Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning...

This study systematically analyzes the land use changes in Changsha City from 2000 to 2023. Three classification models-Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Artificial Neural Network (ANN) were employed to evaluate the accuracy of land use classification. The RF model, with an accuracy of 95.78%, outperformed the others, demonstrating its robustness and generalization ability in handling complex land use classification tasks. The study further conducted a spatiotemporal analysis of urban construction land expansion, identified key driving forces behind urbanization in Changsha. Results indicate that the construction land area expanded nearly threefold, from 563.82 km² in 2000 to 1628.20 km² in 2023, with the most significant growth occurring between 2010 and 2015. This rapid expansion was largely driven by China's "New Urbanization" policy and population growth. Additionally, 12 key factors influencing land use change in Changsha was analyzed, including slope, soil salinity, annual mean temperature, leaf area index, soil moisture, aerosols, aspect, nighttime light index (X8), DEM, population density (X10), vegetation cover, and annual precipitation. Univariate and interaction detection analyses revealed that the nighttime light index (X8) and population density (X10) were the most significant drivers of construction land expansion, consistently exhibiting high q-values across all years. In contrast, natural factors, such as slope (X1) and aerosols (X6), had a lesser impact on land use change, although their influence has gradually increased over time. This is particularly evident in the growing role of annual precipitation (X12) and leaf area index (X4) in influencing ecosystem and vegetation recovery. The study also simulated construction land expansion trends for 2030 under three different scenarios. In the natural development scenario, construction land area is projected to expand to 1920.65 km², reflecting unregulated expansion of urbanization. Under the farmland protection scenario, the area will grow to 1826.32 km², indicating the effectiveness of policy interventions in preserving agricultural land. The ecological control scenario, however, predicts a limited expansion to 1702.66 km², underscoring the importance of ecological protection policies in curbing uncontrolled urban sprawl. This research provides a comprehensive understanding of the driving mechanisms and evolutionary patterns of construction land use change in Changsha. It highlights the significant pressure that urbanization, particularly anthropogenic factors, has placed on land resources. It also demonstrates that policy regulation, particularly through ecological protection measures, can effectively mitigate this expansion trend. The findings offer valuable insights for land use planning and policy formulation in Changsha, underscoring the importance of balancing economic development with ecological preservation to achieve sustainable urban growth.