Modeling urbanization patterns and morphological evolution of Sargodha city, Pakistan using remote sensing and GIS
DOI:
https://doi.org/10.12775/EQ.2026.049Keywords
Google Earth Engine (GEE), CA-Markov, Random Forest (RF) algorithm, Land Use/Land Cover (LULC), Spatio-Temporal AnalysisAbstract
Rapid urbanization has significantly transformed land use and land cover (LULC) patterns in developing countries, resulting in increasing environmental challenges. This study examines the Spatio-temporal dynamics of LULC change in Sargodha City, Pakistan, from 1992 to 2024 using remote sensing, machine learning, and geospatial modeling techniques. Landsat imagery from 1992, 2000, 2008, 2016, and 2024 was processed in Google Earth Engine (GEE), and LULC classes were classified into urban land, vegetation, water bodies, and barren land using the Random Forest (RF) algorithm. The overall accuracy ranged from 88.27 percent in 1992 to 95.26 percent in 2024, and the Kappa statistics fell within a range of 0.82 to 0.91. Spectral indices including NDVI, NDBI, and NDWI were used to assess environmental changes associated with urban expansion. The results indicate that urban land increased from 131 km² (8.5%) in 1992 to 220 km² (14.23%) in 2024, while vegetation cover declined from 1356 km² (87.7%) to 1259 km² (81.43%). Future LULC changes were simulated using the CA–Markov model, which predicts urban expansion to 351 km² (22.69%) and a reduction in vegetation cover to 1080 km² (69.85%) by 2040. The findings highlight the rapid pace of urban growth in Sargodha City and emphasize the need for sustainable urban planning and land management strategies to reduce environmental degradation and support balanced urban development.
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