Hyperspectral Remote Sensing of Vegetation

1. Auflage 2011
ISBN: 978-1-4398-4537-0
Verlag: Taylor & Francis Inc

Buch, Englisch, 782 Seiten, Format (B × H): 182 mm x 261 mm, Gewicht: 1580 g

Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research.
Hyperspectral Remote Sensing of Vegetation integrates this knowledge, guiding readers to harness the capabilities of the most recent advances in applying hyperspectral remote sensing technology to the study of terrestrial vegetation. Taking a practical approach to a complex subject, the book demonstrates the experience, utility, methods and models used in studying vegetation using hyperspectral data. Written by leading experts, including pioneers in the field, each chapter presents specific applications, reviews existing state-of-the-art knowledge, highlights the advances made, and provides guidance for the appropriate use of hyperspectral data in the study of vegetation as well as its numerous applications, such as crop yield modeling, crop and vegetation biophysical and biochemical property characterization, and crop moisture assessment.

This comprehensive book brings together the best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, vegetation classification, biophysical and biochemical modeling, crop productivity and water productivity mapping, and modeling. It provides the pertinent facts, synthesizing findings so that readers can get the correct picture on issues such as the best wavebands for their practical applications, methods of analysis using whole spectra, hyperspectral vegetation indices targeted to study specific biophysical and biochemical quantities, and methods for detecting parameters such as crop moisture variability, chlorophyll content, and stress levels. A collective "knowledge bank," it guides professionals to adopt the best practices for their own work.

Zielgruppe

Professionals and researchers in agricultural and water institutes, remote sensing centers, and aerial imaging companies that conduct overflights and survey for a variety of hyperspectral data, graduate students in earth sciences and spatial sciences, and governmental and non-governmental groups, libraries, and universities.

Autoren/Hrsg.

Thenkabail, Prasad S.

Lyon, John G.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Geowissenschaften Geologie Geodäsie, Kartographie, Fernerkundung
• Geowissenschaften Geographie | Raumplanung Biogeographie
• Geowissenschaften Geographie | Raumplanung Physische Geographie und Topographie
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften
• Geowissenschaften Umweltwissenschaften Umweltüberwachung, Umweltanalytik, Umweltinformatik

Inhaltsverzeichnis

Introduction and Overview
Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Croplands, Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete
Hyperspectral Sensor Systems
Hyperspectral Sensor Characteristics: Airborne, Spaceborne, Hand-Held, and Truck-Mounted; Integration of Hyperspectral Data with LIDARFred Ortenberg
Hyperspectral Remote Sensing in Global Change StudiesJiaguo Qi, Yoshio Inoue, and Narumon Wiangwang
Data Mining, Algorithms, Indices
Hyperspectral Data MiningSreekala G. Bajwa and Subodh S. Kulkarni
Hyperspectral Data Processing AlgorithmsAntonio Plaza, Javier Plaza, Gabriel Martín, and Sergio Sánchez
Leaf and Plant Biophysical and Biochemical Properties
Nondestructive Estimation of Foliar Pigment (Chlorophylls, Carotenoids, and Anthocyanins) Contents: Evaluating a Semianalytical Three-Band ModelAnatoly A. Gitelson
Forest Leaf Chlorophyll Study Using Hyperspectral Remote SensingYongqin Zhang
Estimating Leaf Nitrogen Concentration (LNC) of Cereal Crops with Hyperspectral DataYan Zhu, Wei Wang, and Xia Yao
Characterization on Pastures Using Field and Imaging SpectrometersIzaya Numata
Optical Remote Sensing of Vegetation Water ContentColombo Roberto, Busetto Lorenzo, Meroni Michele, Rossini Micol, and Panigada Cinzia
Estimation of Nitrogen Content in Crops and Pastures Using Hyperspectral Vegetation IndicesDaniela Stroppiana, F. Fava, M. Boschetti, and P.A. Brivio
Vegetation Biophysical Properties
Spectral Bioindicators of Photosynthetic Efficiency and Vegetation StressElizabeth M. Middleton, K. Fred Huemmrich, Yen-Ben Cheng, and Hank A. Margolis
Spectral and Spatial Methods for Hyperspectral Image Analysis for Estimation of Biophysical and Biochemical Properties of Agricultural CropsVictor Alchanatis and Yafit Cohen
Hyperspectral Vegetation IndicesDar A. Roberts, Keely L. Roth, and Ryan L. Perroy
Remote 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. Glenn
Species Identification
Crop Type Discrimination Using Hyperspectral DataLênio Soares Galvão, José Carlos Neves Epiphanio, Fábio Marcelo Breunig, and Antônio Roberto Formaggio
Identification of Canopy Species in Tropical Forests Using Hyperspectral DataMatthew L. Clark
Detecting and Mapping Invasive Plant Species by Using Hyperspectral DataRuiliang Pu
Land Cover Applications
Hyperspectral Remote Sensing for Forest ManagementValerie Thomas
Hyperspectral Remote Sensing of Wetland VegetationElijah Ramsey III and Amina Rangoonwala
Characterization of Soil Properties Using Reflectance SpectroscopyE. Ben-Dor
Detecting Crop Management, Plant Stress, and Disease
Analysis 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 Gang
Using Hyperspectral Data in Precision Farming ApplicationsHaibo Yao, Lie Tang, Lei Tian, Robert L. Brown, Deepak Bhatnagar, and Thomas E. Cleveland
Hyperspectral Data in Global Change Studies
Hyperspectral Data in Long-Term, Cross-Sensor Continuity StudiesTomoaki Miura and Hiroki Yoshioka
Hyperspectral Remote Sensing of Outer Planets
Hyperspectral 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. Geissler
Conclusions and Way Forward
Hyperspectral Remote Sensing of Vegetation and Agricultural Crops: Knowledge Gain and Knowledge Gap After 40 Years of ResearchPrasad S. Thenkabail, John G. Lyon, and Alfredo Huete
Index

Über Autor(innen)

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.