Exploring the impact of multitemporal DEM data on the susceptibility mapping of landslides

Jiaying Li, Weidong Wang, Zheng Han, Yange Li, Guangqi Chen

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.

Original languageEnglish
Article number2518
JournalApplied Sciences (Switzerland)
Issue number7
Publication statusPublished - Apr 1 2020

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


Dive into the research topics of 'Exploring the impact of multitemporal DEM data on the susceptibility mapping of landslides'. Together they form a unique fingerprint.

Cite this