The vegetation of the post-mining areas is subject to constant and significant changes. Reclamation works, carried out after the cessation of mineral extraction, contribute to the intensive development of new plant species. However, secondary deformations, occurring even many years after the end of exploitation, may cause the degradation of the vegetation cover. It is, therefore, an important issue to identify changes in flora conditions and to determine whether and to what extent past mining has a negative impact on the plant cover state. The objectives of this research have been as follows: (1) analysis of the flora condition in the post-mining area in the 1989–2019 period, (2) identification of sites with significant changes in vegetation state, and (3) modeling of the relationship between the identified changes in vegetation and former mining activities. The research was carried out in the area of the former opencast and underground lignite mine “Friendship of Nations—Babina Shaft,” which is located in the present-day Geopark (Western Poland), using Landsat TM/ETM+/OLI derived vegetation indices (NDVI, NDII, MTVI2) and GIS-based spatial regression. The results indicate a general improvement in flora condition, especially in the vicinity of post-mining waste heaps and former opencast excavations, with the exception of the northwestern part of the former mining field where the values of all of the analyzed vegetation indices have decreased. Also, four zones of statistically significant changes in the flora condition were identified. Finally, the developed GWR models demonstrate that former mining activities had a significant influence on changes in the plant cover state of the analyzed region.

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.

The motivation for this study arises from the need to monitor the condition of a rehabilitated post-mining areas even decades after the end of the recovery phase. This can be facilitated with satellite derived spectral vegetation indices and Geographic Information System (GIS) based spatiotemporal analysis. The study area described in this work is located in Western Poland and has unique characteristics, as it was subjected to the combined underground and open pit mining of lignite deposits that had been shaped by glaciotectonic processes. The mining ended in early 1970’ties and the area was subjected to reclamation procedures that ended in the 1980’ties. We used the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) spectral indices derived from Sentinel-2 data for the 2015–2022. period. Then, we applied a combination of GIS-based map algebra statistics (local, zonal and combinatorial) and GI* spatial statistics (hot spot and temporal hot spot) for a complex analysis and assessment of the vegetation cover condition in a post-mining area thought to be in the rehabilitated phase. The mean values of NDVI and EVI for the post-mining study area range from 0.48 to 0.64 and 0.24 to 0.31 and are stable in the analyzed 8 year period. This indicates general good condition of the vegetation and post-recovery phase of the area of interest. However, the combination of spatiotemporal analysis allowed us to identify statistically significant clusters of higher and lower values of the vegetation indices and change of vegetation cover classes on 3% of the study area. These clusters signify the occurrence of local processes such as, the encroachment of aquatic vegetation in waterlogged subsidence basins, and growth of low vegetation in old pits filled with waste material, barren earth zones on external waste dumps, as well as present-day forest management activities. We have confirmed that significant vegetation changes related to former mining occur even five decades later. Furthermore, we identified clusters of the highest values that are associated with zones of older, healthy forest and deciduous tree species. The results confirmed applicability of Sentinel-2 derived vegetation indices for studies of post-mining environment and for the detection of local phenomena related to natural landscaping processes still taking place in the study area. The methodology adopted for this study consisting of a combination of GIS-based data mining methods can be used in combination or separately in other areas of interest, as well as aid their sustainable management.

This study aimed to analyze and assess the spatial and temporal trends in distribution of reserves and production of igneous and metamorphic dimension stones and crushed rocks in the Lower Silesia region, which with 90% of total supply is the key source region of these rock raw materials in Poland. The research utilized descriptive statistics to examine temporal variations of production, as well as to determine sufficiency of these resources for four different scenarios and seven main lithological groups of rocks. Spatial statistics in the geographic information system was used to map changes in spatial distribution of production with the density function, as well as to determine areas of highest concentration in the 2010–2018 period. Then, 169 communes in the region were assessed using the multicriteria analytical hierarchy process (AHP) technique to identify local communities prospective for development of this mining sector. Strong, positive correlation (r = 0.645) between year to year production change with annual rate of gross domestic product was determined. Sufficiency of economic reserves was estimated, in four scenarios, as being the lowest for melaphyre and porphyry (25–49 years), and the highest for marble (380–389 years). Reserves of basalt and amphibolite should last for approximately 50 years (32–60 and 36–67 years, respectively), granite for 82 to 110 years, and gabbro and gneiss for over 100 years (78–159 and 76–189 years, respectively). Maps revealed a possible trend of increasing production in quarries located in the central and eastern parts of Lower Silesia, whereas multicriteria analysis allowed for the identification of nine communes with the highest potential for rock raw material quarrying. The practical outcome of this study is a knowledge database for authorities, upon which sustainable management of regional rock raw materials can be based in the context of economic, social, and environmental impacts of their extraction.

Underground and opencast mining adversely affects the surrounding environment. This process may continue even decades after the end of actual mineral extraction. One of the most significant effects of ceased mining are secondary deformations. Safe, new development of post-mining areas requires reliable information on potential deformation risk zones, which may be difficult to obtain due to a lack of necessary data. This study aimed to investigate and understand the secondary deformation processes in the underground mining area of the former “Babina” lignite mine, located in the unique glaciotectonic environment of the Muskau Arch, in western Poland. A combination of GIS-based historical mapping, geophysical 2D/3D microgravimetry, and Electrical Resistivity Tomography (ERT) measurements allowed the identification of subsidence-prone areas and the determination of potential factors of sinkhole development. The latter are associated with anthropogenic transformation of rock mass and hydrogeological conditions, by shallow underground mining. The results confirmed that multi-level mining of coal deposits in complex and complicated glaciotectonic conditions cause discontinuous deformations, and may be hazardous as long as 50 years after the end of mining operations.

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.