Assessing past, present, and simulated future prediction of land use land cover changes using CA-Markov chain models with Satellite data
Land use land cover changes (LULCC) have gained significant attention during the last few decades. This study aimed to evaluate LULCC in the Gujranwala district from 1998 to 2022 and future prediction of LULCC for the years 2035 and 2048 using the cellular automata (CA) and Markov model. Machine lea...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302501655X |
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| Summary: | Land use land cover changes (LULCC) have gained significant attention during the last few decades. This study aimed to evaluate LULCC in the Gujranwala district from 1998 to 2022 and future prediction of LULCC for the years 2035 and 2048 using the cellular automata (CA) and Markov model. Machine learning was used to classify the image into different LULCC classes, including vegetation areas, bare soil, built-up areas, and water bodies. The results indicated that the built-up area increased by 14,517 ha (4 %) to 40,478 ha (11.13 %) from 1998 to 2022. Future prediction of LULCC observed a slight increase in built-up area from 2022 to 2048, with 36,059 ha (9.92 %), and a considerable decrease in vegetation with 36,059 ha (9.92 %) from 2022 to 2048. From 1998 to 2022, the vegetation area decreased from 334,398 ha (92.01 %) to 314,853 ha (86.63 %) in the study area. The CA–Markov chain model simulations predicted that vegetation area could decrease from 2022 to 2048, with -52,713 ha (14.5 %) in the Gujranwala district. Our findings indicated significant LULCC changes over the study period, including urban expansion and agricultural encroachment. CA–Markov model is calibrated and validated using observed data, ensuring accuracy in predicting spatial shifts and magnitudes of land cover alterations. The future prediction of LULCC will continue to inform our understanding of environmental and socio-economic systems and guide decision-making toward sustainable development. |
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| ISSN: | 2590-1230 |