PROGRESS IN GEOGRAPHY ›› 2012, Vol. 31 ›› Issue (10): 1326-1333.doi: 10.11820/dlkxjz.2012.10.010

• Original Articles • Previous Articles     Next Articles

Effect of DEM Error on Landslide Susceptibility Mapping Models

BAO Lili1,2, QIN Chengzhi1, ZHU A-Xing1, HU Xuemei1,2   

  1. 1. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2012-04-01 Revised:2012-06-01 Online:2012-10-25 Published:2012-10-25

Abstract: Terrain attributes (such as slope gradient and slope aspect) computed from a gridded digital elevation model (DEM) are important input data for landslide susceptibility mapping. Elevation error in DEM can cause uncertainty in terrain attributes and further influence landslide susceptibility mapping. However little research has concerned about this issue. This paper studies this issue by a Monte Carlo simulation method for an expert knowledge-based approach to landslide susceptibility mapping and the logistic regression model, which are chosen as representatives of two main types of current landslide susceptibility mapping models, i.e. the expert- knowledge-based models and the multivariate statistical models. The study area is located in the Kaixian County of Chongqing Municipality, and belongs to the middle-upper reaches of the Yangtze River, China. The grid size of DEM is 5 m. Sequential Gaussian simulation was conducted to simulate a total of 12 DEM error fields with combinations of three error magnitudes (i.e., standard deviation values are 1, 7.5, and 15 m) and four spatial autocorrelation levels of elevation error (i.e., range values are 0, 30, 60, and 120 m). Each simulation included 100 realizations. Overall uncertainty of each landslide susceptibility mapping model associated with each simulated DEM error field was evaluated based on both a map of standard deviation of resulted landslide susceptibility from the simulation and a percentage map of classification consistency of landslide susceptibility from the simulation. The uncertainty assessment shows that the trends of the overall uncertainty of either landslide susceptibility mapping model changed with the spatial autocorrelation level of simulated DEM error are different under each simulated error magnitude. The trends of the overall uncertainty of expert knowledge-based model changed with increasing error magnitude are different under different spatial autocorrelation levels of simulated DEM error, while the overall uncertainty of logistic regression model monotonously increases as the magnitude of simulated DEM error increases. In general, the overall uncertainty of logistic regression model is more sensitive to DEM error magnitude than expert knowledge-based model.

Key words: DEM, error, landslide susceptibility mapping, Monte Carlo simulation, uncertainty