This research work focuses on automated coregistering/aligning multiple point cloud strips of forested areas acquired from UAV LiDAR lack of artificial ground control. Thanks to GPS and IMU system mounted on the UAV, the Z-axes of UAV LiDAR data are assumed to be aligned to world z-axis and there is no variation in scale. The proposed method is based on detecting the tree positions from point cloud segmentation and calculating the stem centers. For each stem position, the distances to all surrounding trees are computed and then a similarity approach is applied to match the corresponding pairs. After graph matching, a rigid transformation is performed between the two coordinate systems to determine the horizontal and vertical shifts along with the rotation around z axis. A heuristic algorithm is performed to determine the optimal tie point subset that warrant the minimum deviation between the matched pairs is obtained. The concept is validated by an experimental analysis using ULS data of sample plots in subtropical forest plantations. The regularity of tree distribution in the planted forest plantations posed even a great challenge for the developed approach. For the sample plot, the rigid transformation can be finally performed using 13 corresponding tree pairs to achieve the alignment of two ULS strips and resulted in mean difference of 0.5m with a range from 0.25m to 0.75m.