Spatiotemporal analysis of forest cover change in southwestern Ethiopia

Supervised classification with maximum likelihood algorithm used for LULC classification • Dense forest was declined by 4% due to agricultural land expansion • About 1952.3km2 of forest land was converted to other land use land cover types. Forest degradation has emerged as a critical environmental challenge in many tropical regions, driven by agricultural expansion, population pressure, and unsustainable land-use practices, resulting in biodiversity loss and ecosystem instability. Accordingly, this study examines the spatiotemporal dynamics of forest cover change using GIS and remote sensing techniques in the Buno Bedele and Ilu Ababor Zones of southwestern Ethiopia. Landsat ETM+ of 2003 ETM+ of 2013 and OLI/TIRS of 2023 were used. Land use/land cover (LULC) types of the study area were classified using a supervised classification approach with the maximum likelihood algorithm. The result revealed that, a significant decrease in both dense forest cover and sparse forest cover by 4% and 3.3% respectively, from 2003 to 2023. In contrast, cultivated land expanded considerably during the same period, rising with 5.7% over the study period. This indicates that agricultural expansion is the leading cause of deforestation in the study area. Furthermore, the conversion matrix analysis reveals that nearly 60% of dense forest loss occurred through degradation into sparse forest before being converted into agricultural land, highlighting a critical forest degradation pathway. These findings underscore the urgent need for effective and targeted land use policies to promote sustainable forest management and to establish land-use zoning strategies that mitigate on going land degradation and enhance resilience against future disturbances

Agricultural land · Agriculture · Biodiversity · Deforestation (computer science) · Environmental degradation · Land cover · Land degradation · Land use · Population · Zoning · Conservation, Biodiversity, and Resource Management · Land Use and Ecosystem Services · Remote Sensing in Agriculture

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