ArticleHeliyon2023
Trend analysis, past dynamics and future prediction of land use and land cover change in upper Wabe-Shebele river basin.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Impacts of land use/land cover change on ecosystem service values in the Dinki Watershed, central highlands of Ethiopia.Scientific reports · 2026Article
- Climate change projections using CMIP6 GCMs and downscaling approaches in the Upper Wabe Shebele Basin, Ethiopia.Scientific reports · 2025Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 km
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Registered trials
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