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<p>Quantifying land use land cover (LULC) dynamics in vulnerable ecosystems of the Ethiopian highlands is crucial for understanding the drivers of environmental degradation and informing sustainable land management to protect ecosystem integrity. The Jema River Sub-basin, a critical contributor to the Upper Blue Nile, is highly vulnerable to agricultural expansion, the loss of native vegetation, and landscape fragmentation. This study quantified the spatiotemporal dynamics of LULC change and identified potential drivers between 1994 and 2024, to support evidence-based climate-resilient land-use planning in this basin. A machine-learning framework was implemented on the Google Earth Engine (GEE) platform to classify LULC. We utilized Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI imageries, which were classified using the Random Forest (RF) algorithm. The classification was optimized by integrating spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI), along with topographic variables (slope and elevation). Classification accuracy, assessed using 3100 ground-truth samples, exceeded 87% overall accuracy with Kappa coefficients above 83%. Temporal change detection, transition matrices, and trend analyses quantified alterations across seven LULC classes. The results show that farmland expanded by 37.4%, and built-up area by 23.5%, while natural vegetation and grazing land declined by 56% and 67.4%, respectively. Focus group discussions and key informant interviews identified population pressure, agricultural intensification, and weak governance as the dominant socio-economic drivers. The integration of multi-temporal geospatial analysis with local knowledge indicates substantial anthropogenic landscape modification and provides empirical evidence to inform sustainable land-use planning and ecosystem restoration initiatives in the Jema Sub-basin.</p>

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Assessing spatiotemporal land dynamics using Google Earth Engine and random forest: trends and drivers of change in Ethiopia’s fragile Jema River Basin

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • Tamrat Selamu,
  • Sileshi Degefa,
  • Wakgari Furi,
  • Tegegne Molla Sitotaw,
  • Ram L. Ray

摘要

Quantifying land use land cover (LULC) dynamics in vulnerable ecosystems of the Ethiopian highlands is crucial for understanding the drivers of environmental degradation and informing sustainable land management to protect ecosystem integrity. The Jema River Sub-basin, a critical contributor to the Upper Blue Nile, is highly vulnerable to agricultural expansion, the loss of native vegetation, and landscape fragmentation. This study quantified the spatiotemporal dynamics of LULC change and identified potential drivers between 1994 and 2024, to support evidence-based climate-resilient land-use planning in this basin. A machine-learning framework was implemented on the Google Earth Engine (GEE) platform to classify LULC. We utilized Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI imageries, which were classified using the Random Forest (RF) algorithm. The classification was optimized by integrating spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI), along with topographic variables (slope and elevation). Classification accuracy, assessed using 3100 ground-truth samples, exceeded 87% overall accuracy with Kappa coefficients above 83%. Temporal change detection, transition matrices, and trend analyses quantified alterations across seven LULC classes. The results show that farmland expanded by 37.4%, and built-up area by 23.5%, while natural vegetation and grazing land declined by 56% and 67.4%, respectively. Focus group discussions and key informant interviews identified population pressure, agricultural intensification, and weak governance as the dominant socio-economic drivers. The integration of multi-temporal geospatial analysis with local knowledge indicates substantial anthropogenic landscape modification and provides empirical evidence to inform sustainable land-use planning and ecosystem restoration initiatives in the Jema Sub-basin.