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<p>Land use and land cover (LULC) change significantly affects environmental processes and sustainable land management in river basins. The Upper Blue Nile River Basin, Ethiopia, has experienced significant LULC changes due to population growth, agricultural expansion, and deforestation. This study examines past and future LULC dynamic patterns using an integrated cloud-based framework implemented in Google Earth Engine. Classification and regression tree, random forest (RF), and support vector machine classifiers were used to process multi-temporal Landsat imagery. RF achieved the highest overall accuracy (94.4%) and kappa (0.879) in 2024. The results reveal pronounced agricultural expansion and substantial forest loss over the past two decades. Using the RF-derived LULC maps, a Cellular Automata–Markov model was calibrated and validated with strong agreement (Kappa = 0.886) to project future changes. Projections to 2034 and then 2044 indicate continued expansion of agricultural and built-up areas at the expense of forests and shrub/grasslands. The results support improved land use planning and environmental sustainability in the basin.</p>

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GEE-integrated ML classifiers evaluation for LULC change detection and CA-Markov-based future prediction in Upper Blue Nile River Basin, 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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  • Shambel Yideg Arega,
  • Denghua Yan,
  • Tianling Qin,
  • Mustafa Shaaban Ismail Ali,
  • Nebiyou Kassahun Getahun,
  • Mahmoud M. Hassanien

摘要

Land use and land cover (LULC) change significantly affects environmental processes and sustainable land management in river basins. The Upper Blue Nile River Basin, Ethiopia, has experienced significant LULC changes due to population growth, agricultural expansion, and deforestation. This study examines past and future LULC dynamic patterns using an integrated cloud-based framework implemented in Google Earth Engine. Classification and regression tree, random forest (RF), and support vector machine classifiers were used to process multi-temporal Landsat imagery. RF achieved the highest overall accuracy (94.4%) and kappa (0.879) in 2024. The results reveal pronounced agricultural expansion and substantial forest loss over the past two decades. Using the RF-derived LULC maps, a Cellular Automata–Markov model was calibrated and validated with strong agreement (Kappa = 0.886) to project future changes. Projections to 2034 and then 2044 indicate continued expansion of agricultural and built-up areas at the expense of forests and shrub/grasslands. The results support improved land use planning and environmental sustainability in the basin.