Classification Map of Smallholder Forest Plantations in Andhra Pradesh, India

<p>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 cropland, 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 both within and outside
of forests, with the latter (as contiguous blocks) being the focus of this
study. Accurately mapping smallholder forest plantations in South and Southeast
Asia is difficult using remotely sensed data due to the plantations’ small size
(average of 2 hectares), short rotation ages (4-7 years for timber species),
and spectral similarities to croplands and natural forests. Cloud-free
Harmonized Landsat Sentinel-2 (HLS) S10 data was acquired over six dates, from
different seasons, over four years (2015-2018). Available in situ data on
forest plantations was supplemented with additional training data resulting in
2,230 high-quality samples aggregated into three land cover classes: nonforest,
natural forest, and forest plantations. Image classification used random
forests on a thirty-band stack consisting of the VNIR bands and NDVI images for
all six 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.</p>