| Description |
1 online resource (400 pages) |
| Contents |
Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Foreword -- Preface -- List of Abbreviations -- Editors -- Section I General Introduction to Forest Monitoring -- Chapter 1 Introduction to Forest Monitoring Using Advanced Remote Sensing Technology -- An Editorial Message -- 1.1 Introduction -- 1.2 Forest Monitoring: Importance and Trends -- 1.3 Advances in Remote Sensing Technology for Forest Monitoring -- 1.4 Summary -- References -- Chapter 2 Geospatial Perspectives of Sustainable Forest Management to Enhance Ecosystem Services and Livelihood Security -- 2.1 Introduction and Background -- 2.2 Major Ecological Disturbances of Forests -- 2.2.1 Livelihood Dependencies -- 2.3 Forest Fires -- 2.4 Invasive Plant Species (IPS) -- 2.5 Climate Change -- 2.6 Forest Ecosystem Services (FESs) -- 2.7 Sustainable Uses of Forests and Their Contributions to Livelihood Security -- 2.8 Landscape Based Approach (LbA) and Ecosystem-Based Approach (EbA) of Sustainable Forests Management (SFM) -- 2.9 Conclusions -- References -- Section II Forest Parameters -- Biochemical and Biophysical Parameters -- Chapter 3 Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data -- 3.1 Introduction -- 3.1.1 Chapter Overview -- 3.1.2 Threats to Riparian Forests -- 3.1.3 Remote Sensing of Riparian Forests -- 3.1.4 Implication of Carotenoids in Plant Stress -- 3.1.5 Advances in Carotenoid Retrieval Using Reflectance Spectroscopy -- 3.1.6 Applying Machine Learning to Reflectance Spectroscopy -- 3.2 Study Area -- 3.3 Data -- 3.3.1 Leaf Sampling and Analysis -- 3.3.2 Reflectance Measurements -- 3.4 Methodology -- 3.4.1 Preprocessing of Reflectance Data -- 3.4.2 ML Algorithms -- 3.4.3 Carotenoid Prediction -- 3.5 Results -- 3.5.1 Leaf Carotenoid Contents |
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3.5.2 Predictions of Carotenoid Contents Using ML Algorithms -- 3.6 Discussion -- 3.6.1 Sources of Variability in the Carotenoid Pool among Species -- 3.6.2 Toward a Broad-Scale Monitoring of Carotenoids? -- 3.6.3 Sensitivity Analysis -- 3.7 Conclusion -- Acknowledgments -- Funding -- References -- Supporting Information -- Chapter 4 Modeling of Abiotic Stress of Conifers with Remote Sensing Data -- 4.1 Introduction -- 4.2 Natural Factors -- 4.2.1 Soils -- 4.3 Anthropogenic Factors -- 4.3.1 Atmospheric Pollution -- 4.3.2 Soil and Groundwater Pollution -- 4.4 Thresholds and Critical Loads -- 4.4.1 Satellite Multi-Band Remote Methods for Detecting Abiotic Stress -- 4.4.2 Satellite Infrared Remote Sensing Methods for Detecting Abiotic Stress -- 4.4.3 Hyperspectral Satellite Remote Sensing Methods for Detecting Abiotic Stress -- 4.4.4 Fluorescent Satellite Remote Sensing Methods for Detecting Vegetation Stress -- 4.4.5 Modeling in Geoscience -- 4.4.6 Models of Geosystems and Abiotic Stress in Ecology and Radioecology -- 4.5 Conclusions -- References -- Chapter 5 Retrieval of Mangrove Forest Properties Using Synthetic Aperture Radar -- 5.1 Introduction -- 5.2 Microwave Remote Sensing -- 5.2.1 Polarization -- 5.2.2 Interaction Mechanism of SAR -- 5.2.3 SAR Based Mangroves Studies -- 5.2.4 SAR Image of the Mangroves -- 5.2.5 Mapping the Mangrove Area -- 5.2.6 Identification of Mangrove Degraded Area Using SAR -- 5.2.7 Mangrove Forest Structure Parameters and SAR -- 5.2.8 Mangrove Biomass and SAR -- 5.3 Conclusions -- References -- Chapter 6 Photosynthetic Variables Estimation in a Mangrove Forest -- 6.1 Introduction -- 6.1.1 Mangroves -- 6.1.2 Photosynthesis/Carbon Sequestration -- 6.1.3 Leaf Area Index -- 6.1.4 Chlorophyll Concentration -- 6.1.5 Solar Induced Fluorescence -- 6.1.6 Gross Primary Productivity (GPP) -- 6.1.7 Vegetation Indices (VIs) |
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6.2 Materials and Methodology -- 6.2.1 Dataset -- 6.2.2 Methods -- 6.3 Results -- 6.3.1 Seasonal Variation of LAI, SIF, and GPP -- 6.3.2 Landsat-8 Predicted LAI -- 6.3.3 Landsat-8 Predicted Canopy Chlorophyll Content (CCC) -- 6.4 Discussion -- 6.4.1 Seasonal Behavior -- 6.4.2 Random Forest-based LAI and LCC estimation -- 6.5 Conclusions -- References -- Chapter 7 Quantifying Carbon Stock Variability of Species Within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery -- 7.1 Introduction -- 7.2 Materials and Methods -- 7.2.1 The Study Site -- 7.2.2 Field Survey and Data Collection -- 7.2.3 Allometric Modeling of Above Ground Biomass and Carbon Stock -- 7.2.4 Image Acquisition and Pre-processing -- 7.2.5 Sentinel-2 MSI Texture Metrics Derivation -- 7.2.6 Statistical Analysis -- 7.2.7 Model Accuracy Assessment -- 7.3 Results -- 7.3.1 Carbon Stock of Reforested Tree Species -- 7.3.2 Prediction Performance of Carbon Stock Using Remotely Sensed Data and the Random Forest Model -- 7.3.3 Carbon Stock Estimates and Variability Between Reforested Tree Species -- 7.4 Discussion -- 7.4.1 Carbon Stock Variability Between Reforested Tree Species -- 7.5 Conclusion -- Acknowledgments -- References -- Chapter 8 Mapping Oil Palm Plantations in the Fringe of Sebangau National Park, Central Kalimantan, Indonesia -- 8.1 Introduction -- 8.2 Methodology -- 8.2.1 Test Site and Datasets -- 8.2.2 Data Processing and Analysis -- 8.3 Results and Discussion -- 8.3.1 Identifying Oil Palm -- 8.3.2 Classification Accuracies -- 8.4 Conclusion -- Acknowledgments -- References -- Section III Remote Sensing Technology for Forest Fire Monitoring -- Chapter 9 Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms -- 9.1 Introduction -- 9.2 Study Area -- 9.3 Materials and Methods -- 9.3.1 Materials |
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9.3.2 Forest Fire Inventory -- 9.3.3 Ignition Factors for Forest Fire Modeling -- 9.3.4 Method for the Multicollinearity Analysis -- 9.3.5 Methods for Forest Fire Susceptibility Modeling -- 9.3.6 Validation of the Models -- 9.4 Results -- 9.4.1 Multicollinearity Analysis -- 9.4.2 Forest Fire Susceptibility Modeling -- 9.4.3 Validation Analysis of the Models -- 9.5 Discussion -- 9.6 Conclusion -- Acknowledgements -- References -- Chapter 10 Leveraging Google Earth Engine (GEE) and Landsat Images to Assess Bushfire Severity and Postfire Short-Term Vegetation Recovery: A Case Study of Victoria, Australia -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Study Area -- 10.2.2 Conceptual Workflow and Vegetation Recovery Predictors -- 10.2.3 Dataset -- 10.2.4 Processing in GEE -- 10.2.5 Fire Severity Characterization -- 10.2.6 Post-Fire Recovery Indices Calculation -- 10.2.7 Bushfire Severity Accuracy Assessment -- 10.3 Results -- 10.3.1 Bushfire Severity Assessment -- 10.3.2 Bushfire Severity Accuracy Assessment Results -- 10.3.3 Post-Fire Recovery Assessment -- 10.3.4 Correlation Among Climatic, Topographic, and Post-fire Recovery Variables -- 10.3.5 Relative Variable Importance in Post-Fire Recovery -- 10.4 Discussion -- 10.4.1 Bushfire Severity Assessment -- 10.4.2 Post-Fire Recovery Assessment -- 10.4.3 Climatic and Topographic Influence of Bushfire Recovery Assessment -- 10.4.4 Limitations of this Study -- 10.5 Conclusions -- Acknowledgements -- References -- Section IV Advancement in RS-Drones and Multi-Sensors Multi-Source for Forest Monitoring -- Chapter 11 Recent Advancement and Role of Drones in Forest Monitoring: Research and Practices -- 11.1 Introduction -- 11.2 Field Sampling Methods in Forest Application: Traditional to Present -- 11.3 Biophysical Parameters Assessment Using Remote Sensing -- 11.3.1 Above Ground Biomass (AGB) |
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11.3.2 Tree Height and Diameter at Breast Height (DBH) -- 11.3.3 Leaf Area Index (LAI) -- 11.4 Biochemical Parameter Assessment Using Remote Sensing -- 11.4.1 Canopy Chlorophyll Content (CCC) -- 11.4.2 Canopy Water Content (CWC) -- 11.5 UAV-Based Remote Sensing -- 11.6 Other Important Forest Research Applications and Practices -- 11.7 Conclusions -- References -- Chapter 12 Applications of Multi-Source and Multi-Sensor Data Fusion of Remote Sensing for Forest Species Mapping -- 12.1 Introduction -- 12.2 Forest Mapping Process -- 12.2.1 Image Acquisition -- 12.2.2 Image Pre-processing -- 12.2.3 Image Enhancement -- 12.2.4 Image Classification -- 12.2.5 Accuracy Assessments -- 12.2.6 Vegetation Indices -- 12.3 Data Fusion -- 12.3.1 Fusion of Satellite and UAV/Drone -- 12.4 Discussion -- 12.5 Conclusion and Future Trends -- Acknowledgments -- References -- Section V Opportunities, Challenges, and Future Aspects in Forest Monitoring -- Chapter 13 Challenges and Monitoring Methods of Forest Management Through Geospatial Application: A Review -- 13.1 Introduction -- 13.2 Importance of Forest Cover -- 13.2.1 Biogeochemical Cycle -- 13.2.2 Climate Change -- 13.2.3 Soil and Nutrients -- 13.2.4 Soil Conservation -- 13.2.5 Microbes -- 13.3 Challenges in the Sustainability of Forest Management -- 13.3.1 Challenges Due to Anthropogenic Activities -- 13.3.2 Application of Geospatial Technology in Monitoring of the Forests -- 13.3.3 Types of Forest Data -- 13.4 Summary -- References -- Chapter 14 Challenges and Future Possibilities Toward Himalayan Forest Monitoring -- 14.1 Introduction -- 14.2 Component of Forest Monitoring -- 14.2.1 Satellite Monitoring -- 14.2.2 Ground Station Monitoring -- 14.2.3 Ground Survey and Inventory -- 14.3 Challenges in Satellite Monitoring -- 14.3.1 Forest Fire Monitoring -- 14.3.2 Challenges in Land-Use Change Monitoring |
| Notes |
14.3.3 Challenges in Species Distribution Monitoring |
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Description based on publisher supplied metadata and other sources |
| Form |
Electronic book
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| Author |
Pandey, Prem C
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| ISBN |
1119788153 |
|
9781119788157 |
|
1119788137 |
|
9781119788133 |
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