高原高植被覆盖型滑坡调查难度大，资料不足，滑坡形态及内部信息获取难，隐蔽性高，对道路威胁大。以四川省小金县肖家坡滑坡为例，在现场详细地质调查的基础上，运用无人机倾斜及正射摄影技术识别滑坡的形态及规模；通过卫星遥感影像分析滑坡的历史变形；并利用机载LiDAR获取滑坡地表点云及数字影像数据，得到去植被后滑坡地表数字高程模型及厘米级滑坡地表三维模型，获取去植被后滑坡地表信息，从而对高原区高植被覆盖滑坡的基本特征及成因机制进行分析。结果表明：肖家坡滑坡顶部海拔3 030 m，植被覆盖度90%，为典型的高原高植被覆盖滑坡，独特的高原沟谷型地貌特征、具有良好临空面、斜坡结构简单地层易滑、雨水易渗透入坡体并形成积水区为诱发滑坡的主要内因；外部因素中长期降水使坡体内部渗水软化，岩土体强度降低，冻融循环进一步弱化其坡体强度，地震与人类工程活动对土体结构扰动一步降低坡体稳定性。在内外诱因的共同驱使下，滑坡变形逐渐加大并产生逐级滑移。研究结果可对高原区高植被覆盖型公路滑坡的预测预报及防灾减灾提供一定的技术支持。

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.