## 2 Study Area and Data Used
### Data Used  
A landslide inventory map is necessary for analyzing landslide susceptibility. To ensure the accuracy of the landslide inventory map, new methods based on satellite and ground-based remote sensing have been developed to enhance the reliability of these maps. In this study, a comprehensive landslide inventory map was created by integrating historical landslide information, high-resolution remote sensing imagery and extensive field observation data. In total, 114 landslide locations were identified, and their centroids are depicted in Figure 1. Additionally, 114 reasonable points were selected from the study area as a negative sample using GIS, resulting in a dataset with both landslide and non-landslide points. All these landslide points and non-landslide points were divided into two subsets, where 70% of the points were randomly selected for model training and the remaining 30% were used for model validation. The locations of all points in the dataset are shown in Figure 2.  
The selection of the influencing factors is crucial for evaluating landslide susceptibility [30]. The selected factors directly affect the competitiveness of the model. However, there is no general standard or guideline on how to select appropriate control variables [31]. In this study, considering previous research, and the geological and environmental charac  
Figure 2: Map of the study area and distribution of landslide points. teristics, 12 landslide conditioning factors were selected to assess the landslide susceptibility. These landslide conditioning factors can be divided into three categories. The first category reflects the geological and environmental conditions, including the normalized difference vegetation index (NDVI) and the distance to rivers. The second category reflects the topographic and geomorphic conditions, including the elevation, slope aspect, slope angle, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index, terrain relief and surface cutting depth. The third category reflects human activity, namely the distance to -roads. Each landslide conditioning factor was processed from the available data, and the final output was a raster image with a resolution of 30 m \(\times\) 30 m. All the landslide conditioning factors used in this study are presented in Figure 3. The raw remote sensing images were subjected to radiometric calibration, atmospheric correction and fusion using ENVI 5.3 software to generate usable images. The calculation of the TWI, SPI, surface cutting depth and terrain relief involved the utilization of the raster calculator. Moreover, the distances from roads and water were derived using the buffer function. Other factors were acquired directly through the respective tools provided by ArcGIS. Slope aspect is a critical factor that can significantly impact a slope's stability due to its influence on solar radiation and exposure to moisture. To accurately represent the actual terrain conditions, slope aspect was derived from a digital elevation model (DEM) and categorized into nine levels at 45-degree intervals. If the input raster data represented a flat area, it was assigned to the flat level. Elevation has been identified as one of the primary factors affecting landslides. Higher elevations generally correspond to steeper slopes and less vegetation cover, leading to higher rates of erosion and greater susceptibility to landslides [32; 33]. Elevation was classified into five categories 695-941 m, 941-1193 m, 1193-1416 m, 1416-1664 m and 1664-2685 m.  
Figure 3: _Cont._  
Figure 3: All evaluation factors: (**a**) elevation; (**b**) slope; (**c**) slope aspect; (**d**) TWI; (**e**) distance to rivers; (**f**) surface cutting depth; (**g**) plan curvature; (**h**) terrain relief; (**i**) distance to roads; (**j**) SPI; (**k**) NDVI; (**f**) terrain roughness index. Slope is a critical factor affecting the occurrence and distribution of landslides.