Application of Sentinel-2 Satellite Data to Map Forest Cover in Southeast Sri Lanka through the Random Forest Classifier

Abstract

Sentinel-2 satellite data has been used for forest cover monitoring for almost five years. Mapping with Sentinel data will be a cost-effective solution for Sri Lanka, where the lack of updated land cover maps with high spatial resolution is a significant challenge in the land resource management of the country. A study area of about 5,000 km2 located in southeast Sri Lanka was selected for this study. Agricultural lands, forests including Yala national park, and villages with perennial crops make up the region. A Level-2A Sentinel-2 image with less than 10 percent cloud cover was used in the European Space Agency's (ESA) SNAP software version 8.0.0 for image processing and the forest cover of the study area was mapped through the Random Forest classifier (RFC). Normalized Difference Vegetation Index (NDVI) is also calculated as a Sentinel product to support RFC output. For RFC, ground truth data were collected through the reference of Google Earth high-resolution data. The classification accuracy was assessed using the Google Earth image as the reference dataset. Furthermore, RFC results were compared with NVDI greenness values. The classification accuracy was calculated using a confusion matrix (error matrix) through randomly selected 100 sample points. The overall accuracy of the land cover map was 85 percent, with a 96 percent accuracy for forest cover identification. The study found RFC as an effective method to isolate forest cover in Sri Lanka.KEYWORDS: Sentinel-2, Random Forest Classifier, Land cover classification, Land cover mapping, Normalized Difference Vegetation Index.