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Large-scale subsidence and uplift pose a significant risk to buildings and infrastructure. While subsidence due to groundwater removal or construction activities can easily be constrained on a local scale, regional changes caused by climate change are more difficult to detect. These phenomena are investigated within the „Umwelt 4.0, Cluster I - Use of digital terrain models and Copernicus data" project, which is carried out by the Hessian Agency for Nature Conservation, Environment and Geology in cooperation with the TU Darmstadt and funded by the Hessian Minister for Digital Strategy and Development. Within the framework of this project, we are creating a systematic workflow to detect ground motion over a period of several years. We focus on the state of Hessen, Germany, where several regions are known for landslide activity, e.g., Hoher Meissner, or for widespread subsidence, e.g., in the industrial areas surrounding Frankfurt a.M.. In this way, occurring ground movements and even mass movements could be detected at an early stage and, if necessary, measures can be initiated. Based on these results, future decisions on regulations or even information for the general public on risk areas can be created.We utilize two major datasets based on remote sensing. High-resolution digital elevation and surface models (DGM 1 and DSM 1) from airborne LiDAR surveys by the Hessian Administration for Land Management and Geoinformation. For the most parts of Hessen, it was possible to calculate differences in elevation between the years 2014, 2019 and 2021. The second dataset are persistent scatterer interferometry points (PSIs) from the BodenBewegungsdienst Deutschland with a temporal resolution of 6 days since 2015. Both datasets are integrated and linked with other data sources, such as geological maps, known subsidence-sensitive layers, hydrogeological and climatic data. For the InSAR data a toolbox has been developed that automatically detects regions with strong movement (Ground Motion Analyzer). A major challenge for integrating both datasets is the large difference in spatial coverage and temporal resolution. Advantages of LiDAR data are the high spatial resolution and the possibility to detect even small-scale movements ( |
