Detection and volume estimation of mining subsidence based on multi-temporal LiDAR data

A new technique for quickly assessing extensive areas of mining subsidence that uses digital elevation models (DEMs) extracted from LiDAR (Airborne light detection and ranging, LiDAR) is presented in this paper. The proposed technique observes the elevation changes by using multi-temporal DEMs. One-meter-resolution DEMs from LiDAR data are applied to detection the mining subsidence in Hebi coal mine area, China. We assess the DEMs produced by the proposed method and their mining subsidence application. Differential DEMs were produced to identify vertical ground displacements over the period. Elevation changes in excess of 15 cm can be detected. We find three main results. 1) The elevation difference error increases with the high coverage of vegetation. 2) The proposed technique well delineated the large-scale mining subsidence. The total rate of successful area detection was over 90%. 3) The mining subsidence volume could be roughly estimated in units of 10³ m³. The developed technique well supports damage assessments of mining subsidence because the location, depth, and volume can be quantitatively determined by LiDAR.