Remote Sensing Handbook, Volume IV: Forests, Biodiversity, Ecology, LULC, and Carbon

Volume IV of the Six Volume Remote Sensing Handbook , Second Edition, is focused on the use of remote sensing in forestry, biodiversity, ecology, land use and land cover, and global terrestrial carbon mapping and monitoring. It discusses remote sensing studies of multi-scale habitat modeling, forest informatics, tree and stand height studies, land cover and land use (LCLU) change mapping, forest biomass and carbon modeling and mapping, and advanced image analysis methods and advances in land remote sensing using optical, radar, LiDAR, and hyperspectral remote sensing. This thoroughly revised and updated volume draws on the expertise of a diverse array of leading international authorities in remote sensing and provides an essential resource for researchers at all levels interested in using remote sensing. It integrates discussions of remote sensing principles, data, methods, development, applications, and scientific and social context. FEATURES Provides the most up-to-date comprehensive coverage of remote sensing science for forests, biodiversity, land cover and land use change (LCLUC), biomass, and carbon. Discusses and analyzes data from old and new generations of satellites and sensors spread across 60 years. Extensive forestry, LCLUC studies, biomass, and carbon using optical, radar, LiDAR, and hyperspectral data. Includes numerous case studies on advances and applications at local, regional, and global scales. Introduces advanced methods in remote sensing such as machine learning, cloud computing, and AI. Highlights scientific achievements over the last decade and provides guidance for future developments. This volume is an excellent resource for the entire remote sensing and GIS community. Academics, researchers, undergraduate and graduate students, as well as practitioners, decision makers, and policymakers, will benefit from the expertise of the professionals featured in this book and their extensive knowledge of new and emerging trends. Cover Half Title Title Copyright Contents Foreword Preface About the Editor List of Contributors Acknowledgments Part I Forests Chapter 1 Characterizing Tropical Forests with Multispectral Imagery Acronyms and Definitions 1.1 Introduction 1.2 Multispectral Imagery and REDD+ 1.2.1 Greenhouse Gas Inventories and Forest Carbon Offsets 1.2.2 The Roles of Multispectral Imagery 1.3 Characteristics of Multispectral Image Types 1.4 Preprocessing Imagery to Address Clouds 1.4.1 Cloud Screening 1.4.2 Filling Cloud and Scan-line Gaps 1.5 Forest Biomass, Degradation, Regrowth Rates From Multispectral Imagery 1.5.1 Tropical Forest Biomass from High-Resolution Multispectral Imagery 1.5.2 The Biomass, Age, and Rates of Biomass Accumulation in Forest Regrowth 1.5.3 Limitations to Mapping Forest Biomass or Age with One Multispectral Image Epoch 1.5.4 Detecting Tropical Forest Degradation with Multispectral Imagery 1.6 Mapping Tropical Forest Types with Multispectral Imagery 1.6.1 Forest Types as Strata for REDD+ and Other C Accounting 1.6.2 High-Resolution Multispectral Imagery for Mapping Finely Scaled Habitats 1.6.3 Remote Tree Species Identification and Forest Type Mapping 1.6.4 Mapping Tropical Forest Types with Medium-Resolution Imagery 1.6.5 Species Richness, Endemism and Functional Traits, and Multispectral Imagery 1.6.6 Tropical Forest Type Mapping at Coarse Spatial Scale 1.6.7 Tropical Forest Type Mapping and Image Spatial Resolution 1.7 Monitoring Effects of Global Change on Tropical Forests 1.7.1 Progress in Monitoring Tropical Forests at Subcontinental to Global Scales 1.7.2 The Feedbacks among Tropical Forest Disturbance, Drought, and Fire 1.7.3 Storm-Related and Other Tree Mortality 1.8 Summary and Conclusions 1.9 Acknowledgments Chapter 2 Remote Sensing of Forests from LiDAR and Radar Acronyms and Definitions 2.1 Introduction 2.2 Conventional Practices for Acquisition of Forest Resource Information 2.2.1 Forest Inventory 2.2.2 Forest Measurements 2.3 General Features of Laser Scanning/LiDAR and Radar 2.4 Obtaining 3D Data from Forestry 2.4.1 Space-borne LiDAR 2.4.2 Space-borne Synthetic Aperture Radar (SAR) 2.4.3 Airborne Laser Scanning, Airborne LiDAR 2.4.4 Terrestrial Laser Scanning 2.4.5 Mobile Laser Scanning 2.5 Processing 3D Data into Forest Information 2.5.1 DTM Processing 2.5.2 DSM Processing and Canopy Height Model 2.5.3 Point Height Metrics 2.5.4 Approaches for Obtaining Forest Data from Point Clouds 2.5.5 Individual Tree Detection or Locating with ALS 2.5.6 Individual Tree Height Derivation 2.5.7 Diameter and Stem Curve Derivation 2.6 Future Operational Possibilities 2.6.1 The Concept and Utility of the LiDAR Plots 2.6.2 Improving Large-Area Mapping of Forest Attributes Using Satellite Radar 2.6.3 Precision Forestry—Toward Individual Tree Inventories and Change Detection 2.6.4 Detection of Forest Cuttings 2.6.5 Automizing Field Inventories 2.6.6 Harvester Laser Scanning 2.6.7 Permanent Laser Scanning Systems for Continuous Forest Monitoring 2.7 Summary 2.8 Acknowledgment Chapter 3 Forest Biophysical and Biochemical Properties from Hyperspectral and LiDAR Remote Sensing Acronyms and Definitions 3.1 Introduction 3.2 HSI and LiDAR Data 3.2.1 HSI Data Sources 3.2.2 LiDAR Data Sources 3.2.3 Data Quality 3.3 HSI Remote Sensing of Forests 3.3.1 Biophysical Properties 3.3.2 Biochemical Properties 3.3.3 Canopy Physiology 3.4 LiDAR Remote Sensing of Forests 3.4.1 Canopy Structure and Biomass 3.4.2 Light Penetration 3.5 Integrating HSI and LiDAR 3.5.1 Benefits of Data Fusion 3.6 Conclusions Chapter 4 Optical Remote Sensing of Tree and Stand Heights Acronyms and Definitions 4.1 Introduction 4.2 Why Measure Tree Heights? 4.2.1 The Determinants of Heights 4.2.2 Importance of Tree Height Distribution for Forest Management and Ecology 4.2.3 Limitations of Field Measurements and How Remote Sensing Can Help Meet Information Requirements 4.3 Two Promising Optical Remote Sensing Techniques for Tree Height Measurements: LiDAR and Digital Photogrammetry 4.3.1 LiDAR 4.3.2 3D Modeling of the Canopy by Digital Photogrammetry 4.3.3 Comparison of ALS and Photogrammetric Products 4.4 Assessing Height Characteristics at the Stand Level 4.4.1 General Presentation of Area-based Approaches 4.4.2 Area-based Model Implementation 4.4.3 Model Extrapolation and Inferences for Large-Area Inventories 4.5 Approaches for Individual Tree Height Assessment 4.5.1 Raster-based Approaches 4.5.2 Point-based Approaches 4.6 Conclusion and Perspectives 4.7 Acknowledgments Part II Biodiversity Chapter 5 Biodiversity of the World: A Study from Space Acronyms and Definitions 5.1 Introduction 5.2 Measuring Biodiversity from Space 5.2.1 Mapping Species and Vegetation Types 5.2.2 Mapping Individual Trees 5.2.3 Mapping Animals from Space 5.2.4 Mapping Species Assemblages 5.3 Modeling Biodiversity from Space 5.3.1 Species Distribution Modeling 5.3.2 Land Cover and Diversity 5.3.3 Spectral Indices and Diversity 5.3.4 Multiple Sensors and Diversity 5.4 Monitoring Biodiversity from Space 5.4.1 Remote Sensing of Protected Areas 5.4.2 Remote Sensing of Urban Areas 5.5 Spaceborne Sensors and Biodiversity 5.5.1 Spectral Sensors and Biodiversity 5.5.2 Radar Sensors and Biodiversity 5.5.3 LiDAR Sensors and Biodiversity 5.5.4 Ideal Biodiversity Satellites and Sensors 5.6 Conclusions Chapter 6 Multi-Scale Habitat Mapping and Monitoring Using Satellite Data and Advanced Image Analysis Techniques Acronyms and Definitions 6.1 Introduction—The Policy Framework 6.1.1 Monitoring Global Change 6.1.2 Biodiversity and Related Policies 6.1.3 Mapping the State of Ecosystems 6.1.4 The EU Habitats Directive 6.2 Satellite Sensor Capabilities 6.2.1 Spatial Resolution—What Detail Can Be Mapped? 6.2.2 Spectral Resolution—Plant and Plant Feature Discrimination 6.2.3 Active Systems—Radar and LiDAR 6.2.4 Revisiting Time—Phenology 6.2.5 Advanced Image Analysis Techniques 6.3 EO-Based Biodiversity and Habitat Mapping 6.3.1 Land Cover, Habitats, and Indicators 6.3.2 Distinguishing between and within Broad Habitat Categories 6.4 Observing Quality, Pressures, and Changes 6.4.1 Measuring Habitat Quality 6.4.2 Identifying Pressures and Changes 6.5 Toward a Biodiversity Monitoring Service Part III Ecology Chapter 7 Ecological Characterization of Vegetation Using Multi-Sensor Remote Sensing in the Solar Reflective Spectrum Acronyms and Definitions 7.1 Introduction 7.2 A Brief History of Key Optical Sensors for Vegetation Mapping 7.2.1 The NOAA/AVHRR Program 7.2.2 MODIS and MISR 7.2.3 Suomi NPP/VIIRS 7.2.4 The Landsat Program 7.2.5 The SPOT Program 7.2.6 Commercial High-Resolution Satellite Era 7.2.7 Future Direction of Optical Remote Sensing 7.3 Optical Remote Sensing of Vegetation Structure 7.3.1 Vegetation Cover 7.3.2 Forest Successional Stages 7.3.3 Remote Sensing of Leaf Area Index and Clumping Index 7.3.4 Biomass 7.3.5 Uncertainties, Errors, and Accuracy 7.4 Optical Remote Sensing of Vegetation Functions 7.4.1 Vegetation Phenology 7.4.2 Fraction of Absorbed Photosynthetically Active Radiation 7.4.3 Leaf Chlorophyll Content 7.4.4 Light Use Efficiency 7.4.5 Gross Primary Productivity (GPP)/Net Primary Productivity (NPP) 7.4.6 Uncertainties, Errors, and Accuracy 7.5 Future Directions 7.6 Acknowledgment Part IV Land Use/Land Cover Chapter 8 Land Cover Change Detection Acronyms and Definitions 8.1 Introduction 8.2 Land Cover Change Detection and Monitoring—Theory and Practice 8.3 Trends in Land Cover Change Detection and Monitoring 8.3.1 Historical Trends—Eight Epochs 8.3.2 Cause of Land Cover Change 8.4 Land Cover Change Detection Approaches 8.4.1 Monotemporal Change Detection—Products for Real Time and Specific Disturbance Types 8.4.2 Bitemporal Change Detection—Map Comparison and Disturbance Analysis 8.4.3 Temporal Trend Analysis—Automation and Big Data 8.4.4 Comparison of Several Automated Change Detection Approaches 8.5 Accuracy Assessment—Beyond Statistics 8.6 Massachusetts Case Study—CLASlite 8.6.1 CLASlite Results 8.6.2 Deforestation and Disturbance Mapping 8.6.3 Gardner, Massachusetts, Forest Change 8.6.4 2011 Tornado Disturbance 8.7 Knowledge Gaps and Future Directions 8.8 Acknowledgments Chapter 9 Land Use and Land Cover Mapping and Monitoring with Radar Remote Sensing Acronyms and Definitions 9.1 Introduction 9.2 Radar System Parameters and Development 9.3 Radar System Parameter Consideration for LULC Mapping 9.4 Classification of Radar Imagery 9.4.1 Image Preprocessing 9.4.2 Feature Extraction and Selection 9.4.3 Selection of Classifiers 9.5 Change Detection Methods for Radar Imagery 9.5.1 Unsupervised Change Detection Methods 9.5.2 Combining Unsupervised Change Detection and Post-classification Comparison 9.6 Applications of Radar Imagery in LULC Mapping and Monitoring 9.6.1 LULC Classification and Change Detection 9.6.2 Forestry Inventory and Mapping 9.6.3 Crop and Vegetation Identification 9.6.4 Application on Urban Environment 9.6.5 Snow and Ice Mapping 9.6.6 Flood Detection and Monitoring 9.6.7 Other Applications 9.7 Future Developments Part V Carbon Chapter 10 Global Carbon Budgets and the Role of Remote Sensing Acronyms and Definitions 10.1 The Global Carbon Budget 10.1.1 The Contemporary Carbon Budget 10.1.2 A History of Carbon Cycle Research 10.1.3 Sources and Sinks of Carbon from Land 10.1.4 A Bookkeeping Model 10.1.5 Spatial Analyses 10.2 Land Use and Land Cover Change (LULCC), Disturbances, and Recovery 10.2.1 Use of Satellite Data 10.3 The Policy Realm: Issues Inherent in Estimating the Flux of Carbon from LULCC, with an Example Using RED, REDD, and REDD+ 10.3.1 Definitions 10.3.2 Assigning a Carbon Density to the Areas Deforested 10.3.3 Committed versus Actual Emissions (Legacy Effects) 10.3.4 Gross and Net Emissions of Carbon from LULCC 10.3.5 Initial Conditions 10.3.6 Full Carbon Accounting 10.3.7 Accuracy and Precision 10.3.8 Attribution 10.3.9 Uncertainties 10.4 The Residual Terrestrial Sink 10.4.1 The Orbiting Carbon Observatory (OCO) 10.4.2 Satellite Monitoring of Vegetation Activity (Greenness) 10.5 Conclusions 10.6 Acknowledgment Chapter 11 Aboveground Terrestrial Biomass and Carbon Stock Estimations from Multi-Sensor Remote Sensing Acronyms and Definitions 11.1 Introduction 11.1.1 Importance of the Terrestrial Ecosystem Carbon and Carbon Changes Estimates 11.1.2 Importance of Tropical Rainforests in Carbon Storage 11.1.3 Summary of Methods Used to Estimate Terrestrial Biomass and Carbon Stocks 11.1.4 The Role of Remote Sensing in Terrestrial Ecosystem Carbon Estimates 11.1.5 Specific Topics Covered in This Chapter 11.2 Conventional Methods of Carbon Stocks Estimates 11.2.1 Biome Average Methods 11.2.2 Allometric Biomass Methods 11.3 Remote Sensing Data 11.3.1 Passive Optical Remote Sensing Data 11.3.2 Radar Data 11.3.3 LiDAR Data 11.4 Research Approaches/Methods 11.5 Remote Sensing Based Aboveground Biomass Estimates 11.5.1 Optical Remote Sensing 11.5.2 Radar 11.5.3 LiDAR 11.5.4 Multi-Sensor Fusion 11.6 Summary 11.7 Conclusions and Future Directions 11.8 Acknowledgment Part VI Summary and Synthesis of Volume IV Chapter 12 Forests, Biodiversity, Ecology, LULC, and Carbon Acronyms and Definitions 12.1 Tropical Forest Characterization Using Multi-Spectral Imagery 12.2 LiDAR and Radar for Forest Informatics 12.3 Hyperspectral Imager (HSI) and LiDAR Data in the Study of Forest Biophysical, Biochemical, and Structural Properties 12.4 Tree and Stand Heights from Optical Remote Sensing 12.5 Study of Biodiversity from Space 12.6 Multi-Scale Habitat Mapping and Monitoring Using Satellite Data and Advanced Image Analysis Techniques 12.7 Ecological Characterization of Vegetation Using Multi-Sensor Remote Sensing 12.8 Land Cover Change Detection 12.9 Radar Remote Sensing in Land Use and Land Cover Mapping and Change Detection 12.10 Global Carbon Budgets and Remote Sensing 12.11 Remote Sensing of Global Terrestrial Carbon 12.12 Acknowledgments Index