Multi-Scale Analysis of Green Space Patterns in Thermal Regulation Using Boosted Regression Tree Model: A Case Study in Central Urban Area of Shijiazhuang, China

Multi-scale thermal regulation of urban green spaces is critical for climate-adaptive planning. Addressing the limited research on key indicators and cross-scale synergies in high-density areas, this study developed an integrated framework combining multi-granularity grids and boosted regression tree (BRT) modeling to investigate nonlinear scale-dependent relationships between landscape parameters and land surface temperature (LST) in the central urban area of Shijiazhuang. Key findings: (1) Spatial heterogeneity and scale divergence: Vegetation coverage (FVC) and green space area (AREA) showed decreasing contributions at larger scales, while configuration metrics (e.g., aggregation index (AI), edge density (ED)) exhibited positive scale responses, confirming a dual mechanism with micro-scale quality dominance and macro-scale pattern regulation. (2) Threshold effects quantification: The BRT model revealed peak marginal cooling efficiency (0.8–1.2 °C per 10% FVC increment) within 30–70% FVC ranges, with minimum effective green patch area thresholds increasing from 0.6 ha (micro-scale) to 3.5 ha (macro-scale). (3) Based on multi-scale cooling mechanism analysis, a three-tier matrix optimization framework for green space strategies is established, integrating “micro-level regulation, meso-level connectivity, and macro-level anchoring”. This study develops a green space optimization paradigm integrating machine learning-driven analysis, multi-scale coupling, and threshold-based management, providing methodological tools for mitigating urban heat islands and enhancing climate resilience in high-density cities.