Illegal small-scale mining in eastern Cameroon has grown tremendously in the last decade and caused significant environmental degradation. Excessive cloud cover in the area has limited the use of optical remote sensing data to map and monitor the extent of these activities. This study investigated the use of annual time-series Sentinel-1 data to map and monitor illegal mining activities along major rivers in Eastern Cameroon between 2017 and 2021. A change detection approach, based on three time-series features—minimum, mean, maximum—was used to compute a backscatter threshold value suitable to identify/detect mining-induced land cover changes in the study area. Compared to the mean and maximum, the minimum time-series feature (in both VH and VV polarization) was found to be more sensitive to changes in backscattering within the period of investigation. Our approach permitted the detection of new illegal mining areas on an annual basis. A backscatter threshold value of +1.55 dB was found suitable for detecting illegal mining activities in the study area. Application of this threshold revealed illegal mining area extents of 42 km2, 85 km2 and 135 km2 for periods 2017/2018–2018/2019, 2018/2019–2019/2020 and 2019/2020–2020/2021, respectively. The upward trend observed in new areas of illegal mining suggests that efforts at stopping illegal mining along rivers in Cameroon have not yielded positive results in the period investigated. Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed that about 25% of the Sentinel-1 data, mostly acquired in March and October (beginning and end of rainy season respectively), were unusable due to atmospheric effects from high intensity rainfall events. Further investigation in other geographies and climatic regions is needed to ascertain the susceptibility of Sentinel-1 data to atmospheric conditions.

Keywords: Sentinel-1; mining; image artifacts; time-series features; Cameroon