Identification of dynamic objects in sequential terrestrial Light Detection And Ranging (LiDAR) point cloud data is important for analyzing activity and usage of coastal environments. This research focuses on identifying non-geomorphological dynamic objects, such as people and bulldozers, in sequential terrestrial LiDAR point cloud data acquired by a permanent laser scanner installed in Noordwijk, the Netherlands. A workflow is proposed and demonstrated, consisting of three main components: ground and non-ground separation using a Cloth Simulation Filter, dynamic point detection through Cloud-to-Cloud comparison, and clustering of individual dynamic objects using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Parameter tuning is performed by evaluating all possible configurations
within a defined range and validated against manually identified dynamic objects. For a week-long dataset, the error in the number of detected large dynamic objects is relatively low at 6.9%, whereas the error for small dynamic objects is higher at 23.0%, attributed to their proximity to the ground and to each other. On a point-to-point basis, the optimized configuration results in an average error of 13.5% for large dynamic objects and 33.7% for small dynamic objects with respect to a reference set. A sensitivity analysis using a Monte Carlo simulation with normally distributed parameter variations around the tuned values demonstrates robustness to moderate parameter fluctuations, particularly for larger dynamic objects, which show a standard deviation of 0.07 detected objects, while smaller objects show greater variability with a standard deviation of 0.34 detected objects. The application of the Cloth Simulation Filter adds value by excluding geomorphological processes, contributing to a reduction in error rate of approximately 95% for large objects and 62% for small objects. Overall, the presented workflow offers a robust automated approach for detecting dynamic objects on sandy beaches in LiDAR point cloud data, with demonstrated potential for scalable, long-term monitoring.