Impacts of green-blue-grey infrastructures on high-density urban thermal environment at multiple spatial scales: A case study in Wuhan

Abstract

Land surface temperature (LST) has been broadly applied to urban climate studies. However, the sensitivity of LST to urban green-blue-grey infrastructures (GBGI) is still not clear, particularly at multiple spatial scales. A case study at a high-density area in Wuhan investigated the impacts of 27 indicators, including area proportion (AP), plot ratio (PR), and landscape fragmentation index (LFI), on LST at ten different spatial scales. The effects of individual and combined GBGI characteristics on LST were examined with correlation tests and multiple regression models, respectively. In single-factor test, LST is most sensitive to blue infrastructure (BI) and grey infrastructure (GYI) at 1600 m. BI_AP, BI_PR, River_AP, GYI_AP, Pavement_AP, and Building_AP showed strong correlations with LST at all scales. Regarding GNI (green infrastructure), LST is the most sensitive to GNI_LFI and Woodland_LFI at 1600 m, while to GNI_AP and GNI_PR at 50 m. In the hybrid state, LST has the highest sensitivity to GBGI sub-components at the district scale (1200–1600 m) based on multiple regression models. Finally, we evaluated the efficiency of different GBGI-based strategies by comparing 12 designed scenarios. This study provides important references for selecting effective spatial scales and indicators to maximize cooling efficiency in urban climate research and planning.