## 3 Data and Methodology  
### Data  
A summary of the data collection parameters is presented in Table 1. The data of 14 June 2020 have been abbreviated to 06.14 as are the other data.  
The FEIMA D2000 quad-rotor UAV equipped with a SONY a6000 camera was used to collect the visible images and simulate the ground flight. A ground sample distance (GSD) of 4 cm/pixel was provided at a flight height of 255 m relative to the ground. Each flight was set with 80% forward overlaps and 60% side overlaps. Photoscan software was used to generate the UAV point clouds, the digital surface model (DSM) and the digital orthophotot maps (DOMs). The D-LIDAR 2000 module carried by the D2000 UAV served to collect the original LiDAR data, and the LAS-format point cloud was obtained by preprocessing. Detailed information about the camera and LiDAR module are shown in Table 2.  
First, the outliers in the point cloud were removed by the statistical outlier removal (SOR) filter tool in CloudCompare software(2.10, [[https://www.danielgm.net/cc/](https://www.danielgm.net/cc/)]([https://www.danielgm.net/cc/](https://www.danielgm.net/cc/))). The filter performs a statistical analysis on the neighborhood of each point and calculates the average distance from it to all adjacent points. Points whose average distance were outside the standard range (defined by the global distance average plus the defined standard deviation) would be defined as outliers and removed from the data. Then, the progressive triangulation densification filtering algorithm in TerraSolid software (2019, Finland, [[https://terrasolid.com/](https://terrasolid.com/)]([https://terrasolid.com/](https://terrasolid.com/))) was used to automatically classify ground points, and the DEM generated from the initial filtering result was visually inspected, the corresponding misclassified  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Time** & **Acquisition** & **Result Form** &
\begin{tabular}{c} **Point Cloud** \\ **Number** \\ \end{tabular} & **Area km2** & **Density per m2** \\ \hline
1 & 14 June 2020 & UAV image & Point cloud, DSM, DOM & \(2.3\times 10^{8}\) & 4.5 & 52 \\
2 & 20 July 2020 & UAV image & Point cloud, DSM, DOM & \(2.4\times 10^{8}\) & 4.5 & 53 \\
3 & 7 September 2020 & UAV image & Point cloud, DSM, DOM & \(2.2\times 10^{8}\) & 4.2 & 53 \\
4 & 15 November 2020 & UAV image & Point cloud, DSM, DOM & \(2.4\times 10^{8}\) & 4.5 & 53 \\
5 & 31 July 2021 & UAV image & Point cloud, DSM, DOM & \(4.7\times 10^{8}\) & 4.3 & 110 \\
6 & 16 January 2022 & Airborne LiDAR & Point cloud & \(2.5\times 10^{8}\) & 4.0 & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time series data collection statistics.  
Figure 1: Schematic for relationship between the working face, observation stations, and study area.  
point clouds were detected through the section line, and the misclassified points were manually corrected using the classification tool to improve the initial filtering result. Finally, multi-temporal DEMs were generated by interpolation of ground point cloud. All data coordinate systems were of the WGS-84 to ensure consistency of the coordinate datum.  
The average density of the original point cloud of 06.14 data is 52 per m\({}^{2}\), the average density of ground point cloud obtained by point cloud filtering is about 3.5 per m\({}^{2}\), the average spacing of point cloud is about 0.5 m, and the resolution of DEM is 0.5 m. In order to enable differential calculation of data in different periods, the DEM data of other periods were resampled to the same spatial resolution.