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.

Coastal environments are vital for ecological stability, human activity, and climate resilience, yet they are increasingly affected by anthropogenic activities. Particularly, construction machinery such as bulldozers plays a critical role in altering the beach environment through beach nourishment purposes and coastal engineering, but their presence and movement are rarely tracked systematically. This research addresses that gap by developing a method to automatically identify bulldozers and other large dynamic objects from multiple epochs of permanent terrestrial laser scanning (TLS) point cloud data using multidimensional feature analysis and supervised machine learning.

This study presents a robust framework for automatically classifying bulldozers and other large dynamic objects from terrestrial laser scanning (TLS) point clouds, achieving a test accuracy of 92.5% with a k-Nearest Neighbours (k-NN) classifier. This framework integrates both 3D point cloud descriptors with 2D projection-based features. Starting from raw TLS data, object clusters are extracted and described using geometric features. For each object, 3D features including linearity, planarity, and verticality are computed; 2D raster-based descriptors, including footprint spread and height variation, are computed from XY and XZ plane projections. These features are aggregated to build an object-level dataset. At the same time, global descriptors of the horizontal and vertical extents are computed to capture the dimension information. Together, these features are then standardised at the object level to train supervised classifiers capable of distinguishing four object classes: 'large bulldozer', 'other bulldozer', 'tractor-trailer', and 'other'.

The evaluation reveals that the instance-based k-NN model consistently outperformed a Support Vector Machine (SVM), which proves less robust to class imbalance and dataset shift due to its reliance on a fixed global decision boundary. Feature importance analysis confirms that a combination of 3D descriptors capturing structural complexity (e.g., eigenentropy, omnivariance) and 2D projection features quantifying vertical profiles is the most discriminative. The framework's real-world applicability is validated on an independently and automatically segmented dataset, where the k-NN model maintains a high overall accuracy of 90.9%. This validation also highlights the classification performance's sensitivity to segmentation quality, as incomplete object data from partial occlusion predictably decreases accuracy

In conclusion, this study establishes a practical and reliable feature-based methodology for monitoring anthropogenic activity in dynamic coastal zones. Future work should focus on enhancing this framework by expanding the training dataset to improve robustness, implementing adaptive binning for 2D feature extraction to better handle scale variance, and integrating more advanced segmentation algorithms to enable a fully automated monitoring pipeline. Such improvements will further solidify the method's utility for long-term environmental monitoring and data-driven coastal management.

In 2003, the Hondsbossche and Pettemer Zeewering, located from Camperduin to Petten, was classified to not be within safety margins of the Dutch Coast. In order to protect the Dutch Coast from sea level rise, the Hondsbossche dunes were created in 2015. This project included the building of an artificial lagoon for recreational purposes. This lagoon is protected from the sea by three sand dunes. Without these dunes, the lagoon will not remain to exist. The aim of this study is to detect changes of the shape and the height of the dunes protecting the lagoon at Camperduin from the sea using annual lidar data.
JARKUS data is obtained from airborne laser scanning for the Dutch coastal areas which is done yearly between January and March. Eight datasets of annual lidar data which include the dunes are used for this study. The datasets consists of point clouds for the years 2016 to 2023.
To obtain information about the changes occurring, several methods are used. The workflow recommended is the C2M method for 3D changes. These changes can be clustered by K-means clustering. 2D changes can be observed using cross sections, contour lines and the volume changes.
The seaward side of the dunes decreases in height due to marine erosion such as storms. Around 4.5 meter erosion on some locations has been found due to large storms in 2022. The lagoon side of the dunes increases in height by aeolian transport. The rate of dune growth is found to be 0.5 meters per year.
The middle dune experiences more erosion than deposition and the volume decreases. The erosion is caused by less vegetation and human impacts. If the trend from before the storm in 2022 continues, the dune will shrink and disappear if no additional maintenance is done. The other two dunes do not experience big volume losses and are classified as stable.