geographic weighted regression, green infrastructure, image analysis, morphological spatial pattern analysis, statistical analysis, urban heat island, Computational linguistics. Natural language processing, P98-98.5, Computer software, QA76.75-76.765

Abstract Green Infrastructure (GI) has garnered increasing attention from various regions due to its potential to mitigate urban heat island (UHI), which has been exacerbated by global climate change. This study focuses on the central area of Fuzhou city, one of the “furnace” cities, and aims to explore the correlation between the GI pattern and land surface temperature (LST) in the spring and autumn seasons. The research adopts a multiscale approach, starting from the urban scale and using urban geographic spatial characteristics, multispectral remote sensing data, and morphological spatial pattern analysis (MSPA). Significant MSPA elements were tested and combined with LST to conduct a geographic weighted regression (GWR) experiment. The findings reveal that the UHI in the central area of Fuzhou city has a spatial characteristic of “high temperature in the middle and low temperature around,” which is coupled with a “central scattered and peripheral concentrated” distribution of GI. This suggests that remote sensing data can effectively be utilised for UHI inversion. Additionally, the study finds that the complexity of GI, whether from the perspective of the overall GI pattern or the classification study based on the proportion of the core area, has an impact on the alleviation of UHI in both seasons. In conclusion, this study underscores the importance of a reasonable layout of urban green infrastructure for mitigating UHI.