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  • 1
    Online Resource
    Online Resource
    Newark :John Wiley & Sons, Incorporated,
    Keywords: Natural resources-Remote sensing. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (531 pages)
    Edition: 1st ed.
    ISBN: 9781119616023
    DDC: 333.70284
    Language: English
    Note: Cover -- Title Page -- Copyright -- Contents -- List of Abbreviations -- List of Contributors -- List of Editors -- Preface -- Section I General Section -- Chapter 1 Introduction to Natural Resource Monitoring Using Remote Sensing Technology -- 1.1 Introduction -- References -- Chapter 2 Spectroradiometry: Types, Data Collection, and Processing -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 The Types of Spectroradiometry -- 2.3.1 Spectroradiometry -- 2.3.2 Photometry and Colorimetry -- 2.4 Principle of the Spectroradiometer -- 2.5 Radiance Measurement -- 2.5.1 Factors Affecting Spectral Reflectance Measurements -- 2.5.2 Data Processing -- 2.5.2.1 Radiometric Calibration -- 2.5.2.2 Reflectance/Transmittance -- 2.5.2.3 Radiance/Irradiance/Emissivity -- 2.5.2.4 1st Derivative -- 2.5.2.5 2nd Derivative -- 2.5.2.6 Parabolic Correction -- 2.5.2.7 Other Methods -- 2.6 Data Collection -- 2.7 Generation of the Metadata -- 2.7.1 Continuum Removal -- 2.8 Applications of ASD in Agriculture and Forestry -- 2.9 Future Importance, Limitations, and Recommendations -- Acknowledgment -- References -- Chapter 3 Geometric‐Optical Modeling of Bidirectional Reflectance Distribution Function for Trees and Forest Stands -- 3.1 Introduction -- 3.2 Model Description -- 3.2.1 Sunlit Surfaces -- 3.2.2 Shaded Surfaces -- 3.2.3 Forest Stand Modeling -- 3.3 General Shape of the Apparent Luminance -- 3.4 Simulation and Discussion -- References -- Section II Vegetation Resource Monitoring (Forest and Agriculture) -- Chapter 4 Mapping Stand Age of Indonesian Rubber Plantation Using Fully Polarimetric L‐Band Synthetic Aperture Radar -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Test Site and Dataset -- 4.2.2 Processing -- 4.3 Results and Discussion -- 4.3.1 Scattering Behavior -- 4.3.2 Classification Using Backscatter Coefficients. , 4.3.3 Classification Using Model‐Based Decomposition -- 4.3.4 The Role of Combining Datasets -- 4.3.5 The Best Subset -- 4.4 Conclusion -- Acknowledgments -- References -- Chapter 5 Responses of Multi‐Frequency Remote Sensing to Forest Biomass -- 5.1 Background -- 5.1.1 Optical Remote Sensing -- 5.1.2 Microwave Remote Sensing -- 5.1.3 LiDAR Remote/Sensing -- 5.1.4 Synergic Use of Multi‐Sensor Data -- 5.2 A Case Study in the Mixed Tropical Deciduous Forest of India -- 5.2.1 Study Area -- 5.2.2 Datasets -- 5.2.3 Methodology -- 5.2.4 Results -- 5.2.5 Conclusion -- 5.3 Uncertainties and Future Scope of Research in Biomass Estimation -- 5.3.1 Summary -- Acknowledgment -- References -- Chapter 6 Crop Water Requirements Analysis Using Geoinformatics Techniques in the Water‐Scarce Semi‐Arid Watershed -- 6.1 Introduction -- 6.1.1 Crop Calendar -- 6.1.2 Crop Type Classification -- 6.1.3 Crop Water Requirements -- 6.1.4 CROPWAT Model -- 6.1.5 Meteorological Data -- 6.2 Reference Evapotranspiration (ETo) -- 6.2.1 Effective Rainfall -- 6.2.2 Crop Coefficient (Kc) -- 6.3 Soil Data -- 6.4 Crop Evapotranspiration (ETc) -- 6.5 Irrigation Water Requirement -- 6.6 Conclusion -- Acknowledgment -- References -- Chapter 7 Biophysical Characterization and Monitoring Large‐Scale Water and Vegetation Anomalies by Remote Sensing in the Agricultural Growing Areas of the Brazilian Semi‐Arid Region -- 7.1 Introduction -- 7.2 Material and Methods -- 7.3 Results and Discussion -- 7.4 Conclusions -- Acknowledgments -- References -- Section III Soil and Land Resource Monitoring -- Chapter 8 SMOS L4 Downscaled Soil Moisture Product Evaluation Over a Two Year - Period in a Mediterranean Setting -- 8.1 Introduction -- 8.2 Experimental Setup -- 8.3 Datasets Description -- 8.3.1 SMOS L4 SM Product (1 km) -- 8.3.2 In‐situ Soil Moisture Data -- 8.4 Methodology. , 8.4.1 SSM Extraction from SMOS -- 8.4.2 Pre‐Processing of SMOS -- 8.4.3 Agreement Evaluation -- 8.5 Results -- 8.5.1 Station ES‐CPA -- 8.5.2 Station N9 -- 8.5.3 Station M5 -- 8.5.4 Station H7 -- 8.5.5 Station K9 -- 8.6 Discussion -- 8.7 Conclusions -- Acknowledgments -- References -- Chapter 9 Estimating Urban Population Density Using Remotely Sensed Imagery Products -- 9.1 Introduction -- 9.2 Spatial Data Disaggregation-MAUP Problem -- 9.2.1 Spatial Interpolation -- 9.3 Materials and Methods -- 9.3.1 Study Area and Data Sources -- 9.3.2 Areal Interpolation Using Cokriging -- 9.4 Areal Interpolation Using Geographically Weighted Regression (GWR) -- 9.5 Results and Discussion -- 9.6 Conclusions -- References -- Chapter 10 Impact of Land Cover Change on Surface Runoff -- 10.1 Introduction -- 10.2 Literature -- 10.3 Methodology -- 10.3.1 Supervised Classification -- 10.3.2 SWAT Model -- 10.3.3 SWAT Inputs -- 10.3.4 SWAT Outputs -- 10.4 Methodology -- 10.5 Study Area -- 10.5.1 Justification for Study Area Selection -- 10.6 Data Used -- 10.6.1 Weather Data -- 10.6.2 Satellite Data -- 10.6.2.1 LANDSAT Dataset -- 10.6.3 Digital Elevation Model -- 10.6.4 Soil Map -- 10.7 Results and Discussion -- 10.7.1 LU/LC Classification -- 10.7.2 LU/LC Map 1987 -- 10.7.3 LU/LC Map 1997 -- 10.7.4 LU/LC Map 2007 -- 10.7.5 LU/LC Map 2017 -- 10.7.6 Watershed Delineation -- 10.8 SWAT Results -- 10.8.1 HRU Analysis Report -- 10.8.2 Runoff Generated in Sub Basins -- 10.9 Conclusion -- Acknowledgment -- References -- Chapter 11 Delineation of Groundwater Potential Zone and Site Suitability of Rainwater Harvesting Structures Using Remote Sensing and In Situ Geophysical Measurements -- 11.1 Introduction -- 11.2 Study Area -- 11.3 Data Used and Methodology -- 11.3.1 Data Used -- 11.3.2 Methodology -- 11.3.3 Vertical Electrical Sounding -- 11.3.4 Weightage Calculation. , 11.4 Results and Discussion -- 11.4.1 Land Use and Land Cover (LULC) -- 11.4.2 Soil -- 11.4.3 Hydro‐Geomorphology -- 11.4.4 Lithology -- 11.4.5 Drainage Density -- 11.4.6 Lineament Density -- 11.5 Resistivity Survey -- 11.5.1 VES Survey and Cross Section -- 11.5.2 Interpolated Subsurface Soil Profile -- 11.5.3 Groundwater Potential Zone -- 11.5.4 Suitable Sites for Rainwater Harvesting Structures -- 11.6 Conclusions -- Acknowledgment -- References -- Chapter 12 Structural Control on the Landscape Evolution of Son Alluvial Fan System in Ganga Foreland Basin -- 12.1 Introduction -- 12.2 Study Area -- 12.2.1 Geomorphological Setting of SAFS -- 12.2.2 Geology of the Son Valley and SAFS -- 12.2.3 Drainage -- 12.2.4 Climate -- 12.3 Materials and Methods -- 12.3.1 Data Used -- 12.3.2 Preprocessing of DEM -- 12.3.3 DEM Derived Parameters -- 12.3.4 Conceptual Background -- 12.3.4.1 Quantitative Measure of River Basin Dynamics/Reorganization -- 12.3.4.2 X (χ)‐Metrics and Cross‐Divide χ‐Anomaly -- 12.3.4.3 Rationale Behind Experimental Use of χ‐Transform for Alluvial Stream Long Profiles -- 12.3.5 Normalized Channel Steepness Index (ksn) and Channel Concavity Index (θ) Computation -- 12.3.6 Stream Sinuosity -- 12.3.7 Hypsometric Curve (HC) -- 12.4 Results and Discussion -- 12.4.1 Zones of (dis)equilibrium Over SAFS in Ganga Foreland Basin (GFB) -- 12.4.2 Sinuosity of Streams and Drainage Behavior Over SAFS -- 12.4.3 Extent of SAFS vis‐à‐vis Evolution of Ganga Plain -- 12.5 Conclusion and Recommendations -- Acknowledgments -- References -- Section IV Water Resource Monitoring -- Chapter 13 Managing the Blue Carbon Ecosystem: A Remote Sensing and GIS Approach -- 13.1 Introduction -- 13.2 Blue Carbon Ecosystem -- 13.2.1 Distribution -- 13.2.2 Mangrove -- 13.2.3 Seagrass -- 13.2.4 Salt Marshes -- 13.3 Factors Affecting Carbon Storage in Blue Carbon Ecosystems. , 13.4 Carbon Storage in the Blue Carbon Ecosystem -- 13.5 Pathways of Carbon in the Blue Carbon Ecosystem -- 13.6 Evaluation of Long‐Term Carbon Deposition in Sediments -- 13.7 Ecosystem Services -- 13.8 Threats to Coastal Blue Carbon Ecosystems -- 13.9 Economy of Blue Carbon Ecosystems -- 13.10 Management -- 13.11 Conservation of Blue Carbon Ecosystem: A Remote Sensing Approach -- 13.11.1 Role of Optical Remote Sensing -- 13.11.2 Mapping the Mangrove Cover and Change Detection -- 13.12 Quantification of Biophysical Variables -- 13.12.1 Phenology -- 13.12.2 Role of Hyperspectral Remote Sensing -- 13.12.3 Mangrove‐Mapping and Dynamics Studies Using Radar Data -- 13.12.4 Dependence on Frequency -- 13.12.5 Species Identification -- 13.13 Conclusion -- Acknowledgment -- References -- Chapter 14 Appraising the Changing Climate and Extent of Snow in the Kashmir Himalaya Using MODIS Data -- 14.1 Introduction -- 14.2 Study Area -- 14.3 Materials and Methods -- 14.4 Results and Discussions -- 14.4.1 Trend in Air Temperature -- 14.4.2 Trend in Snow Cover Area -- 14.4.3 Variations in SCA Under Elevation Zones -- 14.5 Conclusion -- Acknowledgments -- References -- Chapter 15 Knowledge‐Based Mapping of Debris‐Covered Glaciers in the Greater Himalayan Range -- 15.1 Introduction -- 15.1.1 Overview of Ablation Pattern of Glaciers in the Western Himalayas -- 15.1.2 Overview of Glacier Mapping Techniques -- 15.2 Study Area -- 15.3 Data Sources -- 15.4 Methodology -- 15.4.1 Pre‐Processing of Satellite Data -- 15.4.2 Knowledge‐Based Approach -- 15.4.2.1 Segregation of Snow and Ice from Other Land Covers Using Spectral Index -- 15.4.2.2 Segregation Between Snow and Ice Types Using Spectral Indices -- 15.4.2.3 Segregation of Supraglacial Debris Types from Non‐Glacier Area -- 15.5 Results and Discussions. , 15.5.1 Accuracy Assessment of Supraglacial Covers Mapping of Pensilungpa Glacier.
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  • 2
    Online Resource
    Online Resource
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Keywords: Natural disasters. ; Geographic information systems. ; Earth sciences. ; Geomorphology. ; Environmental geography.
    Description / Table of Contents: Introduction -- Landslide/ Slope failures/ Flood and glacial lake outburst floods (GLOFS) /Drought/ Desertification status mapping/ Tsunami/ Lightening/ Forest Fire/ Earthquake / Volcanic eruptions -- Statistical, multi-criteria decision making (MCDM), machine learning models (spatial prediction models) -- Challenges and future needs.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource(XXXVII, 517 p. 203 illus., 192 illus. in color.)
    Edition: 1st ed. 2024.
    ISBN: 9783031510533
    Language: English
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