Direct and indirect effects after mine operations cease operating must ideally be subjected to perpetual monitoring routines in order to detect possible risks or avoid adverse effects on the surrounding ecosystems at an early stage. In this contribution, mining subsidence lakes created inside the nature reserve Kirchheller Heide and Hilsfeld Forest are subjected to analysis for a long-term monitoring scheme. For this purpose, we employ high-resolution unmanned aerial system (UAS)-based multispectral and thermal mapping tools to provide a fast, non-invasive and multitemporal environmental monitoring method. Specifically, we propose to monitor vegetation evolution through multispectral analysis, biotypes identification using machine learning algorithms, and water surface extent detection, together with their thermal behavior. The aim of this contribution is to present the proposed workflow and first results to establish a baseline for future analyses and subsequent surveys for long-term multi-temporal monitoring.

Digital Twin has gained significant attention for future urban management. Since 2020, Building Information Modelling (BIM) has become the standard for high-rise buildings and infrastructures in Germany. Our project BIMSAR is to combine BIM data and SAR-derived movement for urban movement monitoring. The so-called persistent scatterer (PS) points are extracted from a SAR image stack based on PSI. These PS-points, considered pseudo-substructures of buildings, contain geoinformation such as geographic position and movement estimates. They are integrated into the BIM models for various self-contained units like façades and roofs. This fusion step is implemented by machine learning, employing a novel distance metric adapted through dimensionality reduction. To provide a user-friendly analysis tool, the fused data are displayed in 4D (position + movement) on a GIS-based platform, along with other geodata, e.g., geological maps, hydrological maps, underground mining structures. This paper shows the current results around Ahlen in Ruhr region, where many damages to buildings, bridges, and highways are associated with the abnormal deformation. We created the BIM models ourselves by converting open-source 3D models, adhering to Industry Foundation Classes (IFC) format. The SAR data contain Sentinel-1, TerraSAR-X, and PALSAR-2 for a cross-analysis. The primary application is to assist stakeholders with an early warning system to evaluate whether unusual or long-term deformations cause structural damages and thereby threaten the safety of life and property. We demonstrate some examples towards this application.

Integrated geo- and environmental monitoring in mining represents a high-dimensional challenge (location, altitude/depth, time and sensors). This is challenging for experts but poses great problems for a multitude of participants and stakeholders in building up a complete process understanding. The Epe research cooperation aims to elucidate the ground movement at the Epe cavern storage facility with a public participation process. The research cooperation was founded by the city of Gronau, the citizens’ initiative cavern field Epe, the company EFTAS, Münster, and the Research Center of Post-Mining at the Technische Hochschule Georg Agricola, Bochum. This research cooperation is the first in Germany to involve direct collaboration between science and the public. In the cavern field, which has been in operation since the 1970s, brine is extracted, and at the same time natural gas, crude oil and helium, as well as hydrogen in the future, are stored in the subsurface. The technical focus of this work was the development of a high-resolution spatiotemporal analysis of ground movements. The area is monitored annually by the mining company’s mine surveyor. The complexity of the monitoring issue lies in the fact that the western part is a bog area and a former bog area. Furthermore, the soils in the eastern part are very humus-rich and show strong fluctuations in the groundwater and therefore complex hydraulic conditions. At the same time, there are few fixed scatterers or prominent points in the area that allow high-resolution spatiotemporal monitoring using simple radar interferometry methods. Therefore, the SBAS method (Small Baseline Subset), which is based on an aerial method, was used to analyze the radar interferometric datasets. Using an SBAS analysis, it was possible to evaluate a time series of 760 scenes over the period from 2015 to 2023. The results were integrated with the mine survey maps on the ground movement and other open geodata on the surface, the soil layers and the overburden. The results show complex forms of ground movement. The main influence is that of mining. Nevertheless, the influence of organic soils with drying out due to drought years and uplift in wet years is great. Thus, in dry years, ground subsidence accelerates, and in wet years, ground subsidence not only slows down but in some cases also causes uplift. This complexity of ground movements and the necessary understanding of the processes involved has been communicated to the interested public at several public information events as part of the research cooperation. In this way, an understanding of the mining process was built up, and transparency was created in the subsurface use, also as a part of the energy transition. In technical terms, the research cooperation also provides a workflow for developing the annual mine survey maps into an integrated geo- and environmental monitoring system with the development of a transparent participatory geomonitoring process to provide resilience management to a mining location.

As part of the MUSE project aimed at optimizing post-mining dewatering through multi-sensor geomonitoring, additional analyses were conducted based on soil samples. The aim of these analyses was to develop retention curves, which help determine critical soil physical parameters and soil moisture levels. Due to the absence of pressure pot analyses, grain size analysis was used to identify these critical parameters, allowing comparison with other data levels. In conclusion, the project results presented, that the methods used demonstrated a high degree of consistency with official soil data (maps and tables) in the working area, making them suitable for future studies on soil moisture, potentially eliminating the need for time-consuming laboratory analyses. The Soil Map 1:50,000 (BK50) data can serve as a benchmark for estimating soil moisture values in large-scale investigations.

This study explores the use of advanced drone technology with multiple sensors to improve the detection and mapping of fault zones. The goal is to validate a multifaceted approach using LIDAR, multispectral cameras, and thermal imaging, providing a comprehensive analysis of the Earth's surface. LIDAR technology plays a critical role by creating high-resolution digital elevation models (DEMs) and digital surface models (DSMs). These models offer detailed depictions of terrain topography, crucial for identifying subtle variations associated with fault lines. LIDAR's ability to see through vegetation also aids in delivering a clear terrain representation, irrespective of surface cover. Multispectral cameras capture images across various wavelengths, enabling the analysis of vegetation health through indices like GNDVI, NDVI, MSAVI, and VARI. These indices indicate geological disruptions, such as fault zones, since vegetation health often correlates with underlying anomalies. Thermal imaging adds another dimension by detecting minor temperature fluctuations on the ground's surface. These variations can signal active faults, revealing friction or geothermal activities beneath the surface. To verify the sensor data accuracy, a site visit was conducted, comparing drone findings with actual soil profile samples. This ground-truthing step is vital for confirming that remote sensing data reflects real-world conditions accurately. Overall, the study shows that a multisensorial approach using drones significantly enhances fault zone detection and analysis. This integrated method serves as a potent tool for geological research, aiding in understanding fault dynamics and contributing to natural disaster preparedness.

This paper presents the application of mobile geographic information systems (mobile GIS) in the study of successional vegetation in the Schöttelheide area, a degraded heap of the former Prosper-Haniel mine in the Ruhr region. This area, left to natural succession, became the focus of research using Survey123 and Flora Incognita for the automatic identification of plant species. The aim of the study was to make a spatial inventory of the plant communities taking into account the stage of succession, the presence of invasive species and the natural valorisation. The results showed that mobile GIS is an extremely effective and flexible tool for monitoring ecological succession in hard-to-reach and heterogeneous post-mining habitats.