We present the sensor concept and first performance and accuracy assessment results of a novel lightweight topo-bathymetric laser scanner designed for integration on Unmanned Aerial Vehicles (UAVs), light aircraft, and helicopters. The instrument is particularly well suited for capturing river bathymetry in high spatial resolution as a consequence of (i) the low nominal flying altitude of 50–150 m above ground level resulting in a laser footprint diameter on the ground of typically 10–30 cm and (ii) the high pulse repetition rate of up to 200 kHz yielding a point density on the ground of approximately 20–50 points/m². The instrument features online waveform processing and additionally stores the full waveform within the entire range gate for waveform analysis in post-processing. The sensor was tested in a real-world environment by acquiring data from two freshwater ponds and a 500 m section of the pre-Alpine Pielach River (Lower Austria). The captured underwater points featured a maximum penetration of two times the Secchi depth. On dry land, the 3D point clouds exhibited (i) a measurement noise in the range of 1–3 mm; (ii) a fitting precision of redundantly captured flight strips of 1 cm; and (iii) an absolute accuracy of 2–3 cm compared to terrestrially surveyed checkerboard targets. A comparison of the refraction corrected LiDAR point cloud with independent underwater checkpoints exhibited a maximum deviation of 7.8 cm and revealed a systematic depth-dependent error when using a refraction coefficient of n = 1.36 for time-of-flight correction. The bias is attributed to multi-path effects in the turbid water column (Secchi depth: 1.1 m) caused by forward scattering of the laser signal at suspended particles. Due to the high spatial resolution, good depth performance, and accuracy, the sensor shows a high potential for applications in hydrology, fluvial morphology, and hydraulic engineering, including flood simulation, sediment transport modeling, and habitat mapping.

In contrast to topographic laser scanning, laser hydrography must take into account the presence of two media. A pulsed laser beam, which enters the water from the air at an oblique angle, is refracted at the air–water boundary in the direction of the plumb line. This change in the direction described by Snellius’ law is caused by a slower speed of the light wave in the water, i.e., the phase velocity. Light scattering caused by turbidity gives rise to further deviations from the straight path. Together, the slower speed and the turbidity-induced path extension cause a longer pulse round trip time in the water than in the air. For an accurate measurement, it is important to correct this propagation time extension. It is a common practice to assume the phase velocity as the velocity for the laser pulses in water. In a dispersive medium, however, the phase velocity is only an approximation of the velocity of a pulse. In media with chromatic dispersion, the pulses propagate with a different velocity, i.e., the group velocity. In water, using the group velocity instead of the phase velocity reduces the range dependent bias of the depth measurement at a laser wavelength of 532 nm by more than 1.5%. We present an easy to perform an experiment, which shows that the group velocity differs so much from the phase velocity that this difference should be taken into account. We further discuss the use of group velocity to explain the depth bias using examples from the literature.

A well-known technique to enhance the signal to noise ratio (SNR) of repetitive signals is to average them. The coherent parts of the signal add up constructively while the incoherent parts are averaged out. The prerequisite is that the signals are acquired under conditions of high repeatability, i.e., the signals must be sufficiently similar. In the present technical note, we describe an efficient method for maintaining signal similarity by ensuring spatial and temporal proximity of laser waveform signals obtained by a sensor operated from an airborne platform. The method makes use of a few auxiliary parameters such as laser pulse repetition rate, mirror rotation rate, platform altitude and flight speed. The method can be extended to be operated in real time.

A tool for Total Propagated Uncertainty (TPU) estimation is presented, as implemented feature of a bathymetric LiDAR processing software. The algorithm of the TPU Estimator is based on an approach alternative to existing methods. The concept of the novel approach is described, compared with and validated against an established tool. Two examples from different coastal surveys document the application of TPU estimation and allow to observe systematic tendencies of the results. We discuss potential for further improve-ment and consider how to embed TPU estimation into a user interface to generate expressive deliverables as part of hydrographic survey quality verification. Finally, we give an outlook for possible use of LiDAR TPU estimation apart from a-posteriori quality control and outline further development projects.

A well known technique to enhance the signal to noise ratio (SNR) of repetitive signals is to average them. The coherent parts of the signal add up constructively while the incoherent parts are averaged out. The prerequisite is that the signals are acquired under conditions of high repeatability, i.e.\ the signals must be sufficiently similar. In the present technical note we describe an efficient method for maintaining signal similarity by ensuring spatial and temporal proximity of laser waveform signals obtained by a sensor operated from an airborne platform. The method makes use of a few auxiliary parameters such as laser pulse repetition rate, mirror rotation rate, platform altitude, and flight speed. The method can be extended to be operated in real time.

In order to provide proof of consistent quality, survey systems, acquisition methods, and procedures for analyzing the resulting data are subject to regular validation. One method of validation is punctual comparison with data for comparison that entitles as reference data. However, as the conditions at the time of data acquisition critically impact each survey, dedicated parallel surveys employing different methods. This can provide insight into discrepancies and relative consistencies, especially for research purposes. In the literature, multiple techniques and sensors have been presented for the acquisition of underwater reference data, such as multi-beam echo sounders, single-beam echo sounders, and different types of pole measurements. Furthermore, bathymetric LiDAR can be deployed from UAVs, helicopters, and aircraft, each entailing specific data resolution and accuracy. Therefore, this study presents four different bathymetric LiDAR datasets (one UAV, two helicopter and one airplane-based), where for each dataset a different type of reference acquisition approach is used appropriate for the individual water bodies (river, ponds, coastal waters). The results of this comparison display the overall alignment of bathymetric LiDAR and reference data with the highest accuracies for UAV data and pole reference measurements. There, the mean normal distance between the LiDAR data and the reference is 0 cm ± 2 cm standard deviation. The highest difference was seen for the Baltic sea dataset, where airplane-based data and single-beam echo sounder reference were used. In this dataset, the mean normal distance between the LiDAR data and reference is −5 cm ± 10 cm standard deviation. In conclusion, the analyzed bathymetric LiDAR datasets show strong consistency with their respective reference measurements, with observed variations primarily influenced by environmental conditions and system configurations.