243 / 2023-09-29 22:19:38

An automated extraction method for large-scale open pit coal mining disturbance ranges

vegetation disturbance range; automated extraction；large-scale open pit coal mines

Bulk identification of the range of disturbance of vegetation by open pit coal mining is of great significance for the ecological regulation and management of mines and the construction of green mines. In this paper, an automatic extraction method oriented to the disturbance range of large-scale open pit coal mining is proposed. The method is based on the distance attenuation pattern of vegetation disturbance by mining activities, constructs a conceptual model of the spatial trajectory sequence of normalized vegetation index (NDVI) changes with distance, and uses nonlinear fitting to obtain the characteristic parameters of the spatial trajectory sequence changes to realize the automatic extraction of the mining disturbance range. The automatic extraction of mining disturbance distances is divided into the following four steps: (1) Extraction of NDVI spatial trajectory series (2) Curve conceptual model function design (3) Spatial trajectory series fitting and optimization (4) Extraction of mining disturbance range parameters.It utilized the algorithm to batch and automatically extract the disturbance range of all surface coal mines in the Shendong coal in 2020, and summarized the spatial analysis and law of the disturbance range and intensity of the surface coal mines. The results are as follows:(1) Method extraction accuracy: the mining disturbance ranges of 106 surface coal mines in the Shendong Coal Base in 2020 were extracted with an overall MRE of 0.1043. (2) The method estimated the disturbance intensity of the surface coal mine by processing the characteristic parameters of the disturbance curve pattern, and found that there is a strong disturbance direction in the surface coal mining. Distinguishing from the existing mining disturbance range extraction methods, the algorithm proposed in this paper takes into account the directional heterogeneity of vegetation disturbance by coal mining and overcomes the problem of dependence on high-frequency remote sensing image data, which significantly reduces the high-frequency requirement for data acquisition and the complexity of data processing. It provides a fast extraction method for comprehensively monitoring the disturbance of vegetation by surface coal mining activities, which can quickly extract and accurately assess the extent of disturbance and damage to vegetation by coal mining, and provide data support for mining management planning and ecological restoration and governance.Meanwhile, the conclusion of the differential distribution characteristics of the disturbance range and disturbance intensity in different directions of the coal mine is expected to provide scientific guidance for the hierarchical zoning management, precise restoration, and optimization of the production plan in the coal mine area.