Monitoring Urban Areas for Climate Change Adaptation Using Remotely Sensed Indicators in the UrbanGreenEye Project

In the context of climate change adaptation, sustainable urban development, and environmental justice, local civil services must meet a range of dynamic demands. To do so, municipalities require both qualitative and quantitative knowledge about the current state and the development of urban structures such as impervious surfaces and vegetated areas within their boundaries. However, obtaining this data through surveys is costly and time-consuming, and the frequency of these surveys is too low to capture changes consistently. As a result, data availability to support urban planning strategies often depends on financial priorities and is not consistently available to all local authorities in Germany. Remote sensing data, which has extensive spatial coverage and is regularly available, offers a more viable option for effective monitoring of urban structures. However, local authorities often lack knowledge about the benefits and limitations of using such data. The UrbanGreenEye project aims to bridge this gap by developing urban climate indicators based on Earth Observation data that meet the needs of local authorities. Drawing on the experience of nine partner municipalities, the project will demonstrate the use and implementation of these indicators in planning processes and strategies. It will also help create digital twins for urban planning applications and provide a free, regularly updated indicator-geodata foundation for Germany to support decision-making, particularly for climate change adaptation. The indicators will help identify locations experiencing high thermal and hydrological stress and quantify the relief provided by vegetated and pervious areas. Land surface temperature (LST) derived from satellite data from the US Landsat program will be used to monitor thermal stress, while the urban green volume and vegetation vitality indicators, derived from EU Copernicus Sentinel-2 satellite data, will contribute to thermal stress relief. The imperviousness indicator will also be derived from Sentinel-2 data using spectral models. Artificial intelligence algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) and attention-based transformer models, will be used to extract complex information from the urban surfaces, which require large amounts of reference data to capture necessary details. This reference data will be generated from high-resolution aerial images using CNN, supported by local ground truth data from municipal authorities and citizen science projects. It will then be upscaled and used as a reference for satellite-level models to provide nationwide consistent products. The satellite-based indicators will be validated for error ranges and at different spatial scales using the micro-scale climate model PALM-4U. Eventually, the indicators will be used to create a model for urban green volume deficiency to identify hot spots for adaptation measures and support planning strategies.