Application of Geospatial Approaches for Evaluation of Urban Growth Pattern and Trend Prediction of Multan City, Pakistan

Muhammad Hashim*, Atta-ur Rahman, Muhammad Qasim, Muhammad Umar Farooq, Muhammad Dawood, Basit Nadeem, Shazia Muneer ()
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Muhammad Hashim*, Atta-ur Rahman, Muhammad Qasim, Muhammad Umar Farooq, Muhammad Dawood, Basit Nadeem, Shazia Muneer: University of Peshawar, Peshawar, Pakistan. Bahauddin Zakariya University, Multan, Multan, Pakistan. Department of Epidemiology and Public Health, University of Veterinary and Animal Sciences, Lahore

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 9, 108-120

Abstract: This research purposes to evaluate the changes in land use, land cover (LULC) in the study area and scrutinize the urban growth trends in Multan City over a period of 30 years, from 1993 to 2023. Moreover, the research utilizes an Artificial Neural Network (ANN) model to implement urban expansion up to the year 2050. To achieve these goals, geospatial systems and approaches are applied. Satellite imagery and remote sensing data from the years 1993, 2003, 2013, and 2023 are analyzed to detect LULC changes. The classification of these images provides valuable insights into the transformation of Multan’s urban landscape over time. A supervised classification technique is primarily utilized to identify specific land cover classes. Landsat 5 data is used for the years 1993 and 2003, Landsat 7 for 2003, Landsat 8 for more recent observations, and Landsat 9 for the latest satellite imagery. The core geospatial model applied in this study is the Cellular Automata–Artificial Neural Network (CA–ANN) model, which is used to simulate and quantify urban expansion. Based on the CA–ANN model results, the urban area in Multan was approximately 154.84 km² in 1993, which expanded to 587.21 km² by 2023. Projections indicate that this urban area will further increase to 992.64 km² by 2030 and could reach 3,184.59 km² by 2050. These findings highlight a significant and rapid urban expansion expected in the coming decades.

Keywords: ANN-Artificial Neural Network; LULC-Land Use Land Cover; RS-Remote Sensing; CA- Cellular Automata (search for similar items in EconPapers)
Date: 2025
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