Fig 1.
Flowchart shows overall methodology adopted for this study.
Fig 2.
False color composite 3D view of the study area prepared from ALOS image.
Fig 3.
(a) Landslide inventory map for the study area randomly divided into two groups overlaid the shaded relief (10 m DEM): training dataset and validation samples: (b) enlarged view of boxed area in (a) overlaid on 2005 aerial photographs provided by the Midori Niigata and Sado city acquired in 2005.
Fig 4.
Histogram showing the distribution of landslide sizes.
Table 1.
List of data sources used in the study.
Fig 5.
Landslide causative factors: a) elevation, b) slope angle, c) slope aspect, d) total curvature, e) profile curvature, f) plan curvature, g) CTI, h) SPI, i) drainage density (m-1), (j) distance from drainage networks, k) lithology, l) density of geological boundaries, m) distance to geological boundaries, n) distance to faults, and o) NDVI.
Table 2.
CF weights classification according to the range of CF values.
Fig 6.
Correlations between landslide frequency and the causative factors.
Table 3.
Spatial relationship between the causative factors and landslide occurrence by the CF method and SI method.
Fig 7.
LSM maps generated by the SI method using: a) six factors, and b) fifteen factors.
The maps (c) and (d) are enlarged views of the LSM maps (red color boundary shown in (a) and (b)).
Fig 8.
Comparison of landslide susceptibility class obtained from the SI model.
Table 4.
The classes used for susceptibility maps.
Table 5.
Coefficients, statistics of the factors (S.E.-standard error, VIF- variance inflation factor) and the multi-collinearity diagnosis indexes for variables used in the logistic regression equation.
Fig 9.
LSM maps generated by the LR model using: a) six factors, and b) fifteen factors.
The maps (c) and (d) are enlarged views of the LSM maps (red color boundary shown in (a) and (b)).
Fig 10.
Comparison of landslide susceptibility class obtained from the LR model.
Fig 11.
Area under curve (AUC) represents: a) success rate, and b) prediction rate using SI method.
Fig 12.
Area under curve (AUC) represents: a) success rate, and b) prediction rate using LR model.