The tree species and its diversity are two critical components to be monitored for sustainable management of forest as well as biodiversity conservation. In the present study, we have classified the tree species and estimated its diversity based on hyperspectral remote sensing data at a fine scale level in the Saranda forest. This area is situated near the mining fields and has a dense forest cover around it. The forest surrounding the study area is exhibiting high-stress condition as evidenced by the dying and dry plant material, consequently affecting tree species and its diversity. The preprocessing of 242 Hyperion (hyperspectral) spectral wavebands resulted in 145 corrected spectral wavebands. The 21 spectral wavebands were selected through discrimination analysis (Walk's Lambda test) for tree species analysis. The SVM (support vector machine), SAM (spectral angle mapper), and MD (minimum distance) algorithms were applied for tree species classification based on ground spectral data obtained from the spectroradiometer. We have identified six local tree species in the study area at the spatial level. The result shows that Sal and Teak tree species are located in the upper and lower hilly sides of two mines (Meghahatuburu and Kiriburu). We have also used hyperspectral narrow banded vegetation indices (VIs) for species diversity estimation based on the field-measured Shannon diversity index. The statistical result shows that NDVI705 (red edge normalized difference vegetation index) is having the best R2 (0.76) and lowest RMSE (0.04) for species diversity estimation. That is why we have used NDVI705 for species diversity estimation. The result shows that higher species diversity values are located in the upper and lower hilly sides of two mines. The linear regression between Hyperion and field measured Shannon index shows the R2 (0.72) and RMSE (0.15). This study will aid in effective geoenvironmental planning and management of forest in the hilltop mining areas.
Keywords: Hyperspectral remote sensing; Mining activities; Tree species and diversity.