Examining the performances of true color RGB bands from Landsat-8, Sentinel-2 and UAV as stand-alone data for mapping artisanal and Small-Scale Mining (ASM)

Abstract

Conventional satellite sensors provide periodic data for monitoring Artisanal and Small-Scale Mining (ASM). Incorporating Unmanned Aerial Vehicle (UAV) sensors in monitoring ASM can increase data availability and also mitigate the challenge of cloud cover that causes data gaps in satellite data availability. However, most UAV sensors measure only in the Red-Green-Blue (RGB) bands and the utility of RGB for Land Use Land Cover (LULC) mapping is still in the preliminary stages. This study contributes to research by examining the accuracies of true color RGB images for mapping ASM, using the Birim Basin in Ghana as a case study. The study first compared Landsat 8 (L8) and Sentinel-2 (S2) full reflective bands for LULC mapping of the basin. The study then used true color RGB from UAV, L8 and S2 to map a subsection of the basin where there was active ASM. A test experiment was also performed in which other band combinations, single band, and indices were used to map ASM. From the results, the L8 and S2 full reflective bands attained Overall accuracy (OA) and User Accuracy (UA) over 97%, with less than 1% difference between their accuracies. All stand-alone RGB precisely mapped ASM with accuracies of 90% and above, indicating their fit for mapping ASM. However, the effect of image resolution caused differences in the surface area of the ASM estimated from the different sensors. Also, the different band combinations and indices could not accurately map ASM. Nonetheless, the results of the study showed increased scientific grounds for using UAV for mapping ASM. The overall results showed the capabilities of remote sensors in providing frequent and sustainable data for mapping ASM. Additionally, the results from the three images (L8, S2 and UAV) reavelaed much destruction of the forest cover due ASM. These results would be relevant in monitoring and managing the natural resources, which will help achieve environmental sustainability in meeting the SDG goals in 2030.