rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Synthetic aperture radar for disaster management
    sub title: bridging science and solutions
  name:
    name part: Joshi, Pooja.
  name:
    name part: Bhatt, Ashutosh.
  name:
    name part: Sharma, Neelam.
  name:
    name part: Joshi, Kapil.
  type of resource: text
  origin info:
    place:
      place term: nju
    date issued: 2026
    copyright date: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (398 p.)
  abstract: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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).
  note: edited by Pooja Joshi... [and 3 others].
  note: Description based upon print version of record.
  subject:
    topic: Synthetic aperture radar
  subject:
    topic: Emergency management
  classification: HV551.2 .S95 2026
  classification: TK6592.S95 S95 2026
  classification: 363.34/8068028
  classification: 621.3848/5
  related item:
    title info:
      title: Synthetic Aperture Radar for Disaster Management
    name:
      name part: Joshi, Pooja
    origin info:
      publisher: Newark : John Wiley & Sons, Incorporated,c2026
  identifier: 9781394398966
  identifier: 1394398964
  identifier: 9781394398959
  identifier: 1394398956
  identifier: 9781394398935
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 260411
    record change date: 20260810124332.3
    record identifier: in00005790578
    language of cataloging:
      language term: eng