Climate Impacts on Sustainable Natural Resource Management

ISBN-13: 9781119793373 / Angielski / Twarda / 2021 / 384 str.

Pavan Kumar; Ram Kumar Singh; Manoj Kumar

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About the Editors xiiiList of Contributors xvForeword xxiPreface xxiiSection I Sustainable Natural Resource Management 11 Impact of Local REDD+ Intervention on Greenhouse Gas Emissions in East Kalimantan Province, Indonesia 3Kiswanto, Martiwi Diah Setiawati, and Satoshi Tsuyuki1.1 Introduction 31.1.1 Tropical Deforestation 31.1.2 REDD+ 31.1.3 REDD+ in Indonesia 41.2 Materials and Methods 51.2.1 Spatial Dataset 51.2.2 Carbon Stock in Each Land Cover Class 51.2.3 Change in Carbon Stock and CO2 Emission 71.2.4 Historical Baselines and Future Trajectories 71.3 Results 81.3.1 Annual GHG Emissions 81.3.2 Historical Baselines and Future Trajectories 91.4 Discussion 101.5 Conclusions 12Acknowledgement 12Author Contribution 12List of Appendix 13References 142 Role of Geospatial Technologies in Natural Resource Management 19Abhishek K. Kala and Manoj Kumar2.1 Introduction 192.2 Applications of Geospatial Technology in Natural Resource Management 202.2.1 Forest Management 202.2.2 Water Resource Management 212.2.3 Water Quality Monitoring 222.2.4 Agriculture 232.2.5 Combating Desertification 252.2.6 Biodiversity Management 252.3 LiDAR Technology 262.4 Artificial Intelligence and Remote Sensing 262.5 Machine Learning Tools for Natural Resource Management 272.6 Applications of Unmanned Aerial Systems in Natural Resource Management 282.7 Google Earth Engine as a Platform for Environmental Monitoring and NRM 292.8 Conclusion 29References 303 Estimation of Snow Cover Area Using Microwave SAR Dataset 35Shafiyoddin B. Sayyad and Mudassar A. Shaikh3.1 Introduction 353.2 Classification Technique 363.2.1 Unsupervised Classification 363.2.1.1 H A Alpha Unsupervised Classification 363.2.1.2 Wishart H A Alpha Unsupervised Classification 373.2.2 Supervised Classification 373.2.2.1 Wishart Supervised Classification 383.2.2.2 Support Vector Machine (SVM) Supervised Classification 383.3 Statistical Parameters 393.3.1 Mean 393.3.2 Standard Deviation 403.3.3 Coefficient Variance 403.3.4 Equivalence Number of Looks (ENL) 403.4 Error and Accuracy Assessment 403.4.1 Confusion Matrix 413.4.2 Commission Error 413.4.3 Omission Error 423.5 Study Area 423.6 Methodology 433.7 Result and Discussion 443.8 Conclusion and Future Perspective 52References 52Section II Determinants of Forest Productivity 574 Forest Cover Change Detection Across Recent Three Decades in Persian OakForests Using Convolutional Neural Network 59Alireza Sharifi, Shilan Felegari, Aqil Tariq, and Saima Siddiqui4.1 Introduction 594.2 Materials and Methods 614.2.1 Study Area 614.2.2 Dataset 614.2.3 Image Pre-processing 644.2.4 Image Classification 644.3 Results and Discussion 654.4 Conclusion and Future Prospects 68References 695 The Interlinked Mechanisms of Productivity for Developing Process-BasedForest Growth Models 74Keshav Tyagi, Manoj Kumar, Sweta Nisha Phukon, Abhishek Ranjan, Pavan Kumar,and Ram Kumar Singh5.1 Introduction 745.2 Productivity: Definition and Associated Components 765.3 Various Processes and Components Driving Forest Productivity 775.3.1 Photosynthesis 785.3.2 Light Interception 795.3.3 Stomatal Conductance 795.3.4 Leaf Area Index 795.3.5 Gas-Exchange 805.3.6 Plant Respiration 805.3.7 Hydrology 815.3.8 Nitrogen Cycle 815.3.9 Litterfall 815.4 Different Approaches to Productivity Assessment 825.5 Evolution of Process-Based Models 835.6 Conclusion 84References 846 Allometric Equations for the Estimation of Biomass and Carbon in the Sub- tropical Pine Forests of India 89Harshi Jain, Keshav Tyagi, Akshay Paygude, Pavan Kumar, Ram Kumar Singh, andManoj Kumar6.1 Introduction 896.1.1 Species of Pine in India and its Associates 916.1.2 Uses of Chirpine 916.2 Chir Pine - a Boon or Bane? 926.3 Forest Carbon and Forest Biomass 936.4 Composition of Forest Biomass 946.4.1 Indian Forest Biomass and Carbon Estimates 946.4.2 Importance of Forest Biomass Estimation 956.5 Allometric Equations for Biomass Estimation 966.5.1 How Are Allometric Equations Developed? 966.6 Biomass and Carbon Stock Estimation in Chir Pine Forests of India Using Allometric Equations 976.7 Conclusion 101References 102Section III Agriculture and Climate Change 1097 Characterization of Stress-Prone Areas for Dissemination of Suitable Rice Varieties and their Adoption in Eastern India: An Integrated Approach toward Food Security 111Sk Mosharaf Hossain, Devi Dayal Sinha, and Swati Nayak7.1 Introduction 1117.1.1 Characterization of Stress-Prone (Flood and Drought) Areas in Eastern India: Geo-Spatial Based Studies (Submergence and Drought) 1127.1.2 Eastern India (Submergence Study - Assam) 1147.1.3 Eastern India (Drought Study - Uttar Pradesh) 1157.1.4 Rice-Growing Environments in India and Constraints 1167.1.5 Abiotic Stress in the Context of Rice Production 1177.2 Materials and Method (for Submergence-prone: Assam) 1187.3 Results and Discussion 1207.4 Conclusions 127References 1288 Farmers' Perspective and Adaptation Efforts to Tackle the Impacts of Climate Change 132Shivani Mehta and Shridhar Samant8.1 Introduction 1328.2 Methodology 1358.3 Results and Analysis 1378.3.1 Trends in Rainfall Patterns 1378.3.1.1 Trends in Annual Rainfall 1378.3.1.2 Trends in Rainy Days 1408.3.1.3 Trends in Actual and Normal (Expected) Rainfall for Every Month 1448.3.2 Impact of Climate Change on Farmers 1488.3.2.1 Demographic Profile of the Respondents 1488.3.2.2 Livelihood 1488.3.2.3 Pests and Diseases 1498.4 Understanding the Farmer's Perception of Climate Change 1498.5 Adaptation Efforts 1508.6 Conclusion 151References 152Section IV Water Resource Management and Riverine Health 1579 Multicriteria Drought Severity Analysis in Monaragala District Sri Lanka by Utilizing Remote Sensing and GIS 159K.U.J. Sandamali, K.A.M. Chathuranga, B.A.S.C. Kumara, and D.K.D.A. Ranaweera9.1 Introduction 1599.2 Methodology 1629.2.1 Study Area 1629.2.2 Data Sources and Data Collection Techniques 1639.3 Meteorological Drought of Monaragala District 1649.4 Agricultural Drought of Monaragala District 1679.4.1 Normalized Difference Vegetation Index (NDVI) 1679.4.2 Vegetation Condition Index (VCI) 1679.5 Hydrological Drought of Monaragala District 1699.6 Drought Risk Area Map of Monaragala District 1739.7 Conclusion and Recommendations 1779.8 Conclusion 1779.9 Recommendation 179References 18010 Comparative Evaluation of Predicted Hydrologic Response Under Two Extremities of Sustainability Using Transformed Landuse-Landcover and CORDEX-Based Climatic Scenarios: A Case Study of Kangshabati River Basin, West Bengal 183Shreyashi Santra Mitra, Akhilesh Kumar, Abhisek Santra, and Shidharth Routh10.1 Introduction 18310.2 A Brief Account of the Kangshabati River Basin, the Study Area 18510.3 Data and Methodological Description 18710.3.1 Model Data Input 18710.3.2 Land Change Scenarios Using Idrisi Land Change Modeler (LCM) 19010.3.3 SWAT Model Setup for Simulating Hydrologic Responses 19410.4 Results and Observations 19510.4.1 Trends in Climatic Indicators 19510.4.2 Trends in Land Use and Land Cover Change Scenarios 19810.4.3 Trends in Volumetric Runoff 20410.4.4 Trends in Surface Runoff 20910.5 Conclusion 214References 21511 Riverine Health a Function of Riverscape Variable: A Case Study of the River Ganga in Varanasi 219Shikha Sharma, Harshith Clifford Prince, Arijit Roy, and Madhoolika Agarwal11.1 Introduction 21911.2 Material and Methods 22211.2.1 Study Area 22211.2.1.1 Sampling Zones 22211.2.1.2 Survey Sites 22211.2.2 Data Collection 22311.2.2.1 Water Sample Collection and Analysis 22311.2.2.2 Survey Method 22411.2.3 Statistical Analysis 22411.2.3.1 Cluster Analysis 22411.2.3.2 Correlations Between Land Use Classes and Water Quality Parameters 22511.3 Result and Discussion 22511.3.1 Land Use and Water Quality 22511.3.2 Land Use and Biodiversity 22711.3.3 Land Use and Societal Perceptions 22811.3.3.1 Livelihood Earners Perceptions 22811.3.3.2 Tourists' Perception 22911.4 Conclusions 231References 231Section V Climate Change Threat on Natural Resources 23712 Socio-Economic Impacts of Climate Change 239Shubhi Patel, Anwesha Dey, Shani Kumar Singh, Rakesh Singh, and H.P. Singh12.1 Introduction 23912.2 Trends in Climate Variables 24012.3 Welfare Impact of Climate Change 24212.4 Impact on Agriculture 24412.5 Impact of Climate Change on Society 24612.5.1 Food Security 24612.5.2 Labor Productivity 24712.5.3 Health and Nutrition 24812.5.4 Adaptation Risk and Potential 24812.6 Conclusion 262References 26313 The Political Economy of Vulnerable Environment in the Age of Climate Change: A Kerala Experience 268P. RatheeshMon13.1 Introduction 26813.2 Climate Change in Kerala 26913.3 Climate and Sea Level Change Projections 27013.4 Natural Disasters Associated with Climate Change 27013.5 The Political Economy of Climate Change and Associated Disasters 27313.6 Who Are the Affected? 27513.7 Conclusion and Suggestions 276References 27614 Land Use/Land Cover (LULC) Changes in Cameron Highlands, Malaysia: Explore the Impact of the LULC Changes on Land Surface Temperature (LST) Using Remote Sensing 279Mohd Hasmadi Ismail, Darren How Jin Aik, Mohamad Azani Alias, Farrah MelissaMuharam, and Pakhriazad Hassan Zaki14.1 Introduction 27914.2 Effectiveness of Usage of Satellite Imagery in Land Use/Land Cover (LULC) Change 28114.3 The Impact of LULC Changes on Land Surface Temperature (LST) 28214.4 Methodology 28314.4.1 Cameron Highlands 28314.4.2 Data Collection 28414.4.3 Field Verification 28414.4.4 Image Processing 28514.5 Land Use/Cover Changes in Cameron Highland from 2009 to 2019 28714.5.1 Accuracy Assessment 29014.6 Land Surface Temperature Analysis of Comparative Sensors between Landsat Satellite Data and MODIS 29114.7 The LULC Effect on LST in Cameron Highlands 29214.8 Conclusions 296References 297Section VI Linkages between Natural Resources and Biotic-Abiotic Stressors 30315 Emerging Roles of Osmoprotectants in Alleviating Abiotic Stress Response Under Changing Climatic Conditions 305Debasish Pattnaik, Deepali Dash, Ankita Mishra, Aditya Kiran Padhiary, Prajjal Dey,and Goutam Kumar Dash15.1 Introduction 30515.2 Role of Osmoprotectant Under Abiotic Stress 30615.3 Role of Osmoprotectants Under Drought Stress 30615.4 Role of Osmoprotectants Under Salinity Stress 30715.5 Role of Osmoprotectants Under Cold Stress 30715.6 Role of Osmoprotectants Under Submergence Stress 30815.7 Role of Osmoprotectants Under Low Light Stress 30815.8 Mechanisms of Osmoprotectants Under Multiple Abiotic Stress 30915.9 Approaches to Improve Osmoprotectants to Confer Abiotic Stress Tolerance 31315.10 Metabolic Engineering Approach 31515.11 Future Prospect for Osmoprotectants Under Changing Climatic Conditions 316References 31616 Growth Variability of Conifers in Temperate Region of Western Himalayas 325Ufaid Mehraj, Akhlaq Amin Wani, Aasif Ali Gatoo, Mohammd Ajaz-ul-Islam, Shah Murtaza Mushtaq, Amir Farooq, Immad Ahmad Shah, and Tariq Hussain Masoodi16.1 Introduction 32516.2 Material and Methods 32616.2.1 Study Area 32616.2.2 Collection of Core Samples 32616.3 Results 32816.4 Discussion 33216.4.1 Species-Wise 33216.4.2 Site-Wise 33216.4.3 Diameter Class-Wise 33316.5 Conclusion 333References 33417 Process-Based Carbon Sequestration Study with Reference to the Energy-Water-Carbon Flux in a Forest Ecosystem 336Hukum Singh17.1 Introduction 33617.2 Concept of Soil-Vegetation-Atmosphere- Transfer (SVAT) 33817.3 History of Flux Measurements and Recent Advances-Different Methods 33917.4 Exchange Flux Measurements over Forest Ecosystems 34017.4.1 Fast Response System: Eddy Covariance or Eddy Correlation Measurements 34117.4.2 Slow-Response System 34117.4.2.1 Bowen Ratio Measurements 34117.4.2.2 Aerodynamic Flux Profile Method 34217.5 Ecosystem Flux Measurements Network Worldwide and Indian Scenario 34317.5.1 The Worldwide Network: The FLUXNET 34317.5.2 Scenario in India and Prospects 34417.5.3 The Proposed Concept of IndoFlux 34517.6 State of the Current Knowledge at Forest Research Institute, Dehradun 34517.7 Research Gaps and Future Needs 34617.8 Conclusion 347References 347Index 352

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About the EditorsDr Pavan Kumar has more than 7 years' experience in the field of remote sensing, forest monitoring, agricultural resource management and climate change.Dr Ram Kumar Singh has more than 12 years of experience in the field of remote sensing, data dynamic modelling, machine learning for various applications related to natural resource management.Dr Manoj Kumar is a senior scientist working in the field of forestry, environment and climate change to test and apply the computational tools and techniques of simulation, modelling, remote sensing and GIS.Dr Meenu Rani is a research scholar working in the field of remote sensing and water resource management.Dr Pardeep Sharma has more than 5 years' experience in the field of climate change.