Coupling remote sensing data and local meteorological measurements to predict thermal stress and its potential mitigation by urban forests

Abstract

Urban areas face complex challenges including air pollution, land use changes, and rising temperatures driven by Urban Heat Island (UHI) effects. UHIs heighten heat stress risks, impacting human health and well-being. Urban green spaces, particularly urban forests, are key ecosystems to address these challenges by providing cooling effects and enhancing thermal comfort. In this context, monitoring the role of trees in mitigating the temperature is crucial. Remote sensing optical data may represent a powerful tool for this aim, but available sensors generally provide temperature at a very low spatial resolution that is not effective in the urban context. The primary aims of this study are to (i) develop and test a model for assessing thermal comfort in urban settings with an enhanced spatial resolution and (ii) investigate how urban green spaces contribute to increasing thermal comfort. Remote sensing Landsat 8 and Sentinel 2 data were combined to increase the spatial resolution of land surface temperature maps from 30 m to 10 m. The model was validated with ground data. Air temperature, global temperature, and thermal comfort maps were then created for the city of Florence. The results show significantly lower thermal comfort in areas lacking vegetation cover, while tree-lined streets and green spaces exhibit higher thermal comfort. Even vegetation traits such as height and fresh-wood volume positively correlate with thermal comfort, emphasizing the cooling effects of urban trees and their role in mitigating heat stress. This study promotes data and methods accessibility and replicability and supports evidence-based decision-making, highlighting the role of green spaces in mitigating heat stress and climate changes issues.