UrbaNature

Why this project matters

Climate change poses significant challenges for urban environments. Rising temperatures are felt particularly strongly in cities, where heat can accumulate due to sealed surfaces and reduced air circulation.

Extreme heat has direct consequences for human health. Heat stress can increase the risk of dehydration or heatstroke and can exacerbate existing health conditions, like cardiovascular diseases.

One strategy to increase urban resilience to heat is the greening of urban areas. Urban vegetation, such as trees or green roofs, can contribute to climate mitigation by lowering carbon dioxide concentrations in general. In addition, urban trees provide shade that offer protection for residents and through evapotranspiration, urban vegetation can lower surface and air temperatures.

The goals

So far, the interplay between vegetation and urban environments is difficult to assess, and many knowledge gaps exist. However, to determine the effect of vegetation on the climate in cities, fundamental knowledge about how vegetation influences local carbon, energy, and water cycles and how vegetation functioning is impacted by urban environments is needed.

In our UrbaNature project, we combine different methodological approaches to better understand these urban environmental processes. We collect ecophysiological measurements on-site, make observations of urban microclimates, use advanced remote sensing methods, and employ climate as well as ecophysiological modeling across multiple spatial scales. Together, these methods allow us to capture both local conditions and broader system-level dynamics. The two Swiss cities Basel and Zurich serve as case studies to facilitate method development and evaluation.

Our contribution

In our group, Alexander Damm and Lars Groeneveld focus on the development of remote sensing methods to derive information about the health state and functioning of vegetation in complex urban environments. A particular emphasis lies on understanding how fractional and cast shadow influences information retrievals from imaging spectroscopy data and how related effects can be compensated.