Characterization of tropical forests at community level: combining spectral, phenological, structural datasets using random forest algorithm

Since the inception of satellite remote sensing as a technology, characterization of forests has been one of its major applications. Characterization of forests at community level is essential for conservation, restoration and sustainable management of biodiversity. Recent advances in remote sensing offer opportunities to observe not only the reflectance spectra of forests from space, but also their phenology and structure. In this study, Earth Observation (EO) datasets were divided into 3 sets: spectral, structural and phenological. Then, Random Forest (RF) algorithm was applied on these 3 datasets along with field inventory-based tree data to generate community classification map of Purna wildlife sanctuary in Gujarat, India. The classification accuracy achieved from the spectral datasets (79.08–87.23