The study explores a workflow for integrating photogrammetric image blocks with LiDAR point clouds acquired via Unmanned Laser Scanning (ULS) in forestry applications. Hybrid datasets combining UAV imagery and LiDAR data are increasingly used for 3D mapping, yet discrepancies often arise due to independent orientation processes and systematic errors. Traditional solutions rely on numerous ground control points (GCPs), which can be impractical in dense forest environments. To address this, the proposed method fuses bundle adjustment and Iterative Closest Point (ICP) algorithms in a joint optimization process, aligning multispectral images with ULS point clouds without additional observations or GCPs. The workflow includes a GPU-accelerated filtering step to extract representative canopy points, reducing computational load and improving correspondence selection. Implemented using Python and C++ extensions, the system leverages the Ceres Solver for non-linear optimization, minimizing reprojection, GNSS, IMU, and point-to-cloud errors iteratively. Tests conducted in Żednia Forest District, Poland, during leaf-on and leaf-off seasons demonstrated significant improvements in alignment accuracy: average horizontal errors decreased by over 50%, and maximum offsets were reduced by more than 1 meter. These results confirm that the proposed hybrid adjustment substantially enhances geometric consistency between photogrammetric and LiDAR datasets, offering a cost-effective solution for forestry mapping and monitoring.