Understanding vegetation dynamics in three-dimensional, high-temporal resolution is essential for advancing ecological research and sustainable forest management. This study introduces a novel methodology for tracking branch kinematics in trees using hyper-temporal terrestrial laser scanning (TLS) data. Focusing on a solitary pedunculate oak (Quercus robur) over a one-year
period, we employed a geometric feature detection algorithm combined with quantitative structure modeling (QSM) to identify and track distinctive point cloud sections on first- and second-order branches. By leveraging an iterative closest point (ICP) alignment process, branch kinematics were analyzed across multiple epochs, yielding detailed three-dimensional movement trajectories. The results demonstrate that branch movements exhibit screw-shaped patterns. Temporal resolution analysis revealed that a one-week recording interval is sufficient for our study subject to reliably capture kinematic dynamics, whereas longer intervals (e.g., three weeks) result in significant deviations from actual trajectories. The proposed method proved robust against partial occlusions from leaf growth but struggled under extensive occlusions. This research highlights the potential of hyper-temporal TLS for non-contact, high-resolution monitoring of tree canopy dynamics and provides a foundational approach for future studies aimed at modeling vegetation movement and structural changes over time.