Open AccessBest hyperspectral indices for assessing leaf chlorophyll content in a degraded temperate vegetation
Author(s) -
Peng Yu,
Fan Min,
Wang Qinghui,
Lan Wenjuan,
Long Yating
Publication year - 2018
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.4229
Subject(s) - hyperspectral imaging , chlorophyll , chlorophyll a , remote sensing , environmental science , vegetation (pathology) , spectral resolution , mathematics , botany , biology , spectral line , physics , medicine , pathology , geology , astronomy
Extensive studies have focused on assessing leaf chlorophyll content through spectral indices; however, the accuracy is weakened by limited wavebands and coarse resolution. With hundreds of wavebands, hyperspectral data can substantially capture the essential absorption features of leaf chlorophyll; however, few such studies have been conducted on same species in various degraded vegetations. In this investigation, complete combinations of either original reflectance or first‐order derivative spectra we conducted a complete combination on either original reflectance or its first‐order derivative value from 350 to 1000 nm to quantify leaf total chlorophyll (Chll), chlorophyll‐a (Chla), and chlorophyll‐b (Chlb) contents. This was performed using three hyperspectral datasets collected in situ from lightly, moderately, and severely degraded vegetations in temperate Helin County, China. Suitable combinations were selected by comparing the numbers of significant correlation coefficients with leaf Chll, Chla, and Chlb contents. The combinations of reflectance difference ( D ij ), normalized differences ( ND ), first‐order derivative ( FD ), and first‐order derivative difference ( FD ( D )) were found to be the most effective. These sensitive band‐based combinations were further optimized by means of a stepwise linear regression analysis and were compared with 43 empirical spectral indices, frequently used in the literature. These sensitive band‐based combinations on hyperspectral data proved to be the most effective indices for quantifying leaf chlorophyll content ( R 2 > 0.7, p < 0.01), demonstrating great potential for the use of hyperspectral data in monitoring degraded vegetation at a fine scale.