ArticleHeliyon2023
Support vector machine-based spatiotemporal land use land cover change analysis in a complex urban and rural landscape of Akaki river catchment, a Suburb of Addis Ababa, Ethiopia.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Spatio-Temporal Modeling of Land-Use/Land-Cover Change and Land Surface Temperature Using SVM and CA-Markov in Dilla Town, Ethiopia.Global challenges (Hoboken, NJ) · 2026Article
- Determinants and governance of unused rural residential bases in the context of Rural Revitalisation in China.Scientific reports · 2025Article
- Seasonal Land Use and Land Cover Mapping in South American Agricultural Watersheds Using Multisource Remote Sensing: The Case of Cuenca Laguna Merín, Uruguay.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
3 authors.
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Abstract
Intense level of land use land cover (LULC) changes has been observed in Sub-Saharan Africa, particularly in the central highlands of Ethiopia, due to rapid population growth and urbanization process. However, quantifying and identifying the rural-urban landscape changes are challenging. In this study, LULC changes during the years 1984, 1990, 2000, 2010, and 2021 have been analyzed using satellite imageries and Support Vector Machine (SVM) algorithms in a heterogenous rural and urban landscape of the Akaki river catchment, central highlands of Ethiopia. The LULC change drivers were evaluated by applying LULC thematic change analysis combined with key informants' interviews. Seven LULCs that include: Built-up area (BTA), Cropland (CL), Grassland (GL), Waterbody (WB), Plantation Forest (PF), Woodland (WL), and Bareland (BL) were detected. The result shows that 51.3 % of the catchment area has been transformed into other land uses. BTA increased by 24.7 % while GL and WL reduced by 18.1 % and 5.9 % respectively. Large areas of CL (61 %) and GL (22 %) were changed into an urban landscape. The spatial and non-spatial analysis revealed that the major spatiotemporal LULC change drivers between 1984 and 2005 were land use policy and legislation change and the Eucalyptus tree plantation campaign. Whereas, low-cost housing programs, informal settlers, market opportunity, and real estate development were the main drivers for the LULC changes between 2006 and 2021. The study also found the key informant observation and SVM image classification results are aligned and therefore, we found the SVM-based classifications are suited for such complex rural-urban landscape change and pattern analysis. The outcome of this research can contribute to improving land use policy, its management, and public understanding of the LULC dynamics and its implications.
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