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.
Illegal open pit mining might be a very dangerous activity both for the environment and also for the people living in its neighbourhood. This kind of activity is connected with environmental degradation, disruption of sustainable development and lack of the most critical last stage of the mine’s “life”, i.e. land reclamation. An additional element connected with illegal exploitation is the fact of breaking the law and stealing mineral resources. Monitoring of illegal exploitation is therefore an important aspect. The presented here review was intended to investigate which methods can be used directly to detect open pit mining sites and to evaluate their effectiveness. In the reviewed works a wide variety of methods have been used, ranging from manual methods, such as photo-interpretation, to a combination of automatic methods and photo-interpretation, to fully automatic methods. Based on the analysis, it was indicated that different types of classification (supervised, unsupervised, hybrid) are the most commonly u sed. Besides, radar interferometry, image fusion techniques, or images spectral similarity are also used.
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