CLIMATE IMPACTS ON SUSTAINABLE NATURAL RESOURCE MANAGEMENT Climate change has emerged as one of the predominant global concerns of the 21st century. Statistics show that the average surface temperature of the Earth has increased by about 1.18°C since the late 19th century and the sea levels are rising due to the melting of glaciers. Further rise in the global temperature will have dire consequences for the survival of humans on the planet Earth. There is a need to monitor climatic data and associated drivers of changes to develop sustainable planning. The anthropogenic activities that are…mehr
CLIMATE IMPACTS ON SUSTAINABLE NATURAL RESOURCE MANAGEMENT
Climate change has emerged as one of the predominant global concerns of the 21st century. Statistics show that the average surface temperature of the Earth has increased by about 1.18°C since the late 19th century and the sea levels are rising due to the melting of glaciers. Further rise in the global temperature will have dire consequences for the survival of humans on the planet Earth. There is a need to monitor climatic data and associated drivers of changes to develop sustainable planning. The anthropogenic activities that are linked to climate change need scientific evaluation and must be curtailed before it is too late.
This book contributes significantly in the field of sustainable natural resource management linked to climate change. Up to date research findings from developing and developed countries like India, Indonesia, Japan, Malaysia, Sri Lanka and the USA have been presented through selectedcase studies covering different thematic areas. The book has been organised into six major themes of sustainable natural resource management, determinants of forest productivity, agriculture and climate change, water resource management and riverine health, climate change threat on natural resources, and linkages between natural resources and biotic-abiotic stressors to develop the concept and to present the findings in a way that is useful for a wide range of readers. While the range of applications and innovative techniques is constantly increasing, this book provides a summary of findings to provide the updated information.
This book will be of interest to researchers and practitioners in the field of environmental sciences, remote sensing, geographical information system, meteorology, sociology and policy studies related to natural resource management and climate change.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
About the Editors Dr 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.
Inhaltsangabe
About the Editors xiii
List of Contributors xv
Foreword xxi
Preface xxii
Section I Sustainable Natural Resource Management 1
1 Impact of Local REDD+ Intervention on Greenhouse Gas Emissions in East Kalimantan Province, Indonesia 3 Kiswanto, Martiwi Diah Setiawati, and Satoshi Tsuyuki
1.1 Introduction 3
1.1.1 Tropical Deforestation 3
1.1.2 REDD+ 3
1.1.3 REDD+ in Indonesia 4
1.2 Materials and Methods 5
1.2.1 Spatial Dataset 5
1.2.2 Carbon Stock in Each Land Cover Class 5
1.2.3 Change in Carbon Stock and CO2 Emission 7
1.2.4 Historical Baselines and Future Trajectories 7
1.3 Results 8
1.3.1 Annual GHG Emissions 8
1.3.2 Historical Baselines and Future Trajectories 9
1.4 Discussion 10
1.5 Conclusions 12
Acknowledgement 12
Author Contribution 12
List of Appendix 13
References 14
2 Role of Geospatial Technologies in Natural Resource Management 19 Abhishek K. Kala and Manoj Kumar
2.1 Introduction 19
2.2 Applications of Geospatial Technology in Natural Resource Management 20
2.2.1 Forest Management 20
2.2.2 Water Resource Management 21
2.2.3 Water Quality Monitoring 22
2.2.4 Agriculture 23
2.2.5 Combating Desertification 25
2.2.6 Biodiversity Management 25
2.3 LiDAR Technology 26
2.4 Artificial Intelligence and Remote Sensing 26
2.5 Machine Learning Tools for Natural Resource Management 27
2.6 Applications of Unmanned Aerial Systems in Natural Resource Management 28
2.7 Google Earth Engine as a Platform for Environmental Monitoring and NRM 29
2.8 Conclusion 29
References 30
3 Estimation of Snow Cover Area Using Microwave SAR Dataset 35 Shafiyoddin B. Sayyad and Mudassar A. Shaikh
3.1 Introduction 35
3.2 Classification Technique 36
3.2.1 Unsupervised Classification 36
3.2.1.1 H A Alpha Unsupervised Classification 36
3.2.1.2 Wishart H A Alpha Unsupervised Classification 37
3.2.2 Supervised Classification 37
3.2.2.1 Wishart Supervised Classification 38
3.2.2.2 Support Vector Machine (SVM) Supervised Classification 38
3.3 Statistical Parameters 39
3.3.1 Mean 39
3.3.2 Standard Deviation 40
3.3.3 Coefficient Variance 40
3.3.4 Equivalence Number of Looks (ENL) 40
3.4 Error and Accuracy Assessment 40
3.4.1 Confusion Matrix 41
3.4.2 Commission Error 41
3.4.3 Omission Error 42
3.5 Study Area 42
3.6 Methodology 43
3.7 Result and Discussion 44
3.8 Conclusion and Future Perspective 52
References 52
Section II Determinants of Forest Productivity 57
4 Forest Cover Change Detection Across Recent Three Decades in Persian Oak Forests Using Convolutional Neural Network 59 Alireza Sharifi, Shilan Felegari, Aqil Tariq, and Saima Siddiqui
4.1 Introduction 59
4.2 Materials and Methods 61
4.2.1 Study Area 61
4.2.2 Dataset 61
4.2.3 Image Pre-processing 64
4.2.4 Image Classification 64
4.3 Results and Discussion 65
4.4 Conclusion and Future Prospects 68
References 69
5 The Interlinked Mechanisms of Productivity for Developing Process-Based Forest Growth Models 74 Keshav Tyagi, Manoj Kumar, Sweta Nisha Phukon, Abhishek Ranjan, Pavan Kumar,and Ram Kumar Singh
Section I Sustainable Natural Resource Management 1
1 Impact of Local REDD+ Intervention on Greenhouse Gas Emissions in East Kalimantan Province, Indonesia 3 Kiswanto, Martiwi Diah Setiawati, and Satoshi Tsuyuki
1.1 Introduction 3
1.1.1 Tropical Deforestation 3
1.1.2 REDD+ 3
1.1.3 REDD+ in Indonesia 4
1.2 Materials and Methods 5
1.2.1 Spatial Dataset 5
1.2.2 Carbon Stock in Each Land Cover Class 5
1.2.3 Change in Carbon Stock and CO2 Emission 7
1.2.4 Historical Baselines and Future Trajectories 7
1.3 Results 8
1.3.1 Annual GHG Emissions 8
1.3.2 Historical Baselines and Future Trajectories 9
1.4 Discussion 10
1.5 Conclusions 12
Acknowledgement 12
Author Contribution 12
List of Appendix 13
References 14
2 Role of Geospatial Technologies in Natural Resource Management 19 Abhishek K. Kala and Manoj Kumar
2.1 Introduction 19
2.2 Applications of Geospatial Technology in Natural Resource Management 20
2.2.1 Forest Management 20
2.2.2 Water Resource Management 21
2.2.3 Water Quality Monitoring 22
2.2.4 Agriculture 23
2.2.5 Combating Desertification 25
2.2.6 Biodiversity Management 25
2.3 LiDAR Technology 26
2.4 Artificial Intelligence and Remote Sensing 26
2.5 Machine Learning Tools for Natural Resource Management 27
2.6 Applications of Unmanned Aerial Systems in Natural Resource Management 28
2.7 Google Earth Engine as a Platform for Environmental Monitoring and NRM 29
2.8 Conclusion 29
References 30
3 Estimation of Snow Cover Area Using Microwave SAR Dataset 35 Shafiyoddin B. Sayyad and Mudassar A. Shaikh
3.1 Introduction 35
3.2 Classification Technique 36
3.2.1 Unsupervised Classification 36
3.2.1.1 H A Alpha Unsupervised Classification 36
3.2.1.2 Wishart H A Alpha Unsupervised Classification 37
3.2.2 Supervised Classification 37
3.2.2.1 Wishart Supervised Classification 38
3.2.2.2 Support Vector Machine (SVM) Supervised Classification 38
3.3 Statistical Parameters 39
3.3.1 Mean 39
3.3.2 Standard Deviation 40
3.3.3 Coefficient Variance 40
3.3.4 Equivalence Number of Looks (ENL) 40
3.4 Error and Accuracy Assessment 40
3.4.1 Confusion Matrix 41
3.4.2 Commission Error 41
3.4.3 Omission Error 42
3.5 Study Area 42
3.6 Methodology 43
3.7 Result and Discussion 44
3.8 Conclusion and Future Perspective 52
References 52
Section II Determinants of Forest Productivity 57
4 Forest Cover Change Detection Across Recent Three Decades in Persian Oak Forests Using Convolutional Neural Network 59 Alireza Sharifi, Shilan Felegari, Aqil Tariq, and Saima Siddiqui
4.1 Introduction 59
4.2 Materials and Methods 61
4.2.1 Study Area 61
4.2.2 Dataset 61
4.2.3 Image Pre-processing 64
4.2.4 Image Classification 64
4.3 Results and Discussion 65
4.4 Conclusion and Future Prospects 68
References 69
5 The Interlinked Mechanisms of Productivity for Developing Process-Based Forest Growth Models 74 Keshav Tyagi, Manoj Kumar, Sweta Nisha Phukon, Abhishek Ranjan, Pavan Kumar,and Ram Kumar Singh
5.1 Introduction 74 &nbs
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