The Impact of Urban Forest Landscape on Thermal Environment Based on Deep Learning: A Case of Three Main Cities in Southeastern China

Abstract

Accelerated urbanization has exacerbated the urban heat island phenomenon, and urban forests have been recognized as an effective strategy for modulating thermal environments. Nevertheless, there remains a dearth of systematic investigations into the nonlinear associations between the detailed spatial configurations of urban forests and thermal conditions. We proposed a deep learning-based approach to extract forest data, utilizing multisource high-resolution remote sensing data with relative radiometric correction. Subsequently, we employed deep neural networks (DNNs) to quantify the linkages between urban forest landscape patterns and land surface temperature (LST) in summer and winter across Fuzhou, Xiamen, and Zhangzhou in Fujian Province, China. Our findings indicate the following: (1) Our extraction approach outperforms DeepLabv3+, FCN_8S, and SegNet in terms of extraction precision and adaptability, achieving an overall accuracy (OA) of 87.57%; furthermore, the implementation of relative radiometric correction enhances both the extraction precision and model generalizability, improving OA by 0.05%. (2) Geographic and seasonal differences influence the urban forests’ cooling effects, with more pronounced cooling in summer, particularly in Zhangzhou. (3) The significance of forest landscape composition and configuration in affecting the thermal environment varies seasonally; landscape configuration plays a more pivotal role in modulating surface temperatures across the three cities, with a more critical role in winter than in summer. (4) Seasonal and city-specific variations in forest spatial patterns influence LST. Adopting the appropriate forest structures tailored to specific seasons, cities, and scales can optimize cooling effects. These results offer quantitative insights into urban heat island dynamics and carry significant implications for urban planning strategies.