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 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. It
emphasizes the importance of hyperspectral remote sensing tools for
studying vegetation processes and functions as well as the
appropriate use of hyperspectral data for vegetation management
practices. The concluding chapter provides readers with useful
guidance on the highlights and essence of Volume IV through the
editors' perspective. Key Features of Volume IV: Guides readers to
harness the capabilities of the most recent advances in applying
hyperspectral remote sensing technology to the study of terrestrial
vegetation. Includes specific applications on agriculture, crop
management practices, study of crop stress and diseases, crop
characteristics based on inputs (e.g., nitrogen, irrigation), study
of vegetation impacted by heavy metals, gross and net primary
productivity studies, light use efficiency studies, crop water use
and actual evapotranspiration studies, phenology monitoring, land
use and land cover studies, global change studies, plant species
detection, wetland and forest characterization and mapping, crop
productivity and crop water productivity mapping, and modeling.
Encompasses hyperspectral or imaging spectroscopy data in narrow
wavebands used across visible, red-edge, near-infrared,
far-infrared, shortwave infrared, and thermal portions of the
spectrum. Explains the implementation of hyperspectral remote
sensing data processing mechanisms in a standard, fast, and
efficient manner for their applications. Discusses cloud computing
to overcome hyperspectral remote sensing massive big data
challenges. Provides hyperspectral analysis of rocky surfaces on
the earth and other planetary systems.
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