## 2 Methodology
### Displacement Calculation  
The main idea of displacement calculation based on monitoring points is to set several monitoring points in advance on the slope to be measured, and find the monitoring points with the same characteristics in the results of two times reconstruction cloud points. Finally, calculating the coordinate difference based on the center point identified by two homonymous monitoring points, so as to obtain the displacement of this point on the slope.  
Since the generated point clouds are disordered, it is the premise of displacement calculation to accurately distinguish and match the monitoring points with the same characteristics of two measured data from point clouds. In this study, all points in point clouds were searched by traversal. HSL space color was adopted as the recognition standard for the identification of the monitoring points. The monitoring points were extracted, and the displacement was calculated by matching between two times measurements. The main process is as follows (Figure 14):  
(1) Firstly, the recognition range of \(H\) and \(S\) parameters in the color of HSL space was determined according to the collected images and point clouds were screened to extract all points within the range of \([H-\Delta H,\,H+\Delta H],\left[S-\Delta S,\,S+\Delta S\right]\) from point clouds;  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Shooting Time**} & \multirow{2}{*}{**Monitoring Point Number**} & \multicolumn{6}{c}{**Monitoring Point Color**} \\ \cline{3-8}  & & \(R\) & \(G\) & \(B\) & \(H\) & \(S\) & \(L\) \\ \hline \multirow{4}{*}{10:30 a.m.} & MP 1 & 232 & 131 & 145 & 352 & 0.44 & 0.91 \\  & MP 2 & 237 & 138 & 152 & 352 & 0.42 & 0.93 \\  & MP 3 & 237 & 130 & 152 & 348 & 0.45 & 0.93 \\  & MP 4 & 235 & 134 & 151 & 350 & 0.43 & 0.92 \\  & MP 5 & 240 & 132 & 144 & 353 & 0.45 & 0.94 \\ \hline \multirow{4}{*}{3:53 p.m.} & MP 1 & 199 & 101 & 118 & 350 & 0.49 & 0.78 \\  & MP 2 & 209 & 109 & 125 & 350 & 0.48 & 0.82 \\ \cline{1-1}  & MP 3 & 192 & 111 & 119 & 354 & 0.42 & 0.75 \\ \cline{1-1}  & MP 4 & 191 & 113 & 126 & 350 & 0.41 & 0.75 \\ \cline{1-1}  & MP 5 & 201 & 113 & 126 & 351 & 0.44 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison table of RGB color and HSL color of five monitoring points in two time periods.  
2. The square side length of the monitoring point was named as \(D\), and the distance \(d_{ij}\) between the \(i\)th point (\(0<i\leq m\)) and the \(j\)th point (\(0<j\leq m\) and \(j\
eq i\)) among the \(m\) points was calculated and screened out. If \(d_{ij}<0.707\)\(d\) (\(d\), the distance from the center of the square monitoring point to the four corners), the \(j\)th point was divided into the \(i\)th group of data;
3. If there are points in group \(i+1\) that are the same characteristics as those in group \(i\), the data in group \(i+1\) and group \(I\) are considered to represent the same monitoring point, and the two sets of data are combined to obtain n sets of data, where n should be equal to the number of monitoring points arranged;
4. Calculate \(N\) groups of data determined in the previous step to find the centroid of each group of data points, that is, the centroid is considered to be the center point within the painting range of the monitoring point, representing the position of the monitoring point;
5. According to the monitoring point was identified, the closest two points between the monitoring point in the second time registration point clouds and the monitoring points in the first time registration point clouds are regarded as the monitoring point with the same characteristics. Setting the distance threshold avoids matching the situation of the dislocation. When access to two of the coordinates of point clouds of monitoring points with the same characteristics, the calculation can be carried out according to the displacement calculation formula of the two-point coordinates.