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Books > Earth & environment > Geography > Cartography, geodesy & geographic information systems (GIS) > Remote sensing
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Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation - Hyperspectral Remote Sensing of Vegetation (Hardcover, 2nd edition)
Loot Price: R4,257
Discovery Miles 42 570
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Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation - Hyperspectral Remote Sensing of Vegetation (Hardcover, 2nd edition)
Series: Hyperspectral Remote Sensing of Vegetation, Second Edition
Expected to ship within 12 - 17 working days
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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.
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