## 3 Data and Methodology  
### Spatial Database  
Landslide modeling is mainly based on the assumption that landslides will more likely to occur in the future under similar conditions that led to past landslides [2]. Therefore, the landslide inventory plays a fundamental role in landslide susceptibility assessment. The field surveys from 2014 to 2016 by the Department of Nature Resources of Yunnan Province are the major sources of landslide inventory data used in this study. A total of 388 landslide locations were identified in the study area, and were mainly distributed in the middle area of Lushui County, especially along the Nujiang River Grand Canyon and important traffic lines. Those recorded landslides mainly belonged to soil slopes, varying from 80 m\({}^{2}\) to 600,000 m\({}^{2}\) in plane area. Overall, 66.8% had a slide depth \(<\)10 m and 94.04% were classified as being small to medium-sized (\(<\)100 \(\times\) 104 m\({}^{3}\)). Since most of the landslides were small enough against the working scale, their location information could be represented as point features [48,49]. Subsequently, the landslide inventory map of the study area was generated by plotting the centroids of those landslides (Figure 1).  
Landslide occurrence is the result of the negative synergy of various topographical, geological, and hydrological as well as anthropogenic features [50]. The preparation of conditioning factors used for landslide modeling is a crucial step. According to the condition of the study area and the mechanism of landslide occurrence, twelve landslide-related factors were considered firstly: elevation, slope angle, slope aspect, plan curvature, profile curvature, ERG, land use, distance to roads, distance to rivers, distance to faults, annual rainfall, and normalized difference vegetation index (NDVI). Those factors were collected from various available sources. Specifically, the digital elevation models (DEMs) were  
Figure 2: Geological map of the study area.  
derived from a digital contour map (at the scale of 1:10,000) with an equidistance of 5 m. We identified five geomorphic factors including elevation, slope angle, slope aspect, plan curvature, and profile curvature, with a resolution of 100 m. The road network, river network, and land use were acquired from the data of the Second Detailed Land Investigation Nationwide of China (on a county scale) that was provided by the Department of Nature Resources of Yunnan Province. The distance to roads and distance to rivers were then generated by buffering the road network and river network, respectively. Geological factors played an indispensable part in landslide susceptibility analysis [51]. The ERG and distance to faults were considered as important geological factors which were collected from a regional geological map at the scale of 1:100,000. The types of ERGs reflect different physical and mechanical properties of rocks that are related to the failure mechanism. The integrity of rock and slope is usually influenced by distance to the faults. The NDVI map used in this study was constructed based on the Landsat 8 OLI imagery with a resolution of 30 m ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn))). Rainfall is generally regarded as a causative factor for the slope movement [52]. In this work, the Yunnan Digital Village Website ([[http://www.ynszxc.net](http://www.ynszxc.net)]([http://www.ynszxc.net](http://www.ynszxc.net))) was used to collect the annual average rainfall for each village in Lushui County, from which the annual rainfall map of the whole study area was then produced through the Kriging interpolation. Finally, all landslide conditioning factor layers were resampled into 310,604 raster cells with the same pixel size of 100 m \(\times\) 100 m to unify the data scale for further analysis (Figure 3).