The closure of coal and lignite mines has the potential to result in long-term environmental risks and socio-economic issues. To solve these, this research aims to improve the hazard assessment and risk management of former mining regions in a European project funded by the Research Fund for Coal and Steel. A multidisciplinary approach integrated historic, geological, topographical, environmental, and socio-economic data to create a methodology to support stakeholders at different decision-making levels in risk assessment and possible mitigation. For this purpose, a spatial decision support system was developed using a multi-hazard, multi-risk methodology. The individual hazards (post-mining, natural, and technical) are weighted using expert knowledge, their interaction analyzed, and then combined into a spatial multi-hazard index. Together with the other risk factors of social vulnerability and exposure, a comprehensive spatial risk map can be created automatically for individual regions using open-source components. In addition, GIS and statistical tools enable further analysis and visualization for decision-making by the relevant stakeholders. The methodology was validated through the examination of a first case study conducted in the post-mining region of the southern Ruhr area in Germany. The methodology and tool created significant results in two test scenarios, and will be tested and improved using other European mining sites during the next stages of the project.

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.

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.

The closure of coal and lignite mines has the potential to result in long-term environmental risks and socio-economic issues. The concept described in this article aims to improve the hazard assessment and risk management of former mining regions in a European project funded by the Research Fund for Coal and Steel. A multidisciplinary approach integrates historic, geological, topographical, environmental and socio-economic aspects to support stakeholders at different decision-making levels in the risk assessment and possible mitigation. The individual hazards (post-mining, natural, and technical) are weighted using expert knowledge, their interaction analyzed, and then combined into a spatial multi-hazard index. Together with the other risk factors of social vulnerability and exposure a comprehensive, spatial risk map can be created automatically for individual regions using open-source components. In addition, GIS and statistical tools enable further analysis and visualization for decision-making by the relevant stakeholders. The methodology is validated through the examination of case studies conducted in various European countries within the scope of the project.

Direct and indirect effects after mine operations cease must ideally be subject 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 longterm multi-temporal monitoring