Remote sensing and GIS integration

Remote Sensing and GIS Integration Theories, Methods, and Applications Qihao Weng, Ph.D New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2010 by The McGraw-Hill Companies, Inc All rights reserved Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher ISBN: 978-0-07-160654-7 MHID: 0-07-160654-8 The material in this eBook also appears in the print version of this title: ISBN: 978-0-07-160653-0, MHID: 0-07-160653-X All trademarks are trademarks of their respective owners Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark Where such designations 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contract, tort or otherwise Contents Foreword ix Preface xiii Acknowledgments xvii Principles of Remote Sensing and Geographic Information Systems (GIS) 1.1 Principles of Remote Sensing 1.1.1 Concept of Remote Sensing 1.1.2 Principles of Electromagnetic Radiation 1.1.3 Characteristics of Remotely Sensed Data 1.1.4 Remote Sensing Data Interpretation and Analysis 1.2 Principles of GIS 1.2.1 Scope of Geographic Information System and Geographic Information Science 1.2.2 Raster GIS and Capabilities 1.2.3 Vector GIS and Capabilities 1.2.4 Network Data Model 1.2.5 Object-Oriented Data Model References Integration of Remote Sensing and Geographic Information Systems (GIS) 2.1 Methods for the Integration between Remote Sensing and GIS 2.1.1 Contributions of Remote Sensing to GIS 2.1.2 Contributions of GIS to Remote Sensing 2.1.3 Integration of Remote Sensing and GIS for Urban Analysis 2.2 Theories of the Integration 2.2.1 Evolutionary Integration 2.2.2 Methodological Integration 2.2.3 The Integration Models 2.3 Impediments to Integration and Probable Solutions 1 21 21 23 25 29 30 31 43 43 44 46 49 51 51 52 53 57 iii iv Contents 2.3.1 Conceptual Impediments and Probable Solutions 2.3.2 Technical Impediments and Probable Solutions 2.4 Prospects for Future Developments 2.4.1 Impacts of Computer, Network, and Telecommunications Technologies 2.4.2 Impacts of the Availability of Very High Resolution Satellite Imagery and LiDAR Data 2.4.3 Impacts of New Image-Analysis Algorithms 2.5 Conclusions References Urban Land Use and Land Cover Classification 3.1 Incorporation of Ancillary Data for Improving Image Classification Accuracy 3.2 Case Study: Landsat Image-Housing Data Integration for LULC Classification in Indianapolis 3.2.1 Study Area 3.2.2 Datasets Used 3.2.3 Methodology 3.2.4 Accuracy Assessment 3.3 Classification Result by Using Housing Data at the Pre-Classification Stage 3.4 Classification Result by Integrating Housing Data during the Classification 3.5 Classification Result by Using Housing Data at the Post-Classification Stage 3.6 Summary References Urban Landscape Characterization and Analysis 4.1 Urban Landscape Analysis with Remote Sensing 4.1.1 Urban Materials, Land Cover, and Land Use 4.1.2 The Scale Issue 4.1.3 The Image “Scene Models” 4.1.4 The Continuum Model of Urban Landscape 4.1.5 Linear Spectral Mixture Analysis (LSMA) 57 61 68 68 71 73 78 78 91 92 95 95 96 98 105 105 109 111 112 114 117 118 118 120 121 121 123 Contents 4.2 Case Study: Urban Landscape Patterns and Dynamics in Indianapolis 4.2.1 Image Preprocessing 4.2.2 Image Endmember Development 4.2.3 Extraction of Impervious Surfaces 4.2.4 Image Classification 4.2.5 Urban Morphologic Analysis Based on the V-I-S Model 4.2.6 Landscape Change and the V-I-S Dynamics 4.2.7 Intra-Urban Variations and the V-I-S Compositions 4.3 Discussion and Conclusions References Urban Feature Extraction 5.1 Landscape Heterogeneity and Per-Field and Object-Based Image Classifications 5.2 Case Study: Urban Feature Extraction from High Spatial-Resolution Satellite Imagery 5.2.1 Data Used 5.2.2 Image Segmentation 5.2.3 Rule-Based Classification 5.2.4 Post-Classification Refinement and Accuracy Assessment 5.2.5 Results of Feature Extraction 5.3 Discussion 5.4 Conclusions References Building Extraction from LiDAR Data 6.1 The LiDAR Technology 6.2 Building Extraction 6.3 Case Study 6.3.1 Datasets 6.3.2 Generation of the Normalized Height Model 6.3.3 Object-Oriented Building Extraction 6.3.4 Accuracy Assessment 6.3.5 Strategies for Object-Oriented Building Extraction 6.3.6 Error Analysis 6.4 Discussion and Conclusions References 125 125 125 127 130 130 134 139 157 160 165 166 169 169 169 170 171 173 173 178 179 183 185 186 188 188 189 192 196 197 201 205 206 v vi Contents Urban Land Surface Temperature Analysis 7.1 Remote Sensing Analysis of Urban Land Surface Temperatures 7.2 Case Study: Land-Use Zoning and LST Variations 7.2.1 Satellite Image Preprocessing 7.2.2 LULC Classification 7.2.3 Spectral Mixture Analysis 7.2.4 Estimation of LSTs 7.2.5 Statistical Analysis 7.2.6 Landscape Metrics Computation 7.2.7 Factors Contributing to LST Variations 7.2.8 General Zoning, Residential Zoning, and LST Variations 7.2.9 Seasonal Dynamics of LST Patterns 7.3 Discussion and Conclusions: Remote Sensing–GIS Integration in Urban Land-Use Planning References 209 210 211 211 212 213 215 218 219 225 234 237 240 242 Surface Runoff Modeling and Analysis 8.1 The Distributed Surface Runoff Modeling 8.2 Study Area 8.3 Integrated Remote Sensing–GIS Approach to Surface Runoff Modeling 8.3.1 Hydrologic Parameter Determination Using GIS 8.3.2 Hydrologic Modeling within the GIS 8.4 Urban Growth in the Zhujiang Delta 8.5 Impact of Urban Growth on Surface Runoff 8.6 Impact of Urban Growth on Rainfall-Runoff Relationship 8.7 Discussion and Conclusions References 247 248 251 Assessing Urban Air Pollution Patterns 9.1 Relationship between Urban Air Pollution and Land-Use Patterns 9.2 Case Study: Air Pollution Pattern in Guangzhou, China, 1980–2000 9.2.1 Study Area: Guangzhou, China 9.2.2 Data Acquisition and Analysis 267 253 253 257 257 259 261 263 264 268 270 270 272 Contents 10 11 9.2.3 Air Pollution Patterns 9.2.4 Urban Land Use and Air Pollution Patterns 9.2.5 Urban Thermal Patterns and Air Pollution 9.3 Summary 9.4 Remote Sensing–GIS Integration for Studies of Urban Environments References 275 Population Estimation 10.1 Approaches to Population Estimation with Remote Sensing–GIS Techniques 10.1.1 Measurements of Built-Up Areas 10.1.2 Counts of Dwelling Units 10.1.3 Measurement of Different Land-Use Areas 10.1.4 Spectral Radiance 10.2 Case Study: Population Estimation Using Landsat ETM+ Imagery 10.2.1 Study Area and Datasets 10.2.2 Methods 10.2.3 Result of Population Estimation Based on a Non-Stratified Sampling Method 10.2.4 Result of Population Estimation Based on Stratified Sampling Method 10.3 Discussion 10.4 Conclusions References 295 Quality of Life Assessment 11.1 Assessing Quality of Life 11.1.1 Concept of QOL 11.1.2 QOL Domains and Models 11.1.3 Application of Remote Sensing and GIS in QOL Studies 11.2 Case Study: QOL Assessment in Indianapolis with Integration of Remote Sensing and GIS 11.2.1 Study Area and Datasets 11.2.2 Extraction of Socioeconomic Variables from Census Data 11.2.3 Extraction of Environmental Variables 283 288 291 291 292 296 296 299 300 301 303 303 303 308 313 320 321 322 327 328 328 329 330 331 331 332 332 vii viii Contents 11.2.4 Statistical Analysis and Development of a QOL Index 11.2.5 Geographic Patterns of Environmental and Socioeconomic Variables 11.2.6 Factor Analysis Results 11.2.7 Result of Regression Analysis 11.3 Discussion and Conclusions References 12 13 Urban and Regional Development 12.1 Regional LULC Change 12.1.1 Definitions of Land Use and Land Cover 12.1.2 Dynamics of Land Use and Land Cover and Their Interplay 12.1.3 Driving Forces in LULC Change 12.2 Case Study: Urban Growth and Socioeconomic Development in the Zhujiang Delta, China 12.2.1 Urban Growth Analysis 12.2.2 Driving Forces Analysis 12.2.3 Urban LULC Modeling 12.2.4 Urban Growth in the Zhujiang Delta, 1989–1997 12.2.5 Urban Growth and Socioeconomic Development 12.2.6 Major Types of Urban Expansion 12.2.7 Summary 12.3 Discussion: Integration of Remote Sensing and GIS for Urban Growth Analysis References Public Health Applications 13.1 WNV Dissemination and Environmental Characteristics 13.2 Case Study: WNV Dissemination in Indianapolis, 2002–2007 13.2.1 Data Collection and Preprocessing 13.2.2 Plotting Epidemic Curves 13.2.3 Risk Area Estimation 13.2.4 Discriminant Analysis 13.2.5 Results 13.3 Discussion and Conclusions References Index 333 334 335 341 342 343 345 345 346 346 348 350 350 350 351 352 355 357 359 359 360 363 364 365 365 368 368 368 369 377 379 383 Foreword W hen Qihao Weng asked me to write a foreword to his book, I had two immediate reactions I was, of course, at first flattered and honored by his invitation but when I read further in his letter I shockingly realized that 20 years had gone by since Geoffrey Edwards, Yvan Bédard, and I published our paper on the integration of remote sensing and GIS in Photogrammetric Engineering & Remote Sensing (PE&RS) Twenty years is a long time in a fast-moving field such as ours that is concerned with geospatial data collection, management, analysis, and dissemination I am very excited that Qihao had the enthusiasm, the stamina, and, last but not the least, the time to compile a comprehensive summary of the status of GIS/remote sensing integration today When Geoff, Yvan, and I wrote our paper it was not only the first partially theoretical article on the integration of the two very separate technologies at that time, but it was also meant to be a statement for the forthcoming National Center for Geographic Information and Analysis (NCGIA) Initiative 12: Integration of Remote Sensing and GIS The leading scientists for this initiative—Jack Estes, Dave Simonett, Jeff Star, and Frank Davis—were all from the University of California at Santa Barbara NCGIA site, so I thought that we had to something to prove our value to this group of principal scientists To my delight, we achieved the desired result Actually, the making of this paper started to some degree by accident Geoff Edwards discovered that he and I had both submitted papers with very similar titles and content to the GIS National Conference in Ottawa and asked me if we could combine our efforts I immediately agreed and saw the chance to publish a research article in the upcoming special PE&RS issue on GIS Geoff and Yvan worked at Laval University in Quebec, I was at the University of Maine in Orono, and, at this very important time, we all worked with Macintoshes and sent our files back and forth through the Internet without being concerned with data conversion issues When I look back upon those times, I ponder the research questions that we thought were the most pressing ones 20 years ago How many ix FIGURE 3.5 Centroids of Census block 10 15 20 km Block centroid of block N FIGURE 3.6 Housing surface generated by IDW interpolation N High : 92641.6 10 15 20 km Low : FIGURE 3.7 Rasterized housing density N 12 High : 9998 16 km Low : A D B E C F FIGURE 3.8 Examples of typical land use: (a) commercial, (b) industrial, (c) transportation, (d ) high-density residential, (e) medium-density residential, (f ) low-density residential LULC types Water Urban Residential-H Residential-M Residential-L Grass Forest Crop land N 12 16 km FIGURE 3.9 LULC classification image based on intergration of housing and the ETM+ image at the pre-classification stage LULC types Water Urban Residential-H Residential-M Residential-L Grass Forest Crop land LULC types Water Urban Residential-H Residential-M Residential-L Grass Forest Crop land N 12 N 16 km FIGURE 3.10 LULC map based on integration of housing surface into the ETM+ image as an additional layer (during the classification) 12 16 km FIGURE 3.11 LULC map based on integration of housing data at the post-classification stage LULC, 1995 Commercial and industrial Residential Forest Grassland Pasture and agriculture Water LULC, 1991 Commercial and industrial Residential Forest Grassland Pasture and agriculture Water N N W E S 6 S 12 Miles 12 Miles LULC, 2000 Commercial and industrial Residential Forest Grassland Pasture and agriculture Water N W E S E W 12 Miles FIGURE 4.6 LULC maps of 1991, 1995, and 2000 (Adapted from Weng and Lu, 2006.) Impervious fraction value, 1991 0.0–0.1 0.1–0.3 0.3–0.6 0.6–1.0 N W E S 6 12 Miles Impervious fraction value, 1995 0.0–0.1 0.1–0.3 0.3–0.6 0.6–1.0 N E W S 6 12 Miles Impervious fraction value, 2000 0.0–0.1 0.1–0.3 0.3–0.6 0.6–1.0 N E W S 6 12 Miles FIGURE 4.10 Distribution of impervious coverage by year (Adapted from Weng and Lu, 2009.) Original image Impervious surface Building Road Building Building extraction Road extraction Building extraction Original image Impervious surface Road Road extraction FIGURE 5.1 Feature extraction from FIGURE 5.2 Feature extraction from CBD residential area area FIGURE 6.1 Study area in downtown Indianapolis, Indiana N E W S Legend Height (feet) 816 –67 Building extraction area 375 750 1,500 2,250 3,000 Meters N E W S Legend Height (feet) 816 Reference buildings 375 750 1,500 2,250 3,000 Meters N E W S First-return DSM Legend Elevation value (feet) High : 1455.83 Low : 507.63 Last-return DSM Elevation value (feet) High : 1404.96 Low : 558.448 0.1 0.05 FIGURE 6.3 0.1 0.2 0.3 0.4 Miles A comparison of the first return DSM and last return DSM N W E S Legend Raw NHM (unit: feet) –67.73–2.00 –1.99–0 0–40.00 40.01–695.09 Legend Raw NHM (unit: feet) High : 695.09 Low : 0.00 2,000 1,000 FIGURE 6.4 2,000 4,000 6,000 The original and corrected NHM image 8,000 Meters Residential area Central business district Segmentation (scale 10) Merge (scale 86) Resulting ground objects FIGURE 6.5 image An example of segmentation and merge of the corrected NHM N W E S Legend Reference buildings Extracted buildings 375 750 1,500 2,250 3,000 Meters FIGURE 6.6 Building extraction result by using the segmentation scale of 84 and the merge level of (no application of merge) N W E S Legend Reference buildings Extracted buildings 375 750 1,500 2,250 3,000 Meters FIGURE 6.7 Building extraction result by using the segmentation scale of 84 and the merge level of 55 N W E S Legend Reference buildings Extracted buildings FIGURE 6.9 375 750 1,500 2,250 3,000 Meters Building extraction result by supervised object-oriented classification Strategy I Strategy II Strategy III Street trees I Street trees II Yard trees I Yard trees II Urban forest Legend Extracted buildings Reference buildings FIGURE 6.10 Nonbuilding pixels mislabeled as buildings in the three strategies of building extraction (blue pixels not covered by orange ones) Strategy I Strategy II Strategy III Residential buildings Irregular-roof business buildings Regular business buildings Legend Extracted buildings Reference buildings FIGURE 6.11 Building pixels mislabeled as the background in the three strategies of building extraction (orange pixels not covered by blue ones) N N 2.5 10 Kilometers 15 20 2.5 10 Kilometers 15 20 Legend Legend Urban Forest Grasslands Agriculture Water Urban Agriculture Forest Water Barren lands Grasslands Barren lands (a) (b) N 2.5 10 Kilometers 15 20 N 2.5 10 Kilometers 15 20 Legend Legend Urban Agriculture Urban Agriculture Forest Water Forest Water Grasslands Barren lands Grasslands (c) Barren lands (d) FIGURE 7.1 LULC maps of four seasons in Marion County, Indianapolis, Indiana, derived from ASTER images: (a) October 3, 2000; (b) June 16, 2001; (c) April 5, 2004; and (d ) February 6, 2006 (Adapted from Weng et al., 2008.) Mean of normalized LST 0.05–0.38 0.38–0.43 0.43–0.49 0.49–0.55 0.55–0.94 1st_2nd_roads Rivers 1.25 2.5 Miles (a) Standard deviation of normalized LST 0.001–0.036 0.036–0.053 0.053–0.105 0.105–0.278 0.278–4.426 1st_2nd_roads Rivers 1.25 2.5 Miles (b) FIGURE 7.2 Normalized land surface temperatures computed based on the four dates of images: (a) mean value of TN and (b) standard deviation values of TN (Adapted from Weng et al., 2008.) FIGURE 8.5 Surface runoff changes in the Zhujiang Delta, 1989–1997 Scale Kilometers Aggregated runoff zone (8–10) 50 Elevation range (meters) 356–400 311–356 267–311 222–267 178–222 133–178 89–133 44–89 0–44 Contour map N FIGURE 9.2 16 Miles Digital elevation model of Guangzhou (Inset: Contour map.) 0.04 SO2 (mg/m3) NOx (mg/m3) 0.02 District boundary District boundary 02 0.02 03 0.0 0.04 0.07 0.06 0.0 0.10 0.08 0.05 0.0 0.04 0.06 0.04 0.06 04 N N 0.10 8 16 Miles 8 (a) 16 Miles (b) TSP (mg/m3) District boundary 00 Dust (ton/km2/month) District boundary 0.2 0.2 8.0 8.00 0.25 8.00 0.3 0.3 6.00 10.00 10.00 N N 0.2 0.2 0.25 8.0 0.3 (c) 16 Miles 8 16 Miles (d) FIGURE 9.4 Geographic distribution of the concentrations of major air pollutants in Guangzhou Contours are interpolated from point data observed in the monitoring stations Data represent annual averages of pollutant measurements The base map is the LST map of August 29, 1997, derived from Landsat TM thermal infrared data 16.98–19.45 19.45–21.17 21.17–22.89 22.89–25.23 N W E S 7 14 Miles (a) 1989 27.62–29.39 29.39–31.93 31.93–34.47 34.47–39.62 N W E S 7 14 Miles (b) 1997 FIGURE 9.7 A choropleth map showing the geographic distribution of LSTs in 1989 and 1997 Factor score –1.59 to –0.8 –0.8 to –0.34 –0.34 to 0.18 0.18 to 1.00 1.00 to 2.85 2.85 to 5.92 No data 10 15 20 Km N FIGURE 11.2 The first factor score—economic index (Adapted from Li and Weng, 2007.) Factor score –1.88 to –1.04 –1.04 to –0.44 –0.44 to 0.17 0.17 to 0.89 0.89 to 1.89 1.89 to 5.57 No data Factor score –3.65 to –1.90 –1.90 to –0.81 –0.81 to –0.10 –0.10 to 0.50 0.50 to 1.18 1.18 to 2.75 No data N 10 15 20 Km FIGURE 11.3 The second factor score— environmental index (Adapted from Li and Weng, 2007.) QOL index –1.14 to –0.50 –0.50 to –0.18 –0.18 to 0.16 0.16 to 0.58 0.58 to 1.35 1.35 to 2.84 No data 10 15 20 Km N FIGURE 11.4 The third factor score— crowdedness (Adapted from Li and Weng, 2007.) Urban Forest Grasslands Agriculture Water Barren lands 10 20 N W E N 10 15 20 Km FIGURE 11.5 Synthetic quality of life index (Adapted from Li and Weng, 2007.) 30 kilometers S FIGURE 13.1 LULC map of Indianapolis, Indiana, on October 13, 2006 40 Radius: 6.14 km Radius: 5.27 km Radius: 5.37 km Radius: 5.11 km Radius: 4.57 km Radius: 6.5 km (a) July (b) August Radius: 5.62 km Radius: 6.19 km Radius: 5.61 km Radius: 7.13 km Radius: 5.50 km Radius: 4.36 km (c) September (d) October N WNV case W Risk area E S 1.5 12 kilometers Y 2002 Y 2003 Y 2004 Y 2005 Y 2006 Y 2007 Departure from average precipitation Departure from average temperature FIGURE 13.4 Risk areas in July, August, September, and October in the years 2002 through 2007 –1 Y 2002 Y 2003 Y 2004 Y 2005 Y 2006 Y 2007 –1 Mar Apr May Jun Jul Month (a) Aug Sep Oct Mar Apr May Jun Jul Aug Sep Oct Month (b) FIGURE 13.5 Departure from average temperature and precipitation in Indianapolis from March through October in 2002 through 2007 ... on remote sensing, digital image processing, remote sensing GIS integration, and GIS and environmental modeling His research focuses on remote sensing and GIS analysis of urban ecological and. .. on remote sensing and GIS principles is to facilitate the discussion on the integration of remote sensing and GIS set forth in Chap 1.1 Principles of Remote Sensing 1.1.1 Concept of Remote Sensing. .. 2.1.1 Contributions of Remote Sensing to GIS 2.1.2 Contributions of GIS to Remote Sensing 2.1.3 Integration of Remote Sensing and GIS for Urban Analysis