Cloud-free satellite data are not available during the monsoon season (July to September) in this coastal region. In situ data on forest plantations, provided by collaborators, was supplemented with additional training data representing other land cover subclasses in the region: agriculture, water, aquaculture, mangrove, palm, forest plantation, ground, natural forest, shrub/scrub, sand, and urban, with a total sample size of 2,230. These high-quality samples were then aggregated into three land use classes: non-forest, natural forest, and forest plantations. Image classification used random forests within the Julia Decision Tree package on a thirty-band stack that was comprised of the VNIR bands and NDVI images for all dates. The median classification accuracy from the 5-fold cross validation was 94.3%. Our results, predicated on high quality training data, demonstrate that (mostly smallholder) forest plantations can be separated from natural forests even using only the Sentinel 2 VNIR bands when multitemporal data (across both years and seasons) are used.

This study's objective was to develop a method by which smallholder forest plantations can be mapped accurately in Andhra Pradesh, India, using multitemporal visible and near-infrared (VNIR) bands from the Sentinel-2 MultiSpectral Instruments (MSIs). Conversion to agriculture, coupled with secondary dependencies on and scarcity of wood products, has driven the deforestation and degradation of natural forests in Southeast Asia. Concomitantly, forest plantations have been established