Quantifying Non-Linearities and Interactions in Urban Forest Cooling Using Interpretable Machine Learning

The cooling effect of urban forests has been widely investigated to support climate-adaptive spatial planning. However, studies on the impacts of key landscape drivers have often produced conflicting results, limiting their practical applicability. These inconsistencies may stem from an oversimplified focus on the global effects of individual factors, while neglecting non-linear threshold behaviors and pairwise interactions. To address this gap, this study employed an interpretable machine learning framework (XGBoost-SHAP) to quantify the seasonal non-linearities, thresholds, and interaction effects of landscape drivers on urban forest cooling in Suzhou, a subtropical Chinese city. The results indicate that the combined explanatory power of neighboring water body proportion (NWP), neighboring green space proportion (NGP), vegetation density (NDVI), spatial characteristics (Area, SHAPE), and elevation on the cooling intensity of urban forest patches was strongest in summer (R2 = 0.615) and weakest in winter (R2 = 0.316). Among these, NWP, NGP, and NDVI were the dominant drivers, while patch area and shape exhibited weaker marginal effects. NWP significantly enhances cooling only after exceeding seasonal critical thresholds (11%–15%). NGP contributed positively above ~40% in warm seasons but suppressed cooling above 37% in winter. Patch area exhibits a logarithmic relationship with cooling intensity, with a critical threshold of approximately 2.48 ha and saturation thresholds between 12 and 14 ha. SHAPE exerted positive effects in spring and winter, negative effects in summer, and a transition from negative to positive in autumn. Notably, significant, threshold-modulated interactions were identified, including those between NDVI and NWP, SHAPE and NDVI, SHAPE and NGP, NWP and NDVI, NWP and NGP, and NGP and NDVI. In each interaction, the first factor regulates and reverses the effect of the second once specific thresholds are exceeded. This study provides actionable, evidence-based guidance for the planning and optimized design of urban forests.