Abstract
Numerous spectral indices have been developed to assess plant diversity. However, since they are developed in different areas and vegetation type, it is difficult to make a comprehensive comparison among these indices. The primary objective of this study was to explore the optimum spectral indices that can predict plant species richness across different communities in sandy grassland. We use 7339 spectral indices (7217 we developed and 122 that were extracted from literature) to predict plant richness using a two-year dataset of plant species and spectra information at 270 plots. For this analysis, we employed cluster analysis, correlation analysis, and stepwise linear regression. The spectral variability within the 420–480 nm and 760–900 nm ranges, the first derivative value at the sensitive bands, and the normalized difference at narrow spectral ranges correlated well with plant species richness. Within the 7339 indices that were investigated, the first-order derivative values at 606 and 583 nm, the reflectance combinations on red bands: (R802 − R465)/(R802 + R681) and (R750 − R550)/(R750 + R550) showed a stable performance in both the independent calibration and validation datasets (R2 > 0.27, p < 0.001, RMSE < 1.7). They can be regarded as the best spectral indices to estimate plant species richness in sandy grasslands. In addition to these spectral variation indices, the first derivative values or the normalized difference of the sensitive bands also reflect plant diversity. These results can help to improve the estimation of plant diversity using satellite-based airborne and hand-held hyperspectral sensors.
Highlights
Plant species diversity is a key element in the provision of ecosystem services [1,2]
The spectral indices clustered into five distinct groups in the hierarchical cluster tree based on all 7339 hyperspectral indices (Figure S1)
This study assessed 7339 spectral indices to predict plant richness across varying community covers and complexities using a two-year dataset of plant species and hyperspectral wavelength spectra from 270 plots
Summary
Plant species diversity is a key element in the provision of ecosystem services [1,2]. The variations in the remotely sensed spectra can be used to assess plant species diversity. This forms a key basis in the Spectral Variations Hypothesis (SVH) [3,6,7,8]. This means that spectral entropy is potentially represents an efficient and relatively inexpensive means to provide biodiversity estimates when compared to high-cost, labor-intensive field surveys [9]. The greater the detail contained within a spectral range of a remotely sensed data-set, the more useful it will be extract reliable predictors of plant
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