ABSTRACT Coal remains a major global energy source despite ongoing environmental controversies, particularly, regarding climate change and landscape transformation. This study investigates the spatiotemporal dynamics of land use and land cover (LULC) in the Moatize Coal Basin (MCB), Mozambique, between 1990 and 2024, with a specific focus on land degradation processes. Atmospherically and geometrically corrected Landsat imagery was used to generate annual cloud‐free mosaics for LULC mapping. Supervised classification was performed using spectral bands, spectral indices, and temporal metrics within the Google Earth Engine (GEE) platform, achieving an impressive average overall accuracy of 85.67% and Kappa coefficient of 0.7679. Spatial patterns were quantified using fractal dimension ( D ), estimated through the combined application of the box‐counting and least‐squares regression methods, and complemented by Shannon entropy ( H ) to assess class distribution and structural heterogeneity. The results indicate that, following the onset of intensive mining activities in 2011, exposed surfaces expanded rapidly, with an increase of 52.91%, directly attributable to mining operations. The fractal dimension of these areas approached 2.0, indicating highly fragmented and irregular spatial patterns. Concurrently, rising entropy values, particularly after 2011, reflect increasing landscape disorder and reduced ecological coherence. Overall, the findings demonstrate that coal mining has been the dominant driver of LULC reconfiguration in the MCB, promoting landscape fragmentation, expansion of exposed surfaces, and progressive land degradation. The integration of fractal and entropy‐based metrics with long‐term remote sensing data provides a robust framework for assessing mining‐induced landscape transformation in data‐scarce regions.
Naite et al. (Wed,) studied this question.