The ecosystem services of the 'urban forest': area-wide modeling using remote sensing and artificial intelligence
- Subject: Urban trees, ecosystem services
- Study site: Munich, Germany
- Funding: German Federal Environmental Foundation (DBU)
- Duration: 12.2021 - 11.2024
- Contact: Hannes Taubenböck, Andrea Sofia Garcia de León
Urban trees provide valuable ecosystem services (ES), such as reducing surface runoff and heat pollution, sequestering carbon and improving air quality. The spatial distribution of ecosystem services is crucial to improve environmental conditions and, in consequence, the well-being and health of the urban population through informed decision making. However, detailed, comprehensive and systematic information on the growth and ES of trees in European cities is still lacking. Our project uses state-of-the-art remote sensing, artificial intelligence (AI) and modeling technology to comprehensively map urban forests, characterize them in detail and systematically quantify their ES, from which targeted planning measures can be derived.
Usually, urban tree cadastres only record trees in public spaces. Our preliminary studies for Munich, for example, have shown that over 50% of the tree population is located on private land and is therefore not represented in the cadastre. The combination of remote sensing image data with AI algorithms has revolutionized the recording and characterization of urban trees. In our project, we use area-wide remote sensing information and highly accurate in-situ measurements of individual trees to simulate ES of the urban forest in detail for each individual tree using the newly developed tree growth model “CityTree”.
Our aim is to derive action-oriented information for intelligent and sustainable urban planning. The project can contribute to the management of the urban tree population and to build a resilient urban green infrastructure. In this way, the project not only contributes to improving environmental conditions and urban well-being, but also to adapting cities to climate change.