Airborne LiDAR (ALS) and terrestrial LiDAR (TLS) data integration provides complementary perspectives for acquiring detailed 3D forest information. However, challenges in registration arise due to feature instability, low overlap, and differences in cross-platform point cloud density. To address these issues, this study proposes an automatic point cloud registration method based on the consistency of the single-tree position distribution in multi-species and complex forest scenes. In this method, single-tree positions are extracted as feature points using the Stepwise Multi-Form Fitting (SMF) technique. A novel feature point matching method is proposed by constructing a polar coordinate system, which achieves fast horizontal registration. Then, the Z-axis translation is determined through the integration of Cloth Simulation Filtering (CSF) and grid-based methods. Finally, the Iterative Closest Point (ICP) algorithm is employed to perform fine registration. The experimental results demonstrate that the method achieves high registration accuracy across four forest plots of varying complexity, with root-mean-square errors of 0.0423 m, 0.0348 m, 0.0313 m, and 0.0531 m. The registration accuracy is significantly improved compared to existing methods, and the time efficiency is enhanced by an average of 90%. This method offers robust and accurate registration performance in complex and diverse forest environments.