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With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic features are considered as a basis, the DEM should be reproducible based on a combination of such features. The purpose of this study is to develop new DEM generation methods based on sparse modeling. Based on a review of the definitions of sparsity, we developed DEM generation methods based on a discrete cosine transform (DCT), DCT with elastic net, K-singular value decomposition (K-SVD), Fourier regularization, wavelet regularization, and total variation (TV) minimization, and conducted a comparative analysis. The developed methods were applied to artificially deficient DEM and ALB data, and their accuracy was evaluated. Thus, as a conclusion, we can confirm that the K-SVD method is appropriate when the percentage of deficiencies is low, and that the TV minimization method is appropriate when the percentage of deficiencies is high. Based on these results, we also developed a method integrating both methods and achieved an RMSE of 0.128 m.

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.

Accurate bathymetric data in shallow water is of increasing importance for navigation safety, coastal management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatial and temporal resolution combined with extensive repetitive coverage. Many previous empirical SDB approaches are unsuitable for precision bathymetry mapping in various scenarios, due to the assumption of homogeneous bottom over the whole region, as well as the neglect of various interfering factors (e.g., turbidity) causing radiation attenuation. Therefore, this study proposes a bottom-type adaption-based SDB approach (BA-SDB). Under the consideration of multiple factors including suspended particulates and phytoplankton, it uses a particle swarm optimization improved LightGBM algorithm (PSO-LightGBM) to derive depth of each pre-segmented bottom type. Based on multispectral images of high spatial resolution and in situ observations of airborne laser bathymetry and multi-beam echo sounder, the proposed approach is applied in shallow water around Yuanzhi Island, and achieves the highest accuracy with an RMSE value of 0.85 m compared to log-ratio, multi-band, and classical machine learning methods. The results of this study show that the introduction of water-environment parameters improves the performance of the machine learning model for bathymetric mapping.

The Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) is equipped with an Advanced Terrain Laser Altimeter System (ATLAS) with the capability of penetrating water bodies, making it a widely utilized tool for the bathymetry of various aquatic environments. However, the laser sensor often encounters a significant number of noise photons due to various factors such as sunlight, water quality, and after-pulse effect. These noise photons significantly compromise the accuracy of bathymetry measurements. In an effort to address this issue, this study proposes a two-step method for photon denoising by utilizing a method combining the DBSCAN algorithm and a two-dimensional window filter, achieving an F1 score of 0.94. A robust M-estimation method was employed to estimate the water depth of the denoised and refraction-corrected bathymetric photons, achieving an RMSE of 0.30 m. The method proposed in this paper preserves as much information as possible about signal photons, increases the number of bathymetric points, enhances the resistance to gross error, and guarantees the accuracy of bathymetry measurements while outlining the underwater topography. While the method is not fully automated and requires setting parameters, the fixed parameter values allow for efficient batch denoising of underwater photon points in different environments.

Ocean waves are a vital environmental factor that affects the accuracy of airborne laser bathymetry (ALB) systems. As the regional water surface undulates with randomness, the laser propagation direction through the air–water surface will change and impact the underwater topographic result from the ALB system, especially for the small laser divergence system. However, the natural ocean surface changes rapidly over time, and uneven ocean surface point clouds from ALB scanning will cause an uncertain estimation of the laser propagation direction; therefore, a self-adaptive correction method based on the characteristics of the partial wave surface is key to improving the accuracy and applicability of the ALB system. In this paper, we focused on the issues of spatial position deviation caused by surface waves and position correction of the underwater laser footprint, and the dimension-based adaptive method is applied to attempt to correct the laser incidence angle. Simulation experiments and analysis of the actual measurement data from different ALB systems verified that the method can effectively suppress the influence of ocean waves. Furthermore, the inversion result of sea surface inclination changes is consistent with the surface wind wave reanalysis products. Based on the laser underwater propagation model in the strategy, we also quantitatively analyzed the influence of surface waves on laser bathymetry, which can guide the operation selection and data processing of the ALB system at specific water depths and under dynamic ocean conditions.

An important problem associated with the aerial mapping of the seabed is the precise classification of point clouds characterizing the water surface, bottom, and bottom objects. This study aimed to improve the accuracy of classification by addressing the asymmetric amount of data representing these three groups. A total of 53 Synthetic Minority Oversampling Technique (SMOTE) algorithms were adjusted and evaluated to balance the amount of data. The prepared data set was used to train the Multi-Layer Perceptron (MLP) neural network used for classifying the point cloud. Data balancing contributed to significantly increasing the accuracy of classification. The best overall classification accuracy achieved varied from 95.8% to 97.0%, depending on the oversampling algorithm used, and was significantly better than the classification accuracy obtained for unbalanced data and data with downsampling (89.6% and 93.5%, respectively). Some of the algorithms allow for 10% increased detection of points on the objects compared to unbalanced data or data with simple downsampling. The results suggest that the use of selected oversampling algorithms can aid in improving the point cloud classification and making the airborne laser bathymetry technique more appropriate for seabed mapping.

Accurate and reliable reference data are key elements for advancing remote sensing techniques and validating the quality of geo data. This includes remote sensing methods for bathymetric data collection, such as SONAR or bathymetric LiDAR. In this work, we present a method for acquiring accurate bathymetric reference data for shallow water depths. We use a tilt-compensating dual-prism measurement pole combined with two time-synchronized robotic total stations to allow tilted and non-static pole measurements. The accessible water depth of our method is restricted only by the pole length. The error induced by pole tilt and movement, caused by water currents, large water depths, or the operation from unstable vehicles, is minimized by using the time-synchronized tilt information gathered from two reflectors mounted on the measurement pole. With our approach, we show in an air-only experiment that we achieve better than 1 cm height RMSE and 8 cm position RMSE for tilted measurement with a 4.65 m long pole. The variance propagation and precision values derived from our in-water study suggest an even lower position RMSE of 3 cm.

Fractal dimension is a statistical index of complexity to characterize geometries. It is commonly used in signal processing in different fields of research. There, observations of dynamic systems can be translated into numerical values allowing us to classify signals into groups of similar characteristics. In full-waveform LiDAR this methodology can be applied to the reflected echo pulse, thus enabling an analysis based on the overall waveform characteristics. Consequently, the fractal dimension of the full-waveform can be leveraged to differentiate between echo pulses with a high number of returns and single- or low-return echo pulses. This introduces an independent measure, which is calculated prior to the signal processing step. The advantage of this initial classification is that the echo pulse extraction could be further improved without need for human supervision, as the correlation between the number of echo pulses and the fractal dimension hints towards a measure of estimating the number of echo pulses within a recorded full-waveform. To conclude, we expand the concept of the fractal dimension to LiDAR waveforms and use the extracted correlation between the number of echo pulses and the fractal dimension to gain new insights for estimating the total number of echo pulses. This improvement is demonstrated through comparisons with manually annotated data, advancing the state-of-the-art in full-waveform analysis and introducing additional parameters.

This study presents a comparative analysis of airborne laser bathymetry (ALB) and multimedia photogrammetry for the detection and documentation of a submerged Neolithic pile dwelling settlement in Lake Mondsee, Austria. High-resolution ALB data acquired with a UAV-mounted bathymetric laser scanner and aerial images were processed and evaluated for suitability to identify submerged wooden piles and associated archaeological features. The results demonstrate that ALB delivers superior data quality, allowing the detection of small-scale structures even at depths of up to 7 m, while multimedia photogrammetry was limited by water turbidity and depth, with reliable results only up to 3 m. Despite its limitations, photogrammetry proved useful for larger features under favorable conditions. The study confirms ALB as the more robust technique for detailed underwater archaeological documentation in moderately turbid lake environments.

In this paper, we present a practical application of a new bathymetric LiDAR sensor mounted on an unmanned aerial vehicle (UAV). Using the YellowScan Navigator, we conducted four data acquisition campaigns over the Herault River, each during different seasons. All flights followed the same plan, using the same UAV and sensor configuration to ensure consistency. This multi-temporal dataset allows us to monitor riverbed changes with a vertical accuracy of several centimeters. Our results demonstrate the sensor’s capability to detect subtle morphological variations in the riverbed, bridging the gap between traditional multi-echo sonar techniques and topographic LiDAR systems. This new tool offers a valuable addition to the geodetic toolbox for fluvial and environmental monitoring.