# Study on Temporal and Spatial Distribution of Landslides in the Upper Reaches of the Yellow River
## 3 Temporal and Spatial Distribution of Landslides in the Upper Reaches of the Yellow River
### Material and Methods
The specific process of remote sensing interpretation and identification of landslide disasters in the study area is shown in Figure 3.  
During remote sensing imaging, various factors lead to certain phenomena, such as geometric deformation and radiation changes in remote sensing images, which are unfavorable for the analysis of remote sensing images. Therefore, the preprocessing of remote sensing images is a crucial step before conducting image analysis. The process of image preprocessing mainly includes orthorectification, geometric registration, image mosaicking, image fusion, and image clipping. Since the study area is a high and steep canyon region that exceeds the coverage of a single remote sensing image, the precision requirement for image preprocessing is very high. We utilize professional remote sensing software ENVI and auxiliary digital elevation model (DEM) data to preprocess the images and obtain the experimental images. The remote sensing identification of landslides is a technical method that utilizes remote sensing images, combined with human-computer interaction and visual interpretation, to obtain relevant information about landslides. Its principle is based on the differences in tone, shape, shadow, texture, and pattern between landslide bodies and their background geological bodies. These differences manifest as specific combinations of tone, texture, and geometric shapes on remote sensing images, known as direct interpretation signs for landslide identification [21]. Based on field investigation data, representative landslides in the study area were selected for establishing the interpretation markers. Subsequently, the remote sensing characteristics of landslides in high and steep canyon areas were summarized based on remote sensing images. By comparing the field investigation data, typical landslides were selected to analyze their development characteristics. Most of the landslides are water-related and are distributed along both banks of the river. Ultimately, the landslide disaster interpretation markers in the study area were determined to have spectral features and shape characteristics. Spectral features are primarily manifested as: relatively developed vegetation on the surface of the landslide body, with scattered small collapses; and the base of the landslide is the reservoir bank slope. The shape characteristics of the land-slide development can be observed through the distinct gullies on both sides of the landslide, mainly manifesting as rectangle, triangle, side-by-side shape, and so on. Landslides are represented as individual image objects on remote sensing images, and their classification utilizes the spectral and geometric characteristics of these objects. Therefore, the interpretation of landslides in remote sensing adopts the object-oriented  
Figure 3: Remote sensing identification process of landslide disasters in the study area. image classification approach. Object-oriented image classification mainly consists of two parts: image object construction and object classification. The construction of landslide images utilizes the multi-scale segmentation algorithm in e-Cognition software to perform segmentation experiments on the images of the study area. Before conducting the segmentation experiments, the band combination of the images can be performed, which can comprehensively select the characteristics of each band to improve the interpretability of ground features and make the interpretation results more scientific and reasonable [22]. In multiscale segmentation algorithms, it is necessary to consider two factors that affect image heterogeneity: the spectral factor and the shape factor, which in turn include smoothness heterogeneity and compactness heterogeneity. Due to the time-consuming nature of multi-scale segmentation algorithms, representative areas of landslides in the study area were selected. Meanwhile, in order to determine the optimal segmentation scale while ensuring high homogeneity of landslide objects.