Remote Sensing of Plant Biodiversity

Description

Foreword Contents About the Authors About the Editors Chapter 1: The Use of Remote Sensing to Enhance Biodiversity Monitoring and Detection: A Critical Challenge for the Twenty-First Century 1.1 Introduction 1.2 Why a Focus on Plant Diversity? 1.3 The Promise of Remote Sensing to Detect Plant Diversity 1.4 The Contents of the Book 1.5 The Origins of the Book References Chapter 2: Applying Remote Sensing to Biodiversity Science 2.1 What Is Biodiversity? 2.2 The Hierarchical Nature of Biodiversity 2.3 The Making of a Phenotype: Phylogeny, Genes, and the Environment 2.4 Patterns in Plant Diversity 2.5 Functional Traits, Community Assembly, and Evolutionary Legacy Effects on Ecosystems 2.5.1 Functional Traits and the Leaf Economic Spectrum 2.5.2 Plant Traits, Community Assembly, and Ecosystem Function 2.5.3 Phylogenetic, Functional, and Spectral Dispersion in Communities 2.6 Evolutionary Legacy Effects on Ecosystems 2.7 Quantifying Multiple Dimensions of Biodiversity 2.7.1 The Spatial Scale of Diversity: Alpha, Beta, and Gamma Diversity 2.7.2 Taxonomic Diversity 2.7.3 Phylogenetic Diversity 2.7.4 Functional Diversity 2.7.5 Spectral Diversity 2.7.6 Beta Diversity Metrics 2.8 Links Between Plant Diversity, Other Trophic Levels, and Ecosystem Functions 2.9 Incorporating Spectra into Relationships Between Biodiversity and Ecosystem Function 2.10 Links Between Biodiversity and Ecosystem Services 2.11 Trade-Offs Between Biodiversity and Ecosystem Services References Chapter 3: Scaling Functional Traits from Leaves to Canopies 3.1 Introduction 3.1.1 Plant Traits and Functional Diversity 3.1.2 Historical Advances in Remote Sensing of Vegetation 3.1.3 Remote Sensing as a Tool for Scaling and Mapping Plant Traits 3.1.4 Key Considerations for the Use of Imaging Spectroscopy Data for Scaling and Mapping Plant Functional Traits 3.2 Linking Plant Functional Traits to Remote Sensing Signatures 3.2.1 Spectroscopy and Plant Functional Traits 3.2.2 Approaches for Linking Traits and Spectral Signatures 3.2.2.1 Empirical Scaling Approaches 3.2.2.2 Radiative Transfer Models and Scaling Functional Traits 3.3 Important Considerations, Caveats, and Future Opportunities 3.3.1 Field Sampling and Scaling Considerations 3.3.2 Evaluating Functional Trait Maps and the Need to Quantify Uncertainties 3.3.3 Current and Future Opportunities in the Use of Remote Sensing to Characterize Functional Traits and Biodiversity References Chapter 4: The Laegeren Site: An Augmented Forest Laboratory 4.1 Introduction 4.2 The Laegeren Site: Description and History 4.3 Data 4.3.1 In-Situ Data 4.3.1.1 Measurements of Leaf Optical Properties 4.3.1.2 Forest Inventory 4.3.2 RS Data 4.3.2.1 Airborne Laser Scanning 4.3.2.2 Terrestrial Laser Scanning 4.3.3 Multispectral and Imaging Spectroscopy Data 4.4 Methods 4.4.1 In-Situ Data Processing 4.4.1.1 Optical Properties 4.4.1.2 3-D Reconstruction 4.4.1.3 Linking Field and RS Data 4.4.2 Radiative Transfer Modeling 4.4.3 Validation of Trait Predictions Using the RTM Approach 4.4.4 Computation of Functional Richness 4.5 Results and Discussion 4.5.1 Forward Simulation of Passive Optical Imagery and Comparison With EO Data 4.5.1.1 Spectral Validation 4.5.1.2 Spatial Validation 4.5.2 Functional Diversity of Laegeren Site 4.6 Conclusion and Outlook References Chapter 5: Lessons Learned from Spectranomics: Wet Tropical Forests 5.1 Introduction 5.2 Spectranomics Approach 5.3 Lessons Learned from Spectranomics 5.3.1 Nested Geography of Canopy Chemical Traits in Humid Tropical Forest 5.3.2 Spectral Properties of Humid Tropical Forest Canopies 5.3.3 Spectranomics for Biodiversity Mapping 5.3.4 Scientific and Conservation Opportunities References Chapter 6: Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance and Decline for Protection of Biodiversity 6.1 Introduction 6.2 The Basics of Forest Decline 6.3 RS Approaches to Forest Decline Detection 6.4 Spectroscopy of Early Decline Detection 6.5 Techniques for Early Stress Detection 6.6 Using RS to Inform Forest Management 6.7 Management Applications: Limitations and Opportunities 6.8 Conclusions References Chapter 7: Linking Leaf Spectra to the Plant Tree of Life 7.1 Introduction 7.2 Evolutionary Trees 7.2.1 How to Read Phylogenies 7.2.2 Why Care About Phylogenetic Accuracy? 7.3 The Evolution of Quantitative Traits 7.3.1 Macroevolutionary Models of Trait Evolution 7.3.1.1 Brownian Motion 7.3.1.2 Ornstein–Uhlenbeck 7.3.2 Phylogenetic Signal 7.3.2.1 Pagel’s Lambda 7.3.2.2 Blomberg’s K 7.4 Evolution and Spectra 7.4.1 Simulating Leaf Spectra Under Different Evolutionary Regimes 7.4.2 Making Evolutionary Inferences from Leaf Spectra 7.4.3 Leaf Spectra, Biodiversity Detection, and Evolution 7.4.4 Diversity Detection at Large Scales: Challenges and Ways Forward 7.5 Cautionary Notes 7.5.1 Is the Sampling Adequate for Making Evolutionary Inferences? 7.5.2 The More of the Tree of Life That Is Sampled, the More Complex Models Will (or Should) Be 7.5.3 Spectra Do not Evolve∗, Leaves Do! 7.5.4 Ignore Phylogeny at Your Peril 7.6 Moving Forward References Chapter 8: Linking Foliar Traits to Belowground Processes 8.1 Framework 8.2 How Are Belowground Processes and Microbial Communities Influenced by Aboveground Properties? 8.3 Mechanisms by Which Aboveground Vegetation Attributes Influence Belowground Processes 8.3.1 Total Aboveground Inputs 8.3.2 Chemical Composition of Vegetation 8.3.3 Plant Diversity 8.4 Case Studies 8.4.1 Remote Sensing of Belowground Processes via Canopy Chemistry Measurements 8.4.2 Forest Systems: Aspen Clones Example 8.4.3 Experiment Prairie Grassland System: Cedar Creek Example 8.4.4 Challenges and Future Directions References Chapter 9: Using Remote Sensing for Modeling and Monitoring Species Distributions 9.1 Introduction 9.2 Theoretical Background 9.2.1 The BAM Diagram 9.2.2 Where Are We Now? 9.3 Modeling Ecological Niches and Predicting Geographic Distributions 9.3.1 Methods 9.3.1.1 Oak Species Data Sets 9.3.1.2 Environmental Data Sets 9.3.1.3 Modeling Procedure Statistical Analyses 9.3.2 Results 9.4 Perspectives 9.4.1 Should We Use S-RS Data for ENM/SDM? 9.4.2 Enabling Large-Scale Biodiversity Change Detection References Chapter 10: Remote Sensing of Geodiversity as a Link to Biodiversity 10.1 Conserving Nature’s Stage 10.2 Geodiversity Indices 10.3 Remote Sensing of Geodiversity 10.3.1 Lithosphere 10.3.1.1 Lithosphere: Topography 10.3.1.2 Lithosphere: Geology and Soils 10.3.2 Atmosphere: Climate and Weather 10.3.3 Hydrosphere 10.3.4 Cryosphere 10.4 Remote Sensing of Biodiversity 10.5 A Case Study Linking RS of Geodiversity to Tree Diversity in the Eastern United States 10.5.1 Challenges and Opportunities 10.5.1.1 The Interplay Between Biodiversity and Geodiversity over Time 10.5.1.2 Scale and Expertise Mismatches 10.6 Conclusion References Chapter 11: Predicting Patterns of Plant Diversity and Endemism in the Tropics Using Remote Sensing Data: A Study Case from the Brazilian Atlantic Forest 11.1 Introduction 11.2 Study System 11.3 Methods 11.4 Results and Discussion 11.5 Conclusions and Future Directions References Chapter 12: Remote Detection of Invasive Alien Species 12.1 Introduction 12.1.1 Invasive Alien Species and Global Environmental Change 12.1.2 Biodiversity Impacts and Global Relevance 12.1.3 Remote Sensing for Detection of Plant Invasions 12.2 Invasive Plants in Natural and Agroecosystems 12.2.1 Forests 12.2.2 Rangelands and Grasslands 12.2.3 Aquatic Ecosystems 12.2.3.1 Riparian 12.2.3.2 Emergent 12.2.3.3 Floating Macrophytes 12.2.3.4 Submerged Macrophytes 12.2.3.5 Phytoplankton 12.2.4 Agroecosystems 12.2.5 Urban Ecosystems 12.3 Summary, Conclusions, and Prospectus References Chapter 13: A Range of Earth Observation Techniques for Assessing Plant Diversity 13.1 Understanding Plant Diversity with Remote Sensing 13.2 Range of EO Platforms to Assess Plant Diversity 13.2.1 Close-Range EO Approaches 13.2.1.1 Spectral Laboratory 13.2.1.2 Plant Phenomics Facilities 13.2.1.3 Ecotrons 13.2.1.4 WSNs, Sensorboxes 13.2.1.5 Towers 13.2.2 Air- and Spaceborne RS Platforms and Sensors 13.2.2.1 Unmanned Aerial Systems (UAS) 13.2.2.2 Optical RS Alpha Diversity Beta Diversity 13.2.2.3 Thermal RS 13.2.2.4 Light Detection and Ranging (LiDAR) 13.2.2.5 Radar Systems and Techniques Classification and Biophysical Modeling Applications 13.3 Conclusion and Further Work References Chapter 14: How the Optical Properties of Leaves Modify the Absorption and Scattering of Energy and Enhance Leaf Functionality 14.1 Introduction 14.2 On the Optical Spectrum of Seed Plants 14.3 Leaf Reflectance Patterns 14.4 Leaf Transmittance Patterns 14.5 Leaf Absorptance Patterns 14.6 Physical Processes Underlying Leaf Optical Properties 14.7 The Epidermis 14.7.1 Surface Characteristics of Epidermal Cells 14.7.2 Epidermal Cell Shape and Function 14.7.3 Epidermal Cell Index of Refraction 14.8 The Mesophyll 14.8.1 Mesophyll Index of Refraction 14.8.2 Molecular Absorption Processes 14.8.3 Leaf Biochemistry and Energy Absorption in the Solar Spectrum 14.8.4 Photosynthetic Pigments 14.8.5 Accessory Pigments 14.8.6 Xanthophyll Pigments 14.8.6.1 Apparent Concentration vs. Actual Concentration 14.8.7 Non-photosynthetic Pigments 14.8.8 Brown Pigments 14.9 Leaf Water Content 14.10 Cell Wall Constituents 14.11 Conclusion References Chapter 15: Spectral Field Campaigns: Planning and Data Collection 15.1 Introduction 15.1.1 Why Plan? The Data Life Cycle 15.1.2 Spectral Models and Scales of Measurement 15.2 Planning Field Campaigns 15.2.1 Data Organization 15.2.2 Communication 15.2.3 Planning Data Collection 15.2.3.1 Area Selection 15.2.3.2 Range of Values 15.2.3.3 Sampling Design 15.3 Field Data Collection 15.3.1 Leaf-Level Spectroscopy 15.3.2 Proximal Canopy-Level Spectroscopy 15.3.3 Airborne Campaigns 15.3.3.1 Flight Planning 15.3.3.2 Reference Data Collection for Image Processing 15.4 Conclusions References Chapter 16: Consideration of Scale in Remote Sensing of Biodiversity 16.1 Introduction 16.1.1 Why a Chapter on Scale? 16.1.2 What Is Optical (Spectral) Diversity? 16.2 What Is “Scale” and Why Is It Important? 16.2.1 Biological Scale 16.2.2 Spatial Scale 16.2.3 Temporal Scale 16.2.4 Spectral Scale 16.2.5 Angular Scale 16.3 Implementing Scaling Approaches 16.4 Designing a Scale-Aware Biodiversity Monitoring System References Chapter 17: Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study of Ecosystem Functioning and Conservation at Large Spatial Scales 17.1 Introduction 17.2 Ecosystem Functioning 17.2.1 Pollination 17.2.2 Phenology 17.2.3 Carbon Storage 17.2.4 Challenges 17.3 Conservation 17.3.1 Biodiversity Monitoring 17.3.2 Vegetation Condition 17.3.3 Habitat Intactness and Critical Transitions 17.3.4 Protected Area Monitoring 17.3.5 Challenges 17.4 Data Availability and Issues 17.4.1 In-Situ Biodiversity-Related Data 17.4.2 In-Situ Abiotic Factors 17.4.3 Socioeconomic Factors and Land Use 17.4.4 Land Cover 17.5 Methods to Integrate Remotely Sensed Measures of Plant Biodiversity with In-Situ Plant Diversity, Abiotic, and Socioeconomic Data 17.5.1 Fusion 17.5.2 Assimilation 17.5.3 Validation 17.6 Conclusions References Chapter 18: Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling 18.1 Introduction 18.2 The EBV Framework 18.2.1 Definition of Essential Biodiversity Variables 18.2.2 A Space-Time-Biology Cube 18.2.3 Six EBV Classes 18.3 Production Workflows for EBVs 18.3.1 The Need for Open EBV Workflows 18.3.2 From Data Collection to Biodiversity Models 18.3.3 Access Principles 18.4 Seamless Integration of Past Trends to Future Scenarios Using EBVs 18.5 Concluding Remarks References Chapter 19: Prospects and Pitfalls for Spectroscopic Remote Sensing of Biodiversity at the Global Scale 19.1 Introduction 19.2 Characteristics and Objectives for a Global Imaging Spectrometer 19.3 EMIT, HISUI, and EnMAP 19.4 Pitfalls and Opportunities in Remote Sensing of Global Plant Diversity 19.5 Vegetation Structure 19.6 Pixel Size 19.7 Phenology and Temporal Aggregation 19.8 Conclusion References Chapter 20: Epilogue: Toward a Global Biodiversity Monitoring System 20.1 Current Situation 20.2 Remote Sensing for Global Biodiversity Monitoring: Building on GEO BON References Glossary Index