Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes

The population experiencing high temperatures in cities is rising due to anthropogenic climate change, settlement expansion, and population growth. Yet, efficient tools to evaluate potential intervention strategies to reduce population exposure to Land Surface Temperature (LST) extremes are still lacking. Here, we implement a spatial regression model based on remote sensing data that is able to assess the population exposure to LST extremes in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies. We define exposure as the number of days per year where LST exceeds a given threshold multiplied by the total urban population exposed, in person center dot day. Our findings reveal that urban vegetation plays a considerable role in decreasing the exposure of the urban population to LST extremes. We show that targeting high-exposure areas reduces vegetation needed for the same decrease in exposure compared to uniform treatment.

Automated summary

The population experiencing high temperatures in cities is increasing due to anthropogenic climate change, settlement expansion, and population growth. However, there is a lack of efficient tools to evaluate potential intervention strategies to reduce population exposure to Land Surface Temperature (LST) extremes. In this study, a spatial regression model based on remote sensing data is implemented to assess population exposure to LST extremes in urban environments. The findings show that urban vegetation plays a significant role in decreasing population exposure to LST extremes.