Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

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.