Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment / Edition 1

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Urban Remote Sensing is designed for upper level undergraduates, graduates, researchers and practitioners, and will be the first to be written with the clear focus on the development of remote sensing technology for monitoring, synthesis and modeling in the urban environment. It covers four major areas: the use of high-resolution satellite imagery or alternative sources of image date (such as high-resolution SAR and LIDAR) for urban feature extraction; the development of improved image processing algorithms and techniques for deriving accurate and consistent information on urban attributes from remote sensor data; the development of analytical techniques and methods for deriving indicators of socioeconomic and environmental conditions that prevail within urban landscape; and the development of remote sensing and spatial analytical techniques for urban growth simulation and predictive modeling.

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Editorial Reviews

From the Publisher
“This book is a great addition to the very few books on urban remote sensing.”  (Photogrammetric Engineering and Remote Sensing, 1 March 2013)

"This excellent textbook provides a thorough grounding in the uses and types of remote sensing techniques employed for analyzing population, energy use, and other aspects of the urban environment." (Book News, 1 August 2011)

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Product Details

  • ISBN-13: 9780470749586
  • Publisher: Wiley
  • Publication date: 6/15/2011
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 408
  • Product dimensions: 8.80 (w) x 11.30 (h) x 1.00 (d)

Table of Contents

List of Contributors xiii

Author’s Biography xvi

Preface xix


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


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


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


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


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, Jaros³aw Chormañski 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 Mörtberg

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


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

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