•Despite scanning differences, canopy profiles remain regular even in dense areas.•Using canopy profiles as primitives, transforming feature norms for the forest.•A new, high-resolution, 256-dimensional feature descriptor for forest scenes.•A fresh approach to registering UAV- and terrestrial-based forest point clouds. Within this study, we present a pioneering cross-platform point cloud registration (PCR) framework aimed at the automated alignment of UAV and terrestrial forest LiDAR point clouds. This framework leverages canopy profile skyline (CPS) descriptors and feature orientation information to support registration. Given the inherent irregular and natural distribution of point clouds derived from crown environments, conventional registration techniques that operate on geometric primitives such as points, lines, and planes are susceptible to inadequacies. In this article, we first analyze the high resolution and robustness of a skyline formed from successive tree canopy profiles as feature elements in forest data. Subsequently, we highlight the intriguing property that, whether they are UAV-based or terrestrial-based scanning data, the tree CPS obtained from the same location exhibits remarkably similar shapes and trends. Therefore, we propose a novel feature descriptor that encompasses M dimensions across 8 directions, with the aim of establishing reliable feature correspondences, subsequently leveraging directional constraints and employing one-shot estimation RANSAC to achieve automated UAV and terrestrial-based forest point cloud registration. Finally, the standard ICP algorithm combined with the constraint strategy based on tree trunk geometric morphological features will be utilized to refine the registration results. We conducted tests using five datasets with heterogeneous tree species and structures, and the results demonstrate that the proposed approach achieves SOTA performance.