# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 2 Study Area
### Influencing Factors  
Selecting the influencing factors is a key step in a landslide susceptibility assessment [36; 37]; however, the causes of landslides are complex. To date, hundreds of influencing factors have been identified that are potentially important to susceptibility assessments [38]. For example, areas with high elevation and steep slopes are highly prone to landslides [39]. Lithology is also related to landslide occurrences, because different lithologies can withstand different levels of triggering factors. The distances to structure lines, rivers, and roads have important impacts on the spread and size of landslides in the study area [40]. [PERSON] et al. investigated global susceptibility during the period of 2005-2016 [28]. Nearly 100 factors were summarized to reveal the commonly used factors. In accordance with the relevant studies [29; 41; 42] and the study area, we select eight influencing factors: elevation, topography, slope, lithology, distance to a structure line, distance to a river, average annual rainfall, and distances to roads.  
Table 1 lists all the DEM data for the study area and their sources. The Open Topography Facility provided the GMRT data, which is hosted at the San Diego Supercomputer Center, University of California San Diego. This facility has built a strong cyberinfrastructure framework for managing and processing high-resolution topography data from light detection and ranging (LiDAR) ([[http://opentop.sdssc.edu/datasets?listAll=true](http://opentop.sdssc.edu/datasets?listAll=true)]([http://opentop.sdssc.edu/datasets?listAll=true](http://opentop.sdssc.edu/datasets?listAll=true))). Meanwhile, the Shuttle Radar Topography Mission (SRTM) DEM and Global Digital Elevation Model (GDEM) DEM were provided by the Geospatial Data Cloud site of the Computer Network Information Center in the Chinese Academy of Sciences ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/))). The DEM data at the same resolution for 1992, 2000, and 2009 were then obtained using a resampling tool in the GIS environment (Figure 4). There are three resampling methods in the resampling tool, namely nearest neighbor, bilinear interpolation, and cubic convolution methods. Nearest neighbor is selected in the present study because the method is simple, fast, and applicable.  
The DEM data for 1992, 2000, and 2009 were used to analyze factors affected by the DEM. Meanwhile, the data of other factors, namely lithology, distance to the structure line, distance to the river, and average annual rainfall, remained the same and were provided by China Railway Number 4 Engineering Group Co., Ltd. (745 Heping Road, Wuhan, Hubei, China), and the Roads and Traffic Authority of China. Because of the diversity of lithological layers in the study area, the lithology is divided into four groups in Table 2 [43]. Thus, the zoning maps of the four factors were obtained (Figure 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **Structure** & **Lithology** \\ \hline Group 1 & Loose structure & Clay, gravelly soil, clay rock, thin layer siltstone \\ Group 2 & Cataclastic structure & Silstone, shale, phyllite, thin layer slate \\ Group 3 & Stratified structure & Thick layer sandstone, conglomerate with argillaceous rocks, \\  & & siliceous rock with argillaceous shale \\ Group 4 & Block structure & Limestone, siliceous rock, thick layer conglomerate, \\  & & dolomite, phosphate rock \\ \hline \hline \end{tabular}
\end{table}
Table 2: Lithology and structure in the study area.  
Figure 5: Zoning map of (**a**) lithology, (**b**) distance to a structure line, (**c**) distance to a river, (**d**) and average annual rainfall.