Hyperspectral Remote Sensing of Vegetation, Second Edition, Four Volume Set  book cover
2nd Edition

Hyperspectral Remote Sensing of Vegetation, Second Edition, Four Volume Set

ISBN 9781138066250
Published December 11, 2018 by CRC Press
1632 Pages 414 Color & 204 B/W Illustrations

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Book Description

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.

Table of Contents

Contents of Volume I:

Section I: Introduction to Hyperspectral Remote Sensing of Agricultural Crops and Vegetation

1. Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

[Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete]

Section II: Hyperspectral Sensor Systems

2. Hyperspectral Sensor Characteristics: Airborne, Spaceborne, Hand-Held, and Truck-Mounted; Integration of Hyperspectral Data with LiDAR

[Fred Ortenberg]

3. Hyperspectral Remote Sensing in Global Change Studies

[Jiaguo Qi, Yoshio Inoue, and Narumon Wiangwang]

Section III: Hyperspectral Libraries of Agricultural Crops and Vegetation

4. Monitoring Vegetation Diversity and Health through Spectral Traits and Trait Variations Based on Hyperspectral Remote Sensing

[Angela Lausch and Pedro J. Leitão]

5. The Use of Hyperspectral Proximal Sensing for Phenotyping of Plant Breeding Trials

[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, and David R. Jordan]

6. Linking Online Spectral Libraries with Hyperspectral Test Data through Library Building Tools and Code

[Muhammad Al-Amin Hoque and Stuart Phinn]

7. The Use of Spectral Databases for Remote Sensing of Agricultural Crops

[Andreas Hueni, Lola Suarez, Laurie A. Chisholm, and Alex Held]

8. Characterization of Soil Properties Using Reflectance Spectroscopy

[E. Ben-Dor, S. Chabrillat, and José A. M. Demattê]

Section IV: Hyperspectral Data Mining, Data Fusion, and Algorithms

9. Spaceborne Hyperspectral EO-1 Hyperion Data Pre-Processing: Methods, Approaches, and Algorithms

[Itiya P. Aneece, Prasad S. Thenkabail, John G. Lyon, Alfredo Huete, and Terrance Slonecker]

10. Hyperspectral Image Data Mining

[Sreekala G. Bajwa, Yu Zhang, and Alimohammad Shirzadifar]

11. Hyperspectral Data Processing Algorithms

[Antonio Plaza, Javier Plaza, Gabriel Martín, and Sergio Sánchez]

12. Methods for Linking Drone and Field Hyperspectral Data to Satellite Data

[Muhammad Al-Amin Hoque and Stuart Phinn]

13. Integrating Hyperspectral and LiDAR Data in the Study of Vegetation

[Jessica J. Mitchell, Nancy F. Glenn, Kyla M. Dahlin, Nayani Ilangakoon, Hamid Dashti, and Megan C. Maloney]

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[Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete]


Contents of Volume II:

Section I: Hyperspectral Vegetation Indices

  1. Hyperspectral vegetation indices
  2. [Dar A. Roberts, Keely L. Roth, Erin B. Wetherley, Susan K. Meerdink, and Ryan L. Perroy]

  3. Derivative hyperspectral vegetation indices in characterizing forest biophysical and biochemical quantities
  4. [Quan Wang, Jia Jin, Rei Sonobe, and Jing Ming Chen]

    Section II: Hyperspectral Image Classification Methods and Approaches

  5. Hyperpsectral image classification methods in vegetation and agricultural
  6. cropland studies

    [Edoardo Pasolli, Saurabh Prasad, Melba M. Crawford, and James C. Tilton]

  7. Big Data Processing on Cloud Computing Architectures for Hyperspectral Remote Sensing
  8. [Zebin Wu, Jin Sun, and Yi Zhang]

    Section III: Hyperspectral Vegetation Indices Applications to Agriculture and Vegetation

  9. Non-invasive Quantification of Foliar Pigments: Principles and Implementation
  10. [Anatoly Gitelson and Alexei Solovchenko]

  11. Hyperspectral Remote Sensing of Leaf Nitrogen Concentration in Cereal Crops
  12. [Tao Cheng, Yan Zhu, Dong Li, Xia Yao, and Kai Zhou]

  13. Optical remote sensing of vegetation water content
  14. [Colombo Roberto, Busetto Lorenzo, Meroni Michele, Rossini Micol, and Panigada Cinzia]

  15. Estimation of nitrogen content in herbaceous plants using hyperspectral vegetation indices
  16. [D. Stroppiana, F. Fava, M. Boschetti, and P.A. Brivio]

  17. Hyperspectral remote sensing of leaf chlorophyll content: from leaf, canopy, to landscape scales
  18. [Yongqin Zhang]

    Section IV: Conclusions

  19. Fifty-years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation: Summary, Insights, and Highlights of Volume II: Hyperspectral Vegetation Indices and Image Classifications for Agriculture and Vegetation

[Prasad S. Thenkabil, John G. Lyon, and Alfredo Huete]


Contents of Volume III:

Section I: Vegetation Biophysical and Biochemical Properties

1. Recent Developments in Remote Estimation of Crop Biophysical and Biochemical Properties at Various Scales

[Anatoly A. Gitelson]

2. Hyperspectral Assessment of Ecophysiological Functioning for Diagnostics of Crops and Vegetation

[Yoshio Inoue, Roshanak Darvishzadeh, and Andrew Skidmore]

3. Spectral and Spatial Methods for Hyperspectral and Thermal Image-Analysis to Estimate Biophysical and Biochemical Properties of Agricultural Crops

[Yafit Cohen and Victor Alchanatis]

4. Spectral and 3D Nonspectral Approaches to Crop Trait Estimation Using Ground and UAV Sensing

[Helge Aasen and Georg Bareth]

5. Photosynthetic Efficiency and Vegetation Stress

[Elizabeth M. Middleton, K. Fred Huemmrich, Qingyuan Zhang, Petya K.E. Campbell, and David R. Landis]

Section II: Plant Species Identification and Discrimination

6. Crop Type Discrimination Using Hyperspectral Data: Advances and Perspectives

[Lênio Soares Galvão, José Carlos Neves Epiphanio, Fábio Marcelo Breunig, and Antônio Roberto Formaggio]

7. Identification of Canopy Species in Tropical Forests Using Hyperspectral Data

[Matthew L. Clark]

8. Characteristics of Tropical Tree Species in Hyperspectral and Multispectral Data

[Matheus Pinheiro Ferreira, Cibele Hummel do Amaral, Gaia Vaglio Laurin, Raymond Kokaly, Carlos Roberto de Souza Filho, and Yosio Edemir Shimabukuro]

9. Detecting and Mapping Invasive Plant Species Using Hyperspectral Data

[Ruiliang Pu]

10. Visible, Near Infrared, and Thermal Spectral Radiance On-Board UAVs for High-Throughput Phenotyping of Plant Breeding Trials

[Scott C. Chapman, Bangyou Zheng, Andries B. Potgieter, Wei Guo, Frederic Baret, Shouyang Liu, Simon Madec, Benoit Solan, Barbara George-Jaeggli, Graeme L. Hammer, and David R. Jordan]

Section III: Conclusions

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

[Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete]


Contents of Volume IV:

Section I: Detecting Crop Management Practices, Plant Stress, and Disease

1. Using Hyperspectral Data in Precision Farming Applications

[Haibo Yao, Yanbo Huang, Lie Tang, Lei Tian, Deepak Bhatnagar, and Thomas E. Cleveland]

2. Hyperspectral Narrowbands and Their Indices in Study of Nitrogen Content of Cotton Crops

[Jianlong Li, Jingjing Zhang, Chunliu Tao, Dehua Zhao, and Chengcheng Gang]

3. Analysis of the Effects of Heavy Metals on Vegetation Hyperspectral Reflectance Properties

[E. Terrence Slonecker]

Section II: Vegetation Processes and Function (ET, Water Use, GPP, LUE, Phenology)

4. Mapping the Distribution and Abundance of Flowering Plants Using Hyperspectral Sensing

[Tobias Landmann, Hannes Feilhauer, Miaogen Shen, Jin Chen, and Suresh Raina]

5. Crop Water Productivity Estimation with Hyperspectral Remote Sensing

[Michael Marshall, Itiya Aneece, Daniel Foley, Cai Xueliang, and Trent Biggs]

6. Hyperspectral Remote Sensing Tools for Quantifying Plant Litter and Invasive Species in Arid Ecosystems

[Pamela Lynn Nagler, B. B. Maruthi Sridhar, Aaryn Dyami Olsson, Willem J. D. van Leeuwen, and Edward P. Glenn]

7. Hyperspectral Applications to Landscape Phenology

[Alfredo Huete, Werapong Koedsin, and Jin Wu]

Section III: Land Cover, Forests, and Wetland and Urban Applications Using Hyperspectral Data

8. The Use of Hyperspectral Earth Observation Data for Land Use/Cover Classification: Present Status, Challenges, and Future Outlook

[Prem Chandra Pandey, Kiril Manevski, Prashant K. Srivastava, and George P. Petropoulos]

9. Hyperspectral Remote Sensing for Forest Management

[Valerie Thomas]

10. Characterization of Pastures Using Field and Imaging Spectrometers

[Izaya Numata]

11. Hyperspectral Remote Sensing of Wetland Vegetation

[Elijah Ramsey III and Amina Rangoonwala]

Section IV: Thermal, SWIR, and Visible Remote Sensing
12. Hyperspectral Remote Sensing of Fire: A Review

[Sander Veraverbeke, Philip Dennison, Ioannis Gitas, Glynn Hulley, Olga Kalashnikova, Thomas Katagis, Le Kuai, Ran Meng, Dar Roberts, and Natasha Stavros]

Section V: Hyperspectral Data in Global Change Studies
13. Hyperspectral Data in Long-Term, Cross-Sensor Continuity Studies

[Tomoaki Miura and Hiroki Yoshioka]

Section VI: Hyperspectral Remote Sensing of Other Planets
14. Hyperspectral Analysis of Rocky Surfaces on Earth and Other Planetary Bodies

[R. Greg Vaughan, Timothy N. Titus, Jeffrey R. Johnson, Justin J. Hagerty, Laurence A. Soderblom, Paul E. Geissler, David P. Mayer, and Will M. Grundy]

Section VII: Conclusions
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

[Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete]

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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. Thenkabail’s 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 (2007–2017). 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 Agency’s 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 ORD’s 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.


"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 Earth’s 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."


"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