Earth observation technology, in conjunction with in situ data collection, can be used to observe, monitor, measure, and model many of the components that comprise urban ecosystems cycles. Over the past decade, urban remote sensing has rapidly emerged as a new frontier in the Earth observation technology by focusing primarily on understanding the biophysical properties, patterns, and processes of urban landscapes, and mapping and monitoring of urban land cover and spatial extent.

Table of Contents

• Preface
• Editors
• Contributors
• Section I Data, Sensors, and Systems Considerations and Algorithms for Urban Feature Extraction
• 1 The Global Urban Footprint - Thomas Esch, Wieke Heldens, and Andreas Hirner
• 1.1 Introduction
• 1.2 Generating the GUF
• 1.2.1 Global Urban Mapping Using Earth Observation
• 1.2.2 From TanDEM-X Imagery to GUF
• 1.2.2.1 Feature Extraction
• 1.2.2.2 Unsupervised Classification
• 1.2.2.3 Mosaicking
• 1.2.2.4 Automated Postprocessing
• 1.3 The GUF Data Set
• 1.4 Analyzing Urban Footprints
• 1.5 Summary and Outlook
• Acknowledgments
• References
• 2 Development of On-Demand Human Settlement Mapping System Using Historical Satellite Archives - Hiroyuki Miyazaki and Ryosuke Shibasaki
• 2.1 Introduction
• 2.2 Methodology of Automated Time-Series Human Settlement Mapping
• 2.2.1 Introduction
• 2.2.2 Learning with Local and Global Consistency
• 2.2.3 Composition of LLGC Results
• 2.3 Experimental Implementations of Time-Series Mapping System
• 2.3.1 Data and Processing
• 2.3.2 System Implementation
• 2.3.3 Experiment Results and Discussions
• 2.4 Development of On-Demand System
• 2.4.1 Web-Based Mapping and Data Processing Standards
• 2.4.2 Implementation of On-Demand Data Processing Service
• 2.5 Conclusions
• Acknowledgments
• References
• 3 Morphological Building Index (MBI) and Its Applications to Urban Areas - Xin Huang and Tao Zhang
• 3.1 Introduction
• 3.2 The Original MBI
• 3.3 Morphological Building/Shadow Index
• 3.4 Enhanced Building Index
• 3.5 Applications of MBI
• 3.5.1 Building Extraction
• 3.5.2 Change Detection
• 3.5.3 Image Classification
• 3.5.4 Urban Environment
• 3.6 Conclusion
• References
• Section II Assessing and Modeling Urban Landscape Compositions, Patterns, and Structures
• 4 Stereo-Based Building Roof Mapping in Urban Off-Nadir VHR Satellite Images: Challenges and Solutions - Alaeldin Suliman and Yun Zhang
• 4.1 Introduction
• 4.2 Principle of Stereo-Based Building Detection
• 4.2.1 Stereo Image Processing
• 4.2.1.1 Stereo-Based Information Generation
• 4.2.1.2 Stereo-Based Information Co-Registration
• 4.2.1.3 Stereo-Based Information Normalization
• 4.2.2 Building Detection
• 4.2.2.1 Image Fusion and Segmentation
• 4.2.2.2 Vegetation Suppression
• 4.2.2.3 Building Detection and Finishing
• 4.2.2.4 Map Geo-Referencing
• 4.2.3 Performance Evaluation
• 4.3 Identified Challenges
• 4.3.1 Efficient Image-Elevation Co-Registration
• 4.3.2 Disparity Gaps and Normalization
• 4.3.3 Direct Map Geo-Referencing
• 4.4 Developed Solutions
• 4.4.1 Line-of-Sight DSM Solution
• 4.4.2 Registration-Based Mapping of the Aboveground Disparities
• 4.4.3 Disparity-Based Elevation Co-Registration
• 4.4.3.1 Image Projection and Rectification
• 4.4.3.2 Scale-Based Formulas Derivation
• 4.4.3.3 Applying the Derived Formulas
• 4.5 Experimental Results and Analysis
• 4.5.1 Data Sets
• 4.5.2 Intermediate Results and Discussion
• 4.5.2.1 LoS-DSM Results and Discussion
• 4.5.2.2 RMAD Results and Discussion
• 4.5.2.3 DECR Results and Discussion
• 4.5.3 Results of Stereo-Based Building Detection
• 4.5.3.1 Building Detection Results Based on LoS-DSM Solution
• 4.5.3.2 Building Detection Results Based on RMAD Solution
• 4.5.3.3 Building Detection Results Based on DECR Solution
• 4.6 Conclusions
• Acknowledgments
• References
• 5 Beyond Built-Up: The Internal Makeup of Urban Areas - Benjamin Bechtel, Martino Pesaresi, Aneta J. Florczyk, and Gerald Mills
• 5.1 Introduction
• 5.2 Urban Data for Global Climate Science
• 5.3 GHSL-LABEL
• 5.4 WUDAPT-LCZ
• 5.5 Comparison LABEL-LCZ
• 5.5.1 Comparison Methods
• 5.5.2 Visual Comparison
• 5.5.3 Set Comparisons
• 5.5.4 Overall Agreement
• 5.5.5 Discussion
• 5.6 Conclusions
• Acknowledgments
• References
• Appendix A
• Appendix B
• Appendix C
• 6 Urban Change Detection Utilizing High-Resolution Optical Images Taken from Different Viewing Angles and Different Platforms - Shabnam Jabari and Yun Zhang
• 6.1 Introduction
• 6.2 Patch-Wise MAD Approach
• 6.2.1 Patch-Wise Co-Registration
• 6.2.1.1 Segmentation
• 6.2.1.2 Co-Registered Patch Generation
• 6.2.1.3 Solution to Occlusion
• 6.2.1.4 Null Segments
• 6.2.2 Comparison of the Spectral Properties Using MAD Transform
• 6.3 Experiments and Discussion
• 6.3.1 Study Data Sets
• 6.3.2 Co-Registration Results
• 6.3.3 Co-Registration Accuracy Assessment and Discussion
• 6.3.3.1 Area Ratio
• 6.3.3.2 Error Propagation
• 6.4 Final Remarks
• 6.5 Conclusion
• Acknowledgment
• References
• Section III Monitoring, Analyzing, and Modeling Urban Growth
• 7 Urbanization in India: Patterns, Visualization of Cities, and Greenhouse Gas Inventory for Developing an Urban Observatory - Bharath Haridas Aithal, Mysore Chandrashekar Chandan, Shivamurthy Vinay, and T.V. Ramachandra
• 7.1 Introduction
• 7.2 Carbon Footprint Analysis
• 7.3 Necessity of Understanding Carbon Footprint of a Region through Quantification
• 7.4 Study Area and Data
• 7.5 Method
• 7.5.1 Data Creation
• 7.5.2 Land Use Analysis
• 7.5.3 Integrated Model Generation and Validation
• 7.5.4 Estimation of GHG Footprint
• 7.6 Result and Outcomes
• 7.6.1 Land Use Analysis
• 7.6.2 Validation
• 7.6.3 Modeling and Prediction
• 7.6.4 Transition Probability
• 7.6.5 GHG Footprint
• 7.7 Discussion and Conclusion
• Acknowledgment
• References
• 8 Mapping Impervious Surfaces in the Greater Hanoi Area, Vietnam, from Time Series Landsat Image 1988–2015 - Hung Q. Ha and Qihao Weng
• 8.1 Introduction
• 8.2 Time Series Analysis of Impervious Surfaces
• 8.2.1 Mapping Impervious Surfaces
• 8.2.2 Time Series Image Analysis
• 8.2.3 Framework for Time Series Analysis of Impervious Surfaces
• 8.2.3.1 Stacking Time Series of Landsat Data for Annual Impervious Surface Estimation
• 8.2.3.2 Estimation of NDVI and LST for Time Series Analysis
• 8.2.3.3 Training Sample Selection
• 8.2.3.4 Time Series Similarity Measures
• 8.2.3.5 Decision Tree Classifier
• 8.2.3.6 Temporal Filtering
• 8.2.4 Data
• 8.3 Study Area
• 8.4 Impervious Surface Time Series
• 8.4.1 Annual Distance Maps
• 8.4.2 Annual Impervious Surface Maps
• 8.4.3 Trend of Urbanization Shown by the Changes in Impervious Surfaces
• 8.4.4 Accuracy Assessment
• 8.5 Discussion and Conclusions
• 8.5.1 Discussion
• 8.5.2 Recommendations
• 8.5.3 Conclusions
• References
• 9 City in Desert: Mapping Subpixel Urban Impervious Surface Area in a Desert Environment Using Spectral Unmixing and Machine Learning Methods - Chengbin Deng and Weiying Lin
• 9.1 Introduction
• 9.2 Study Area and Data
• 9.3 Methodology
• 9.3.1 Spectral Unmixing
• 9.3.2 Machine Learning
• 9.3.3 Accuracy Assessment
• 9.4 Results
• 9.5 Discussion
• 9.6 Conclusions
• References
• 10 Application of Remote Sensing and Cellular Automata Model to Analyze and Simulate Urban Density Changes - Santiago Linares and Natasha Picone
• 10.1 Introduction
• 10.1.1 Remote Sensing and GIS to Study Urban Areas
• 10.1.2 Cellular Automata Applied to Analyze Urban Growth
• 10.2 Study Area
• 10.3 Methodology
• 10.3.1 Urban Density Study
• 10.3.2 Urban Growth Modeling
• 10.4 Results
• 10.4.1 Urban Density 2003–2011 Using Satellite Images
• 10.4.2 Modeling Sceneries of Urban Growth 2003–2033
• 10.4.3 Potential Environmental Consequences of the Urbanization Process
• 10.5 Conclusion
• References
• Section IV Urban Planning and Socioeconomic Applications
• 11 Developing Multiscale HEAT Scores from H-Res Airborne Thermal Infrared Imagery to Support Urban Energy Efficiency: Challenges Moving Forward - Bharanidharan Hemachandran, Geoffrey J. Hay, Mir Mustafiz Rahman, Isabelle Couloigner, Yilong Zhang, Bilal Karim, Tak S. Fung, and Christopher D. Kyle
• 11.1 Introduction
• 11.1.1 Human Behavior and Energy Efficiency
• 11.1.2 Energy Rating Systems
• 11.1.3 Thermal Imaging and Home Energy Efficiency
• 11.1.4 Heat Energy Assessment Technologies
• 11.2 Study Area and Data
• 11.2.1 Climatic Considerations for Thermal Imaging
• 11.2.2 Effects of Emissivity in Thermal Imagery
• 11.2.3 Effects of Microclimatic Variability in Thermal Imagery
• 11.2.4 Cadastral Data and Building Attributes
• 11.3 Methods—HEAT Scores
• 11.3.1 HEAT Score Method 1—The Standardized Score
• 11.3.1.1 Limitations of the Standardized Score Method
• 11.3.2 HEAT Score Method 2—The WUFI Model and Logistic Regression
• 11.3.2.1 Rationale behind the Selection of Independent Variables
• 11.3.2.2 Logistic Regression for HEAT Scores
• 11.3.2.3 Limitations of the Logistic Regression Method
• 11.3.3 HEAT Score Method 3—Criteria Weights
• 11.3.3.1 Summary of Four Criteria Weights
• 11.3.3.2 Advantages of the Criteria Weights Method
• 11.3.4 Evaluation of the Criteria Weights HEAT Scores Method
• 11.4 Results and Discussion
• 11.4.1 Criteria-Weighted Multiscale HEAT Scores and User Interface—City Level
• 11.4.2 Criteria-Weighted Multiscale HEAT Scores—Community Level
• 11.4.3 Multiscale HEAT Scores—Residential Level
• 11.4.4 Comparison of Three Different HEAT Scores
• 11.4.5 Statistical Distribution of Three Different HEAT Scores
• 11.4.6 Challenges
• 11.4.6.1 Geometric Correction and HEAT Scores
• 11.4.6.2 Vegetation and HEAT Scores
• 11.4.6.3 Emissivity and HEAT Scores
• 11.4.6.4 Microclimate and HEAT Scores
• 11.4.6.5 Defining Optimal Rooftop Temperature for HEAT Scores
• 11.4.6.6 Hotspots and Their Relation to Energy Consumption
• 11.5 Conclusion
• 11.6 Future Work
• Acknowledgments
• References
• 12 Air Quality and Health Monitoring in Urban Areas Using EO and Clinical Data - Andrea Marinoni and Paolo Gamba
• 12.1 Introduction: Background and Driving Forces
• 12.2 Methods
• 12.2.1 Retrieving Air Quality Maps from EO Data
• 12.2.2 Investigating Heterogeneous Data by Information Theory–Based Approach
• 12.3 Experimental Results
• 12.3.1 Estimating the Effect of Air Pollution on Patients Affected by Diabetes
• 12.3.2 Affine Pattern Search in Multispectral Remotely Sensed Data Enlarged with Clinical Records
• 12.4 Conclusions and Final Remarks
• References
• 13 Urban Green Mapping and Valuation - Stefan Lang, Thomas Blaschke, Gyula Kothencz, and Daniel Hölbling
• 13.1 Introduction
• 13.2 EO-Based Urban Green Monitoring
• 13.3 Adding Qualitative Aspects of Urban Green
• 13.4 Quantifying Urban Green and Its Perception
• 13.4.1 The Salzburg Green Monitoring Study
• 13.4.2 Mimicking Humans’ Perceptions with EO and GIS Data
• 13.4.3 Delineating Green Valuation Units
• 13.4.4 Adding Height and Volumetric Information
• 13.5 Conclusion
• Acknowledgments
• References