Artificial-Intelligence-Based Investigation on Land Use and Land Cover (LULC) Changes in Response to Population Growth in South Punjab, Pakistan

Tanweer Abbas, Muhammad Shoaib (), Raffaele Albano (), Muhammad Azhar Inam Baig, Irfan Ali, Hafiz Umar Farid and Muhammad Usman Ali
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Tanweer Abbas: Department of Agricultural Engineering, Bahauddin Zakariya University, Multan 60800, Pakistan
Muhammad Shoaib: Department of Agricultural Engineering, Bahauddin Zakariya University, Multan 60800, Pakistan
Raffaele Albano: Department of Health Science, University of Basilicata, 85100 Potenza, Italy
Muhammad Azhar Inam Baig: Department of Agricultural Engineering, Bahauddin Zakariya University, Multan 60800, Pakistan
Irfan Ali: Pakistan Agricultural Research Council, Islamabad 44000, Pakistan
Hafiz Umar Farid: Department of Agricultural Engineering, Bahauddin Zakariya University, Multan 60800, Pakistan
Muhammad Usman Ali: Department of Agricultural Engineering, Bahauddin Zakariya University, Multan 60800, Pakistan

Land, 2025, vol. 14, issue 1, 1-34

Abstract: Land use and land cover (LULC) changes are significantly impacting the natural environment. Human activities and population growth are negatively impacting the natural environment. This negative impact directly relates to climate change, sustainable agriculture, inflation, and food security at local and global levels. Remote sensing and GIS tools can provide valuable information about change detection. This study examines the correlation between population growth rate and LULC dynamics in three districts of South Punjab, Pakistan—Multan, Bahawalpur, and Dera Ghazi Khan—over a 30-year period from 2003 to 2033. Landsat 7, Landsat 8, and Sentinel-2 satellite imagery within the Google Earth Engine (GEE) cloud platform was utilized to create 2003, 2013, and 2023 LULC maps via supervised classification with a random forest (RF) classifier, which is a subset of artificial intelligence (AI). This study achieved over 90% overall accuracy and a kappa value of 0.9 for the classified LULC maps. LULC was classified into built-up, vegetation, water, and barren classes in Multan and Bahawalpur, with an additional “rock” class included for Dera Ghazi Khan due to its unique topography. LULC maps (2003, 2013, and 2023) were prepared and validated using Google Earth Engine. Future predictions for 2033 were generated using the MOLUSCE model in QGIS. The results for Multan indicated substantial urban expansion as built-up areas increased from 8.36% in 2003 to 25.56% in 2033, with vegetation and barren areas displaying decreasing trends from 82.96% to 70% and 7.95% to 3.5%, respectively. Moreover, areas containing water fluctuated and ultimately changed from 0.73% in 2003 to 0.9% in 2033. In Bahawalpur, built-up areas grew from 1.33% in 2003 to 5.80% in 2033, while barren areas decreased from 79.13% to 74.31%. Dera Ghazi Khan expressed significant increases in built-up and vegetation areas from 2003 to 2033 as 2.29% to 12.21% and 22.53% to 44.72%, respectively, alongside reductions in barren and rock areas from 32.82% to 10.83% and 41.23% to 31.2%, respectively. Population projections using a compound growth model for each district emphasize the demographic impact on LULC changes. These results and findings focus on the need for policies to manage unplanned urban sprawl and focus on environmentally sustainable practices. This study provides critical awareness to policy makers and urban planners aiming to balance urban growth with environmental sustainability.

Keywords: climate change; sustainable agriculture; food security; inflation; artificial intelligence; remote sensing; GIS (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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