The issue of monitoring surface motions in post-mining areas in Europe is important due to the fact that a significant number of post-mining areas lie in highly-urbanized and densely-populated regions. Examples can be found in: Belgium, the Czech Republic, France, Germany, the Netherlands, Spain, the United Kingdom, as well as the subject of this study, the Polish Walbrzych Hard Coal Basin. Studies of abandoned coal fields show that surface deformations in post-mining areas occur even several dozen years after the end of underground coal extraction, posing a threat to new development of these areas. In the case of the Walbrzych area, fragmentary, geodetic measurements indicate activity of the surface in the post-mining period (from 1995 onward). In this work, we aimed at determining the evolution of surface deformations in time during the first 15 years after the end of mining, i.e., the 1995–2010 period using ERS 1/2 and Envisat satellite radar data. Satellite radar data from European Space Agency missions are the only source of information on historical surface movements and provide spatial coverage of the entirety of the coal fields. In addition, we attempted to analyze the relationship of the ground deformations with hydrogeological changes and geological and mining data. Three distinct stages of ground movements were identified in the study. The ground motions (LOS (Line Of Sight)) determined with the PSInSAR (Persistent Scatterer Interferometry) method indicate uplift of the surface of up to +8 mm/a in the first period (until 2002). The extent and rate of this motion was congruent with the process of underground water table restoration in separate water basins associated with three neighboring coal fields. In the second period, after the stabilization of the underground water table, the surface remained active, as indicated by local subsidence (up to −5 mm/a) and uplift (up to +5 mm/a) zones. We hypothesize that this surface activity is the result of ground reaction disturbed by long-term shallow and deep mining. The third stage is characterized by gradual stabilization and decreasing deformations of the surface. The results accentuate the complexity of ground motion processes in post-mining areas, the advantages of the satellite radar technique for historical studies, and provide information for authorities responsible for new development of such areas, e.g., regarding potential flood zones caused by restoration of groundwater table in subsided areas.

Mining operations cause negative changes in the environment. Therefore, such areas require constant monitoring, which can benefit from remote sensing data. In this article, research was carried out on the environmental impact of underground hard coal mining in the Bogdanka mine, located in the southeastern Poland. For this purpose, spectral indexes, satellite radar interferometry, Geographic Information System (GIS) tools and machine learning algorithms were utilized. Based on optical, radar, geological, hydrological and meteorological data, a spatial model was developed to determine the statistical significance of the selected factors’ individual impact on the occurrence of wetlands. Obtained results show that Normalized Difference Vegetation Index (NDVI) change, terrain height, groundwater level and terrain displacement had a considerable influence on the occurrence of wetlands in the research area. Moreover, the machine learning model developed using the Random Forest algorithm allowed for an efficient determination of potential flooding zones based on a set of spatial variables, correctly detecting 76% area of wetlands. Finally, the GWR (Geographically Weighted Regression (GWR) modelling enabled identification of local anomalies of selected factors’ influence on the occurrence of wetlands, which in turn helped to understand the causes of wetland formation.

In this article, we present a possible approach to use satellite radar data for a complete description of the formation process of a subsidence trough resulting from an induced seismic event—a mining tremor. Our main goal was to verify whether SAR data allow for the calculation of the basic indicators for the trough (w—subsidence, T—trough slope, K—curvature, u—horizontal displacements, $\epsilon$—horizontal deformations). We verified the extent to which the Mogi and Yang models can be fitted to match the actual displacements recorded after an induced seismic tremor. The calculations were performed for the Legnica-Glogow Copper Belt (LGCB) area in southwest Poland. Due to intensive mining operations and specific geological and tectonic conditions, the area shows a high level of induced seismic activity. Our detailed analysis focused on four powerful mining tremors: the first tremor occurred on 29 November 2016 (M${}_W$3.4), the second on 7 December 2017 (M${}_W$3.3), the next on 26 December 2017 (M${}_W$3.6) and the last tremor on 29 January 2019 (M${}_W$3.7). For each analyzed event, we determined the displacements based on the Differential Interferometric Synthetic Aperture Radar (DInSAR) method and Sentinel 1 synthetic aperture radar (SAR) data from two paths (22 and 73). Additionally, for the period from November 2014 to October 2020, we calculated the displacements using the Small Baseline Subset method (SBAS) time series method. In all cases, the tremor was followed by the development of long-lasting surface deformations. The obtained results allowed us to conclude that it is possible to calculate indicators that result from a specific induced mining event. Considering the full moment tensor and nature of the tremor source, we demonstrated that the Mogi and Yang models can be employed to describe the influence of an induced tremor on the surface in an area of mining activity. We also confirmed the global character of the influence of the reduced troposphere on SAR data calculations. Our conclusions indicate that accounting for the tropospheric correction does not distort horizontal and vertical displacement values in regions influenced by mining activity/tremors.

The article presents a proposal to make simultaneous allowance for both ionospheric and tropospheric corrections in differential synthetic aperture radar interferometry (DInSAR) measurements. Atmospheric delay in the interferometric phase may cause the detection of terrain-surface changes to be impossible or significantly distorted. This fact remains of special importance in the case of surface changes that show limited amplitude and spatial range. Two areas were chosen to verify the validity of the proposed solution. The first area includes terrains affected by underground copper-ore mining activity (Poland), which shows high induced seismic activity. Mining tremors recorded in this area cause the terrain surface to locally subside. The authors analyzed three tremors that were recorded in 2016, 2017, and 2019. Each of the tremors exceeded a magnitude of Mw 4.0. The second area is located in the coastal region of Chile, in the Cardenal Caro province. In this case, the authors focused on a series of three earthquakes recorded on 11 March 2010. The strongest of the earthquakes was of Mw 7.0 magnitude. In the first case, calculations were based on obtained data from the Sentinel 1 satellites, and in the second case from the ALOS-1 satellite. It is demonstrated that simultaneous allowance for both the tropospheric and ionospheric corrections significantly improves the final results. The authors were also able to use the analyzed cases to demonstrate that implementation of the corrections does not have negative influence on the range and magnitude of local ground-surface deformations. At the same time, such implementation minimizes local displacement fluctuations and reduces displacement values in areas affected by deformations. The examples used in the article served to show that tropospheric correction is mainly responsible for global corrections (i.e., within the whole analyzed spatial range), while ionospheric correction reduces local fluctuations.

Dynamic climate changes are particularly apparent in polar regions. Glaciers are retreatng towards the land at a very fast pace. This study demonstrates the application of the feature tracking method in the analysis of ice flow velocity in the region of the Hornsund fiord, southern Spitsbergen, in the years 2018–2022. The calculations were based on the Geogrid and autoRIFT environments and on the Sentinel 1 images. The study also employed external data, such as a numerical terrain model and reference velocity values. The input data, e.g., the chip size and the search limit, were prepared accounting for the specific character of the investigated area. The velocities were calculated for nine biggest glaciers which terminated in the fiord. The accuracy of the results was identified by calculating the median absolute deviation (MAD) of the obtained displacement velocity values from the reference value for areas identified as stable. The study also attempted a causal analysis of the influence of weather factors on the dynamics of ice mass displacement. A systematic year-to-year decrease of the velocity was observed for the entire fiord. In the case of several glaciers, changes related to the ablation season (summer) are also clearly visible. The research results are promising and fill a research gap related to the absence of permanent monitoring and analysis of the dynamics of ice flow in polar regions. It is the first complex and precise study of glacier surface velocity changes, performed on the basis of satellite radar images for the entire Hornsund fiord.

Despite successful land reclamation efforts, post-mining areas are still prone to secondary effects of mineral extraction. These effects include surface deformations, damage to infrastructure and buildings, and periodic or permanent changes to surface water resources. This study focused on analyzing a former copper mine in southwest Poland in terms of surface water changes, which may be caused by the restoration of groundwater conditions in the region after mine closure. The main objective of the study was to detect areas with statistically significant changes in surface water between 2015 and 2024, as well as to identify the main factors influencing the observed changes. The methodology integrated open remote sensing datasets from Landsat and Sentinel-1 missions for deriving spectral indices—Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Moisture Index (NDMI), as well as Surface Soil Moisture index (SSM); spatial statistics methods, including Emerging Hot Spot analysis; and regression models—Random Forest Regression (RFR) and Geographically Weighted Regression (GWR). The results obtained indicated a general increase in vegetation water content, a reduction in the extent of surface water, and minor soil moisture changes during the analyzed period. The Emerging Hot Spot analysis revealed a number of new hot spots, indicating regions with statistically significant increases in surface water content in the study area. Out of the investigated regression models, global regression (RFR) outperformed local (GWR) models, with R2 ranging between 74.7% and 87.3% for the studied dependent variables. The most important factors in terms of influence were the distance from groundwater wells, surface topography, vegetation conditions and distance from active mining areas, while surface geology conditions and permeability had the least importance in the regression models. Overall, this study offers a comprehensive framework for integrating multi-source data to support the analysis of environmental changes in post-mining regions.