Mapping heat-related risks in Swiss cities under different urban tree scenarios

About three quarter of Swiss residents live in urban areas, and this proportion is expected to grow in future decades. An increasing number of people will therefore be exposed to urban heat, which can have adverse effects on human wellbeing, productivity and physical health.

We explore the possibility to detect high-risk areas in five Swiss cities with the development of an urban heat-based risk-mapping approach. The included cities are Basel, Bern, Geneva, Lausanne and Zurich. The analysis is based on a combination of biophysical, including Landsat 8 derived Land Surface Temperature (LST), and socioeconomic data. Additionally, we assess the impact of urban trees on urban heat within the districts of these cities, helping to estimate how risk levels would change under two scenarios: one with increased tree cover (MaxTree) and another with no (NoTree) urban trees.

The assessment on the impact of urban trees on heat showed that the areas with urban trees generally experience cooler temperatures compared to those without, both at the city and district levels. This underscores the positive role of urban trees in mitigating the urban heat effect.

The risk mapping approach revealed a distinct spatial pattern for each city and high risk areas were identified.

Generally, the high-risk areas in the analyzed cities cover the city centers and areas with high vulnerability.

The ‘NoTree’ scenario showed higher risks compared to the baseline situation, illustrating that urban trees currently mitigate heat related risks in Swiss cities. The ‘MaxTree’ scenario results in lower risks, especially in the cities of Lausanne and Bern.

The presented risk mapping approach, including the two idealized scenarios, can be used by policy- and decision-makers (e.g. city planners) can be a tool to determine where urban planning actions are the most urgent and where trees could be most beneficial in terms of adaptation to heat. The approach is easily adaptable and transferable to other cities, since it relies on a clear and simple methodological framework, openly available LST data, and basic socioeconomic variables at district scale that are available for many cities.