Table of Contents: Remote Sensing of Plant Biodiversity.

Remote Sensing of Plant Biodiversity.

Bibliographic Details
Main Author:	Cavender-Bares, Jeannine.
Other Authors:	Gamon, John A., Townsend, Philip A.
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing AG, 2020.
Edition:	1st ed.
Subjects:	Electronic books.
Online Access:	Click to View

Table of Contents:

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.
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.

Remote Sensing of Plant Biodiversity.

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