Advances in Remote Sensing for Forest Monitoring

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
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)
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
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)
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
14.3.3 Challenges in Species Distribution Monitoring
14.3.4 Challenges in Climate Monitoring
14.3.5 Challenges in Wildlife Monitoring
14.4 Challenges in Ground Survey and Inventory
14.4.1 Challenges in Forest Surveying
14.4.2 Challenges in Biodiversity Monitoring
14.4.3 Challenges in Socio-Economic Survey
14.4.4 Challenges in Forest Production Monitoring
14.5 Future Possibilities in Forest Monitoring
14.5.1 Application of High Spatial and Spectral Resolution Satellites and Cameras
14.5.2 Application of Drones and Aircraft
14.5.3 Application of LiDAR
14.5.4 Carbon Credits and Attracting Funds for Nations
14.6 Conclusion
References
Web Sources
Index
EULA