The high-altitude & dense-vegetation landslide is difficult to investigate and lack of data, its appearance and subsurface information also difficult to get. Therefore it is difficult to identify and threat the road greatly. A case study of the Xiaojiapo landslide was presented. Detailed on-site geological surveys were performed, and drone-based oblique and orthophoto imaging techniques were employed to get the landslide's characteristics. Historical deformation of the landslide was analyzed using satellite image, airborne LiDAR was used to collect point cloud and digital imagery data of the landslide surface. A digital elevation model and centimeter-level three-dimensional model of the landslide surface were created following vegetation removal to assess the surface characteristics. The combined investigations reveal that the Xiaojiapo landslide is situated at an elevation of 3 030 m with a vegetation coverage reaching up to 90%. It is a typical landslide with high-altitude and dense vegetation. The key internal factors contributing to the landslide including the unique alpine valley topography, the slope structure with advantageous free faces, and easily erodible geological layers. Precipitation induces internal water infiltration, which reduces the strength of the rock and soil mass. Additionally, freeze-thaw cycles further diminish slope strength, while earthquakes and road constructions disturb the internal structure, weakening it further. These combined internal and external factors drive the landslide deformation. This study offers technical insights for the landslide identifying, early-warning, and mitigation of road landslides in high-altitude regions with dense vegetation.