{"product_id":"advances-in-remote-sensing-for-natural-resource-monitoring-9781119615972","title":"Advances in Remote Sensing for Natural Resource","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSustainable management of natural resources is an urgent need, given the changing climatic conditions of Earth systems. The ability to monitor natural resources precisely and accurately is increasingly important. New and advanced remote sensing tools and techniques are continually being developed to monitor and manage natural resources in an effective way. Remote sensing technology uses electromagnetic sensors to record, measure and monitor even small variations in natural resources. The addition of new remote sensing datasets, processing techniques and software makes remote sensing an exact and cost-effective tool and technology for natural resource monitoring and management.    Advances in Remote Sensing for Natural Resources Monitoring provides a detailed overview of the potential applications of advanced satellite data in natural resource monitoring. The book determines how environmental and - ecological knowledge and satellite-based information can be effectively combined to addre\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Abbreviations xix\u003c\/p\u003e \u003cp\u003eList of Contributors xxix\u003c\/p\u003e \u003cp\u003eList of Editors xxxv\u003c\/p\u003e \u003cp\u003ePreface xxxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection I General Section \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Natural Resource Monitoring Using Remote Sensing Technology \u003c\/b\u003e\u003cb\u003e3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePrem Chandra Pandey and Laxmi Kant Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003eReferences 6\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Spectroradiometry: Types, Data Collection, and Processing \u003c\/b\u003e\u003cb\u003e9\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePrem Chandra Pandey, Manish Kumar Pandey, Ayushi Gupta, Prachi Singh, and Prashant K. Srivastava\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 9\u003c\/p\u003e \u003cp\u003e2.2 Literature Review 10\u003c\/p\u003e \u003cp\u003e2.3 The Types of Spectroradiometry 12\u003c\/p\u003e \u003cp\u003e2.3.1 Spectroradiometry 13\u003c\/p\u003e \u003cp\u003e2.3.2 Photometry and Colorimetry 13\u003c\/p\u003e \u003cp\u003e2.4 Principle of the Spectroradiometer 13\u003c\/p\u003e \u003cp\u003e2.5 Radiance Measurement 16\u003c\/p\u003e \u003cp\u003e2.5.1 Factors Affecting Spectral Reflectance Measurements 17\u003c\/p\u003e \u003cp\u003e2.5.2 Data Processing 18\u003c\/p\u003e \u003cp\u003e2.5.2.1 Radiometric Calibration 18\u003c\/p\u003e \u003cp\u003e2.5.2.2 Reflectance\/Transmittance 19\u003c\/p\u003e \u003cp\u003e2.5.2.3 Radiance\/Irradiance\/Emissivity 20\u003c\/p\u003e \u003cp\u003e2.5.2.4 1st Derivative 20\u003c\/p\u003e \u003cp\u003e2.5.2.5 2nd Derivative 20\u003c\/p\u003e \u003cp\u003e2.5.2.6 Parabolic Correction 20\u003c\/p\u003e \u003cp\u003e2.5.2.7 Other Methods 21\u003c\/p\u003e \u003cp\u003e2.6 Data Collection 21\u003c\/p\u003e \u003cp\u003e2.7 Generation of the Metadata 21\u003c\/p\u003e \u003cp\u003e2.7.1 Continuum Removal 22\u003c\/p\u003e \u003cp\u003e2.8 Applications of ASD in Agriculture and Forestry 23\u003c\/p\u003e \u003cp\u003e2.9 Future Importance, Limitations, and Recommendations 23\u003c\/p\u003e \u003cp\u003eAcknowledgment 24\u003c\/p\u003e \u003cp\u003eReferences 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Geometric-Optical Modeling of Bidirectional Reflectance Distribution Function for Trees and Forest Stands \u003c\/b\u003e\u003cb\u003e28\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNour El Islam Bachari, Salim Lamine, and Khaled Meharrar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 28\u003c\/p\u003e \u003cp\u003e3.2 Model Description 29\u003c\/p\u003e \u003cp\u003e3.2.1 Sunlit Surfaces 31\u003c\/p\u003e \u003cp\u003e3.2.2 Shaded Surfaces 31\u003c\/p\u003e \u003cp\u003e3.2.3 Forest Stand Modeling 32\u003c\/p\u003e \u003cp\u003e3.3 General Shape of the Apparent Luminance 33\u003c\/p\u003e \u003cp\u003e3.4 Simulation and Discussion 35\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection II Vegetation Resource Monitoring (Forest and Agriculture) \u003c\/b\u003e\u003cb\u003e43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Mapping Stand Age of Indonesian Rubber Plantation Using Fully Polarimetric L-Band Synthetic Aperture Radar \u003c\/b\u003e\u003cb\u003e45\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBambang H. Trisasongko\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 45\u003c\/p\u003e \u003cp\u003e4.2 Methodology 46\u003c\/p\u003e \u003cp\u003e4.2.1 Test Site and Dataset 46\u003c\/p\u003e \u003cp\u003e4.2.2 Processing 47\u003c\/p\u003e \u003cp\u003e4.3 Results and Discussion 48\u003c\/p\u003e \u003cp\u003e4.3.1 Scattering Behavior 48\u003c\/p\u003e \u003cp\u003e4.3.2 Classification Using Backscatter Coefficients 50\u003c\/p\u003e \u003cp\u003e4.3.3 Classification Using Model-Based Decomposition 51\u003c\/p\u003e \u003cp\u003e4.3.4 The Role of Combining Datasets 51\u003c\/p\u003e \u003cp\u003e4.3.5 The Best Subset 52\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 55\u003c\/p\u003e \u003cp\u003eAcknowledgments 55\u003c\/p\u003e \u003cp\u003eReferences 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Responses of Multi-Frequency Remote Sensing to Forest Biomass \u003c\/b\u003e\u003cb\u003e58\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSuman Sinha, A. Santra, Laxmi Kant Sharma, Anup Kumar Das, C. Jeganathan, Shiv Mohan, S.S. Mitra, and M.S. Nathawat\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Background 58\u003c\/p\u003e \u003cp\u003e5.1.1 Optical Remote Sensing 59\u003c\/p\u003e \u003cp\u003e5.1.2 Microwave Remote Sensing 62\u003c\/p\u003e \u003cp\u003e5.1.3 LiDAR Remote\/Sensing 63\u003c\/p\u003e \u003cp\u003e5.1.4 Synergic Use of Multi-Sensor Data 65\u003c\/p\u003e \u003cp\u003e5.2 A Case Study in the Mixed Tropical Deciduous Forest of India 66\u003c\/p\u003e \u003cp\u003e5.2.1 Study Area 66\u003c\/p\u003e \u003cp\u003e5.2.2 Datasets 67\u003c\/p\u003e \u003cp\u003e5.2.3 Methodology 67\u003c\/p\u003e \u003cp\u003e5.2.4 Results 67\u003c\/p\u003e \u003cp\u003e5.2.5 Conclusion 67\u003c\/p\u003e \u003cp\u003e5.3 Uncertainties and Future Scope of Research in Biomass Estimation 71\u003c\/p\u003e \u003cp\u003e5.3.1 Summary 71\u003c\/p\u003e \u003cp\u003eAcknowledgment 72\u003c\/p\u003e \u003cp\u003eReferences 72\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Crop Water Requirements Analysis Using Geoinformatics Techniques in the Water-Scarce Semi-Arid Watershed \u003c\/b\u003e\u003cb\u003e81\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eK. Ibrahim-Bathis, S.A. Ahmed, V. Nischitha, and M.A. Mohammed-Aslam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 81\u003c\/p\u003e \u003cp\u003e6.1.1 Crop Calendar 82\u003c\/p\u003e \u003cp\u003e6.1.2 Crop Type Classification 83\u003c\/p\u003e \u003cp\u003e6.1.3 Crop Water Requirements 86\u003c\/p\u003e \u003cp\u003e6.1.4 CROPWAT Model 86\u003c\/p\u003e \u003cp\u003e6.1.5 Meteorological Data 86\u003c\/p\u003e \u003cp\u003e6.2 Reference Evapotranspiration (ETo) 86\u003c\/p\u003e \u003cp\u003e6.2.1 Effective Rainfall 88\u003c\/p\u003e \u003cp\u003e6.2.2 Crop Coefficient (Kc) 89\u003c\/p\u003e \u003cp\u003e6.3 Soil Data 89\u003c\/p\u003e \u003cp\u003e6.4 Crop Evapotranspiration (ETc) 90\u003c\/p\u003e \u003cp\u003e6.5 Irrigation Water Requirement 90\u003c\/p\u003e \u003cp\u003e6.6 Conclusion 91\u003c\/p\u003e \u003cp\u003eAcknowledgment 92\u003c\/p\u003e \u003cp\u003eReferences 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Biophysical Characterization and Monitoring Large-Scale Water and Vegetation Anomalies by Remote Sensing in the Agricultural Growing Areas of the Brazilian Semi-Arid Region \u003c\/b\u003e\u003cb\u003e94\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAntônio Heriberto de Castro Teixeira, Janice Freitas Leivas, Edson Patto Pacheco, Edlene Aparecida Monteiro Garçon, and Celina Maki Takemura\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 94\u003c\/p\u003e \u003cp\u003e7.2 Material and Methods 96\u003c\/p\u003e \u003cp\u003e7.3 Results and Discussion 99\u003c\/p\u003e \u003cp\u003e7.4 Conclusions 104\u003c\/p\u003e \u003cp\u003eAcknowledgments 105\u003c\/p\u003e \u003cp\u003eReferences 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection III Soil and Land Resource Monitoring \u003c\/b\u003e\u003cb\u003e111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 SMOS L4 Downscaled Soil Moisture Product Evaluation Over a Two Year – Period in a Mediterranean Setting \u003c\/b\u003e\u003cb\u003e113\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePatrick N.L. Lamptey, George P. Petropoulos, and Prashant K. Srivastava\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 113\u003c\/p\u003e \u003cp\u003e8.2 Experimental Setup 116\u003c\/p\u003e \u003cp\u003e8.3 Datasets Description 116\u003c\/p\u003e \u003cp\u003e8.3.1 SMOS L4 SM Product (1 km) 116\u003c\/p\u003e \u003cp\u003e8.3.2 In-situ Soil Moisture Data 118\u003c\/p\u003e \u003cp\u003e8.4 Methodology 119\u003c\/p\u003e \u003cp\u003e8.4.1 SSM Extraction from SMOS 119\u003c\/p\u003e \u003cp\u003e8.4.2 Pre-Processing of SMOS 119\u003c\/p\u003e \u003cp\u003e8.4.3 Agreement Evaluation 119\u003c\/p\u003e \u003cp\u003e8.5 Results 120\u003c\/p\u003e \u003cp\u003e8.5.1 Station ES-CPA 120\u003c\/p\u003e \u003cp\u003e8.5.2 Station N9 122\u003c\/p\u003e \u003cp\u003e8.5.3 Station M5 123\u003c\/p\u003e \u003cp\u003e8.5.4 Station H7 123\u003c\/p\u003e \u003cp\u003e8.5.5 Station K9 124\u003c\/p\u003e \u003cp\u003e8.6 Discussion 126\u003c\/p\u003e \u003cp\u003e8.7 Conclusions 127\u003c\/p\u003e \u003cp\u003eAcknowledgments 128\u003c\/p\u003e \u003cp\u003eReferences 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Estimating Urban Population Density Using Remotely Sensed Imagery Products \u003c\/b\u003e\u003cb\u003e132\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDimitris Triantakonstantis, Demetris Stathakis, and Zoi Papadopoulou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 132\u003c\/p\u003e \u003cp\u003e9.2 Spatial Data Disaggregation–MAUP Problem 134\u003c\/p\u003e \u003cp\u003e9.2.1 Spatial Interpolation 135\u003c\/p\u003e \u003cp\u003e9.3 Materials and Methods 136\u003c\/p\u003e \u003cp\u003e9.3.1 Study Area and Data Sources 136\u003c\/p\u003e \u003cp\u003e9.3.2 Areal Interpolation Using Cokriging 137\u003c\/p\u003e \u003cp\u003e9.4 Areal Interpolation Using Geographically Weighted Regression (GWR) 138\u003c\/p\u003e \u003cp\u003e9.5 Results and Discussion 139\u003c\/p\u003e \u003cp\u003e9.6 Conclusions 144\u003c\/p\u003e \u003cp\u003eReferences 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Impact of Land Cover Change on Surface Runoff \u003c\/b\u003e\u003cb\u003e150\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eApoorv Sood, S.K. Ghosh, and Priyadarshi Upadhyay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 150\u003c\/p\u003e \u003cp\u003e10.2 Literature 151\u003c\/p\u003e \u003cp\u003e10.3 Methodology 152\u003c\/p\u003e \u003cp\u003e10.3.1 Supervised Classification 152\u003c\/p\u003e \u003cp\u003e10.3.2 SWAT Model 153\u003c\/p\u003e \u003cp\u003e10.3.3 SWAT Inputs 153\u003c\/p\u003e \u003cp\u003e10.3.4 SWAT Outputs 154\u003c\/p\u003e \u003cp\u003e10.4 Methodology 154\u003c\/p\u003e \u003cp\u003e10.5 Study Area 154\u003c\/p\u003e \u003cp\u003e10.5.1 Justification for Study Area Selection 154\u003c\/p\u003e \u003cp\u003e10.6 Data Used 155\u003c\/p\u003e \u003cp\u003e10.6.1 Weather Data 156\u003c\/p\u003e \u003cp\u003e10.6.2 Satellite Data 158\u003c\/p\u003e \u003cp\u003e10.6.2.1 LANDSAT Dataset 158\u003c\/p\u003e \u003cp\u003e10.6.3 Digital Elevation Model 158\u003c\/p\u003e \u003cp\u003e10.6.4 Soil Map 158\u003c\/p\u003e \u003cp\u003e10.7 Results and Discussion 158\u003c\/p\u003e \u003cp\u003e10.7.1 LU\/LC Classification 158\u003c\/p\u003e \u003cp\u003e10.7.2 LU\/LC Map 1987 161\u003c\/p\u003e \u003cp\u003e10.7.3 LU\/LC Map 1997 161\u003c\/p\u003e \u003cp\u003e10.7.4 LU\/LC Map 2007 161\u003c\/p\u003e \u003cp\u003e10.7.5 LU\/LC Map 2017 161\u003c\/p\u003e \u003cp\u003e10.7.6 Watershed Delineation 163\u003c\/p\u003e \u003cp\u003e10.8 SWAT Results 164\u003c\/p\u003e \u003cp\u003e10.8.1 HRU Analysis Report 164\u003c\/p\u003e \u003cp\u003e10.8.2 Runoff Generated in Sub Basins 164\u003c\/p\u003e \u003cp\u003e10.9 Conclusion 167\u003c\/p\u003e \u003cp\u003eAcknowledgment 168\u003c\/p\u003e \u003cp\u003eReferences 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Delineation of Groundwater Potential Zone and Site Suitability of Rainwater Harvesting Structures Using Remote Sensing and In Situ Geophysical Measurements \u003c\/b\u003e\u003cb\u003e170\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePrachi Singh, Akash Anand, Prashant K. Srivastava, Arjun Singh, and Prem Chandra Pandey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 170\u003c\/p\u003e \u003cp\u003e11.2 Study Area 171\u003c\/p\u003e \u003cp\u003e11.3 Data Used and Methodology 172\u003c\/p\u003e \u003cp\u003e11.3.1 Data Used 172\u003c\/p\u003e \u003cp\u003e11.3.2 Methodology 173\u003c\/p\u003e \u003cp\u003e11.3.3 Vertical Electrical Sounding 173\u003c\/p\u003e \u003cp\u003e11.3.4 Weightage Calculation 174\u003c\/p\u003e \u003cp\u003e11.4 Results and Discussion 175\u003c\/p\u003e \u003cp\u003e11.4.1 Land Use and Land Cover (LULC) 175\u003c\/p\u003e \u003cp\u003e11.4.2 Soil 175\u003c\/p\u003e \u003cp\u003e11.4.3 Hydro-Geomorphology 176\u003c\/p\u003e \u003cp\u003e11.4.4 Lithology 176\u003c\/p\u003e \u003cp\u003e11.4.5 Drainage Density 178\u003c\/p\u003e \u003cp\u003e11.4.6 Lineament Density 178\u003c\/p\u003e \u003cp\u003e11.5 Resistivity Survey 179\u003c\/p\u003e \u003cp\u003e11.5.1 VES Survey and Cross Section 179\u003c\/p\u003e \u003cp\u003e11.5.2 Interpolated Subsurface Soil Profile 181\u003c\/p\u003e \u003cp\u003e11.5.3 Groundwater Potential Zone 181\u003c\/p\u003e \u003cp\u003e11.5.4 Suitable Sites for Rainwater Harvesting Structures 182\u003c\/p\u003e \u003cp\u003e11.6 Conclusions 185\u003c\/p\u003e \u003cp\u003eAcknowledgment 186\u003c\/p\u003e \u003cp\u003eReferences 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Structural Control on the Landscape Evolution of Son Alluvial Fan System in Ganga Foreland Basin \u003c\/b\u003e\u003cb\u003e189\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eManish Pandey, Yogesh Ray, Aman Arora, U.K. Shukla, and Shyam Ranjan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 189\u003c\/p\u003e \u003cp\u003e12.2 Study Area 192\u003c\/p\u003e \u003cp\u003e12.2.1 Geomorphological Setting of SAFS 192\u003c\/p\u003e \u003cp\u003e12.2.2 Geology of the Son Valley and SAFS 196\u003c\/p\u003e \u003cp\u003e12.2.3 Drainage 196\u003c\/p\u003e \u003cp\u003e12.2.4 Climate 197\u003c\/p\u003e \u003cp\u003e12.3 Materials and Methods 198\u003c\/p\u003e \u003cp\u003e12.3.1 Data Used 198\u003c\/p\u003e \u003cp\u003e12.3.2 Preprocessing of DEM 199\u003c\/p\u003e \u003cp\u003e12.3.3 DEM Derived Parameters 199\u003c\/p\u003e \u003cp\u003e12.3.4 Conceptual Background 199\u003c\/p\u003e \u003cp\u003e12.3.4.1 Quantitative Measure of River Basin Dynamics\/Reorganization 200\u003c\/p\u003e \u003cp\u003e12.3.4.2 X (χ)-Metrics and Cross-Divide χ-Anomaly 200\u003c\/p\u003e \u003cp\u003e12.3.4.3 Rationale Behind Experimental Use of χ-Transform for Alluvial Stream Long Profiles 203\u003c\/p\u003e \u003cp\u003e12.3.5 Normalized Channel Steepness Index (ksn) and Channel Concavity Index (θ) Computation 205\u003c\/p\u003e \u003cp\u003e12.3.6 Stream Sinuosity 205\u003c\/p\u003e \u003cp\u003e12.3.7 Hypsometric Curve (HC) 206\u003c\/p\u003e \u003cp\u003e12.4 Results and Discussion 206\u003c\/p\u003e \u003cp\u003e12.4.1 Zones of (dis)equilibrium Over SAFS in Ganga Foreland Basin (GFB) 206\u003c\/p\u003e \u003cp\u003e12.4.2 Sinuosity of Streams and Drainage Behavior Over SAFS 211\u003c\/p\u003e \u003cp\u003e12.4.3 Extent of SAFS vis-à-vis Evolution of Ganga Plain 212\u003c\/p\u003e \u003cp\u003e12.5 Conclusion and Recommendations 214\u003c\/p\u003e \u003cp\u003eAcknowledgments 215\u003c\/p\u003e \u003cp\u003eReferences 215\u003c\/p\u003e \u003cp\u003e12.A Appendix A: Supplementary Figures 226\u003c\/p\u003e \u003cp\u003e12.B Field Evidences of Neotectonic Activity (Source: Google Earth Pro) 240\u003c\/p\u003e \u003cp\u003e12.C Longitudinal Profile of the Ganga and its Right Bank Tributaries Flowing over SAFS 242\u003c\/p\u003e \u003cp\u003e12.D Lines of Cross-Sectional and Longitudinal Profiles 244\u003c\/p\u003e \u003cp\u003e12.E SAFS Profiles from Pandey 2014 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection IV Water Resource Monitoring \u003c\/b\u003e\u003cb\u003e247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Managing the Blue Carbon Ecosystem: A Remote Sensing and GIS Approach \u003c\/b\u003e\u003cb\u003e249\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eParul Maurya, Anup Kumar Das, and Rina Kumari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 249\u003c\/p\u003e \u003cp\u003e13.2 Blue Carbon Ecosystem 249\u003c\/p\u003e \u003cp\u003e13.2.1 Distribution 250\u003c\/p\u003e \u003cp\u003e13.2.2 Mangrove 251\u003c\/p\u003e \u003cp\u003e13.2.3 Seagrass 251\u003c\/p\u003e \u003cp\u003e13.2.4 Salt Marshes 252\u003c\/p\u003e \u003cp\u003e13.3 Factors Affecting Carbon Storage in Blue Carbon Ecosystems 253\u003c\/p\u003e \u003cp\u003e13.4 Carbon Storage in the Blue Carbon Ecosystem 254\u003c\/p\u003e \u003cp\u003e13.5 Pathways of Carbon in the Blue Carbon Ecosystem 254\u003c\/p\u003e \u003cp\u003e13.6 Evaluation of Long-Term Carbon Deposition in Sediments 255\u003c\/p\u003e \u003cp\u003e13.7 Ecosystem Services 256\u003c\/p\u003e \u003cp\u003e13.8 Threats to Coastal Blue Carbon Ecosystems 256\u003c\/p\u003e \u003cp\u003e13.9 Economy of Blue Carbon Ecosystems 257\u003c\/p\u003e \u003cp\u003e13.10 Management 258\u003c\/p\u003e \u003cp\u003e13.11 Conservation of Blue Carbon Ecosystem: A Remote Sensing Approach 258\u003c\/p\u003e \u003cp\u003e13.11.1 Role of Optical Remote Sensing 259\u003c\/p\u003e \u003cp\u003e13.11.2 Mapping the Mangrove Cover and Change Detection 259\u003c\/p\u003e \u003cp\u003e13.12 Quantification of Biophysical Variables 260\u003c\/p\u003e \u003cp\u003e13.12.1 Phenology 260\u003c\/p\u003e \u003cp\u003e13.12.2 Role of Hyperspectral Remote Sensing 260\u003c\/p\u003e \u003cp\u003e13.12.3 Mangrove-Mapping and Dynamics Studies Using Radar Data 261\u003c\/p\u003e \u003cp\u003e13.12.4 Dependence on Frequency 261\u003c\/p\u003e \u003cp\u003e13.12.5 Species Identification 261\u003c\/p\u003e \u003cp\u003e13.13 Conclusion 262\u003c\/p\u003e \u003cp\u003eAcknowledgment 262\u003c\/p\u003e \u003cp\u003eReferences 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Appraising the Changing Climate and Extent of Snow in the Kashmir Himalaya Using MODIS Data \u003c\/b\u003e\u003cb\u003e269\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSeema Rani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 269\u003c\/p\u003e \u003cp\u003e14.2 Study Area 270\u003c\/p\u003e \u003cp\u003e14.3 Materials and Methods 271\u003c\/p\u003e \u003cp\u003e14.4 Results and Discussions 273\u003c\/p\u003e \u003cp\u003e14.4.1 Trend in Air Temperature 273\u003c\/p\u003e \u003cp\u003e14.4.2 Trend in Snow Cover Area 275\u003c\/p\u003e \u003cp\u003e14.4.3 Variations in SCA Under Elevation Zones 278\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 282\u003c\/p\u003e \u003cp\u003eAcknowledgments 283\u003c\/p\u003e \u003cp\u003eReferences 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Knowledge-Based Mapping of Debris-Covered Glaciers in the Greater Himalayan Range \u003c\/b\u003e\u003cb\u003e287\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSwagata Ghosh and Raaj Ramsankaran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 287\u003c\/p\u003e \u003cp\u003e15.1.1 Overview of Ablation Pattern of Glaciers in the Western Himalaya 288\u003c\/p\u003e \u003cp\u003e15.1.2 Overview of Glacier Mapping Techniques 288\u003c\/p\u003e \u003cp\u003e15.2 Study Area 290\u003c\/p\u003e \u003cp\u003e15.3 Data Sources 291\u003c\/p\u003e \u003cp\u003e15.4 Methodology 292\u003c\/p\u003e \u003cp\u003e15.4.1 Pre-Processing of Satellite Data 293\u003c\/p\u003e \u003cp\u003e15.4.2 Knowledge-Based Approach 295\u003c\/p\u003e \u003cp\u003e15.4.2.1 Segregation of Snow and Ice from Other Land Covers Using Spectral Index 295\u003c\/p\u003e \u003cp\u003e15.4.2.2 Segregation Between Snow and Ice Types Using Spectral Indices 298\u003c\/p\u003e \u003cp\u003e15.4.2.3 Segregation of Supraglacial Debris Types from Non-Glacier Area 298\u003c\/p\u003e \u003cp\u003e15.5 Results and Discussions 299\u003c\/p\u003e \u003cp\u003e15.5.1 Accuracy Assessment of Supraglacial Covers Mapping of Pensilungpa Glacier 303\u003c\/p\u003e \u003cp\u003e15.5.2 Knowledge-Based Approach Versus Manual Digitization for Mapping Pensilungpa Glacier 304\u003c\/p\u003e \u003cp\u003e15.5.3 Uncertainty Analysis 306\u003c\/p\u003e \u003cp\u003e15.5.4 Knowledge-Based Approach Versus Supervised Classification for Mapping Pensilungpa Glacier 307\u003c\/p\u003e \u003cp\u003e15.5.5 Evaluation of Spatiotemporal Application Potential of the Knowledge-Based Approach 311\u003c\/p\u003e \u003cp\u003e15.6 Summary and Conclusions 312\u003c\/p\u003e \u003cp\u003e15.7 Future Scope 315\u003c\/p\u003e \u003cp\u003eReferences 315\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Seawater Intrusion and Salinity Mapping in Coastal Aquifers: A Geospatial Approach \u003c\/b\u003e\u003cb\u003e323\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTanushree and Rina Kumari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 323\u003c\/p\u003e \u003cp\u003e16.1.1 Water Stress in Coastal Aquifers Due to Salinity: A Global Concern 323\u003c\/p\u003e \u003cp\u003e16.1.2 Salinization of Aquifers in Semiarid Regions 324\u003c\/p\u003e \u003cp\u003e16.1.3 Seawater Intrusion: Basic Concept 324\u003c\/p\u003e \u003cp\u003e16.1.4 Various Approaches to Study Seawater Intrusion 325\u003c\/p\u003e \u003cp\u003e16.2 Aquifer Vulnerability Concept 326\u003c\/p\u003e \u003cp\u003e16.2.1 Vulnerability Types 327\u003c\/p\u003e \u003cp\u003e16.2.1.1 Intrinsic Vulnerability 327\u003c\/p\u003e \u003cp\u003e16.2.1.2 Specific Vulnerability 327\u003c\/p\u003e \u003cp\u003e16.2.2 Aquifer Vulnerability Due to Seawater Intrusion 327\u003c\/p\u003e \u003cp\u003e16.2.3 Methods to Assess Vulnerability 327\u003c\/p\u003e \u003cp\u003e16.2.3.1 Sensitivity Analysis 328\u003c\/p\u003e \u003cp\u003e16.2.4 Significance 331\u003c\/p\u003e \u003cp\u003e16.2.5 Geophysical Approaches 332\u003c\/p\u003e \u003cp\u003e16.2.5.1 Electromagnetic Surveys 332\u003c\/p\u003e \u003cp\u003e16.2.5.2 Time Domain Electromagnetic (TDEM) 333\u003c\/p\u003e \u003cp\u003e16.2.5.3 Frequency Domain Electromagnetic (FEM) 333\u003c\/p\u003e \u003cp\u003e16.2.5.4 Self-Potential 333\u003c\/p\u003e \u003cp\u003e16.2.5.5 Ground Penetrating Radar 333\u003c\/p\u003e \u003cp\u003e16.2.6 Numerical Model for Explaining Seawater Intrusion 334\u003c\/p\u003e \u003cp\u003e16.2.7 Remote Sensing for Salinity Mapping 334\u003c\/p\u003e \u003cp\u003e16.2.7.1 Optical Remote Sensing for Salinity Mapping 334\u003c\/p\u003e \u003cp\u003e16.2.7.2 Hyperspectral Remote Sensing 335\u003c\/p\u003e \u003cp\u003e16.2.7.3 Microwave Remote Sensing for Salinity Mapping 335\u003c\/p\u003e \u003cp\u003e16.3 Conclusion 336\u003c\/p\u003e \u003cp\u003eAcknowledgments 337\u003c\/p\u003e \u003cp\u003eReferences 337\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Wetland-Inundated Area Modeling and Monitoring Using Supervised and Machine Learning Classifiers \u003c\/b\u003e\u003cb\u003e346\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSwapan Talukdar, Sakshi Mankotia, Md Shamimuzzaman, Shahfahad, and Susanta Mahato\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 346\u003c\/p\u003e \u003cp\u003e17.2 Study Area 348\u003c\/p\u003e \u003cp\u003e17.3 Data Sources and Methods 349\u003c\/p\u003e \u003cp\u003e17.3.1 Data Sources 349\u003c\/p\u003e \u003cp\u003e17.3.2 Methods for Wetland-Inundated Area Mapping 349\u003c\/p\u003e \u003cp\u003e17.3.2.1 Methods for Machine Learning Classifiers 350\u003c\/p\u003e \u003cp\u003e17.3.2.2 Method for Supervised Classifiers 352\u003c\/p\u003e \u003cp\u003e17.3.3 Methods for Accuracy Assessment of Wetland-Inundation Area Mapping 352\u003c\/p\u003e \u003cp\u003e17.3.4 Methods of Modeling Wetland Landscape Transformation 353\u003c\/p\u003e \u003cp\u003e17.4 Results and Discussion 353\u003c\/p\u003e \u003cp\u003e17.4.1 Wetland Mapping Using Different Classifiers 353\u003c\/p\u003e \u003cp\u003e17.4.2 Validation of the Methods 354\u003c\/p\u003e \u003cp\u003e17.4.3 Spatiotemporal Analysis of Hydrological Variability of the Wetlands 356\u003c\/p\u003e \u003cp\u003e17.4.4 Fragmentation Analysis of the Hydrological Variability 357\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 360\u003c\/p\u003e \u003cp\u003eAcknowledgment 360\u003c\/p\u003e \u003cp\u003eReferences 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 A Focus on Reaggregation of Playa Wetland scapes in the Face of Global Ecological Disconnectivity \u003c\/b\u003e\u003cb\u003e366\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLaxmi Kant Sharma, Rajashree Naik, and Prem Chandra Pandey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 366\u003c\/p\u003e \u003cp\u003e18.2 Global Ecological Disconnectivity 367\u003c\/p\u003e \u003cp\u003e18.3 Playa Wetland scapes 367\u003c\/p\u003e \u003cp\u003e18.3.1 Importance 368\u003c\/p\u003e \u003cp\u003e18.3.2 Threats 368\u003c\/p\u003e \u003cp\u003e18.3.3 Playas of India 370\u003c\/p\u003e \u003cp\u003e18.4 Indian Playa Wetland scapes for Global Ecological Connectivity 371\u003c\/p\u003e \u003cp\u003e18.5 Reaggregation of Playa Wetland scapes 374\u003c\/p\u003e \u003cp\u003e18.6 Recent Approaches Used for Wetland scape Studies 375\u003c\/p\u003e \u003cp\u003e18.7 Limitations of Current Wetland scape Studies 377\u003c\/p\u003e \u003cp\u003e18.8 Scope of Integrated Playa Wetland scape Modeling 380\u003c\/p\u003e \u003cp\u003eAcknowledgment 381\u003c\/p\u003e \u003cp\u003eReferences 381\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection V Disaster Monitoring of Natural Resources \u003c\/b\u003e\u003cb\u003e389\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Flood Damage Assessment in a Part of the Ganga-Brahmaputra Plain Region, India \u003c\/b\u003e\u003cb\u003e391\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRajesh Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 391\u003c\/p\u003e \u003cp\u003e19.2 Study Area 393\u003c\/p\u003e \u003cp\u003e19.3 Materials and Methods 393\u003c\/p\u003e \u003cp\u003e19.4 Results and Discussion 395\u003c\/p\u003e \u003cp\u003e19.4.1 Flood-Prone and Flooded Areas 395\u003c\/p\u003e \u003cp\u003e19.4.2 Flood Damage and Flood Protection Works 396\u003c\/p\u003e \u003cp\u003e19.4.3 Trends in Flood Damage and Peak Flood Discharge 398\u003c\/p\u003e \u003cp\u003e19.5 Conclusions 400\u003c\/p\u003e \u003cp\u003eAcknowledgments 401\u003c\/p\u003e \u003cp\u003eDeclaration 401\u003c\/p\u003e \u003cp\u003eReferences 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Texture-Based Riverine Feature Extraction and Flood Mapping Using Satellite Images \u003c\/b\u003e\u003cb\u003e405\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKuldeep, P.K. Garg, and R.D. Garg\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 405\u003c\/p\u003e \u003cp\u003e20.2 Related Work 406\u003c\/p\u003e \u003cp\u003e20.3 The Study Area and Data Resources 408\u003c\/p\u003e \u003cp\u003e20.4 Methodology 408\u003c\/p\u003e \u003cp\u003e20.4.1 Geometric Correction and Image Enhancement 408\u003c\/p\u003e \u003cp\u003e20.4.2 Texture Feature Extraction and Optimal Feature Selection 409\u003c\/p\u003e \u003cp\u003e20.4.3 Texture-Based Classification 411\u003c\/p\u003e \u003cp\u003e20.4.4 Flood Hazard Mapping for Identification of Safe Islands 411\u003c\/p\u003e \u003cp\u003e20.4.4.1 Flood Inundation Mapping 411\u003c\/p\u003e \u003cp\u003e20.4.4.2 Validation of Flood Extent 412\u003c\/p\u003e \u003cp\u003e20.4.4.3 Damage Assessment 412\u003c\/p\u003e \u003cp\u003e20.5 Results and Discussions 413\u003c\/p\u003e \u003cp\u003e20.5.1 Feature Selection and Classification 413\u003c\/p\u003e \u003cp\u003e20.5.2 Flood Hazard Mapping 418\u003c\/p\u003e \u003cp\u003e20.5.3 HEC-RAS Processing and Model Validation 419\u003c\/p\u003e \u003cp\u003e20.5.4 Flood Damage Assessment 421\u003c\/p\u003e \u003cp\u003e20.6 Conclusion 424\u003c\/p\u003e \u003cp\u003eAcknowledgment 426\u003c\/p\u003e \u003cp\u003eReferences 426\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Numerical Simulation and Comparison of Tsunami Inundation for Different Satellite-Derived Datasets for the Gujarat Coast of India \u003c\/b\u003e\u003cb\u003e431\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShafique Matin and S.S. Praveen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 431\u003c\/p\u003e \u003cp\u003e21.2 Study Area 432\u003c\/p\u003e \u003cp\u003e21.3 Methodology 432\u003c\/p\u003e \u003cp\u003e21.3.1 Extraction of Different Satellite-Derived Datasets 432\u003c\/p\u003e \u003cp\u003e21.3.2 Numerical Modeling 434\u003c\/p\u003e \u003cp\u003e21.4 Results and Discussion 436\u003c\/p\u003e \u003cp\u003e21.4.1 Analysis of Datasets 439\u003c\/p\u003e \u003cp\u003e21.4.2 Parallel Transects 440\u003c\/p\u003e \u003cp\u003e21.4.3 Perpendicular Transects 440\u003c\/p\u003e \u003cp\u003e21.5 Conclusions 442\u003c\/p\u003e \u003cp\u003eAcknowledgments 442\u003c\/p\u003e \u003cp\u003eReferences 443\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection VI Future Aspect of Natural Resource Monitoring \u003c\/b\u003e\u003cb\u003e445\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Future Aspects and Potential of the Remote Sensing Technology to Meet the Natural Resource Needs \u003c\/b\u003e\u003cb\u003e447\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLaxmi Kant Sharma, Rajit Gupta, and Prem Chandra Pandey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 447\u003c\/p\u003e \u003cp\u003e22.2 Advances in Remote Sensing for Natural Resources Monitoring 449\u003c\/p\u003e \u003cp\u003e22.3 Potential Applications in Natural Resource Monitoring 451\u003c\/p\u003e \u003cp\u003e22.4 Challenges and Future Aspects 453\u003c\/p\u003e \u003cp\u003e22.5 Conclusion 455\u003c\/p\u003e \u003cp\u003eAcknowledgment 456\u003c\/p\u003e \u003cp\u003eReferences 456\u003c\/p\u003e \u003cp\u003eIndex 465\u003c\/p\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":49407100125527,"sku":"9781119615972","price":149.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119615972.jpg?v=1730498178","url":"https:\/\/bookcurl.com\/products\/advances-in-remote-sensing-for-natural-resource-monitoring-9781119615972","provider":"Book Curl","version":"1.0","type":"link"}