HR: 1340h
AN: H43E-1663    [Abstracts]
TI: Estimation Of Landslide Processes And Slope Evolution From LiDAR-Derived DEMs
AU: * Kasai, M
EM: m-kasai@pwri.go.jp
AF: Public Works Research Institute, 1-6 Minamihara, Tsukuba, 305-0031, Japan
AU: Ikeda, M
EM: m-iked44@pwri.go.jp
AF: Public Works Research Institute, 1-6 Minamihara, Tsukuba, 305-0031, Japan
AU: Asahina, T
EM: toshihiro_asahina@pasco.co.jp
AF: Pasco Corporation, 1-1-2 Higashiyama, Meguro-ku, Tokyo, 153-0043, Japan
AU: Fujisawa, K
EM: fujisawa@pwri.go.jp
AF: Public Works Research Institute, 1-6 Minamihara, Tsukuba, 305-0031, Japan
AB: LiDAR-derived DEMs are making an increasing contribution towards landslide hazard mitigation owing to their increasing availability. They not only produce detailed contour maps and visualized images to create more accurate and complete landslide inventories, but also can be analyzed to understand the surface process of landslides with the notion that their activity relates to fine-scale surface roughness. At present, surface fabric filtering of these DEMs has been used to examine individual landslides, although landslide processes can be better understood when they are considered as a part of hillslope evolution of the area of interest. This presentation introduces the work that the eigenvalue ratio, which represents the 3-dimensional surface roughness, can be used to analyze landslide processes and their influence on hillslope evolution. A case study is presented from a 5 km² steep (average slope = 36 %) area of Kii Peninsula, Japan, where a 1-m grid LiDAR-derived DEM was used to derive eigenvalue ratios and related terrain attributes. The slopes of the v- shaped valleys are particularly landslide-prone and there is little valley margin except for occasional terraces along the river. Based on a combination of the eigenvalue ratio and field survey, the slopes of the study area were categorized into 4 principal types. Slopes with a filter value of less than 2.50 (roughest) are bedrock-dominant, while those between 2.50 and 2.75 partly represent surface collapses and loosened bedrock, and those between 2.75 and 3.00 are colluvial slopes with large rock materials. Where the value exceeds 3.00 the slopes are inactive or covered with fine colluvial materials. The results imply that more attention should be paid to landslide blocks with a filter value of between 2.50 and 2.75, e.g. by continuous monitoring, as they are at higher risk of entire slope collapse in the near future. The eigenvalue ratio tends to increase and the terrain becomes smoother uphill, indicating that slopes evolve by losing stability from the valley floor. The blocks with a value between 2.75 and 3.00 may in future gain similar slope features to those with a value between 2.50 and 2.75, although how long this transition takes is uncertain. The process has probably been driven by the river eroding the base of hillslopes and striping away earth at high flows. The results suggest that, not only can the current activity of landslides be evaluated, but also their future behavior can be estimated by applying surface fabric filters to an area of interest and considering their spatial distribution. Together with creating detailed landslide inventories, utilizing LiDAR-derived DEMs in this manner will in future contribute to the planning of landslide hazard mitigation programs.
DE: 1815 Erosion
DE: 1826 Geomorphology: hillslope (1625)
DE: 1855 Remote sensing (1640)
SC: Hydrology [H]
MN: 2007 Fall Meeting