Abstract: [Purposes] To address the issues of insufficient displacement inversion accuracy in landslide monitoring using multi-temporal UAV LiDAR point clouds under conditions of fine registration requirements and lack of stable control points in mountainous areas,a 3D deformation monitoring method for landslides based on bidirectional nearest-neighbor(BNN) matching and local plane fitting is proposed. [Methods] Firstly,multi-temporal ground point clouds are constructed through flight strip adjustment,noise removal,and ground point filtering.Then,strong-matching point pairs are selected via bidirectional nearest-neighbor matching,and horizontal and vertical displacements are decomposed using local plane fitting.Finally,weighted principal component analysis is applied to invert the 3D displacement field and determine the primary sliding direction. [Findings] Experiments conducted on the Tudi Ridge landslide in the Three Gorges Reservoir area demonstrate that,without external control points,the method can stably detect horizontal displacement changes as small as 2~3 cm,with an absolute error of 4~6 mm compared to GNSS measurements,and the displacement direction aligns well with the main sliding direction of the landslide. [Conclusions] Compared with DEM differencing,C2C,and M3C2 methods,this approach effectively reduces the accumulation of registration errors in complex terrain,showing good adaptability and reliability in scenarios lacking control points,thus providing technical support for landslide deformation monitoring and disaster early warning in vegetated areas．

Key words: UAV-borne LiDAR, landslide deformation monitoring, bidirectional nearest-neighbor matching, local plane fitting, control-point-free monitoring, displacement field inversion