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<p>Assessing long-term vegetation cover dynamics is essential for understanding environmental changes and promoting sustainable land management. This study evaluates vegetation cover patterns in Ethiopia’s&#xa0;middle Gibe River sub-basin from 1992 to 2024 and forecasts trends for 2056 using the CA–Markov model. Using ERDAS Imagine 2014 and ArcGIS 10.8.2, Landsat images from 1992, 2008, and 2024 were classified via maximum likelihood and analysed using post-classification change detection. Mean NDVI was derived at two-year intervals, and the CA–Markov model achieved a high predictive accuracy (kappa index: 84%). The study reveals a substantial reduction in vegetation cover from 1992 to 2024: forests Decreased from 22.8% to 11.07% (loss rate:12.52 km2/year). Grasslands: Decreased from 32.2% to 23.7% (loss rate: 9.06 km<sup>2</sup>/year). Agriculture: Increased significantly from 44.7% to 63.02%. Built-up area: Increased from 0.13% to 1.43%. Waterbodies: Increased from 0.16% to 0.78%. Concurrently, the mean NDVI declined from 0.21 to 0.14, confirming vegetation degradation. By 2056, the CA–Markov model predicts forest cover will shrink to 6.15% and grassland to 14.84%, primarily driven by agricultural expansion. This transformation exacerbates environmental issues, including soil erosion, biodiversity loss, and increased runoff at the Gibe III dam. To mitigate these impacts, policymakers should prioritise agroforestry and natural regeneration over agricultural expansion along steep slopes and riparian zones.</p>

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Evaluation and prediction of the spatiotemporal vegetation cover changes in the Middle Gibe River Sub-basin, Southwestern Ethiopia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Mulugeta Wolde,
  • Berhanu Terefe,
  • Mikias Biazen Molla

摘要

Assessing long-term vegetation cover dynamics is essential for understanding environmental changes and promoting sustainable land management. This study evaluates vegetation cover patterns in Ethiopia’s middle Gibe River sub-basin from 1992 to 2024 and forecasts trends for 2056 using the CA–Markov model. Using ERDAS Imagine 2014 and ArcGIS 10.8.2, Landsat images from 1992, 2008, and 2024 were classified via maximum likelihood and analysed using post-classification change detection. Mean NDVI was derived at two-year intervals, and the CA–Markov model achieved a high predictive accuracy (kappa index: 84%). The study reveals a substantial reduction in vegetation cover from 1992 to 2024: forests Decreased from 22.8% to 11.07% (loss rate:12.52 km2/year). Grasslands: Decreased from 32.2% to 23.7% (loss rate: 9.06 km2/year). Agriculture: Increased significantly from 44.7% to 63.02%. Built-up area: Increased from 0.13% to 1.43%. Waterbodies: Increased from 0.16% to 0.78%. Concurrently, the mean NDVI declined from 0.21 to 0.14, confirming vegetation degradation. By 2056, the CA–Markov model predicts forest cover will shrink to 6.15% and grassland to 14.84%, primarily driven by agricultural expansion. This transformation exacerbates environmental issues, including soil erosion, biodiversity loss, and increased runoff at the Gibe III dam. To mitigate these impacts, policymakers should prioritise agroforestry and natural regeneration over agricultural expansion along steep slopes and riparian zones.