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Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China |
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| Authors: | Weitao Chen Xianju Li Yanxin Wang Shengwei Liu |
| Institution: | 1. College of Computer Science and Department for Geodynamics and Deep Space Exploration of NRSCC, China University of Geosciences, Wuhan, 430074, China 2. Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs, Beijing, 100124, China 3. School of Information Engineering, China University of Geosciences, Wuhan, 430074, China 4. State Key Laboratory of Biogeology and Environmental Geology and School of Environmental Studies, China, University of Geosciences, Wuhan, 430074, China 5. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing, 100083, China |
| Abstract: | The objective of this study is to map landslide susceptibility in Zigui segment of the Yangtze Three Gorges area that is known as one of the most landslide-prone areas in China by using data from light detection and ranging (LiDAR) and digital mapping camera (DMC). The likelihood ratio (LR) and logistic regression model (LRM) were used in this study. The work is divided into three phases. The first phase consists of data processing and analysis. In this phase, LiDAR and DMC data and geological maps were processed, and the landslide-controlling factors were derived such as landslide density, digital elevation model (DEM), slope angle, aspect, lithology, land use and distance from drainage. Among these, the landslide inventories, land use and drainage were constructed with both LiDAR and DMC data; DEM, slope angle and aspect were constructed with LiDAR data; lithology was taken from the 1:250,000 scale geological maps. The second phase is the logistic regression analysis. In this phase, the LR was applied to find the correlation between the landslide locations and the landslide-controlling factors, whereas the LRM was used to predict the occurrence of landslides based on six factors. To calculate the coefficients of LRM, 13,290,553 pixels was used, 29.5 % of the total pixels. The logical regression coefficients of landslide-controlling factors were obtained by logical regression analysis with SPSS 17.0 software. The accuracy of the LRM was 88.8 % on the whole. The third phase is landslide susceptibility mapping and verification. The mapping result was verified using the landslide location data, and 64.4 % landslide pixels distributed in “extremely high” zone and “high” zone; in addition, verification was performed using a success rate curve. The verification result show clearly that landslide susceptibility zones were in close agreement with actual landslide areas in the field. It is also shown that the factors that were applied in this study are appropriate; lithology, elevation and distance from drainage are primary factors for the landslide susceptibility mapping in the area, while slope angle, aspect and land use are secondary. |
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