Vegetation Characterization at Community Level Using Sentinel-2 Satellite Data and Random Forest Classifier in Western Himalayan Foothills, Uttarakhand

Abstract

Characterization of forests is an important aspect for the management and conservation of biodiversity and satellite remote sensing provides opportunity to rapidly assess biodiversity patterns across large geographical areas. The present study aimed to characterize vegetation in a landscape with heterogenous topography using medium resolution Sentinel-2 satellite data and random forest (RF) machine learning algorithm. The study area falls in the Nandhaur landscape of western Himalayan foothills and forms an important part of the Terai Arc Landscape. Sentinel-2 satellite data of January, April and May months was used along with SRTM Digital Elevation Model. Primary field data was collected for dominant vegetation community characteristics and used for training and validation of the model in RF. A total of eleven forest vegetation communities was characterized with an accuracy of 70–87% for individual imageries as well as in collocation of images. Satellite image of April was found most useful for discriminating forest communities with high spectral separability among communities. Variable important analysis revealed Vegetation Red Edge, Narrow NIR and NIR bands were useful for discriminating communities with maximum information along with SWIR and Red bands. Study found that collocation of the multi-date images was useful for classifying communities with higher accuracies. This attempt at characterizing heterogeneous vegetation communities with advanced machine learning tools in Himalayan foothills showed encouraging outcomes which can be used in future studies for monitoring and management of key habitats in this area.