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<p>Analyzing changes in land use and land cover (LULC) is crucial for ensuring long-term ecological sustainability. This study investigated the spatio-temporal dynamics of LULC, with a particular focus on irrigation expansion and its effects on ecosystem services in the Lake Tana sub-basin. An ensemble machine learning algorithm, random forest, was employed in the LULC classification for 1985, 2003, and 2021. The Normalized Vegetation Index (NDVI) was used to differentiate between rain-fed and irrigated lands, and the impact of LULC dynamics on Ecosystem Service Values (ESVs) was evaluated using modified valuation coefficients tailored to Ethiopian biome conditions. Based on the classified maps, the agricultural land increased significantly from 43.95 to 55.22%, and the irrigated agriculture rose from 1.24 to 8.69% between 1985 and 2021, whereas grassland and shrub land decreased from 14.38 to 5.35% and 18.85 to 15.34%, respectively, within the same periods. The largest gain was observed for irrigated agriculture, while the greatest loss was observed for grasslands over 36&#xa0;years. The total ESVs of the sub-basin were estimated at US$2.98 billion in 1985, US$2.96 billion in 2003, and US$2.995 billion in 2021. Over the 36&#xa0;years, approximately US$0.015 billion (0.52%) in ESVs was gained, highlighting the effects of LULC changes on ecosystem services. To mitigate these impacts, strategic land use planning should incorporate ecosystem service valuation into decision-making, reinforce regulatory frameworks, and advance sustainability initiatives. Future research should focus on enhancing classification accuracy by integrating higher-resolution satellite imagery, incorporating socio-economic variables, and simulating future ESV scenarios under various development pathways.</p>

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Assessment of irrigation expansion and land use land cover dynamics: impacts on ecosystem service values in the Lake Tana sub-basin, Ethiopia

  • 期刊论文

  • 发表日期：

    2026年3月5日

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  • Walelign K. Endalew,
  • Fasikaw A. Zimale,
  • Seifu A. Tilahun ,
  • Ayenew D. Ayalew,
  • Ashebir Sewale Belay,
  • Nicola Fohrer

摘要

Analyzing changes in land use and land cover (LULC) is crucial for ensuring long-term ecological sustainability. This study investigated the spatio-temporal dynamics of LULC, with a particular focus on irrigation expansion and its effects on ecosystem services in the Lake Tana sub-basin. An ensemble machine learning algorithm, random forest, was employed in the LULC classification for 1985, 2003, and 2021. The Normalized Vegetation Index (NDVI) was used to differentiate between rain-fed and irrigated lands, and the impact of LULC dynamics on Ecosystem Service Values (ESVs) was evaluated using modified valuation coefficients tailored to Ethiopian biome conditions. Based on the classified maps, the agricultural land increased significantly from 43.95 to 55.22%, and the irrigated agriculture rose from 1.24 to 8.69% between 1985 and 2021, whereas grassland and shrub land decreased from 14.38 to 5.35% and 18.85 to 15.34%, respectively, within the same periods. The largest gain was observed for irrigated agriculture, while the greatest loss was observed for grasslands over 36 years. The total ESVs of the sub-basin were estimated at US$2.98 billion in 1985, US$2.96 billion in 2003, and US$2.995 billion in 2021. Over the 36 years, approximately US$0.015 billion (0.52%) in ESVs was gained, highlighting the effects of LULC changes on ecosystem services. To mitigate these impacts, strategic land use planning should incorporate ecosystem service valuation into decision-making, reinforce regulatory frameworks, and advance sustainability initiatives. Future research should focus on enhancing classification accuracy by integrating higher-resolution satellite imagery, incorporating socio-economic variables, and simulating future ESV scenarios under various development pathways.