## 1 Introduction  
Monitoring of surface subsidence plays a vital role in protecting the ecological environment of mining areas and ensuring the sustainable development of modern coal mines [1]. Traditional observation technologies, such as the total work station, have some limitations, such as low efficiency, high labor costs, and an inability to produce surface subsidence basins. With the development of UAV platform and computer vision technologies, the use of structure from motion (SfM) and Multiview-Stereo (MVS) algorithms to process UAV images are increasingly being used to produce terrain data of high resolution, namely, point cloud, digital surface model (DSM), and digital orthophoto maps (DOM) for research and applications in the earth sciences [2, 3]. Compared with methods based on satellite or airborne LiDAR, the dynamic, safe, low-cost, and efficient data acquisition afforded by a UAV mean that the UAV based technique has great potential for monitoring subsidence caused by coal mining. In determining the ground displacement using a UAV, research has, to date, focused on assessing its effectiveness in monitoring subsidence. Most studies have compared the DEM of two different measurement periods [4, 5, 6, 7]. [PERSON] et al. [8] and [PERSON] et al. [9] constructed dynamic surface subsidence basins based on DEM of difference (DoD) and retrieved mining subsidence-related parameters. [PERSON] et al., optimized the parameters of the filtering algorithm of the progressive triangulation densification filtering, and determined the subsidence area and the maximum subsidence value using airborne LiDAR [10]. [PERSON] et al., constructed the DoDbased on the fuzzy inference system, and obtained the surface deformation information of the study area more accurately by using Bayesian estimation based on the weight filter window [11]. [PERSON] et al., calculated the DoD, extracted surface subsidence information by setting the elevation difference and area threshold, and adopted the method based on elevation difference analysis [12]. The research of [PERSON] et al., showed that UAV can obtain high-density DEM, which can be used for other activities during and after mine excavation, and has good consistency with GNSS RTK data [13]. There are also studies that use dense point clouds derived from UAVs and algorithms that allow point cloud to point cloud comparison. [PERSON] et al., used UAV photogrammetry to obtain multi-temporal point cloud data, and quantified the subsidence of the two periods through the nearest neighbor point cloud to cloud (C2C) comparison method [14]. [PERSON] et al., carried out multi-temporal point cloud comparison experiments based on UAV photogrammetry in open-pit mines, and verified the effectiveness of a Multiscale Model to Model Cloud Comparison (M3C2) algorithm for accurate change detection [15]. [PERSON] et al., compared and analyzed the effectiveness of different image registration algorithms in multi-temporal DOM matching, and proposed a workflow to automatically determine the horizontal displacement caused by underground mining [16]. [PERSON] et al., combined UAV photogrammetry with interferometric synthetic aperture radar (InSAR) technology to monitor ground mining subsidence basins and obtained relatively reliable mining subsidence parameters [17]. [PERSON] et al., presented a practical framework for the integration of UAV based photogrammetry and terrestrial laser scanning (TLS) with application to open-pit mine areas, showing that the accuracy of geo-positioning based on UAV imagery can be improved [18]. UAV technology is highly appropriate for monitoring surface subsidence in coal mining. Generally, the monitoring accuracy is at the decimeter level, and a few studies have reported a centimeter capability [19; 20]. Given that the level of accuracy reported in independent studies differ, it is desirable to use a variety of sensors, including LiDAR, to obtain multi-source monitoring data to facilitate the development of UAVs for multi-source monitoring [21].