http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Synthetic aperture radar for disaster management
Empower your response to global climate extremes by mastering the transformative power of synthetic aperture radar technology with this essential guide to its role in disaster management.
Cover -- Series Page -- Title Page -- Copyright Page -- Dedication Page -- Foreword -- Preface -- Part 1: Foundations of Synthetic Aperture Radar (SAR) and Disaster Management -- Chapter 1 Fundamentals of Synthetic Aperture Radar Technology: The Intersection of Physics, Engineering, and Geospatial Analysis for Advanced Imaging Systems -- 1.1 Introduction to Synthetic Aperture Radar Technology -- 1.1.1 Overview of a Synthetic Aperture Radar -- 1.1.2 Key Principles of Synthetic Aperture Radar Imaging -- 1.1.3 Applications and Importance of Synthetic Aperture Radar -- 1.2 Physics behind Synthetic Aperture Radar Systems -- 1.2.1 Electromagnetic Wave Propagation -- 1.2.2 Doppler Effect and Range Resolution -- 1.2.3 Radar Signal and Wave Interaction with Surfaces -- 1.2.4 Fundamentals of Synthetic Aperture Radar Image Formation -- 1.3 Engineering of Synthetic Aperture Radar Systems -- 1.3.1 Radar Hardware Components -- 1.3.2 Antenna Design and Beamforming -- 1.3.3 Data Acquisition and Signal Processing -- 1.3.4 Advances in Synthetic Aperture Radar System Architecture -- 1.4 Geospatial Analysis with Synthetic Aperture Radar Data -- 1.4.1 Georeferencing and Coordinate Systems -- 1.4.2 Synthetic Aperture Radar Image Calibration and Interpretation -- 1.4.3 Advanced Data Analysis Techniques: Change Detection, Interferometry, and Polarimetry -- 1.4.4 Application Case Studies: Mapping, Surveillance, and Disaster Management -- 1.5 Challenges and Future Directions -- 1.5.1 Signal Processing Challenges in Synthetic Aperture Radar -- 1.5.2 Technological Innovations: From Traditional to Quantum Synthetic Aperture Radar -- 1.5.3 Emerging Applications and the Future of Synthetic Aperture Radar Technology -- 1.6 Conclusion -- 1.6.1 Summary of Key Insights -- 1.6.2 The Role of Synthetic Aperture Radar in Future Earth Observation Systems -- References.
Chapter 2 Extracting Reservoir Surface Features and Land Cover Changes from Optical and Synthetic Aperture Radar Imagery: Tumariya Reservoir Case Study -- 2.1 Introduction -- 2.2 Study Area -- 2.3 Materials and Methods -- 2.3.1 Remote Sensing Dataset Used -- 2.3.1.1 Landsat Data -- 2.3.1.2 Synthetic Aperture Radar Data -- 2.3.2 Auxiliary Data -- 2.4 Generation of Spectral Indices -- 2.4.1 Synthetic Aperture Radar-Based Water Spread -- 2.4.1.1 Pre-Processing of Sentinel-1 Data -- 2.4.2 Water Surface Extraction -- 2.5 Wetland and Land Use Land Cover Mapping -- 2.6 Conclusion -- Acknowledgments -- References -- Part 2: Techniques for Disaster Monitoring and Assessment -- Chapter 3 Synthetic Aperture Radar Polarimetry for Disaster Analysis -- 3.1 Introduction -- 3.2 The Evolution of Synthetic Aperture Radar Technology: From Imaging to Polarimetry -- 3.3 Synthetic Aperture Radar Polarimetry as a Game-Changer in Disaster Management -- 3.4 Fundamentals of Synthetic Aperture Radar Polarimetry -- 3.4.1 Core Principles of Synthetic Aperture Radar -- 3.5 Polarization and Its Critical Role in Synthetic Aperture Radar Imaging -- 3.6 Polarimetric Synthetic Aperture Radar Modes: Single, Dual, and Full Polarization -- 3.7 Comparative Advantages of Optical and Passive Radar Systems -- 3.8 Techniques in Synthetic Aperture Radar Polarimetry -- 3.8.1 Polarimetric Decomposition Methods: Freeman-Durden, Cloude-Pottier, and Yamaguchi Models -- 3.9 Analysis of Coherence and Phase Information for Damage Assessment -- 3.9.1 Incoherent Change Detection -- 3.9.2 Coherent Change Detection -- 3.10 Feature Extraction Using Advanced Algorithms -- 3.10.1 Polarimetric Characteristics of a Stricken Area -- 3.10.1.1 H-a-Wishart Unsupervised Classification Algorithm -- 3.10.2 Yamaguchi Model -- 3.10.2.1 Normalized Circular-Pol Correlation Coefficient.
3.10.2.2 Texture Feature Grey-Level Co-Occurrence Matrix -- 3.11 Automated Change Detection with Artificial Intelligence and Machine Learning Techniques -- 3.12 Applications in Disaster Analysis -- 3.12.1 Flood Monitoring and Mapping -- 3.12.2 Earthquake Damage Assessment -- 3.12.3 Landslide Detection and Monitoring -- 3.12.3.1 Detection -- 3.12.3.2 Monitoring -- 3.12.4 Urban Disaster Resilience -- 3.13 Integration with Emerging Technologies -- 3.13.1 Harnessing Machine Learning and Artificial Intelligence for Polarimetric Data Interpretation -- 3.14 Multisensor Fusion: Combining Synthetic Aperture Radar with Optical, Light Detection and Ranging, and Ground-Based Systems -- 3.15 Real-Time Data Processing for Faster Disaster Response -- 3.16 Cloud-Based Platforms for Global Accessibility and Scalability -- 3.17 Case Studies -- 3.17.1 Flood Mapping in Southeast Asia: Real-Time Decision Support for Humanitarian Aid -- 3.17.1.1 Flood Detection -- 3.17.2 Earthquake Damage in Urban Environment Comments: Polarimetric Insights from the 2011 Tohoku Earthquake -- 3.17.3 Polarimetric Insights from the 2011 Tohoku Earthquake -- 3.17.4 Landslide Detection in Himalayan Regions: Enhancing Early Warning Systems -- 3.17.5 Urban Resilience Monitoring: Post-Hurricane Reconstruction in Coastal Cities -- 3.18 Challenges and Future Directions -- 3.18.1 Technical Constraints: Resolution, Coverage, and Volume of Data -- 3.18.2 Breaking Down Operational Barriers: Cost, Accessibility, and Training Requirements -- 3.18.3 Improving the Spatial and Temporal Resolution of Synthetic Aperture Radar Systems -- 3.18.4 Global Disaster Management Network Vision with Artificial Intelligence-Augmented Automation -- 3.19 Conclusion -- References -- Chapter 4 Synthetic Aperture Radar Interferometry -- 4.1 Introduction -- 4.2 Historical Evolution of Synthetic Aperture Radar Interferometry.
4.2.1 Foundational Developments (1960s-1970s) -- 4.2.2 Emergence of the Interferometric Synthetic Aperture Radar (1980s) -- 4.2.3 Advent of Spaceborne Interferometric Synthetic Aperture Radar (1990s) -- 4.2.4 Global Digital Elevation Model Production and Advanced Time-Series Techniques (2000s) -- 4.2.5 Modern Interferometric Synthetic Aperture Radar Era and Next-Generation Missions (2010-Present) -- 4.2.6 Overview of Key Spaceborne Synthetic Aperture Radar Missions Relevant to the Interferometric Synthetic Aperture Radar -- 4.2.7 Summary Table of Key Interferometric Synthetic Aperture Radar Missions -- 4.3 Fundamental Principles and Working Mechanism of Synthetic Aperture Radars and Interferometric Synthetic Aperture Radars -- 4.3.1 Radar Signal Transmission and Reception -- 4.3.2 Range Resolution (dr) -- 4.3.3 Azimuth Resolution (da) -- 4.3.4 Slant Range and Ground Range -- 4.3.5 Doppler Frequency and Synthetic Aperture Formation -- 4.3.6 Signal Processing and Image Formation -- 4.3.7 Topographic Mapping: Digital Elevation Model Generation -- 4.3.8 Deformation Monitoring: Earthquakes, Landslides, and Subsidence -- 4.3.9 Glaciology and Ice Monitoring: Ice Sheet Movement and Permafrost Changes -- 4.3.10 Volcanic Activity Monitoring -- 4.4 Case Studies -- 4.4.1 Co-Seismic Deformation from the 2015 Nepal Earthquake -- 4.4.2 Landslide Monitoring: The Kotrupi Event -- 4.4.3 Mining-Induced Deformation: Jharia Coalfield -- 4.4.4 Polarimetric Synthetic Aperture Radar-Based Forest Height Estimation and Digital Elevation Model Generation -- 4.4.5 Terrain Hazard Zonation: Rudraprayag-Srinagar Corridor -- 4.4.6 Joshimath Town Multi-Temporal Interferometric Synthetic Aperture Radar Analysis (2019-2023) -- 4.5 Advanced Interferometric Synthetic Aperture Radar Techniques -- 4.6 Challenges and Future Trends in Interferometric Synthetic Aperture Radar.
4.7 Conclusion -- References -- Chapter 5 Calibration and Validation Techniques for Disaster Response -- 5.1 Introduction -- 5.2 Fundamentals of Synthetic Aperture Radar Calibration and Validation -- 5.2.1 Calibration Techniques: Internal, External, and Cross-Calibration -- 5.2.2 Validation Processes and Their Role -- 5.2.3 Limitations of Traditional Methods in Disaster Scenarios -- 5.3 Supervised Learning in Synthetic Aperture Radar for Disaster Response -- 5.3.1 Application of Supervised Algorithms -- 5.3.2 Classification of Disaster Types -- 5.3.3 Workflow: Data Labeling, Model Training, and Evaluation -- 5.3.4 Example: Flood Extent Mapping Using Synthetic Aperture Radar Imagery -- 5.4 Unsupervised Learning Techniques for Synthetic Aperture Radar Data Analysis -- 5.4.1 Overview of Clustering and Anomaly Detection -- 5.4.2 Use Cases: Identifying Unexpected Disaster Impacts -- 5.4.3 Example: Detection of Infrastructure Damage -- 5.5 Reinforcement Learning for Adaptive Calibration -- 5.5.1 Fundamentals of Reinforcement Learning and Its Relevance to Synthetic Aperture Radar -- 5.5.2 Dynamic Optimization of Synthetic Aperture Radar Calibration -- 5.5.3 Example: Continuous Adaptation to Changing Weather Conditions During Floods -- 5.6 Multi-Source Data Integration for Enhanced Calibration and Validation -- 5.6.1 Combining Synthetic Aperture Radar Data with Optical Imagery, Ground-Based Observations, and Unmanned Aerial Vehicle Inputs -- 5.6.2 Artificial Intelligence-Driven Techniques for Multi-Source Data Fusion -- 5.6.3 Case Study: Integrating Synthetic Aperture Radar and Optical Data for Wildfire Impact Assessment -- 5.7 Advanced Validation Techniques -- 5.7.1 Artificial Intelligence-Based Simulations for Synthetic Aperture Radar Validation in Disaster Environments -- 5.7.2 Cross-Validation with Auxiliary Sensors (LiDAR, GPS).
edited by Pooja Joshi... [and 3 others].
Description based upon print version of record.
Newark : John Wiley & Sons, Incorporated,c2026