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%) was better than the phenological dataset (80.94%); and the latter in turn was better than the structural datasets (74.11–81.49%). An RF model with combination of the best predictors from the three datasets increased the classification accuracy upto 90.29%. In case of spectral dataset, the last image before the start of summer monsoon season gave the best accuracy. Also the new spectral bands which first became available in relatively newer satellites contributed significantly more to the model as compared to relatively older spectral bands which have been available in remote sensing satellites for quite some time. Overall, this study develops an empirical framework for mapping tropical tree communities by improving accuracy across the readily available remote sensing datasets and can be upscaled with sufficient field inventory data to generate a national level forest tree community map in India.