Urban remote sensing: monitoring, synthesis and modeling in the urban environment (Oxford; Chichester; Hoboken, 2011). - ОГЛАВЛЕНИЕ / CONTENTS
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ОбложкаUrban remote sensing: monitoring, synthesis and modeling in the urban environment / ed. by X.Yang. - Oxford; Chichester; Hoboken: Wiley-Blackwell, 2011. - xx, 388 p.: ill., maps. - Incl. bibl. ref. - Ind.: p.383-388. - ISBN 978-0-4707-4958-6
 

Оглавление / Contents
 
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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