Rapid urbanization and agricultural intensification in Punjab, Pakistan, have triggered significant Land Use/Land Cover (LULC) changes, threatening the ecological balance and food security. Quantifying land entropy through LULC change analysis is essential for sustainable urban and agricultural planning, particularly in rapidly developing regions such as Punjab, Pakistan. This study aims to assess the spatial disorder and fragmentation of land caused by urban expansion and agricultural land conversion in major cities using multi-temporal Landsat and Sentinel-2 satellite imagery from 1995 to 2025. Employing supervised classification techniques and Shannon’s entropy index within a GIS framework, the research quantifies LULC transitions among agricultural land, built-up areas, barren land, and water bodies. Further incorporating machine learning (e.g., Random Forest, CNN) to predict future land entropy trends under different urbanization and policy scenarios would allow policymakers to simulate the effects of land use regulations, urban growth boundaries, or agricultural conservation policies.
- Entropy Trends: Urban zones showed high entropy (SEI > 1.5), indicating chaotic growth, while agricultural areas exhibited moderate entropy (SEI 0.8–1.2) due to monoculture expansion.
- LULC Shifts: A 15% net urban growth (1995–2025) encroached on 20% of the fertile farmland, with vegetation declining by 22% in central Punjab.
- Hotspots: Lahore and Faisalabad districts had the highest entropy values (SEI > 1.8), correlating with GDP growth but also groundwater depletion.
The entropy index reveals increasing spatial disorder, particularly along urban–rural fringes, highlighting fragmented agricultural patches and unplanned urban growth.
This study concludes that quantifying land entropy via LULC changes provides critical insights into the dynamics of land transformation in Punjab, facilitating informed decision-making for sustainable urban expansion and agricultural conservation. Incorporating Earth observation data with entropy metrics offers a robust approach to monitoring land use patterns, enabling policymakers to balance development needs with environmental sustainability and food security objectives. Future work should integrate socio-economic drivers and predictive modelling to enhance planning frameworks in the region.
