Impact of COVID-19 on the Urban Heat Island in Daegu Using Downscaled Land Surface Temperature

Abstract

The COVID-19 pandemic significantly reduced human activities globally, leading to various changesin urban environments. Previousstudies analyzing the pandemic’simpact on urban heat islands (UHI) have heavily relied on the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) dataset with a spatial resolution of 1 km. However, such a coarse resolution of MODIS failed to adequately capture the complexity of urban structures, making it difficult to explore the change of UHI accurately. The objective of this study is to analyze the impact of changes in human activities on UHI before and after the COVID-19 pandemic using downscaled MODIS 250 m LST in Daegu Metropolitan City, South Korea. The spatial downscaling process employed a local linear forest model, with Lasso feature selection used to extract input kernels with high spatial correlation. The validation was conducted using reference LST data from Landsat and ECOSTRESS that overlapped with MODIS LST. The downscaled LST250m had a correlation coefficient (r) of 0.790–0.929 and a root mean square error (RMSE) of 0.731–1.333°C during the daytime, and an r of 0.892 and RMSE of 0.771°C at night. Compared to MODIS LST1km (daytime: r of 0.771–0.882, RMSE of 0.990–1.497°C; nighttime: r of 0.857, RMSE of 0.906°C), the downscaled LST250m exhibited better accuracy. The intensity of surface UHI (SUHI) was calculated using the LST250m to analyze its spatiotemporal changes. The average normalized SUHI intensity before and after COVID-19 at the administrative district level revealed varied across regions. Residential areas showed an increase in normalized SUHI intensity during the night, while commercial areas exhibited a decrease, which was associated with the landcover ratio within each district. The results showed that such changes were dominant in residential, commercial, and transportation areas that were highly associated with human activities. The exploration of the impact of changes in human activities by COVID-19 on UHI using downscaled LST data will contribute to a further understanding of urban climate change and our knowledge of urban resilience.