The use of texture analysis and band transformation on multispectral imagery to map open-pit mines using machine learning

Abstract

Indonesia has the potential for abundant natural mining resources. The Indonesian government needs to monitor mining activity to maintain environmental sustainability and the availability of mining materials in Indonesia. This study aims to map open mining areas based on remote sensing data. This mapping is one of the actions to support sustainable development goals for ensuring sustainable management and efficient use of natural resources. This study was conducted in Central Bangka Regency, Bangka Belitung Island Province, Indonesia, using the multitemporal Sentinel-2 year of 2020-2021. Gray Level Co-Occurrence Matrix and Principal Component Analysis were applied to improve the input band capability in mapping the distribution of open-pit mining locations. A pixel-based machine learning algorithm, Random Forest, was applied to classify mining and non-mining. Classification using texture analysis and spectral transformation mapped an open mining area of 30.67 km2. Classification using only image bands resulted from a mining area of 18.38 km2. The assessment showed that texture analysis and spectral transformation provided an accuracy of 1.22 % higher than the classification using a direct image input band. Overall, the accuracy obtained by both methods was 96.93 % and 95.71 %. Further research on validation with high-resolution data is still needed.