Remote Sensing Assessment of Land-Cover and Surface-Water Changes Associated with Black-Sand Mining Areas in an Arid Environment: A Multi-Index Exploratory Case Study from N’Diago, Mauritania (2014–2026)

Black-sand mining is an under-studied anthropogenic pressure on arid coastal environments, where sparse vegetation and slow natural recovery limit conventional impact assessment. Despite the recent expansion of heavy-mineral extraction along the Mauritanian coast, no spatio-temporal analysis has quantified its effects on land cover and surface-water dynamics in the N’Diago region. We analysed the N’Diago–LGUWYCHICH coastal sector (south-western Mauritania) using three Landsat scenes (2014, 2020, and 2026) in QGIS (free and open-source Geographic Information System), four spectral indices (NDVI-Normalized Difference Vegetation Index, NDWI-Normalized Difference Water Index, BSI- Bare Soil Index, and CI -Coloration Index), supervised Support Vector Machine classification and a 30 m SRTM Digital Elevation Model over a 97.82 ha area of interest. Bare soil dominated the landscape at every date (92.6–95.7%) and vegetation stayed below 7%, indicating that canopy-based metrics underestimate disturbance in this setting. The clearest change was hydrological: an inland water body shrank from 1.02 ha in 2020 to 0.40 ha in 2026, a 60.6% loss. This individual inland water body, measured directly and independently from the NDWI index, is our primary hydrological observation: the wet feature was smaller in the 2026 image than in the 2020 image and was located near the mapped concession areas, but the available data do not establish the cause of this change. Similar bare-soil and colour index values (BSI ≈ 0.23, CI ≈ 0.79–0.80) were observed in the processed images, while residual seasonal and radiometric differences between the Landsat 8 and DOS-corrected Landsat 9 products cannot be excluded; BSI and CI are treated only as candidate or contextual spectral patterns, so any delineation of the disturbed footprint is provisional and requires confirmation from independent field data. This study illustrates a low-cost exploratory workflow that may support preliminary monitoring in data-scarce arid coastal settings, pending validation with denser time series and field observations.