Integrating Multi-Source Data to Explore Spatiotemporal Dynamics and Future Scenarios of Arid Urban Agglomerations: A Geodetector–PLUS Modelling Framework for Sustainable Land Use Planning

Land use and landscape changes undermine the balance between humans and the environment, threatening sustainable regional development, yet their driving mechanisms and future trends remain insufficiently understood, particularly in arid areas. This study establishes a long-term analytical framework for the temporal evolution and driving mechanisms of land use and landscape patterns in arid areas, based on Landsat remote sensing imagery and socio-economic data. We investigate spatiotemporal evolution trends, driving mechanisms, and spatial non-stationarity of regional landscapes, and apply the Patch-generating Land Use Simulation (PLUS) model to predict future landscape changes under business-as-usual (BAU), economic development (ED), and ecological protection (EP) scenarios. The results show that: (1) Grassland and unused land together account for over 80% of the total area. From 1990 to 2020, built-up land expanded by 1471.58 km2, an increase of 190.09%. The comprehensive land use dynamic degree in the Urumqi–Changji–Shihezi (UCS) region was 0.22%, with the highest value observed between 2000 and 2010. (2) At the class level, spatial heterogeneity and fragmentation of different landscape types increased, enhancing regional landscape diversity. (3) Spatiotemporal changes in land use and landscape patterns were driven by the combined effects of natural factors, socio-economic conditions, and policy influences. (4) By 2030, under all three scenarios, unused land is expected to decrease, with the most significant reduction under the EP scenario. Grassland will increase most notably under the EP scenario, built-up land will expand, especially under the ED scenario, and cropland will also grow, mainly under the EP scenario. Forest and water areas will show slight decreases with minimal fluctuations. Overall, the proposed framework effectively captures the spatiotemporal dynamics and driving forces of land use and landscape changes, providing support for the formulation of long-term sustainable development policies.