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The mining of coal resources is necessary to support the smooth growth of the social economy, but doing so underground has resulted in significant ecological and environmental issues [1,2]. Surface cracks are one of the environmental issues brought on by coal mining in western China, particularly in the arid and semi-arid regions , which also results in building deformation, destruction of arable land, accelerated soil moisture evaporation, vegetation destruction, and soil erosion [4,5,6]. Additionally, it was discovered that cracks of various widths had a variety of noteworthy impacts on soil water content and soil respiration . Therefore, in order to assess the degree of damage and crack development in the study area and to provide data support and assurance for land reclamation work, it is necessary to first obtain real-time, objective, and high-precision distribution information of surface cracks in the mining area . This information must also be acquired and quantitatively described.
Surface crack extraction through UAV (unmanned aerial vehicle) images has achieved wide application . UAVs have significant advantages such as a high resolution, flexibility and mobility, high efficiency and speed, and low operating costs , providing an ideal data source for information extraction of surface cracks in mining areas. The current methods for surface crack extraction through UAV images are mainly object-oriented [11,12], edge detection , threshold segmentation , manual visual interpretation , etc. Some scholars have also conducted experimental studies based on image processing and pattern recognition techniques to achieve crack measurement and statistical aspects with some results [16,17]. However, these studies are mainly based on image processing to extract information about cracks from UAV images. There is no mature method for evaluating the damage of cracks in coal mining subsidence areas, which would be useful for the data support of land reclamation and treatment plan design, so there is an urgent need to propose a more reliable method for evaluating the damage of cracks.
To solve the above problems, this article proposes a new surface crack damage evaluation method using the kernel density estimation method commonly used in geographic information analysis . KDE (kernel density estimation) in two dimensions has been widely used in the field of geographic information analysis research and is an effective tool for spatial clustering analysis, hotspots, or risk point identification [19,20,21]. In this article, we use the kernel density estimation method to construct an evaluation method for surface crack damage caused by mining in the arid and semi-arid areas of Yulin city in northern Shaanxi Province, using the coal mining area as the study area. First, we obtain high-precision crack extraction results based on machine learning methods. Then, we calculate the surface crack nucleus density in the study area and take it as a grading index. Finally, combined with the results of the field investigation by crack management experts, the classification assessment of cracks is carried out. The damage degree of the study area is divided into three levels: light damage, moderate damage, and severe damage.
We used the DJI Matrice 210 RTK equipped with a Zenmuse X5S camera to obtain high-resolution images of the study area. During our flight campaign, the parameters of the UAV and camera were set as shown in Table 1, and we used DJI GS Pro software to operate the drone flight. After removing the images with poor imaging quality, such as those with blur and color cast, Pix4D mapper software was used to process the UAV image to produce the digital orthophoto map (DOM) of the study area with a resolution of 0.013 m (Figure 3).
This article proposes a surface crack damage evaluation method based on nuclear density estimation for UAV images, and its flow chart is shown in Figure 4. Firstly, the UAV images were acquired and cracks were extracted. Secondly, the kernel density estimation method was used to calculate the density of the study cracks. Then, the kernel density of the surface crack was used as the basis, combined with the field survey results of the crack management experts, to determine the grading index. Finally, the damage degree of the study area was evaluated.
The geological environment of the mining area is complex, and the surface vegetation is overgrown; furthermore, the spectral color characteristics of the ground withered vegetation and surface cracks are similar, resulting in a low accuracy and efficiency in extracting cracks based on UAV images. In recent years, scholars have gradually applied artificial intelligence methods to image recognition and crack detection with good results. For the extraction of surface cracks in the mining area, Zhang Fan et al.  cut the complete UAV image into small sub-images for crack extraction through image cutting, which effectively avoided the interference of vegetation and obtained better results. Therefore, this article proposes a crack extraction method based on machine learning for UAV sub-images considering this method. First, MATLAB was used to convert the UAV image into sub-images with cut pixels of 50 50. Second, the sub-images containing cracks were identified by the support vector machine (SVM) machine learning method, the dimensionality reduction method via PCA (principal component analysis), and the image enhancement method via Laplace sharpening, and the crack extraction results of the sub-images were obtained using the threshold segmentation method. Third, the sub-images that do not contain cracks were image-processed to make their background black. Fourth, all processed images were restitched according to the original cut sequence number, obtaining the final UAV image crack extraction results. Fifth, the kappa coefficient method was used to evaluate the crack extraction accuracy, and 2000 sample points were randomly selected, with 1000 crack pixels and no-crack pixels each, and the manual visual interpretation results were used as the true values to verify the accuracy of the crack extraction results. The specific SVM, PCA, Laplace sharpening, and threshold segmentation methods are described in detail below.
Before performing machine learning, images are usually preprocessed first, in which image enhancement methods are widely used. Image enhancement is a common image-processing method. It can emphasize the local features of an image . Laplace sharpening is an image color-enhancement method that can effectively enhance the crack information of the land in the mining area and has achieved a good classification effect.
In the process of KDE analysis, a reasonable bandwidth h selection is very important. h determines the smoothness of the spatial distribution of the kernel density, and the larger the h, the smoother the density distribution. In existing geographic information analysis studies, reasonable bandwidths are often determined based on the degree of focus on clustering features and local features from a global perspective . In this research, there is a range of impacts of surface cracks on the surrounding surface ecology, so the size of the bandwidth h cannot be set arbitrarily.
The surface damage caused by surface cracks in coal mining subsidence areas mainly comes from the changes in soil physicochemical properties and mechanical damage to vegetation roots caused by them [34,35,36]. In a study of the effect of surface cracks on soil moisture in the adjacent mine in the study area of this paper, Yingbin Ma found that the surface cracks did not affect more than 1.5 m, while the aboveground biomass of vegetation was affected within 3 m around the cracks . In a study on the effect of coal mining subsidence cracks on soil physical and moisture properties in wind and sand areas, Han Zhenying found that there was a more significant difference between the 2 m range on both sides of the cracks and the control area . Xu Chuanyang et al. showed that coal mining subsidence cracks do not affect soil properties and crop growth beyond 1.2 m . The results of Wang Qiangmin et al. showed that the effect of coal mining subsidence cracks on soil moisture transport in the wind and sand areas was not more than 1.5 m . According to the existing research results, this article determined the bandwidth h to be 3 m according to the influence range of coal mining subsidence cracks on the surface.
Since no research has been conducted on the grading criteria of ground crack damage evaluation, this study hired experts experienced in surface crack management research to conduct field surveys and selected three sample squares of damage grades in the study area, where each damage grade corresponds to three sample areas with a sample size of 5 5 m2, as shown in Figure 5. Then, the density of crack nuclei in the interior of the sample squares was counted, followed by an analysis of their statistical characteristics and the determination of the grading criteria for crack damage.