Application of remote sensing-based spectral variability hypothesis to improve tree diversity estimation of seasonal tropical forest considering phenological variations

Abstract

Global decline in biodiversity warrants its systematic monitoring in space and time. Remote sensing derived Rao’s Q index has been proposed as a proxy for species diversity yet its scope for seasonal tropical forest is untested. The study assessed the influence of phenology on Rao’s Q index derived using multi-date Sentinel-2 NDVI to estimate tree diversity. Plot level vegetation inventory data (n = 61) was used to estimate tree diversity (Shannon-Wiener index (H')) of Nandhaur landscape in North-West Himalayan foothills. Rao’s Q index and H' showed lower correlation at the landscape level than individual forest types. Rao’s Q index based on NDVI observed higher correlation with H', especially during the leaf flushing period. NDVI-based multi-dimensional Rao’s Q index offered better performance for dry deciduous (R2 =0.69) followed by moist deciduous forest. The present approach can be used for estimating tree diversity, especially in seasonal tropical forests.