Mining activities, and especially open-pit mines, have a significant impact on the Earth’s surface. They influence vegetation cover, soil properties, and hydrological conditions, both during mining and for many years after the mines have been deactivated. Exploring a fast, accurate, and low-cost method to monitor changes, through years, in such an anthropogenic environment is, therefore, an open challenge for the Earth Science community. We selected a case study located in the northeast of Beijing, to assess geomorphic changes related to mining activities. In 2014 and 2016, an unmanned aerial vehicle (UAV) collected two series of high-resolution images. Through the structure-from-motion photogrammetric technique, the images were used to generate high-resolution digital elevation models (DEMs). The assessment of geomorphic changes was carried out by two methodologies. At first, we quantitatively estimated the detectable area, volumetric changes, and the mined tonnage by using the DEM of difference (DoD), which calculated the differences between two DEMs on a cells-by-cells basis. Secondly, the slope local length of autocorrelation (SLLAC) allowed determining the surface covered by open-pit mining by using an empirical model extracting the extent of the open-pit. The analysis of the DoD allows estimating the areal changes and the volumetric changes. The analysis of the SLLAC and its derived parameter allows for the accurate depiction of terraces and the extent of changes within the open-pit mine. Our results underlined how UAVs equipped with high-resolution cameras can be fast, precise, and low-cost instruments for obtaining multi-temporal topographic information, especially when combined with suitable methodologies to analyze the surface geomorphology, for dynamic monitoring of open-pit mines.
UAV DEM SLLAC DoD Open-pit mine
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The author would like to acknowledge the Miyun mine company for their cooperation, and we would like to thank Zhen Yanwei for manipulation of UAV, Zheng Yongxin, Lai Zili and Huang Haozhong for assisting with the processing of the data. The technical support for the UAV 2014 survey was provided by Sky View Technology Co., Ltd. (Taiwan). This research was financially supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2006BAB01A01), Joint Evaluation of Geological Hazards in Beijing by Beijing Education Commission (2015282-49), and China MOST project “Method and model for quantitative prediction of deep geologic anomalies” (2017YFC0601502). The algorithms used in this works were elaborated and tested by the digital terrain analysis research group at University of Padova (Italy), and supported by the grant 60A08-5455/15 “the analysis of the topographic signature of anthropogenic processes”.
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