# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 1 Introduction  
Geohazards are some of the most uncontrolled impacts on local and global economies, as well as on people's livelihoods. Almost nowhere on the planet is free from the damage of geohazards [1]. An essential component of predicting possible geohazard zones is the identification of an area that is vulnerable to future landslides [2, 3]. Landslides are common geohazards that destroy local resources and environments. Therefore, susceptibility assessments of landslides have been widely investigated to improve their capability for use with these hazards [4, 5]. Current studies commonly focus on two major issues regarding the susceptibility assessments of landslides--selection of evaluation indices and establishment of a rational assessment model. Relevant reviews [6, 7, 8, 9] demonstrate that exiting studies have proposed various remarkable models for susceptibility assessment of landslides, such as logistic regression models (LRMs) [10]. Meanwhile, in recent decades, the rapid development of machine learning algorithms in susceptibility assessmentshas been seen over time. LRMs are some of the most classic and commonly used methods, which have the advantages of minimal computation, high detection speed, and good adaptability [11]. LRMs have extensive applications in forecasting [12; 13] and susceptibility assessment [14; 15]. These studies provide a solid foundation for the susceptibility assessment of landslides, and advance the knowledge of susceptibility assessment with machine learning algorithms. In addition to an assessment model, the rational selection of indices is quite important for susceptibility assessment of landslides. Generally, indices such as lithology, elevation, and topography are introduced as the controlling factors, which are strongly related to the susceptibility assessment of landslides [16; 17; 18; 19; 20]. The rational selection of these indices remains a subject of scientific debate in many studies because the importance of different indices may vary from case to case. A review of previous studies [21] provides a guideline for addressing this issue. However, the generalizability of these results is subject to certain limitations, while another essential problem regarding the temporal variation of indices and the influence of this on susceptibility is still confusing and requires more attention. The susceptibility result will be questionable if the data for one index is out of date owing to the absence of a current data source, which is the case in digital elevation models (DEMs). So far, however, few researchers have highlighted the impact of outdated data sources on the reliability of susceptibility results, and few previous studies have investigated the mechanisms involved. A DEM is the digital representation of a terrain surface. These models have been widely applied in geohazard planning, terrain surface analysis, and other fields [22]. Generating a DEM generally involves data from different sources [23], including global multiresolution topography (GMRT) models, shuttle radar topography mission digital elevation models, and advanced space-borne thermal emission and reflection radiometer global digital elevation models (ASTER GDEMs). Among the abovementioned indices, elevation, topography, and slope are closely related to the DEM data [24]. In the majority of the previous studies, it has been suggested that DEM data should be consistent with the study date [25; 26; 27; 28; 29]. However, because of landscape changes and reshaping processes (e.g., earthquakes and engineering construction), the DEM data usually show obvious temporal variation. An example of significant temporal variation of DEM data was shown by [PERSON] et al., who substantiated that the DEM difference of the eastern Italian Alps within one month is up to 0.14 m [30]. [PERSON] et al. demonstrated that the changes experienced by a DEM over time are obvious and unpredictable [31].