Study in landslide hazard zonation based on factor weighting-rating in Wan County, Three Gorges Reservoir area

Zhengjun Liu; Jian Wang; Changyan Chi

doi:10.1117/12.816202

24 November 2008 Study in landslide hazard zonation based on factor weighting-rating in Wan County, Three Gorges Reservoir area

Zhengjun Liu, Jian Wang, Changyan Chi

Author Affiliations +

Proceedings Volume 7123, Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China; 712314 (2008) https://doi.org/10.1117/12.816202
Event: Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China, 2007, Beijing, China

Abstract

Multi-source earth observation data is highly desirable in current landslide hazard prediction modeling, as well as Landslide Hazard Zonation(LHZ) is a very important content of landslide hazard prediction modeling. In this paper, take Wan County for instance, we investigate the potentials of derivation from multi-source data sets to study landslide hazard zonation based on the ordinal scale relative weighting-rating technique. LHZ is then performed with chosen factor layers including: buffer map of thrusts, lithology, slope angle and relative relief calculated from DEM, NDVI, buffer map of drainage and lineaments extracted from the digital satellite imagery(TM). Then Landslide Hazard Index (LHI) value is calculated and landslide hazard zonation is decided by slicing LHI histogram. The statistics results demonstrate that high stable slope zone, stable slope zone, quasi-stable slope zone, relatively unstable slope zone, unstable slope zone and defended slope zone account for 2.20%, 14.02%, 39.88%, 28.27%, 12.17% and 3.47% respectively. Then, GPS deformation control points on the landslide bodies are used to verify the validity of the LHZ technique.

Citation Download Citation

Zhengjun Liu, Jian Wang, and Changyan Chi "Study in landslide hazard zonation based on factor weighting-rating in Wan County, Three Gorges Reservoir area", Proc. SPIE 7123, Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China, 712314 (24 November 2008); https://doi.org/10.1117/12.816202

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