Advances in Remote Sensing for Natural Resource Monitoring

Dr Prem C. Pandey is Assistant Professor in the Center for Environmental Sciences & Engineering, Shiv Nadar University, UP, India.Dr Laxmi K. Sharma is Associate Professor, at the Department of Environmental Science, Central University of Rajasthan, Ajmer, India.

• List of Abbreviations xixList of Contributors xxixList of Editors xxxvPreface xxxviiSection I General Section 11 Introduction to Natural Resource Monitoring Using Remote Sensing Technology 3Prem Chandra Pandey and Laxmi Kant Sharma1.1 Introduction 3References 62 Spectroradiometry: Types, Data Collection, and Processing 9Prem Chandra Pandey, Manish Kumar Pandey, Ayushi Gupta, Prachi Singh, and Prashant K. Srivastava2.1 Introduction 92.2 Literature Review 102.3 The Types of Spectroradiometry 122.3.1 Spectroradiometry 132.3.2 Photometry and Colorimetry 132.4 Principle of the Spectroradiometer 132.5 Radiance Measurement 162.5.1 Factors Affecting Spectral Reflectance Measurements 172.5.2 Data Processing 182.5.2.1 Radiometric Calibration 182.5.2.2 Reflectance/Transmittance 192.5.2.3 Radiance/Irradiance/Emissivity 202.5.2.4 1st Derivative 202.5.2.5 2nd Derivative 202.5.2.6 Parabolic Correction 202.5.2.7 Other Methods 212.6 Data Collection 212.7 Generation of the Metadata 212.7.1 Continuum Removal 222.8 Applications of ASD in Agriculture and Forestry 232.9 Future Importance, Limitations, and Recommendations 23Acknowledgment 24References 243 Geometric-Optical Modeling of Bidirectional Reflectance Distribution Function for Trees and Forest Stands 28Nour El Islam Bachari, Salim Lamine, and Khaled Meharrar3.1 Introduction 283.2 Model Description 293.2.1 Sunlit Surfaces 313.2.2 Shaded Surfaces 313.2.3 Forest Stand Modeling 323.3 General Shape of the Apparent Luminance 333.4 Simulation and Discussion 35References 39Section II Vegetation Resource Monitoring (Forest and Agriculture) 434 Mapping Stand Age of Indonesian Rubber Plantation Using Fully Polarimetric L-Band Synthetic Aperture Radar 45Bambang H. Trisasongko4.1 Introduction 454.2 Methodology 464.2.1 Test Site and Dataset 464.2.2 Processing 474.3 Results and Discussion 484.3.1 Scattering Behavior 484.3.2 Classification Using Backscatter Coefficients 504.3.3 Classification Using Model-Based Decomposition 514.3.4 The Role of Combining Datasets 514.3.5 The Best Subset 524.4 Conclusion 55Acknowledgments 55References 555 Responses of Multi-Frequency Remote Sensing to Forest Biomass 58Suman Sinha, A. Santra, Laxmi Kant Sharma, Anup Kumar Das, C. Jeganathan, Shiv Mohan, S.S. Mitra, and M.S. Nathawat5.1 Background 585.1.1 Optical Remote Sensing 595.1.2 Microwave Remote Sensing 625.1.3 LiDAR Remote/Sensing 635.1.4 Synergic Use of Multi-Sensor Data 655.2 A Case Study in the Mixed Tropical Deciduous Forest of India 665.2.1 Study Area 665.2.2 Datasets 675.2.3 Methodology 675.2.4 Results 675.2.5 Conclusion 675.3 Uncertainties and Future Scope of Research in Biomass Estimation 715.3.1 Summary 71Acknowledgment 72References 726 Crop Water Requirements Analysis Using Geoinformatics Techniques in the Water-Scarce Semi-Arid Watershed 81K. Ibrahim-Bathis, S.A. Ahmed, V. Nischitha, and M.A. Mohammed-Aslam6.1 Introduction 816.1.1 Crop Calendar 826.1.2 Crop Type Classification 836.1.3 Crop Water Requirements 866.1.4 CROPWAT Model 866.1.5 Meteorological Data 866.2 Reference Evapotranspiration (ETo) 866.2.1 Effective Rainfall 886.2.2 Crop Coefficient (Kc) 896.3 Soil Data 896.4 Crop Evapotranspiration (ETc) 906.5 Irrigation Water Requirement 906.6 Conclusion 91Acknowledgment 92References 927 Biophysical Characterization and Monitoring Large-Scale Water and Vegetation Anomalies by Remote Sensing in the Agricultural Growing Areas of the Brazilian Semi-Arid Region 94Antônio Heriberto de Castro Teixeira, Janice Freitas Leivas, Edson Patto Pacheco, Edlene Aparecida Monteiro Garçon, and Celina Maki Takemura7.1 Introduction 947.2 Material and Methods 967.3 Results and Discussion 997.4 Conclusions 104Acknowledgments 105References 105Section III Soil and Land Resource Monitoring 1118 SMOS L4 Downscaled Soil Moisture Product Evaluation Over a Two Year – Period in a Mediterranean Setting 113Patrick N.L. Lamptey, George P. Petropoulos, and Prashant K. Srivastava8.1 Introduction 1138.2 Experimental Setup 1168.3 Datasets Description 1168.3.1 SMOS L4 SM Product (1 km) 1168.3.2 In-situ Soil Moisture Data 1188.4 Methodology 1198.4.1 SSM Extraction from SMOS 1198.4.2 Pre-Processing of SMOS 1198.4.3 Agreement Evaluation 1198.5 Results 1208.5.1 Station ES-CPA 1208.5.2 Station N9 1228.5.3 Station M5 1238.5.4 Station H7 1238.5.5 Station K9 1248.6 Discussion 1268.7 Conclusions 127Acknowledgments 128References 1289 Estimating Urban Population Density Using Remotely Sensed Imagery Products 132Dimitris Triantakonstantis, Demetris Stathakis, and Zoi Papadopoulou9.1 Introduction 1329.2 Spatial Data Disaggregation–MAUP Problem 1349.2.1 Spatial Interpolation 1359.3 Materials and Methods 1369.3.1 Study Area and Data Sources 1369.3.2 Areal Interpolation Using Cokriging 1379.4 Areal Interpolation Using Geographically Weighted Regression (GWR) 1389.5 Results and Discussion 1399.6 Conclusions 144References 14510 Impact of Land Cover Change on Surface Runoff 150Apoorv Sood, S.K. Ghosh, and Priyadarshi Upadhyay10.1 Introduction 15010.2 Literature 15110.3 Methodology 15210.3.1 Supervised Classification 15210.3.2 SWAT Model 15310.3.3 SWAT Inputs 15310.3.4 SWAT Outputs 15410.4 Methodology 15410.5 Study Area 15410.5.1 Justification for Study Area Selection 15410.6 Data Used 15510.6.1 Weather Data 15610.6.2 Satellite Data 15810.6.2.1 LANDSAT Dataset 15810.6.3 Digital Elevation Model 15810.6.4 Soil Map 15810.7 Results and Discussion 15810.7.1 LU/LC Classification 15810.7.2 LU/LC Map 1987 16110.7.3 LU/LC Map 1997 16110.7.4 LU/LC Map 2007 16110.7.5 LU/LC Map 2017 16110.7.6 Watershed Delineation 16310.8 SWAT Results 16410.8.1 HRU Analysis Report 16410.8.2 Runoff Generated in Sub Basins 16410.9 Conclusion 167Acknowledgment 168References 16811 Delineation of Groundwater Potential Zone and Site Suitability of Rainwater Harvesting Structures Using Remote Sensing and In Situ Geophysical Measurements 170Prachi Singh, Akash Anand, Prashant K. Srivastava, Arjun Singh, and Prem Chandra Pandey11.1 Introduction 17011.2 Study Area 17111.3 Data Used and Methodology 17211.3.1 Data Used 17211.3.2 Methodology 17311.3.3 Vertical Electrical Sounding 17311.3.4 Weightage Calculation 17411.4 Results and Discussion 17511.4.1 Land Use and Land Cover (LULC) 17511.4.2 Soil 17511.4.3 Hydro-Geomorphology 17611.4.4 Lithology 17611.4.5 Drainage Density 17811.4.6 Lineament Density 17811.5 Resistivity Survey 17911.5.1 VES Survey and Cross Section 17911.5.2 Interpolated Subsurface Soil Profile 18111.5.3 Groundwater Potential Zone 18111.5.4 Suitable Sites for Rainwater Harvesting Structures 18211.6 Conclusions 185Acknowledgment 186References 18612 Structural Control on the Landscape Evolution of Son Alluvial Fan System in Ganga Foreland Basin 189Manish Pandey, Yogesh Ray, Aman Arora, U.K. Shukla, and Shyam Ranjan12.1 Introduction 18912.2 Study Area 19212.2.1 Geomorphological Setting of SAFS 19212.2.2 Geology of the Son Valley and SAFS 19612.2.3 Drainage 19612.2.4 Climate 19712.3 Materials and Methods 19812.3.1 Data Used 19812.3.2 Preprocessing of DEM 19912.3.3 DEM Derived Parameters 19912.3.4 Conceptual Background 19912.3.4.1 Quantitative Measure of River Basin Dynamics/Reorganization 20012.3.4.2 X (χ)-Metrics and Cross-Divide χ-Anomaly 20012.3.4.3 Rationale Behind Experimental Use of χ-Transform for Alluvial Stream Long Profiles 20312.3.5 Normalized Channel Steepness Index (ksn) and Channel Concavity Index (θ) Computation 20512.3.6 Stream Sinuosity 20512.3.7 Hypsometric Curve (HC) 20612.4 Results and Discussion 20612.4.1 Zones of (dis)equilibrium Over SAFS in Ganga Foreland Basin (GFB) 20612.4.2 Sinuosity of Streams and Drainage Behavior Over SAFS 21112.4.3 Extent of SAFS vis-à-vis Evolution of Ganga Plain 21212.5 Conclusion and Recommendations 214Acknowledgments 215References 21512.A Appendix A: Supplementary Figures 22612.B Field Evidences of Neotectonic Activity (Source: Google Earth Pro) 24012.C Longitudinal Profile of the Ganga and its Right Bank Tributaries Flowing over SAFS 24212.D Lines of Cross-Sectional and Longitudinal Profiles 24412.E SAFS Profiles from Pandey 2014 245Section IV Water Resource Monitoring 24713 Managing the Blue Carbon Ecosystem: A Remote Sensing and GIS Approach 249Parul Maurya, Anup Kumar Das, and Rina Kumari13.1 Introduction 24913.2 Blue Carbon Ecosystem 24913.2.1 Distribution 25013.2.2 Mangrove 25113.2.3 Seagrass 25113.2.4 Salt Marshes 25213.3 Factors Affecting Carbon Storage in Blue Carbon Ecosystems 25313.4 Carbon Storage in the Blue Carbon Ecosystem 25413.5 Pathways of Carbon in the Blue Carbon Ecosystem 25413.6 Evaluation of Long-Term Carbon Deposition in Sediments 25513.7 Ecosystem Services 25613.8 Threats to Coastal Blue Carbon Ecosystems 25613.9 Economy of Blue Carbon Ecosystems 25713.10 Management 25813.11 Conservation of Blue Carbon Ecosystem: A Remote Sensing Approach 25813.11.1 Role of Optical Remote Sensing 25913.11.2 Mapping the Mangrove Cover and Change Detection 25913.12 Quantification of Biophysical Variables 26013.12.1 Phenology 26013.12.2 Role of Hyperspectral Remote Sensing 26013.12.3 Mangrove-Mapping and Dynamics Studies Using Radar Data 26113.12.4 Dependence on Frequency 26113.12.5 Species Identification 26113.13 Conclusion 262Acknowledgment 262References 26214 Appraising the Changing Climate and Extent of Snow in the Kashmir Himalaya Using MODIS Data 269Seema Rani14.1 Introduction 26914.2 Study Area 27014.3 Materials and Methods 27114.4 Results and Discussions 27314.4.1 Trend in Air Temperature 27314.4.2 Trend in Snow Cover Area 27514.4.3 Variations in SCA Under Elevation Zones 27814.5 Conclusion 282Acknowledgments 283References 28315 Knowledge-Based Mapping of Debris-Covered Glaciers in the Greater Himalayan Range 287Swagata Ghosh and Raaj Ramsankaran15.1 Introduction 28715.1.1 Overview of Ablation Pattern of Glaciers in the Western Himalaya 28815.1.2 Overview of Glacier Mapping Techniques 28815.2 Study Area 29015.3 Data Sources 29115.4 Methodology 29215.4.1 Pre-Processing of Satellite Data 29315.4.2 Knowledge-Based Approach 29515.4.2.1 Segregation of Snow and Ice from Other Land Covers Using Spectral Index 29515.4.2.2 Segregation Between Snow and Ice Types Using Spectral Indices 29815.4.2.3 Segregation of Supraglacial Debris Types from Non-Glacier Area 29815.5 Results and Discussions 29915.5.1 Accuracy Assessment of Supraglacial Covers Mapping of Pensilungpa Glacier 30315.5.2 Knowledge-Based Approach Versus Manual Digitization for Mapping Pensilungpa Glacier 30415.5.3 Uncertainty Analysis 30615.5.4 Knowledge-Based Approach Versus Supervised Classification for Mapping Pensilungpa Glacier 30715.5.5 Evaluation of Spatiotemporal Application Potential of the Knowledge-Based Approach 31115.6 Summary and Conclusions 31215.7 Future Scope 315References 31516 Seawater Intrusion and Salinity Mapping in Coastal Aquifers: A Geospatial Approach 323Tanushree and Rina Kumari16.1 Introduction 32316.1.1 Water Stress in Coastal Aquifers Due to Salinity: A Global Concern 32316.1.2 Salinization of Aquifers in Semiarid Regions 32416.1.3 Seawater Intrusion: Basic Concept 32416.1.4 Various Approaches to Study Seawater Intrusion 32516.2 Aquifer Vulnerability Concept 32616.2.1 Vulnerability Types 32716.2.1.1 Intrinsic Vulnerability 32716.2.1.2 Specific Vulnerability 32716.2.2 Aquifer Vulnerability Due to Seawater Intrusion 32716.2.3 Methods to Assess Vulnerability 32716.2.3.1 Sensitivity Analysis 32816.2.4 Significance 33116.2.5 Geophysical Approaches 33216.2.5.1 Electromagnetic Surveys 33216.2.5.2 Time Domain Electromagnetic (TDEM) 33316.2.5.3 Frequency Domain Electromagnetic (FEM) 33316.2.5.4 Self-Potential 33316.2.5.5 Ground Penetrating Radar 33316.2.6 Numerical Model for Explaining Seawater Intrusion 33416.2.7 Remote Sensing for Salinity Mapping 33416.2.7.1 Optical Remote Sensing for Salinity Mapping 33416.2.7.2 Hyperspectral Remote Sensing 33516.2.7.3 Microwave Remote Sensing for Salinity Mapping 33516.3 Conclusion 336Acknowledgments 337References 33717 Wetland-Inundated Area Modeling and Monitoring Using Supervised and Machine Learning Classifiers 346Swapan Talukdar, Sakshi Mankotia, Md Shamimuzzaman, Shahfahad, and Susanta Mahato17.1 Introduction 34617.2 Study Area 34817.3 Data Sources and Methods 34917.3.1 Data Sources 34917.3.2 Methods for Wetland-Inundated Area Mapping 34917.3.2.1 Methods for Machine Learning Classifiers 35017.3.2.2 Method for Supervised Classifiers 35217.3.3 Methods for Accuracy Assessment of Wetland-Inundation Area Mapping 35217.3.4 Methods of Modeling Wetland Landscape Transformation 35317.4 Results and Discussion 35317.4.1 Wetland Mapping Using Different Classifiers 35317.4.2 Validation of the Methods 35417.4.3 Spatiotemporal Analysis of Hydrological Variability of the Wetlands 35617.4.4 Fragmentation Analysis of the Hydrological Variability 35717.5 Conclusion 360Acknowledgment 360References 36018 A Focus on Reaggregation of Playa Wetland scapes in the Face of Global Ecological Disconnectivity 366Laxmi Kant Sharma, Rajashree Naik, and Prem Chandra Pandey18.1 Introduction 36618.2 Global Ecological Disconnectivity 36718.3 Playa Wetland scapes 36718.3.1 Importance 36818.3.2 Threats 36818.3.3 Playas of India 37018.4 Indian Playa Wetland scapes for Global Ecological Connectivity 37118.5 Reaggregation of Playa Wetland scapes 37418.6 Recent Approaches Used for Wetland scape Studies 37518.7 Limitations of Current Wetland scape Studies 37718.8 Scope of Integrated Playa Wetland scape Modeling 380Acknowledgment 381References 381Section V Disaster Monitoring of Natural Resources 38919 Flood Damage Assessment in a Part of the Ganga-Brahmaputra Plain Region, India 391Rajesh Kumar19.1 Introduction 39119.2 Study Area 39319.3 Materials and Methods 39319.4 Results and Discussion 39519.4.1 Flood-Prone and Flooded Areas 39519.4.2 Flood Damage and Flood Protection Works 39619.4.3 Trends in Flood Damage and Peak Flood Discharge 39819.5 Conclusions 400Acknowledgments 401Declaration 401References 40120 Texture-Based Riverine Feature Extraction and Flood Mapping Using Satellite Images 405Kuldeep, P.K. Garg, and R.D. Garg20.1 Introduction 40520.2 Related Work 40620.3 The Study Area and Data Resources 40820.4 Methodology 40820.4.1 Geometric Correction and Image Enhancement 40820.4.2 Texture Feature Extraction and Optimal Feature Selection 40920.4.3 Texture-Based Classification 41120.4.4 Flood Hazard Mapping for Identification of Safe Islands 41120.4.4.1 Flood Inundation Mapping 41120.4.4.2 Validation of Flood Extent 41220.4.4.3 Damage Assessment 41220.5 Results and Discussions 41320.5.1 Feature Selection and Classification 41320.5.2 Flood Hazard Mapping 41820.5.3 HEC-RAS Processing and Model Validation 41920.5.4 Flood Damage Assessment 42120.6 Conclusion 424Acknowledgment 426References 42621 Numerical Simulation and Comparison of Tsunami Inundation for Different Satellite-Derived Datasets for the Gujarat Coast of India 431Shafique Matin and S.S. Praveen21.1 Introduction 43121.2 Study Area 43221.3 Methodology 43221.3.1 Extraction of Different Satellite-Derived Datasets 43221.3.2 Numerical Modeling 43421.4 Results and Discussion 43621.4.1 Analysis of Datasets 43921.4.2 Parallel Transects 44021.4.3 Perpendicular Transects 44021.5 Conclusions 442Acknowledgments 442References 443Section VI Future Aspect of Natural Resource Monitoring 44522 Future Aspects and Potential of the Remote Sensing Technology to Meet the Natural Resource Needs 447Laxmi Kant Sharma, Rajit Gupta, and Prem Chandra Pandey22.1 Introduction 44722.2 Advances in Remote Sensing for Natural Resources Monitoring 44922.3 Potential Applications in Natural Resource Monitoring 45122.4 Challenges and Future Aspects 45322.5 Conclusion 455Acknowledgment 456References 456Index 465