Hyperspectral Remote Sensing of Vegetation

Dr. Prasad S. Thenkabail has more than 25 years experience working as a well recognized international expert in remote sensing and geographic information systems and their applications to agriculture, natural resource management, water resources, sustainable development, and environmental studies. His work experience spans over 25 countries spread across West and Central Africa, Southern Africa, South Asia, Southeast Asia, the Middle East, East Asia, Central Asia, North America, South America, and the Pacific. Dr. Thenkabail has a wealth of work experience in premier global institutes, holding key lead research positions. He is a member of the Landsat Science Team (2007-2011) and is on the editorial boards of two remote sensing journals, Remote Sensing of Environment and Journal of Remote Sensing. He led the global irrigated area mapping (GIAM) project and the global mapping of rainfed croplands (GMRCA) project, and has conducted pioneering work in hyperspectral remote sensing. Currently, he is a research geographer at the U.S. Geological Survey (USGS) and a coordinator of the Committee for Earth Observation Systems (CEOS) Agriculture Societal Beneficial Area (SBA). He co-leads an IEEE Water for the World Project and is an active participant in Group on Earth Observations (GEO) and the Global Earth Observation System of Systems (GEOSS) and CEOS activities. Dr. Thenkabail has more than 80 publications, mostly peer-reviewed and published in major international remote sensing journals. He is the chief editor of two pioneering books, Remote Sensing of Global Croplands for Food Security (2009) and Hyperspectral Remote Sensing of Vegetation (2011). Dr. John G. Lyon’s research has involved advanced remote sensing and GIS applications to water and wetland resources, agriculture, natural resources, and engineering applications. He is the author of books on wetland landscape characterization, wetland and environmental applications of GIS, and accuracy assessment of GIS and remote sensing technologies. Lyon currently serves as a senior scientist (ST) in the EPA Office of the Science Advisor in Washington, District of Columbia, and is co-lead for work on the Group on Earth Observations and the Global Earth Observation System of Systems, and research on geospatial issues in the agency.Dr. Alfredo Huete is currently a professor in the Faculty of Science, Plant Functional Biology and Climate Change Cluster, at the University of Technology Sydney, Australia. Dr. Huete’s research interests focus on understanding large-scale soil–vegetation–climate interactions, processes, and changes with remotely sensed measurements from satellites. He is also involved with field-based and tower optical instrumentation in support of remote sensing studies coupling satellite observations with eddy covariance tower flux measurements. He has done extensive research in the phenology of tropical rain forests and savannas in the Amazon and Southeast Asia and has over 100 research publications in peer-reviewed journals, a book, and more than 20 chapter contributions.

• Introduction and OverviewAdvances in Hyperspectral Remote Sensing of Vegetation and Agricultural Croplands, Prasad S. Thenkabail, John G. Lyon, and Alfredo HueteHyperspectral Sensor SystemsHyperspectral Sensor Characteristics: Airborne, Spaceborne, Hand-Held, and Truck-Mounted; Integration of Hyperspectral Data with LIDARFred OrtenbergHyperspectral Remote Sensing in Global Change StudiesJiaguo Qi, Yoshio Inoue, and Narumon Wiangwang Data Mining, Algorithms, IndicesHyperspectral Data MiningSreekala G. Bajwa and Subodh S. KulkarniHyperspectral Data Processing AlgorithmsAntonio Plaza, Javier Plaza, Gabriel Martín, and Sergio SánchezLeaf and Plant Biophysical and Biochemical PropertiesNondestructive Estimation of Foliar Pigment (Chlorophylls, Carotenoids, and Anthocyanins) Contents: Evaluating a Semianalytical Three-Band ModelAnatoly A. GitelsonForest Leaf Chlorophyll Study Using Hyperspectral Remote SensingYongqin ZhangEstimating Leaf Nitrogen Concentration (LNC) of Cereal Crops with Hyperspectral DataYan Zhu, Wei Wang, and Xia YaoCharacterization on Pastures Using Field and Imaging SpectrometersIzaya NumataOptical Remote Sensing of Vegetation Water ContentColombo Roberto, Busetto Lorenzo, Meroni Michele, Rossini Micol, and Panigada CinziaEstimation of Nitrogen Content in Crops and Pastures Using Hyperspectral Vegetation IndicesDaniela Stroppiana, F. Fava, M. Boschetti, and P.A. BrivioVegetation Biophysical PropertiesSpectral Bioindicators of Photosynthetic Efficiency and Vegetation StressElizabeth M. Middleton, K. Fred Huemmrich, Yen-Ben Cheng, and Hank A. MargolisSpectral and Spatial Methods for Hyperspectral Image Analysis for Estimation of Biophysical and Biochemical Properties of Agricultural CropsVictor Alchanatis and Yafit CohenHyperspectral Vegetation IndicesDar A. Roberts, Keely L. Roth, and Ryan L. PerroyRemote Sensing Estimation of Crop Biophysical Characteristics at Various ScalesAnatoly A. Gitelson Vegetation Processes and Function (ET, Water Use, GPP, LUE, Phenology)Hyperspectral Remote Sensing Tools for Quantifying Plant Litter and Invasive Species in Arid EcosystemsPamela Lynn Nagler, B.B. Maruthi Sridhar, Aaryn Dyami Olsson, Willem J.D. van Leeuwen, and Edward P. GlennSpecies IdentificationCrop Type Discrimination Using Hyperspectral DataLênio Soares Galvão, José Carlos Neves Epiphanio, Fábio Marcelo Breunig, and Antônio Roberto FormaggioIdentification of Canopy Species in Tropical Forests Using Hyperspectral DataMatthew L. ClarkDetecting and Mapping Invasive Plant Species by Using Hyperspectral DataRuiliang PuLand Cover ApplicationsHyperspectral Remote Sensing for Forest ManagementValerie ThomasHyperspectral Remote Sensing of Wetland VegetationElijah Ramsey III and Amina RangoonwalaCharacterization of Soil Properties Using Reflectance SpectroscopyE. Ben-DorDetecting Crop Management, Plant Stress, and DiseaseAnalysis of the Effects of Heavy Metals on Vegetation Hyperspectral Reflectance PropertiesE. Terrence Slonecker Hyperspectral Narrowbands and Their Indices on Assessing Nitrogen Contents of Cotton Crop ApplicationsJianlong Li, Cherry Li, Dehua Zhao, and Chengcheng GangUsing Hyperspectral Data in Precision Farming ApplicationsHaibo Yao, Lie Tang, Lei Tian, Robert L. Brown, Deepak Bhatnagar, and Thomas E. ClevelandHyperspectral Data in Global Change StudiesHyperspectral Data in Long-Term, Cross-Sensor Continuity StudiesTomoaki Miura and Hiroki YoshiokaHyperspectral Remote Sensing of Outer PlanetsHyperspectral Analysis of Rocky Surfaces on the Earth and Other Planetary BodiesR. Greg Vaughan, Timothy N. Titus, Jeffery R. Johnson, Justin J. Hagerty, Lisa R. Gaddis, Laurence A. Soderblom, and Paul E. GeisslerConclusions and Way ForwardHyperspectral Remote Sensing of Vegetation and Agricultural Crops: Knowledge Gain and Knowledge Gap After 40 Years of ResearchPrasad S. Thenkabail, John G. Lyon, and Alfredo HueteIndex

"The authors solicited the help of numerous high-quality hyperspectral remote sensing scientists to write this book. The characteristics of hyperspectral remote sensing systems are explained clearly. Fundamental hyperspectral data analysis, hyperspectral indices, and data mining methods are introduced. I am particularly impressed with the in-depth treatment on leaf and plant biophysical and biochemical properties, especially related to remote sensing of: chlorophyll content, leaf nitrogen concentration, photosynthetic efficiency, quantifying plant litter, leaf-area-index, and vegetation stress detection. The book documents numerous practical applications of hyperspectral remote sensing for forest management, precision farming, monitoring invasive species, and local to global land cover change detection. No other book contains such detailed information about hyperspectral remote sensing of vegetation." —Dr. John R. Jensen, PhD, Carolina Distinguished Professor, Department of Geography, University of South Carolina, Columbia, USAHyperspectral Remote Sensing of Vegetation fills an important gap in today’s literature. This comprehensive text covers all aspects of hyperspectral sensing of plants and vegetation, from sensor systems, data mining, biophysical properties and plant functioning, to species mapping and land cover applications. This book will greatly increase the research communities' understanding of how to use hyperspectral data to solve otherwise intractable problems in plant applications from crops to forests. —Susan L. Ustin, Professor of Environmental and Resource Sciences, Department of Land, Air, and Water Resources, University of California at Davis, USA"Hyperspectral Remote Sensing of Vegetation provides excellent coverage of the research and application of high spectral resolution measurements for vegetation mapping, monitoring and analysis. This book brings together an enormous range of topical areas, leaving the reader with a much improved understanding of the vital role and use of the hyperspectral sensing for plant and ecosystem studies." —Greg Asner, Professor, Department of Global Ecology, Carnegie Institution for Science, Stanford University, California, USA"The publication of this book, Hyperspectral Remote Sensing of Vegetation, marks a milestone in the application of imaging spectrometry to studies of the 70% of the Earth’s landmass which is vegetated. This book shows not only the breadth of international involvement in the use of hyperspectral data but also in the breadth of innovative application of mathematical techniques to extract information from the image data."—From the Foreword by Alexander F. H. Goetz, Chairman and Chief Scientist, Analytical Spectral Devices Inc., Boulder, Colorado, USA"I would highly recommend [this book] to anyone dealing with the subject. … very well written … The following anecdote illustrates of the usefulness of the book. When I received my copy at work, colleagues were quickly interested and browsing through it. Soon after I took the book home for review, my colleagues kept on asking me when I was returning it to work, so they could start using it."—Dr. Pieter Kempeneers, VITO, Belgium, in earthzine