Muutke küpsiste eelistusi

Hyperspectral Remote Sensing of Vegetation, Second Edition, Four Volume Set 2nd edition [Multiple-component retail product]

Edited by (U.S. Geological Survey, Arizona, USA), Edited by (University of Technology, Sydney), Edited by (Clifton, Virginia, USA)
  • Formaat: Multiple-component retail product, 1632 pages, kõrgus x laius: 254x178 mm, kaal: 4055 g, 139 Tables, black and white; 414 Illustrations, color; 204 Illustrations, black and white, Contains 4 hardbacks
  • Sari: Hyperspectral Remote Sensing of Vegetation, Second Edition
  • Ilmumisaeg: 11-Dec-2018
  • Kirjastus: CRC Press
  • ISBN-10: 1138066257
  • ISBN-13: 9781138066250
  • Formaat: Multiple-component retail product, 1632 pages, kõrgus x laius: 254x178 mm, kaal: 4055 g, 139 Tables, black and white; 414 Illustrations, color; 204 Illustrations, black and white, Contains 4 hardbacks
  • Sari: Hyperspectral Remote Sensing of Vegetation, Second Edition
  • Ilmumisaeg: 11-Dec-2018
  • Kirjastus: CRC Press
  • ISBN-10: 1138066257
  • ISBN-13: 9781138066250
Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation.





Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. Volume II, Hyperspectral Indices and Image Classifications for Agriculture and Vegetation evaluates the performance of hyperspectral narrowband or imaging spectroscopy data with specific emphasis on the uses and applications of hyperspectral narrowband vegetation indices in characterizing, modeling, mapping, and monitoring agricultural crops and vegetation. Volume III, Biophysical and Biochemical Characterization and Plant Species Studies demonstrates the methods that are developed and used to study terrestrial vegetation using hyperspectral data. This volume includes extensive discussions on hyperspectral data processing and how to implement data processing mechanisms for specific biophysical and biochemical applications such as crop yield modeling, crop biophysical and biochemical property characterization, and crop moisture assessments. Volume IV, Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation discusses the use of hyperspectral or imaging spectroscopy data in numerous specific and advanced applications, such as forest management, precision farming, managing invasive species, and local to global land cover change detection.

Arvustused

"Very comprehensive and an excellent reference, both for practitioners in the field as well as students hoping to learn more about the uses of Hyperspectral Data for characterizing a diverse set of vegetation...There are books by other authors on Hyperspectral approaches and vegetation characterization(non-hyperspectral), but I believe this book stands alone as the final word on Hyperspectral characterization of vegetation. In fact, all the premier works in literature on Hyperspectral characterization of vegetation have been authored by Thenkabail et al.!"

--Dr. Thomas George, CEO, SaraniaSat Inc.

"The publication of the four-volume set, Hyperspectral Remote Sensing of Vegetation, Second Edition, is a landmark effort in providing an important, valuable, and timely contribution that summarizes the state of spectroscopy-based understanding of the Earths terrestrial and near shore environments."

--Susan L. Ustin, John Muir Institute

"The second edition of the book is major revision effort and covers all the aspects most descriptively and explicitly for the students, academia and professionals across the discipline. The book provides breadth of innovative applications of mathematical techniques to extract information from the hyperspectral image data. The chapters are contributed by internationally renowned authors in their respective fields...The hand book Hyperspectral Remote Sensing of Vegetation by Prasad S. Thenkabail, John G. Lyon and Alfredo Huete is most comprehensive, designed for learning and the best book in the discipline today."

--Dr. P.S. Roy, ICRISAT-CGIAR

"This book is an absolute gem. The history, the contemporary and the future of hyperspectral remote sensing of vegetation is contained within these pages. New topics on data mining and machine learning are hugely helpful to understand how scientists can go about processing these massive data sets. With great societal challenges such as food security, sustainability, deforestation and land use change, the research presented in this book provides clear evidence that hyperspectral remote sensing has an important and valuable role to play.

The book is a great resource for undergraduate, postgraduate students, research and academics. There is something in this book for everyone. I want it on my shelf."

--Prof. Kevin Tansey, Leicester Institute for Space & Earth Observation

Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation
Foreword to the First Edition
xi
Foreword to the Second Edition
xv
Preface
xix
Acknowledgments
xxiii
Editors
xxvii
Contributors
xxxi
Acronyms and Abbreviations
xxxv
Section I Detecting Crop Management Practices, Plant Stress and Disease
Chapter 1 Using Hyperspectral Data in Precision Farming Applications
3(34)
Haibo Yao
Yanbo Huang
Lie Tang
Lei Tian
Deepak Bhatnagar
Thomas E. Cleveland
Chapter 2 Hyperspectral Narrowbands and Their Indices in Study of Nitrogen Content of Cotton Crops
37(12)
Jianlong Li
Jingjing Zhang
Chunliu Tao
Dehua Zhao
Chengcheng Gang
Chapter 3 Analysis of the Effects of Heavy Metals on Vegetation Hyperspectral Reflectance Properties
49(20)
E. Terrence Slonecker
Section II Vegetation Processes and Function (ET, Water Use, GPP, LUE, Phenology)
Chapter 4 Mapping the Distribution and Abundance of Flowering Plants Using Hyperspectral Sensing
69(10)
Tobias Landmann
Hannes Feilhauer
Miaogen Shen
Jin Chen
Suresh Raina
Chapter 5 Crop Water Productivity Estimation with Hyperspectral Remote Sensing
79(18)
Michael Marshall
Itiya Aneece
Daniel Foley
Cai Xueliang
Trent Biggs
Chapter 6 Hyperspectral Remote Sensing Tools for Quantifying Plant Litter and Invasive Species in Arid Ecosystems
97(34)
Pamela Lynn Nagler
B.B. Maruthi Sridhar
Aaryn Dyami Olsson
Willem J.D. van Leeuwen
Edward P. Glenn
Chapter 7 Hyperspectral Applications to Landscape Phenology
131(16)
Alfredo Huete
Werapong Koedsin
Jin Wu
Section III Land Cover, Forests And Wetland and Urban Applications Using Hyperspectral Data
Chapter 8 The Use of Hyperspectral Earth Observation Data for Land Use/Cover Classification: Present Status, Challenges and Future Outlook
147(28)
Chandra Pandey
Kiril Manevski
Prashant K. Srivastava
George P. Petropoulos
Chapter 9 Hyperspectral Remote Sensing for Forest Management
175(22)
Valerie Thomas
Chapter 10 Characterization of Pastures Using Field and Imaging Spectrometers
197(22)
Izaya Numata
Chapter 11 Hyperspectral Remote Sensing of Wetland Vegetation
219(30)
Elijah Ramsey III
Amina Rangoonwala
Section IV Thermal, SWIR and Visible Remote Sensing
Chapter 12 Hyperspectral Remote Sensing of Fire: A Review
249(32)
Sander Veraverbeke
Philip Dennison
Ioannis Gitas
Glynn Hulley
Olga Kalashnikova
Thomas Katagis
Le Kuai
Ran Meng
Dar Roberts
Natasha Stavros
Section V Hyperspectral Data in Global Change Studies
Chapter 13 Hyperspectral Data in Long-Term, Cross-Sensor Continuity Studies
281(20)
Tomoaki Miura
Hiroki Yoshioka
Section VI Hyperspectral Remote Sensing of Other Planets
Chapter 14 Hyperspectral Analysis of Rocky Surfaces on Earth and Other Planetary Bodies
301(38)
R. Greg Vaughan
Timothy N. Titus
Jeffrey R. Johnson
Justin J. Hagerty
Laurence A. Soderblom
Paul E. Geissler
David P. Mayer
Will M. Grundy
Section VII Conclusions
Chapter 15 Fifty Years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation-Summary, Insights and Highlights of Volume IV: Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation
339
Prasad S. Thenkabail
John G. Lyon
Alfredo Huete
Index
379
Hyperspectral Indices and Image Classifications for agriculture and Vegetation
Foreword to the First Edition
ix
Foreword to the Second Edition
xiii
Preface
xvii
Editors
xxv
Contributors
xxix
Acronyms and Abbreviations
xxxiii
Section I Hyperspectral Vegetation Indices
Chapter 1 Hyperspectral Vegetation Indices
3(24)
Dar A. Roberts
Keely L. Roth
Erin B. Wetherley
Susan K. Meerdink
Ryan L. Perroy
Chapter 2 Derivative Hyperspectral Vegetation Indices in Characterizing Forest Biophysical and Biochemical Quantities
27(40)
Quan Wang
Jia Jin
Rei Sonobe
Jing Ming Chen
Section II Hyperspectral Image Classification Methods and Approaches
Chapter 3 Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies
67(38)
Edoardo Pasolli
Saurabh Prasad
Melba M. Crawford
James C. Tilton
Chapter 4 Big Data Processing on Cloud Computing Architectures for Hyperspectral Remote Sensing
105(30)
Zebin Wu
Jin Sun
Yi Zhang
Section III Hyperspectral Vegetation Index Applications to Agriculture and Vegetation
Chapter 5 Noninvasive Quantification of Foliar Pigments: Principles and Implementation
135(28)
Anatoly Gitelson
Alexei Solovchenko
Chapter 6 Hyperspectral Remote Sensing of Leaf Nitrogen Concentration in Cereal Crops
163(20)
Tao Cheng
Van Zhu
Dong Li
Xia Yao
Kai Zhou
Chapter 7 Optical Remote Sensing of Vegetation Water Content
183(18)
Colombo Roberto
Busetto Lorenzo
Meroni Michele
Rossini Micol
Panigada Cinzia
Chapter 8 Estimation of Nitrogen Content in Herbaceous Plants Using Hyperspectral Vegetation Indices
201(26)
D. Stroppiana
F. Fava
M. Boschetti
P.A. Brivio
Chapter 9 Hyperspectral Remote Sensing of Leaf Chlorophyll Content: From Leaf, Canopy to Landscape Scales
227(24)
Yongqin Zhang
Section IV Conclusions
Chapter 10 Fifty Years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation-Summary, Insights and Highlights of Volume II: Hyperspectral Indices and Image Classifications for Agriculture and Vegetation
251
Prasad S. Thenkabail
John G. Lyon
Alfredo Huete
Index
287
BioPhysical and Biochemical Characterization and Plant Species Studies
Foreword to the First Edition
ix
Foreword to the Second Edition
xiii
Preface
xvii
Acknowledgments
xxi
Editors
xxv
Contributors
xxix
Acronyms and Abbreviations
xxxiii
Section I Vegetation Biophysical and Biochemical Properties
Chapter 1 Recent Developments in Remote Estimation of Crop Biophysical and Biochemical Properties at Various Scales
3(22)
Anatoly A. Gitelson
Chapter 2 Hyperspectral Assessment of Ecophysiological Functioning for Diagnostics of Crops and Vegetation
25(48)
Yoshio Inoue
Roshanak Darvishzadeh
Andrew Skidmore
Chapter 3 Spectral and Spatial Methods for Hyperspectral and Thermal Image-Analysis to Estimate Biophysical and Biochemical Properties of Agricultural Crops
73(30)
Yafit Cohen
Victor Alchanatis
Chapter 4 Spectral and 3D Nonspectral Approaches to Crop Trait Estimation Using Ground and UAV Sensing
103(30)
Helge Aasen
Georg Bareth
Chapter 5 Photosynthetic Efficiency and Vegetation Stress
133(50)
Elizabeth M. Middleton
K. Fred Huemmrich
Qingyuan Zhang
Petya K.E. Campbell
David R. Landis
Section II Plant Species Identification and Discrimination
Chapter 6 Crop Type Discrimination Using Hyperspectral Data: Advances and Perspectives
183(28)
Lenio Soares Galvao
Jose Carlos Neves Epiphanio
Fabio Marcelo Breunig
Antonio Roberto Formaggio
Chapter 7 Identification of Canopy Species in Tropical Forests Using Hyperspectral Data
211(22)
Matthew L. Clark
Chapter 8 Characteristics of Tropical Tree Species in Hyperspectral and Multispectral Data
233(22)
Matheus Pinheiro Ferreira
Cibele Hummel do Amaral
Gaia Vaglio Laurin
Raymond Kokaly
Carlos Roberto de Souza Filho
Yosio Edemir Shimabukuro
Chapter 9 Detecting and Mapping Invasive Plant Species Using Hyperspectral Data
255(20)
Ruiliang Pu
Chapter 10 Visible, Near Infrared and Thermal Spectral Radiance On-Board UAVs for High-Throughput Phenotyping of Plant Breeding Trials
275(28)
Scott C. Chapman
Bangyou Zheng
Andries B. Potgieter
Wei Guo
Frederic Baret
Shouyang Liu
Simon Madec
Benoit Solan
Barbara George-Jaeggli
Graeme L. Hammer
David R. Jordan
Section III Conclusions
Chapter 11 Fifty Years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation-Summary, Insights and Highlights of Volume III: Biophysical and Biochemical Characterization and Plant Species Studies
303
Prasad S. Thenkabail
John G. Lyon
Alfredo Huete
Index
343
Fundamentals, Sensor Systems, Spectral Libraries and Data Mining for Vegetation
Foreword to the First Edition
ix
Foreword to the Second Edition
xiii
Preface
xvii
Acknowledgments
xxi
Editors
xxv
Contributors
xxix
Acronyms and Abbreviations
xxxiii
Section I Introduction to Hyperspectral Remote Sensing of Agricultural Crops and Vegetation
Chapter 1 Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops
3(38)
Prasad S. Thenkabail
John G. Lyon
Alfredo Huete
Section II Hyperspectral Sensor Systems
Chapter 2 Hyperspectral Sensor Characteristics: Airborne, Spaceborne, Hand-Held and Truck-Mounted; Integration of Hyperspectral Data with LiDAR
41(30)
Fred Ortenberg
Chapter 3 Hyperspectral Remote Sensing in Global Change Studies
71(24)
Jiaguo Qi
Yoshio Inoue
Narumon Wiangwang
Section III Hyperspectral Libraries of Agricultural Crops and Vegetation
Chapter 4 Monitoring Vegetation Diversity and Health through Spectral Traits and Trait Variations Based on Hyperspectral Remote Sensing
95(32)
Angela Lausch
Pedro J. Leitao
Chapter 5 The Use of Hyperspectral Proximal Sensing for Phenotyping of Plant Breeding Trials
127(22)
Andries B. Potgieter
James Watson
Barbara George-Jaeggli
Gregory McLean
Mark Eldridge
Scott C. Chapman
Kenneth Laws
Jack Christopher
Karine Chenu
Andrew Borrell
Graeme L. Hammer
David R. Jordan
Chapter 6 Linking Online Spectral Libraries with Hyperspectral Test Data through Library Building Tools and Code
149(16)
Muhammad Al-Amin Hoque
Stuart Phinn
Chapter 7 The Use of Spectral Databases for Remote Sensing of Agricultural Crops
165(22)
Andreas Hueni
Lola Suarez
Laurie A. Chisholm
Alex Held
Chapter 8 Characterization of Soil Properties Using Reflectance Spectroscopy
187(64)
E. Ben-Dor
S. Chabrillat
Jose A.M. Dematte
Section IV Hyperspectral Data Mining, Data Fusion, and Algorithms
Chapter 9 Spaceborne Hyperspectral EO-1, Hyperion Data Pre-Processing: Methods, Approaches and Algorithms
251(22)
Itiya P. Aneece
Prasad S. Thenkabail
John G. Lyon
Alfredo Huete
Terrance Slonecker
Chapter 10 Hyperspectral Image Data Mining
273(30)
Sreekala G. Bajwa
Yu Zhang
Alimohammad Shirzadifar
Chapter 11 Hyperspectral Data Processing Algorithms
303(18)
Antonio Plaza
Javier Plaza
Gabriel Martin
Sergio Sanchez
Chapter 12 Methods for Linking Drone and Field Hyperspectral Data to Satellite Data
321(34)
Muhammad Al-Amin Hoque
Stuart Phinn
Chapter 13 Integrating Hyperspectral and LiDAR Data in the Study of Vegetation
355(40)
Jessica J. Mitchell
Nancy F. Glenn
Kyla M. Dahlin
Nayani Ilangakoon
Hamid Dashti
Megan C. Maloney
Chapter 14 Fifty-Years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation-Summary, Insights, and Highlights of Volume I: Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation
395(44)
Prasad S. Thenkabail
John G. Lyon
Alfredo Huete
Index
439
Dr. Prasad S. Thenkabail, Research Geographer-15, U.S. Geological Survey (USGS), is a world-recognized expert in remote sensing science with multiple major contributions in the field sustained over more than 30 years. He obtained his PhD from the Ohio State University in 1992 and has over 140+ peer-reviewed scientific publications. Dr. Thenkabail has conducted pioneering cutting-edge research in the area of hyperspectral remote sensing of vegetation (https://www.usgs.gov/wgsc/GHISA/) and in that of global croplands and their water use for food security (www.croplands.org). Dr. Thenkabails contributions to series of leading edited books on remote sensing science along with his research and other contributions in the subject places his as a noted global expert in remote sensing science. He edited three-volume book entitled Remote Sensing Handbook published by Taylor and Francis, with 82 chapters and more than 2000 pages, widely considered a "magnus opus" encyclopedic standard reference for students, scholars, practitioners, and major experts in remote sensing science. He has recently completed editing four-volume Hyperspectral Remote Sensing of Vegetation. He has also edited a book on Remote Sensing of Global Croplands for Food Security. He is currently an editor-in-chief of the Remote Sensing open access journal published by MDPI; an associate editor of the journal Photogrammetric Engineering and Remote Sensing (PERS) of the American Society of Photogrammetry and Remote Sensing (ASPRS); and an editorial advisory board member of the International Society of Photogrammetry and Remote Sensing (ISPRS) Journal of Photogrammetry and Remote Sensing. NASA and USGS selected him on the Landsat Science team (2007-2011). Earlier, he served on the editorial board of Remote Sensing of Environment for many years (20072017). He has won three best paper awards from ASPRS for his publications in PE&RS. Detailed bio of Dr. Thenkabail can be found here: https://www.usgs.gov/staff-profiles/prasad-thenkabail

John G. Lyon has conducted scientific and engineering research and administrative functions throughout his career. He is formerly the senior physical scientist in the U.S. Environmental Protection Agencys Office of Research and Development (ORD) and Office of the Science Advisor in Washington, DC, where he co-led work on the Group on Earth Observations and the USGEO subcommittee of the Committee on Environment and Natural Resources, and research on geospatial issues. Lyon was director of ORDs Environmental Sciences Division for approximately eight years. He was educated at Reed College in Portland, Oregon, and the University of Michigan in Ann Arbor.

Professor Alfredo Huete leads the Ecosystem Dynamics Health and Resilience research program within the Climate Change Cluster (C3) at the University of Technology Sydney, Australia. His main research interest is in using remote sensing to study and analyze broad scale vegetation health and functioning. Recently, he used remote sensing and field measurements to understand the phenology patterns of tropical rainforests and savannas in the Amazon and Southeast Asia and his Amazon work was featured in a National Geographic television special entitled "The Big Picture". Currently his research involves coupling eddy covariance tower flux measurements with ground spectral sensors and satellite observations to study carbon and water cycling across Australian landscapes. He is actively involved with several international space programs, including the NASA-EOS MODIS Science Team, the Japanese JAXA GCOM-SGLI Science Team, the European PROBA-V User Expert Group, and NPOESS-VIIRS advisory group.