Mining processes have a strong impact on the surrounding environment. The effects of mining activities are particularly visible in areas where mines have been established. 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.

Each phenomenon has its cause and effect. During the research on the post-mining processes, the reasons can be found in ongoing processes taking place after the end of the mining exploitation. Therefore, a very important aspect is Geomonitoring. Currently, the post-mining processes taking place all over the world, should be considered into two groups of processes: such taking place in subsurface regions and such on the surface on the Earth. Through an integrated Geomonitoring it is possible to observe, among others, the vegetation of the mining areas. The observation of the state of vegetation is an aspect of research using remote sensing methods, e.g. indicators of vegetation. Of course, the reduction of plant vegetation may be caused by other reasons as well. Therefore, an important aspect is to distinguish changes in vegetation resulting affects coming from natural factors (climate changes, long-term droughts or sudden weather phenomena) and the resulting from post-mining processes. This article presents the indicators that have been discovered and used in the research of plant vegetation, which can be used in the post-mining.

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.

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.