
24.4.1 The model design ............................... 351
24.4.2 The model construction ......................... 352
24.4.3 The model calibration .......................... 352
24.4.4 The model validation ........................... 353
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
25.1.1 Using urban remote sensing to develop land
cover maps for ecological modeling ............. 360
25.1.2 One example of ecological modeling: modeling
species habitat ................................ 360
25.1.3 Predicting future land use and land cover ...... 361
25.1.4 Integrating models to predict future
biodiversity ................................... 362
25.2 Predicting changes in land cover and avian
biodiversity for an area north of Seattle,
Washington ............................................ 362
25.2.1 Land cover maps ................................ 362
25.2.2 Land use change model .......................... 362
25.2.3 Land cover change model ........................ 364
25.2.4 Avian biodiversity model ....................... 365
25.2.5 Predicted land cover change for study area ..... 365
25.2.6 Predicted changes in avian biodiversity for
study area ..................................... 365
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
26.3.1 Cities as forms - the spatial morphology
tradition ...................................... 374
26.3.2 Cities as machines - the social physics
tradition ...................................... 375
26.3.3 Cities as organisms - the social biology
tradition ...................................... 375
26.3.4 Cities as arenas - the spatial event
tradition ...................................... 376
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
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