Spatiotemporal analysis of urban expansion in Hail City, Saudi Arabia, using GIS, remote sensing, and Markov chain model

Abstract Spatiotemporal urban expansion has raised concerns about land consumption, environmental sustainability, and infrastructure planning in rapidly growing cities in South West Asia. This study utilizes geographic information systems (GIS), remote sensing, and Markov chain-based model for predictive analysis to examine the spatiotemporal patterns of land cover change in Hail city, Saudi Arabia, between 2015 and 2024. Landsat 8 images satellite data with 30 m (multispectral) were utilized for the spatiotemporal urban expansion analysis. While the 2015 classification was based on a 6-band composite, the 2024 final classified raster was based on a 3-band clipped composite. Land cover classes were mapped, changes were assessed, and future urban expansion trends were generated using classified satellite images. The result reveals an increase in built-up lands and a decrease in agriculture and barren lands, signaling unplanned urban sprawl. A Kappa value of 0.73 signifies a substantial level of agreement between the classified image and the ground truth data, justifying that the classification results are reliable and not significantly influenced by random chance. The Markov chain prediction suggests the city’s continuous urban expansion by 2033. A GIS-based interactive dashboard was developed to visualize land cover changes and support data-driven urban planning for Hail city. The analysis highlights key urban growth areas and offers insights into the implications for future infrastructure, environmental sustainability, and threat zones. The study emphasizes the importance of integrating spatiotemporal intelligence-supported management tools to guide efficient, resilient city development.