Mapping Smallholder Forest Plantations in Andhra Pradesh, India using Multitemporal Harmonized Landsat Sentinel-2 S10 Data

• Main Author: Williams, Paige T.
• Other Authors: Not found
• Format: Others
• Published: Virginia Tech 2021
• Subjects: remote sensing; random forest; NDVI; trees outside forests; machine learning; classification
• Online Access: http://hdl.handle.net/10919/104234

The objective of this study was to develop a method by which smallholder forest plantations can be mapped accurately in Andhra Pradesh, India using multitemporal (intra- and inter-annual) visible and near-infrared (VNIR) bands from the Sentinel-2 MultiSpectral Instruments (MSIs). Dependency on and scarcity of wood products have driven the deforestation and degradation of natural forests in Southeast Asia. At the same time, forest plantations have been established both within and outside of forests, with the latter (as contiguous blocks) being the focus of this study. The ecosystem services provided by natural forests are different from those of plantations. As such, being able to separate natural forests from plantations is important. Unfortunately, there are constraints to accurately mapping planted forests in Andhra Pradesh (and other similar landscapes in South and Southeast Asia) using remotely sensed data due to the plantations' small size (average 2 hectares), short rotation ages (often 4-7 years for timber species), and spectral similarities to croplands and natural forests. The East and West Godavari districts of Andhra Pradesh were selected as the area for a case study. Cloud-free Harmonized Landsat Sentinel-2 (HLS) S10 data was acquired over six dates, from different seasons, as follows: December 28, 2015; November 22, 2016; November 2, 2017; December 22, 2017; March 1, 2018; and June 15, 2018. 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. === The objective of this study was to develop a method by which smallholder forest plantations can be mapped accurately in Andhra Pradesh, India using multitemporal (intra- and inter-annual) visible (red, green, blue) and near-infrared (VNIR) bands from the European Space Agency satellite Sentinel-2. Dependency on and scarcity of wood products have driven the deforestation and degradation of natural forests in Southeast Asia. At the same time, forest plantations have been established both within and outside of forests, with the latter (as contiguous blocks) being the focus of this study. The ecosystem services provided by natural forests are different from those of plantations. As such, being able to separate natural forests from plantations is important. Unfortunately, there are constraints to accurately mapping planted forests in Andhra Pradesh (and other similar landscapes in South and Southeast Asia) using remotely sensed data due to the plantations' small size (average 2 hectares), short rotation ages (often 4-7 years for timber species), and spectral (reflectance from satellite imagery) similarities to croplands and natural forests. The East and West Godavari districts of Andhra Pradesh were selected as the area for a case study. Cloud-free Harmonized Landsat Sentinel-2 (HLS) S10 images were acquired over six dates, from different seasons, as follows: December 28, 2015; November 22, 2016; November 2, 2017; December 22, 2017; March 1, 2018; and June 15, 2018. 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 points (X and Y locations with land cover class) 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 of 2,230 training points. 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 DecisionTree package on a thirty-band stack that was comprised of the VNIR bands and NDVI (calculation related to greenness, i.e. higher value = more vegetation) 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.