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<p>Under the background of rapid urbanization, accurately identifying the trend of land-use change becomes key to optimizing urban spatial structure and formulating land policies. Urban land-use modeling, as an important analytical tool, plays a central role in understanding and managing future land dynamics. This study designates the urban agglomeration located within the middle and lower reaches of the Yangtze River as its primary area of investigation. Based on Landsat remote sensing data from 1990 to 2020 and multi-source socioeconomic indicators, it builds a coupled Markov-Future Land Use Simulation model to simulate urban land-use changes from 2020 to 2045 under three scenarios: natural growth, farmland protection, and ecological priority. The model integrates the Markov model's transition probability and the spatial distribution simulation function of the Future Land Use Simulation model, and incorporates policy constraints and spatial driving factors for optimization. Verification using the authentic 2020 land-use mapping demonstrates an aggregate precision of 0.89 and a Kappa coefficient of 0.84, indicating substantial predictive reliability. The results reveal significant spatial heterogeneity in urban land expansion, with especially severe farmland loss under the natural growth scenario. Landscape pattern indices reflect an increasingly fragmented and complex land-use pattern. Standard deviation ellipse analysis further confirms the dynamic migration of construction land along urban core directions. These findings provide scientific support for spatial planning and land management policies in rapidly urbanizing regions.</p>

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Spatiotemporal prediction and spatial pattern evolution of urbanized land based on the Markov-FLUS model

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Guihua Wei

摘要

Under the background of rapid urbanization, accurately identifying the trend of land-use change becomes key to optimizing urban spatial structure and formulating land policies. Urban land-use modeling, as an important analytical tool, plays a central role in understanding and managing future land dynamics. This study designates the urban agglomeration located within the middle and lower reaches of the Yangtze River as its primary area of investigation. Based on Landsat remote sensing data from 1990 to 2020 and multi-source socioeconomic indicators, it builds a coupled Markov-Future Land Use Simulation model to simulate urban land-use changes from 2020 to 2045 under three scenarios: natural growth, farmland protection, and ecological priority. The model integrates the Markov model's transition probability and the spatial distribution simulation function of the Future Land Use Simulation model, and incorporates policy constraints and spatial driving factors for optimization. Verification using the authentic 2020 land-use mapping demonstrates an aggregate precision of 0.89 and a Kappa coefficient of 0.84, indicating substantial predictive reliability. The results reveal significant spatial heterogeneity in urban land expansion, with especially severe farmland loss under the natural growth scenario. Landscape pattern indices reflect an increasingly fragmented and complex land-use pattern. Standard deviation ellipse analysis further confirms the dynamic migration of construction land along urban core directions. These findings provide scientific support for spatial planning and land management policies in rapidly urbanizing regions.