Agroecology-based land use/land cover change detection, prediction and its implications for land degradation: A case study in the Upper Blue Nile Basin

This study examined land use/land cover (LULC) changes in Chemoga watershed of the Upper Blue Nile Basin, comprising four distinct agroecological regions: Wet Wurch, Moist Dega, Moist Weyna Dega, and Moist Kolla. We used multi-temporal Landsat images from 1985 to 2020, a hybrid classification method and the Cellular Automata-Markov model to analyze historical and predict future (2020-2060) LULC changes under business-as-usual (BAU) and land conservation (LC) scenarios. Magnitudes and patterns of spaciotemporal LULC changes were analyzed using intensity analysis. Cropland expanded across all agroecologies from 1985 to 2020, with Moist Kolla experiencing the highest increase at the expense of woodland, due the introduction of commercial farming to this hotter, less populated and inaccessible area. Moist Dega exhibited the highest allocation changes within cropland and forest, attributable to farmers' adoption of rotational land use to rehabilitate extensively degraded cultivated lands. Under the BAU scenario, projections suggest further cropland expansion at expense of woodland in Moist Kolla and built-up areas at the expense of cropland and grassland in Moist Dega. Under the LC scenario, forest cover is expected to increase at the expense of cropland across all agroecologies. The historical and projected BAU LULC change scenario substantially increased soil erosion and reduced ecosystem services. These effects can be minimized if LC scenario is properly implemented. The agroecology-based LULC intensity analysis reveals local drivers of change and associated impacts, providing vital insights for targeted land use planning in this study watershed and other watersheds facing similar challenges. (c) 2024 International Research and Training Center on Erosion and Sedimentation, China Water and Power Press, and China Institute of Water Resources and Hydropower Research. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY- NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).