Recent advances in photogrammetry and remote sensing have highlighted the advantages of three-dimensional point cloud data for accurately reconstructing forest and agricultural environments. LiDAR (Light Detection and Ranging) systems represent the state of the art for acquiring 3D data, offering high geometric precision and adaptability to various platforms. Compared to traditional mapping methods, LiDAR enables greater spatial coverage and efficiency, particularly in large-scale applications. However, automatic registration of point clouds in complex and irregular environments, such as forests, remains a significant challenge due to occlusions, repetitive patterns, and low overlap between scans. This paper presents a coarse-to-fine registration approach designed explicitly for tree-dense environments. The method begins by processing each of the point clouds (Model and Scene), acquired from two stations, in order to generate the CHM (Canopy Height Model) for both point clouds, followed by slicing each of the trunks at breast height. Subsequently, the relaxation labeling algorithm is applied to match tree centroids to estimate an initial 2D transformation between the available scans based on probabilistic similarity and spatial relationships. This initial alignment is then refined using the Iterative Closest Point (ICP) algorithm to compute the final 3D transformation. Experiments using terrestrial laser scans of low overlap point clouds (< 30%) yielded a root mean square error (RMSE) of approximately 3 cm, without artificial targets. The camera-ready version will provide a more detailed explanation of this work.