Time-series unmanned aerial vehicle photogrammetry monitoring method without ground control points to measure mining subsidence

Surface subsidence and its secondary effects caused by underground coal mining continually threaten the safety and property of residents in mining areas. The commonly used total station and leveling approach is time-consuming and laborious, and the spatial range is limited. In this study, an FEIMA D2000 quadrotor unmanned aerial vehicle (UAV) was used to conduct 4-cm/pixel photogrammetry in four trial periods over a surface influence area (3.75 km(2)) of a working mine face. The performance comparison experiments from five point-cloud filtering algorithms in two sub-regions of the study area show that the ATIN algorithm provides the best filtering for this study area. Coal mining subsidence monitoring in mountainous area based on time-series UAV photogrammetry technology is proposed. Using this approach, the ATIN algorithm was used to filter the overall point cloud data in the study area to obtain the digital elevation model (DEM). The surface dynamic subsidence basin was determined with two phases of DEM subtraction. The results show that, compared with the measured leveling data in the field in the same period, the average root mean square error is 165 mm, and the maximum subsidence monitoring accuracy reaches 98%. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)