Coal dust is the main pollutant in coal mining areas. Such pollutants easily diffuse and are difficult to monitor, which increases the cost of environmental pollution control. Remote sensing technology can be used to dynamically monitor mining areas at a low cost, and thus, this is a common means of mining area management. According to the spectral characteristics of various ground objects in remote sensing images, a variety of remote sensing indexes can be constructed to extract the required information. In this study, the Wucaiwan open-pit coal mine was selected as the study area, and the Enhanced Coal Dust Index (ECDI) was established to extract the coal dust pollution information for the mining area. A new mining area pollution monitoring method was developed, which can provide technical support for environmental treatment and mining planning in Zhundong. The results of this study revealed the following: (1) Compared with the normalized difference coal index, the ECDI can expand the difference between the spectral information about the coal dust and the surrounding features, so it has a significant recognition ability for coal dust information. (2) From 2010 to 2021, the coal dust pollution in the study area initially increased and then decreased. With the continued exploitation of the coal mines in the study area, the coal dust pollution area increased from 14.77 km2 in 2010 to 69.49 km2 in 2014. After 2014, the local government issued various environmental pollution control policies, which had remarkable results. The coal dust pollution area decreased to 36.85 km2 and 17.85 km2 in 2018 and 2021, respectively. (3) There was a great deal of pollution around mines and roads, around which the pollution was more serious. Various factors, such as wind, coal type, and the mining, processing, and transportation modes, affect the distribution of the coal dust pollution.
Coal mining has brought a series of environmental problems. Local government departments have issued relevant governance policies, but the premise of scientific prevention and control is to correctly grasp the actual distribution of various ground objects in the mining area. Using classification methods to extract ground object information based on remote sensing images can effectively realize mining area monitoring and provide reference for land and space planning and environmental protection in the mining area. Therefore, it is very important to select the appropriate scale and method to identify the ground object information of remote sensing image. in this paper, Landsat 8 images of the Wucaiwan mining area and GF-2 images of the Tebian coal mine were taken as the research objects, and unsupervised classification, supervised classification and object-oriented classification were used to identify and monitor the mining area's surface. The results showed that: (1) the classification effect of the Mahalanobis distance method was the best in terms of comprehensive operation process and classification accuracy. This method had high classification accuracy for GF-2 and Landsat 8 images. When classifying GF-2 images, the kappa coefficient reached 0.90, and the overall classification accuracy was 94.27%. When classifying Landsat 8 images, the kappa coefficient reached 0.85, and the overall classification accuracy was 90.02%. (2) The factors causing the classification error were 'homospectral foreign bodies' and 'mixed pixels'. (3) When combined with the actual needs and image characteristics, the extensive use of medium and high-resolution remote sensing images to identify and monitor the surface elements of mining areas can greatly improve the work efficiency and minimize the image costs. (4) The construction layout of tailings pond in the Tebian coal mine was conducive
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