Using GlobeLand30 data and cellular automata modeling to predict urban expansion and sprawl in Kigali City

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Katabarwa Murenzi Gilbert
Yishao Shi


The growth and expansion of urban regions in various cities worldwide, especially in developing economies, leads to changes in land usage. Thus, the study assessed the changes in land use and land cover within Kigali City using the Land Change Modeler of the TerrSet software system. The study focused on 2010 to 2020, using classified GlobeLand30 maps to identify significant land cover transitions, which were then classified into submodels. An enhanced multi-layer perceptron neural network was also used to analyze these transitions. Urban expansion was predicted using five key variables: elevation, slope, distance from rivers, roads, and built-up areas. The multi-layer perceptron neural network achieved an accuracy of 81.90% in predicting land use and land cover changes. The Cellular Automata-Markov chain model in the Land Change Modeler was implemented to forecast land use and land cover patterns for 2030. Results indicated that (1) over the past decade (2010-2020), urban areas expanded by 20.89 km², while forests, grasslands, shrublands, and wetlands decreased by 1.31 km², 8.63 km², 0.15 km², and 0.05 km², respectively. The study also predicts that (2) from 2020 to 2030, urban areas and artificial surfaces will expand by 15.83%, with a considerable decrease in grassland and cultivated land. The study further predicts a slight decrease in wetland areas and for land use and land cover in Kigali City, highlighting the expansion of urban areas and their potential impact on other land uses. It serves as a critical tool to support sustainable urban planning and policies aimed at ensuring the long-term ecological and environmental sustainability of Kigali City.

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Murenzi Gilbert, K., & Shi , Y. . (2024). Using GlobeLand30 data and cellular automata modeling to predict urban expansion and sprawl in Kigali City . Advanced Remote Sensing, 4(1), 46–57. Retrieved from


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