Abstract
Coal mining is well known to be very important in the industry and helpful in contributing to the country’s economy. Vegetation and land degradation are a huge environmental problem facing every country around the world. To manage the issue, it is best to implement the best rehabilitation strategies. The introduction of remote sensing and geographical information systems has helped this study with mapping vegetation and land degradation at a regional scale. The first objective was to analyse the temporal and spatial vegetation and land degradation using Landsat observation data for the years 1987,1999,2011 and 2023. The data results showed that dense vegetation and normal vegetation are slowly decreasing with an increase of barren land. When compared to other land use classified classes barren land has the highest record between the years 1987 (46.02%), 1999(60,02%), 2011 (29,98%) and 2023 (42,8%).
The spatial distribution and mapping land cover classes including degraded area showed the overall accuracy for the distribution of degraded areas is (80.47%) and user accuracy (of 75%), while Built-up areas showed the lowest result of (48.78%) overall accuracy and user accuracy (48.78%). The second objective of this study was to assess the levels and causes of vegetation and land degradation in the study area. Combining geographical information systems and satellite imagery the study was able to identify degradation patterns. This was done through the adoption of vegetation indices, normalized difference vegetation index and soil-adjusted vegetation index to monitor vegetation health and assess ecological impacts. However, the results show a decline in Normalized different vegetation index values affected by mining and a decrease in vegetation health and density.
Soil Adjusted Vegetation Index is used for soil brightness in the study area to show land degradation, the study showed an increase in barre land over the past years. The spatial distribution of classified land use classes presented a prom overall accuracy (1987)88.00%, (1999) 96.00%, (2011)95.00%, (2023) 100.00%, with the kappa coefficient of 76%,96%,95% and 100%. In this regard, the dense vegetation class achieved the highest classification accuracy, while built-up areas achieved the lowest percentage of the four years. There was a huge difference between the land use and cover accuracies. Coal mining operations directly impact vegetation through land clearing, soil erosion and habitat destruction. The mining of coal in the study goes
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beyond vegetation and land degradation but the effect it also has on the surrounding communities, livelihood and the alteration of agricultural activities and noise pollution, health issues compromising their health.
The levels and causes of degradation involve water contamination and dust. However, the results are reasonably good with few errors. The study shows that there is a change in land cover health linked with mining activities. Historically degradation is known to be associated with poverty and rurality, and this is a result of the high demand for human and economic resources. The results obtained from this study show that there are degraded areas and there is a need for conservation. The study concluded that both tools, remote sensing and geographical information systems are suitable for mapping the difficulties of land cover classes mostly degraded areas.