Uncertainty due to DEM error in landslide susceptibility mapping
文献类型：SCI/SSCI论文
| 作者 | Qin C. Z. ; Bao L. L. ; Zhu A. X. ; Wang R. X. ; Hu X. M. |
| 发表日期 | 2013 |
| 关键词 | DEM error landslide susceptibility mapping error propagation Monte Carlo simulation uncertainty 3 gorges area digital elevation models logistic-regression hazard assessment yangtze-river fuzzy-logic gis propagation knowledge china |
| 英文摘要 | 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 250km(2)) and the other for model extrapolation (approximately 4600km(2)). 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 15m) for error magnitude and three range values (0, 60, and 120m) 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. |
| 出处 | International Journal of Geographical Information Science |
| 卷 | 27 |
| 期 | 7 |
| 页 | 1364-1380 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 1365-8816 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/30306]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Qin C. Z.,Bao L. L.,Zhu A. X.,et al. Uncertainty due to DEM error in landslide susceptibility mapping. 2013. |
入库方式： OAI收割
来源：地理科学与资源研究所
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