Trend analysis, past dynamics and future prediction of land use and land cover change in upper Wabe-Shebele river basin

Heliyon. 2023 Aug 19;9(9):e19128. doi: 10.1016/j.heliyon.2023.e19128. eCollection 2023 Sep.

Abstract

A growing population has led to extensive farming at the expense of a natural environment. Changes in land use and cover have caused land degradation, and problematic groundwater recharge. The objective of this study was to evaluate the historical trend, simulations, and predictions of land use land cover change in the Upper Wabe-Shebele River Basin. The study accounted for 1992, 2007 and 2022 as well as it will predict the change for 2037 and 2052. Landsat TM for 1992, ETM + for 2007, and Landsat-8 OLI for 2022 were used. In QGIS 3.16, the maximum likelihood method was utilized for supervised image classification. Using CA-Markov and the Land Change Modeler land use and land cover change for 2037 and 2052 were predicted. Validity and accuracy of the model was evaluated using actual and predicted land use and land cover changes of 2022. Topography, proximity to a town, stream, roads, and population density were used as input for the model. The results showed that between 1992 and 2007, cultivated land increased by 17.07% on average at a rate of 1.05%, while settlement increased by 17.51% at a rate of 1.08% per year. Agricultural and settlement land increased by 22.97% and 30.12%, respectively. Between 1992 and 2022, the transition area matrix showed 2,330.25 and 1,145.77 km2 of forest and grazing land were changed to settlement and cultivated land, respectively. Meanwhile, from 2022 to 2037, the quantity of land used for cultivated, grazing, and settlement is predicted to increase by 0.19, 3.66, and 23.8% in order. For 2037 and 2052, settlement and cultivated land were increased by 1.3 and 7.32% respectively. Finally, since natural ecosystem had been significantly disturbed by change in the study area, comprehensive rehabilitation and management is demanded.

Keywords: CA-Markov; Land change modeler; Land cover; Prediction; Simulation; TerrSet.