The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics.

Lokoja, the capital of Kogi State, Nigeria, situated at the confluence of the Niger and Benue Rivers, has experienced rapid urban expansion alongside heightened environmental risks, including flooding and ecosystem degradation. Using multi-temporal Landsat imagery (2000, 2010, 2020, 2024), Random Forest classification, and landscape metrics, this study analyses spatio-temporal patterns of urban growth and fragmentation in this underrepresented mid-sized African city. Urban land cover expanded from 6668 ha in 2000 to 15,985 ha in 2024 (net ~140% growth), following a non-linear trajectory of rapid expansion (2000–2010), partial consolidation (2010–2020), and renewed growth with intensified fragmentation (2020–2024). This growth caused severe ecological impacts: dense forest declined by 99.7% (from 373 ha to 1 ha), woodland by 73.9%, and core natural land cover by 23% to 13.8% of the landscape, below critical ecological thresholds. Edge density rose by 121%, exacerbating urban heat, runoff, and biodiversity loss, while apparent gains in grassland largely reflect secondary succession rather than recovery. This study recommends enforcing development restrictions below 10 m in elevation, with 100 m riparian buffers; restoring 500 ha of native corridors; mandating 20% urban tree canopy cover; and establishing community-based green space monitoring. The findings provide empirical insights into sustainability challenges facing mid-sized African cities and offer transferable strategies for ecologically sensitive urban planning.