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Hyperspectral remote sensing of vegetation

January 1, 2013

Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research.

Citation Information

Publication Year 2011
Title Hyperspectral remote sensing of vegetation
DOI 10.1201/b11222
Authors Prasad S. Thenkabail, John G. Lyon, Alfredo Huete
Publication Type Book
Index ID 70047065
Record Source USGS Publications Warehouse
USGS Organization Western Geographic Science Center