How Coarse Analysis Can Obscure Key Trends - A Multi-Scale Study Using Very High-Resolution Intra-Urban Land Data

Land use change (LUC) mechanisms are vital for understanding change processes and policy formulation. However, most previous studies neglected intra-urban land use evolution and the influence of spatial resolution. This study designed a multi-scale LUC driver analysis framework to analyze the drivers of intra-urban land use expansion in selected cities in Hunan Province, China, and explored the impact of spatial resolution on driver analysis. First, very high-resolution intra-urban land use survey data were used to explore the driving mechanisms based on Random Forest and Spearman's correlation analysis at 2 m resolution. Then, results from other resolutions (5 m, 10 m, 30 m, 60 m, 90 m, and 120 m) were compared to the 2 m resolution. A scale effect assessment strategy based on Spearman's correlation analysis, Sen's slope, and Mann-Kendall (MK) trend test was devised to analyze the patterns of change in the driver mining results with spatial resolution. Findings reveal that economic, topographic, demographic, transportation and industrial factors predominantly drive intra-urban land use expansion, with different categories emphasizing specific factors. Analyzing intra-urban land use expansion mechanisms at low spatial resolutions can significantly distort results and render the ranking of driving elements unstable. The loss of patch information due to reduced spatial resolution is the primary cause of distorted driving factor analysis results. It is recommended that related studies use data with a resolution of 30 m or higher. These findings offer valuable insights for optimizing land use policies and serve as a reference for selecting appropriate data resolutions in future studies.