Detection and mapping abandoned areas of artisanal and small-scale gold mining (ASGM) using multi-sensor data on Google Earth Engine: A case study of Kuantan Singingi, Riau

Abstract

Artisanal and small-scale gold mining (ASGM) activities in Kuantan Singingi, Riau have been operating over a decade without proper permits and using unsafe procedures for the environment. Mercury releases and degraded land have been the leading factors in the decreased environmental functions. ASGM activities are nomadic and secluded, posing a considerable challenge in detecting their location and extent. The aims of this study are to provide a method for detecting and mapping ASGM footprints utilizing multi-sensor data on cloud computing platforms. The detection method is performed using a supervised random forest algorithm. The result successfully mapped an ASGM footprints, estimating an area of 10,044.38 ha with 89.23% accuracy through Sentinel-1 data and an area of 12,308.57 ha with 87.25% accuracy through Sentinel-2 data. The spatial distribution of ASGM footprints is scattered over the streams and tributaries across all regions. These maps are pivotal in establishing regulatory measures for environmental restoration and preventing further expansion of degraded land.