## 2 Material and Methods
### DEM Acquisition
#### 2.3.2 DEM Construction  
In this study, UAV has been used for 2 missions, which were carried out in January and November 2015. A total of 3529 images were captured for the first time, with a survey area of 67.5 km\({}^{2}\). The second flight mission was completed in November. A total of 1697 images were captured, with a survey area of 69.8 km\({}^{2}\). The Overlay and Sidelap are \(>\)80% and \(>\)60%, respectively. The Pix4 Dmapper software developed by the Swiss Pix4D company was used in this study. The aerial photographs taken by the UAVs were exported to the Pix4 Dmapper to build DEMs for calculations of landslide volume and change in river channel depth in based on structure-from-motion (SfM) and multi-view stereo (MVS) photogrammetric techniques [45; 46], then analysis by ArcGIS application. The principle of the Pix4 Dmapper model production is split into the following five steps [47]: (1) search for feature points in the images; (2) aerial triangulation adjustment to reconstruct the precise position and camera direction; (3) verification of feature points and calculation of 3D coordinates; (4) interpolation of feature points to construct an irregular triangular mesh to obtain DEMs and building a dense 3D model to increase the spatial resolution of the triangular mesh; (5) projection images on DEMs to make orthophotos.  
To reduce such discrepancies, ground control points (GCPs) should be used. However, the study area is situated on high relief mountainous range, the access is limited, so, only part of GCPs situated on the valley closed to the village are available. It is believed that airborne LiDAR has the best quality [44; 48]. Alternatively, we focused on the geomorphic changes, only the difference is a major concern, thus the relative precision of the datasets is feasible for such analysis. Caused by inaccessibility and availability, distinct topographic features are chosen from the airborne LiDAR dataset. In this study different GCPs have been used for DEM production. Figure 2 denotes the distribution of GCPs. The control points were assigned based on the permanent points within the landscapes, such as the intersection of roads and mountain peaks (Figure 2). Especially for leveling control, the area was selected first without a significant change within the DEMs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Camera**} & \multicolumn{2}{c}{**Coverage**} & \multirow{2}{*}{\begin{tabular}{c} **GSD 1** \\ **(cm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **DEM** \\ **Grid** \\ **Spacing (m)** \\ \end{tabular} } & \multirow{2}{*}{**Platform**} & \multirow{2}{*}{\
\begin{tabular}{c} **AGL** \\ **2 (m)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Rainfall** \\ **Intensity** \\ **(mm/rainy** \\ **days)** \\ \end{tabular} } \\ \hline
10 April 2009 & DMC & 80 & 30 & 2 & Aircraft & 4004 & - \\
28 August 2009 & ADS40 & 32 & 15 & 2 & Aircraft & 4000 & 120.04 \\
2010 & LiDAR & \(>\)150 & – & 1 & Aircraft & – & – \\
23 January 2015 & Nikon D800E & 67.5 & 15 & 0.17 & UAV & 1500– & 45.50 \\
6 November & Nikon D800E & 69.8 & 15 & 0.20 & UAV & 1500– & 41.97 \\
2015 & D800E & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}  
\({}^{1}\) Ground sampling distance; \({}^{2}\) Above ground level.  
\end{table}
Table 1: Information on the image source.