Uncertainty due to DEM error in landslide susceptibility mapping |
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| Authors: | Cheng-Zhi Qin Li-Li Bao Rong-Xun Wang Xue-Mei Hu |
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| Institution: | 1. State Key Laboratory of Resources and Environmental Information System , Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences , Anwai , Beijing , PR China;2. State Key Laboratory of Resources and Environmental Information System , Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences , Anwai , Beijing , PR China;3. Department of Geography , University of Wisconsin–Madison , Madison , WI , USA;4. Department of Geography , University of Wisconsin–Madison , Madison , WI , USA;5. College of Resources and Environment , University of Chinese Academy of Sciences , Beijing , PR China |
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| Abstract: | Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km2) and the other for model extrapolation (approximately 4600 km2). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression. |
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| Keywords: | DEM error landslide susceptibility mapping error propagation Monte Carlo simulation uncertainty |
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