Inferring Species Diversity and Variability over Climatic Gradient with Spectral Diversity Metrics.

Filling in the void between forest ecology and remote sensing through monitoring biodiversity variables is of great interest. In this study, we utilized imaging spectroscopy data from the ISRO-NASA Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) India campaign to investigate how the measurements of biodiversity attributes of forests over wide areas can be augmented by synchronous field- and spectral-metrics. Three sites, Shoolpaneshwar Wildlife Sanctuary (SWS), Vansda National Park (VNP), and Mudumalai Tiger Reserve (MTR), spread over a climatic gradient (rainfall and temperature), were selected for this study. Abundant species maps of three sites were produced using a support vector machine (SVM) classifier with a 76-80% overall accuracy. These maps are a valuable input for forest resource management. Convex hull volume (CHV) is computed from the first three principal components of AVIRIS-NG spectra and used as a spectral diversity metric. It was observed that CHV increased with species numbers showing a positive correlation between species and spectral diversity. Additionally, it was observed that the abundant species show higher spectral diversity over species with lesser spread, provisionally revealing their functional diversity. This could be one of the many reasons for their expansive reach through adaptation to local conditions. Higher rainfall at MTR was shown to have a positive impact on species and spectral diversity as compared to SWS and VNP. Redundancy analysis explained 13-24% of the variance in abundant species distribution because of climatic gradient. Trends in spectral CHVs observed across the three sites of this study indicate that species assemblages may have strong local controls, and the patterns of co-occurrence are largely aligned along climatic gradient. Observed changes in species distribution and diversity metrics over climatic gradient can help in assessing these forests' responses to the projected dynamics of rainfall and temperature in the future.