UID:
almahu_9949301198702882
Format:
1 online resource (594 pages)
ISBN:
9783030331573
Note:
Intro -- 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.
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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.
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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.
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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.
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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.
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14.2 On the Optical Spectrum of Seed Plants.
Additional Edition:
Print version: Cavender-Bares, Jeannine Remote Sensing of Plant Biodiversity Cham : Springer International Publishing AG,c2020 ISBN 9783030331566
Language:
English
Keywords:
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