Abstract: The West-East Power Transmission Project is a major initiative to safeguard China's power security and optimize energy resource allocation. The West Guizhou transmission corridor, as part of this project, is one of the areas most severely affected by landslides and collapses. InSAR technology is suitable for large-scale landslide identification; however, geometric distortions in SAR imagery caused by complex mountainous terrain can easily lead to missed or false detections. Therefore, visibility analysis is essential to ensure reliable landslide identification. This study utilizes open-access Sentinel-1A ascending and descending orbit data and uses true incidence angles to conduct a quantitative SAR visibility analysis of the study area. SBAS-InSAR-derived deformation results are integrated with field investigations to identify potential landslides along the west Guizhou power transmission corridor. The research results show that the incidence angles of ascending and descending track imagery gradually increase from near to far range, with increments of 11.5°and 12.2°, respectively, significantly affecting SAR visibility. When 90.5% of the mountainous terrain has slope angles smaller than the incidence angle, the proportions of geometric distortion zones (layover, shadow, and foreshortening) in single-track observations reach 46.8% for ascending and 51.2% for descending images. In contract, combined ascending-descending orbit observations significantly reduce the geometric distortion area significantly to 6.1%, greatly improving visibility in foreshortened regions. Two potential landslides/collapses were identified through Line-of-Sight (LOS) deformation analysis and field surveys. The ground-range incidence angle algorithm proposed in this paper shows high consistency with the pixel-column-based approach, with a maximum deviation of only 3.3%. Additionally, deformation velocity maps and cumulative displacement profiles were used to distinguish collapse and accumulation zones, providing technical support for in-depth analysis of potential landslides.