Static and kinematic point clouds using a single terrestrial laser scanning system for forest structure characterization

Terrestrial laser scanning (TLS) enables the detailed three-dimensional characterization of forest stands, capturing structural elements from stems to individual branches in an objective and reproducible way. This high-resolution structural information is valuable for a wide range of applications, including precision forestry, forest management, and ecological and biodiversity monitoring. In addition, TLS-derived forest structure can serve as reference data for the calibration of area-wide remote sensing products, such as airborne laser scanning (ALS) point clouds, and for identifying the structural contributions to synthetic aperture radar (SAR) backscatter signals. Recent technological developments, particularly devices becoming lighter, easier to operate, and capable of functioning in both static and kinematic modes, have considerably broadened the applicability of TLS in forest environments. While multi-scan static TLS acquisitions still represent the gold standard in terms of geometric accuracy, kinematic laser scanning setups are increasingly able to provide point clouds suitable for many forest-related applications and offer advantages with respect to acquisition time and field logistics. In addition, improved usability and increasingly automated processing workflows have expanded the user base of TLS beyond remote sensing and surveying experts. As a result, TLS is now frequently integrated into applied forestry as well as inter- and transdisciplinary forestry research and academic education and training. Within the Earth Sensing Summer School 2025 in San Vito di Cadore (Italy), a student project group conducted forest point cloud acquisitions using multiple terrestrial laser scanning systems operated in both static and kinematic modes, complemented by UAV-based laser scanning (ULS) data. In the presented study, we show results derived from the data of this campaign, focusing on data acquired with a RIEGL VZ-600i terrestrial laser scanner operated in both static and kinematic acquisition setups. Both data acquisitions are performed with long-baseline RTK GNSS to provide absolute georeferencing, although GNSS accuracy is severely degraded within the forest. The analysis is based on a representative forest plot of approximately 2500 m², including around 150 trees. The plot is dominated by coniferous species, primarily Picea abies (Norway spruce), and is located on sloped terrain with sparse understory vegetation. We systematically compare the static and kinematic TLS acquisitions and the resulting point clouds with respect to acquisition time, data processing, point cloud completeness and occlusion effects. Furthermore, the point clouds are analyzed at the individual tree level, including semantic segmentation of individual trees and the derivation of key tree metrics. ULS data are used as a reference for the assessment of tree heights and the representation of upper canopy elements. The data acquisition was performed by students unexperienced with TLS after giving a 30 min introduction to the device and TLS in forest environments. Comparing data acquisitions, the two kinematic acquisitions took ~10 min each, the static acquisition resulted in 22 scan positions and an acquisition time of 1 h 35 min. Preliminary results of the initial data inspection indicate that the kinematic point clouds provide a more complete representation of tree tops than static point clouds.