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 investigates driving factors of intra-urban land use expansion in selected cities of Hunan Province, China, and examines the effect of spatial resolution on driving factor analysis. Very high-resolution intra-urban land use survey data was used for the first time, and driving mechanisms were explored at a 2 m resolution using random forest and Spearman correlation analysis. The results were compared with other resolutions (5 m, 10 m, 30 m, 60 m, 90 m, and 120 m). Spearman correlation analysis, Sen's slope and Mann-Kendall (MK) trend test were employed to analyze the changing pattern of driver importance 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.