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Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation [Hardcover]

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  • Category: Books (Technology & Engineering)
  • ISBN-10:  1138058548
  • ISBN-10:  1138058548
  • ISBN-13:  9781138058545
  • ISBN-13:  9781138058545
  • Publisher:  CRC Press
  • Publisher:  CRC Press
  • Pages:  489
  • Pages:  489
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-Jun-2018
  • Pub Date:  01-Jun-2018
  • SKU:  1138058548-11-MPOD
  • SKU:  1138058548-11-MPOD
  • Item ID: 105942779
  • Seller: ShopSpell
  • Ships in: 2 business days
  • Transit time: Up to 5 business days
  • Delivery by: Oct 12 to Oct 14
  • Notes: Brand New Item. Not shipped to AK, HI, APO, FPO, AE.

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.
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