Sustainability, Год журнала: 2025, Номер 17(11), С. 4874 - 4874
Опубликована: Май 26, 2025
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 boosted regression tree (BRT) modeling to investigate nonlinear scale-dependent relationships between landscape parameters land surface temperature (LST) central area Shijiazhuang. Key findings: (1) Spatial heterogeneity scale divergence: Vegetation coverage (FVC) space (AREA) showed decreasing contributions at larger scales, while configuration metrics (e.g., aggregation index (AI), edge density (ED)) exhibited positive responses, confirming a dual mechanism with micro-scale quality dominance 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% ranges, minimum effective patch thresholds increasing from 0.6 ha (micro-scale) 3.5 (macro-scale). (3) Based multi-scale analysis, three-tier matrix optimization strategies established, integrating “micro-level regulation, meso-level connectivity, macro-level anchoring”. This develops paradigm machine learning-driven coupling, threshold-based management, providing methodological tools mitigating heat islands enhancing climate resilience cities.
Язык: Английский