Spatiotemporal Dynamics and Driving Mechanisms of Impervious Surface Expansion in Changsha County Using Landsat Time-Series

Long-term monitoring of impervious surface dynamics is essential for understanding urbanization processes and supporting sustainable land-use planning. However, existing studies often lack comprehensive analyses that integrate both spatial patterns and underlying driving mechanisms. In this study, we first generate a consistent long-term impervious surface dataset for Changsha County from 2000 to 2025 using Landsat time-series imagery, incorporating feature selection and temporal grouping to ensure reliability. Leveraging the generated long-term impervious surface dataset, we perform a detailed and multi-dimensional analysis of urban expansion, systematically quantifying changes in area, expansion intensity, and growth rate, while simultaneously examining spatial dynamics through centroid shifts and kernel density estimation, and uncovering underlying driving factors through rigorous modelling. The results indicate that impervious surface area increased continuously over the study period, with a clear phased pattern characterized by rapid growth (2000–2010), decelerated expansion (2010–2020), and further slowdown (2020–2025). Spatially, edge expansion dominates the overall growth (approximately 80%), while infilling gradually increases, indicating a transition from outward sprawl to more compact development. Moreover, expansion hotspots shift from the urban core to peripheral and rural areas, accompanied by a northward migration of the center of gravity. Driving factor analysis further indicates that topographic constraints weakened progressively over the study period, road proximity retained a stable spatial association with new impervious expansion as an accessibility-related proxy, and policy-related influences gained increasing importance in later stages. These findings provide new insights into the spatiotemporal evolution and driving mechanisms of county-level urbanization in central China, and offer a reliable methodological framework and scientific basis for long-term land-use monitoring and sustainable urban planning.