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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

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

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

      Hyperspectral Remote Sensing of Vegetation

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

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

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

      Hyperspectral Remote Sensing of Vegetation
      Edited ByPrasad S. Thenkabail, John G. Lyon, Alfredo Huete
      Edition 2nd Edition
      First Published 2018
      eBook Published 7 December 2018
      Pub. Location Boca Raton
      Imprint CRC Press
      DOI https://doi.org/10.1201/9781315164151
      Pages 489
      eBook ISBN 9781315164151
      Subjects Environment & Agriculture
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      Thenkabail, P.S., Lyon, J.G., & Huete, A. (Eds.). (2018). Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation: Hyperspectral Remote Sensing of Vegetation (2nd ed.). CRC Press. https://doi.org/10.1201/9781315164151

      ABSTRACT

      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. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the 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, modeling, 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 the 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 with other data, such as the LiDAR data, in the study of vegetation.
      • Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.

      TABLE OF CONTENTS

      section Section I|1 pages

      Introduction to Hyperspectral Remote Sensing of Agricultural Crops and Vegetation

      chapter 1|35 pages

      Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

      ByPrasad S. Thenkabail, John G. Lyon, Alfredo Huete

      section Section II|1 pages

      Hyperspectral Sensor Systems

      chapter 2|29 pages

      Hyperspectral Sensor Characteristics

      Airborne, Spaceborne, Hand-Held, and Truck-Mounted; Integration of Hyperspectral Data with LiDAR
      ByFred Ortenberg

      chapter 3|24 pages

      Hyperspectral Remote Sensing in Global Change Studies

      ByJiaguo Qi, Yoshio Inoue, Narumon Wiangwang

      section Section III|1 pages

      Hyperspectral Libraries of Agricultural Crops and Vegetation

      chapter 4|32 pages

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

      ByAngela Lausch, Pedro J. Leitão

      chapter 5|21 pages

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

      ByAndries 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|16 pages

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

      ByMuhammad Al-Amin Hoque, Stuart Phinn

      chapter 7|21 pages

      The Use of Spectral Databases for Remote Sensing of Agricultural Crops

      ByAndreas Hueni, Lola Suarez, Laurie A. Chisholm, Alex Held

      chapter 8|61 pages

      Characterization of Soil Properties Using Reflectance Spectroscopy

      ByE. Ben-Dor, S. Chabrillat, José A. M. Demattê

      section Section IV|1 pages

      Hyperspectral Data Mining, Data Fusion, and Algorithms

      chapter 9|22 pages

      Spaceborne Hyperspectral EO-1 Hyperion Data Pre-Processing

      Methods, Approaches, and Algorithms
      ByItiya P. Aneece, Prasad S. Thenkabail, John G. Lyon, Alfredo Huete, Terrance Slonecker

      chapter 10|30 pages

      Hyperspectral Image Data Mining

      BySreekala G. Bajwa, Yu Zhang, Alimohammad Shirzadifar

      chapter 11|17 pages

      Hyperspectral Data Processing Algorithms

      ByAntonio Plaza, Javier Plaza, Gabriel Martín, Sergio Sánchez

      chapter 12|34 pages

      Methods for Linking Drone and Field Hyperspectral Data to Satellite Data

      ByMuhammad Al-Amin Hoque, Stuart Phinn

      chapter 13|39 pages

      Integrating Hyperspectral and LiDAR Data in the Study of Vegetation

      ByJessica J. Mitchell, Nancy F. Glenn, Kyla M. Dahlin, Nayani Ilangakoon, Hamid Dashti, Megan C. Maloney

      chapter 14|42 pages

      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
      ByPrasad S. Thenkabail, John G. Lyon, Alfredo Huete
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