Acid Mine Drainage Discrimination Using Very High Resolution Imagery Obtained by Unmanned Aerial Vehicle in a Stone Coal Mining Area

Abstract

Mining of mineral resources exposes various minerals to oxidizing environments, especially sulfide minerals, which are decomposed by water after oxidation and make the water in the mine area acidic. Acid mine drainage (AMD) from mining can pollute surrounding rivers and lakes, causing serious ecological problems. Compared with traditional field surveys, unmanned aerial vehicle (UAV) technology has advantages in terms of real-time imagery, security, and image accuracy. UAV technology can compensate for the shortcomings of traditional technology in mine environmental surveys and effectively improve the implementat ion efficiency of the work. UAV technology has gradually become one of the important ways of mine environmental monitoring. In this study, a UAV aerial photography system equipped with a Red, Green, Blue (RGB) camera collected very-high-resolution images of the stone coal mining area in Ziyang County, northwest China, and classified the very-high-resolution images by support vector machine (SVM), random forest (RF), and U-Net methods, and detected the distribution of five types of land cover, including AMD, roof, water, vegetation, and bare land. Finally, the accuracy of the recognition results was evaluated based on the land-cover map using the confusion matrix. The recognition accuracy of AMD using the U-Net method is significantly better than that of SVM and RF traditional machine-learning methods. The results showed that a UAV aerial photography system equipped with an RGB camera and the depth neural network algorithm could be combined for the competent detection of mine environmental problems.

Infiltration Efficiency Index for GIS Analysis Using Very-High-Spatial-Resolution Data

“…In the fifth step (5), the quality of the created LULC was improved. When utilizing very-high-resolution geospatial technologies in urban environments and classifying numerous classes, misclassification becomes a significant concern [91][92][93]. The selection of appropriate remotely sensed datasets and a suitable classification algorithm are the two key factors for achieving an accurate LULC model [94].…”

Open-Pit Granite Mining Area Extraction Using UAV Aerial Images and the Novel GIPNet

“…Thiruchittampalam et al [21] used UAV remote sensing technology to characterize coal mine waste, extracting texture and spectral features from real-time on-site data, and employing machine learning algorithms combined with expert experience for waste classification. Kou et al [22] used high-resolution images obtained by UAVs to identify acidic mine drainage in coal mining areas, comparing three methods-SVM, Random Forest (RF), and UNet-and proposing an efficient and economical monitoring approach. Utilizing oblique photography, these UAVs can create 3D models and generate point cloud data [7,23].…”

Substantiation of Drilling Parameters for Undermined Drainage Boreholes for Increasing Methane Production from Unconventional Coal-Gas Collectors

“…The universality of the obtained results consists in the possibility of expanding the use of the authors' approach to modeling nonlinear processes for a wider range of tasks, for example, to improve the methodology for presenting the results of "carbon polygons" [57,58] or assessing the impact of mining wastes on the environment [108][109][110].…”