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Derivative Hyperspectral Vegetation Indices in Characterizing Forest Biophysical and Biochemical Quantities

Authored by: Quan Wang , Jia Jin , Rei Sonobe , Jing Ming Chen

Hyperspectral Indices and Image Classifications for Agriculture and Vegetation

Print publication date:  December  2018
Online publication date:  December  2018

Print ISBN: 9781138066038
eBook ISBN: 9781315159331
Adobe ISBN:

10.1201/9781315159331-2

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Abstract

Derivative hyperspectral vegetation indices (dHVIs) are extensively examined for their capabilities in characterizing forest biophysical and biochemical parameters at different spatial scales. Pros and cons of these dHVIs are explicitly revealed based on simulated datasets generated from radiative transfer models (virtual experiments) as well as measured datasets. Results suggest that derivative hyperspectral VIs generally perform better than those based on the original reflectance but are more sensitive to noise, especially for higher-order derivative indices. With hyperspectral remote sensing data becoming abundant, first-order derivative VIs are probably a good choice for balancing accuracy and robustness in retrieving both biophysical and biochemical parameters and hence may play an increasing role in forest dynamics monitoring.

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