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Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation

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Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation is the first volume in the comprehensive four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition. It introduces readers to the essential principles of hyperspectral or imaging spectroscopy data, covering sensor systems, spectral libraries, and advanced data mining methods. The book presents insights into hyperspectral narrowband data collected from ground-based, airborne, and spaceborne platforms, highlighting both capabilities and limitations. This volume also emphasises modern approaches such as reflectance spectroscopy, machine learning algorithms, and integrating hyperspectral data with other sources like LiDAR, making it a vital resource for studying and managing agricultural crops and natural vegetation.
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Format: Paperback / softback
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This volume is suited for researchers, professionals, and students in environmental science, agriculture, remote sensing, and ecosystem management seeking an in-depth understanding of hyperspectral data and its applications for vegetation analysis.

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This book discusses both the strengths and the limitations of the topics covered: (a) hyperspectral processes, (b) sensors, and (c) data analysis. Each chapter reviews existing “state-of-art” knowledge, highlights the advances made, and provides guidance for appropriate use of hyperspectral data in study of vegetation and its numerous applicatio

Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! 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. It covers both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands over the entire spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors’ perspective.

Key Features of Volume I:

  • Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies.
  • Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands.
  • Develops online hyperspectral libraries, proximal sensing, and phenotyping for understanding, modelling, mapping, and monitoring crop and vegetation traits.
  • Implements reflectance spectroscopy of soils and vegetation.
  • Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms.
  • Explores methods and approaches for data mining and overcoming data redundancy.
  • Highlights advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine.
  • Integrates hyperspectral data with other data, such as LiDAR, in the study of vegetation.
  • Includes the best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity, and water productivity mapping and modelling.

Series: Hyperspectral Remote Sensing of Vegetation, Second Edition

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Book Details

INFORMATION

ISBN: 9781032475882

Publisher: Taylor & Francis Ltd

Format: Paperback / softback

Date Published: 09 January 2023

Country: United Kingdom

Imprint: CRC Press

Edition: 2nd edition

Illustration: 118 Illustrations, color; 57 Illustrations, black and white

Contributors:

  • Edited by Prasad S. Thenkabail
  • Edited by John G. Lyon
  • Edited by Alfredo Huete

Audience: Professional and scholarly

DIMENSIONS

Width: 178.0mm

Height: 254.0mm

Weight: 920g

Pages: 490

About the Author

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.

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