期刊
GEOMORPHOLOGY
卷 86, 期 1-2, 页码 12-24出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.geomorph.2006.08.002
关键词
weights-of-evidence; GIS; landslide; susceptibility; hazard
A GIS-based model for the assessment of the landslide susceptibility in a selected area of the Jurassic escarpment in the Swabian A1b (SW-Germany) is described, using the weights-of-evidence method. A quantitative model applied to landslides and their causative factors was created and illustrated in susceptibility maps. While previous research work in this area concentrated on large-scale investigations, the present study was carried out at a regional level with a target scale of 1: 150,000. The method is based on the assumption that future landslides will occur under the conditions similar or equal to those of past comparable landslides of the same type. Therefore the analysis was limited to one single type of landslides where the causative factors can be assumed as stable over a period of time. Due to uncertainties in the model, mainly because of variances of the weights assigned to the causative factors, the derived probability values, representing the susceptibility for future landslides, have to be considered relative. However, potential susceptible areas can be delineated and landslide indicators can be identified from the available data set. Slopes with angles from 11 degrees to 26 degrees, composed of the Oxford limestone/marls as well as strongly argillaceous and silty colluvial material such as solifluction layers and colluvial cones, are susceptible. The main soil type of the escarpment and the other steep slopes of the Swabian A1b valleys are Rendzinas formed in solifluction layers. Rendzina profiles including rock debris and clay, which are superimposed on marl debris, were also identified as landslide indicators. These findings are in agreement with previous geomorphological studies in the same area. The methodology seems to have widespread applicability beyond this local research area, with the limitation that the knowledge of past landslides input to the model affects the absolute value of the final probability. (c) 2006 Elsevier B.V. All rights reserved.
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