Search Results (121)

This research presents a comprehensive analysis of the spatial extent and temporal change in benthic habitats within the Puck Lagoon in the southern Baltic Sea, utilizing integrated machine learning classification and multi-sourced remote sensing. Object-based image analysis was integrated with Random Forest, Support Vector Machine, and K-Nearest Neighbors algorithms for benthic habitat classification based on airborne bathymetric LiDAR (ALB), multibeam echosounder (MBES), satellite bathymetry, and high-resolution aerial photography. Ground-truth data collected by 2023 field surveys were supplemented with long temporal datasets (2010–2023) for seagrass meadow analysis. Boruta feature selection showed that geomorphometric variables (aspect, slope, and terrain ruggedness index) and optical features (ALB intensity and spectral bands) were the most significant discriminators in each classification case. Binary classification models were more effective (93.3% accuracy in the presence/absence of Zostera marina) compared to advanced multi-class models (43.3% for EUNIS Level 4/5), which identified the inherent equilibrium between ecological complexity and map validity. Change detection between contemporary and 1957 habitat data revealed extensive Zostera marina loss, with 84.1–99.0% cover reduction across modeling frameworks. Seagrass coverage declined from 61.15% of the study area to just 9.70% or 0.63%, depending on the model. Seasonal mismatch may inflate loss estimates by 5–15%, but even adjusted values (70–94%) indicate severe ecosystem degradation. Spatial exchange components exhibited patterns of habitat change, whereas net losses in total were many orders of magnitude larger than any redistribution in space. These findings recorded the most severe seagrass habitat destruction ever described within Baltic Sea ecosystems and emphasize the imperative for conservation action at the landscape level. The methodology framework provides a reproducible model for analogous change detection analysis in shallow nearshore habitats, creating critical baselines to inform restoration planning and biodiversity conservation activities. It also demonstrated both the capabilities and limitations of automatic techniques for habitat monitoring. Full article

The DD Guyot, a flat-topped seamount located in the Western Pacific, was completely mapped using multibeam echosounders (MBESs) in 2024. Clarifying substratum patterns is crucial for understanding seafloor evolution, sediment transport processes, and resource assessment. This study integrates near-bottom video data from the manned submersible Jiaolong, multibeam bathymetry and backscatter data from EM124, and a convolutional neural network (CNN) model to classify the four substratum types (exposed bedrock, thinly sedimented bedrock, sediment–rock transition zone, and continuous sediment) of the DD Guyot. The results indicate that exposed bedrock predominates on the summit platform, while sediment cover increases with water depth along the flank. The base of the guyot is almost entirely covered by sediments. Two landslide areas were identified, with clear main scarps, sidewalls, and debris accumulations. These features, together with underflow erosion, collectively influence sediment distribution patterns. The resulting substratum maps provide guidance for seabed resource exploration. The results are consistent with a post-drowning onlap framework, which points to a drowning unconformity, but video and surface acoustic data alone are insufficient for definitive confirmation. Further investigation is required to more clearly elucidate the substratum characteristics of the DD Guyot. Full article

Accurate mapping of seafloor morphological features, such as pockmarks, is essential for marine spatial planning, geological hazard assessment, and environmental monitoring. Traditional manual delineation methods are often subjective and inefficient when applied to large, high-resolution bathymetric datasets. This study presents a semi-automated workflow based on the CoMMa (Confined Morphologies Mapping) toolbox to classify pockmarks in Flensburg Fjord, Germany–Denmark. Initial detection employed the Bathymetric Position Index (BPI) with intentionally permissive parameters to ensure high recall of morphologically diverse features. Morphometric descriptors were then extracted and used to train a Random Forest classifier, enabling noise reduction and refinement of overinclusive delineations. Validation against expert-derived mappings showed that the model achieved an overall classification accuracy of 86.16%, demonstrating strong performance across the validation area. These findings highlight how integrating a GIS-based geomorphometry toolbox with machine learning yields a reproducible, objective, and scalable approach to seabed mapping, supporting decision-making processes and advancing standardized methodologies in marine geomorphology. Full article

The detection of seabed boulders is a critical step in mitigating geological hazards during the planning and construction of offshore wind energy infrastructure, as well as in supporting benthic ecological and palaeoglaciological studies. Traditionally, side-scan sonar (SSS) has been favoured for such detection, but the growing availability of high-resolution multibeam echosounder (MBES) data offers a cost-effective alternative. This study presents a semi-automated, hybrid GIS-AI approach that combines bathymetric position index filtering and a Random Forest classifier to detect boulders and delineate boulder fields from MBES data. The method was tested on a 0.24 km² site in Long Island Sound using 0.5 m resolution data, achieving 83% recall, 73% precision, and an F1-score of 77—slightly outperforming the average of expert manual picks while offering a substantial improvement in time-efficiency. The workflow was validated against a consensus-based master dataset and applied across a 79 km² study area, identifying over 75,000 contacts and delineating 89 contact clusters. The method enables objective, reproducible, and scalable boulder detection using only MBES data. Its ability to reduce reliance on SSS surveys while maintaining high accuracy and offering workflow customization makes it valuable for geohazard assessment, benthic habitat mapping, and offshore infrastructure planning. Full article

Multibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry surveying, this study proposes a novel error adjustment method integrating crossover error density clustering and beam incident angle (BIA) compensation. Firstly, a bathymetry error detection model was developed based on adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN). By optimizing the neighborhood radius and minimum sample threshold through analyzing sliding-window curvature, the method achieved the automatic identification of outliers, reducing crossover discrepancies from ±150 m to ±50 m in the deep sea at a depth of approximately 5000 m. Secondly, an asymmetric quadratic surface correction model was established by incorporating the BIA as a key parameter. A dynamic weight matrix ω = 1/(1 + 0.5θ²) was introduced to suppress edge beam errors, combined with Tikhonov regularization to resolve ill-posed matrix issues. Experimental validation in the Western Pacific demonstrated that the RMSE of crossover points decreased by about 30.4% and the MAE was reduced by 57.3%. The proposed method effectively corrects residual systematic errors while maintaining topographic authenticity, providing a reference for improving the quality of multibeam bathymetric data obtained via USVs and enhancing measurement efficiency. Full article

Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies. Full article

Unmanned aerial and autonomous surface vehicles (UAVs and ASVs, respectively) are two emerging technologies for the mapping of coastal and marine environments. Using UAV photogrammetry, the sea-bottom composition can be resolved with very high fidelity in shallow waters. At greater depths, acoustic methodologies have far better propagation properties compared to optics; therefore, ASVs equipped with multibeam echosounders (MBES) are better-suited for mapping applications in deeper waters. In this work, a sea-bottom classification methodology is presented for mapping the protected habitat of Mediterranean seagrass Posidonia oceanica (habitat code 1120) in a coastal subregion of Heraklion (Crete, Greece). The methodology implements a machine learning scheme, where knowledge obtained from UAV imagery is embedded (through training) into a classifier that utilizes acoustic backscatter intensity and features derived from the MBES data provided by an ASV. Accuracy and precision scores of greater than 85% compared with visual census ground-truth data for both optical and acoustic classifiers indicate that this hybrid mapping approach is promising to mitigate the depth-induced bias in UAV-only models. The latter is especially interesting in cases where the studied habitat boundaries extend beyond depths that can be studied via aerial devices’ optics, as is the case with P. oceanica meadows. Full article

Marine geophysical and geological investigations are crucial for evaluating the construction suitability of artificial fish reefs (AFRs). Key factors such as seabed topography, geomorphology, sub-bottom structure, and sediment type significantly influence AFR design and site selection. Challenges such as material sinking, sediment instability, and scouring effects should be critically considered and addressed in the construction of AFR, particularly in areas with soft mud or dynamic environments. In this study, detailed investigations were conducted approximately seven months after the deployment of reef materials in the AFR experimental zones around Xiaoguan Island, located in the western South Yellow Sea, China. Based on morphological factors, using data from multibeam echosounders and side-scan sonar, the study area was divided into three geomorphic zones, namely, the tidal flat (TF), underwater erosion-accumulation slope (UEABS), and inclined erosion-accumulation shelf plain (IEASP) zones. The focus of this study was on the UEABS and IEASP experimental zones, where reef materials (concrete or stone blocks) were deployed seven months earlier. The comprehensive interpretation results of multi-source high-resolution acoustic images showed that the average settlement of individual reefs in the UEABS experimental zone was 0.49 m, and their surrounding seabed experienced little to no scouring. This suggested the formation of an effective range and height, making the zone suitable for AFR construction. However, in the IEASP experimental zone, the seabed sediment consisted of soft mud, causing the reef materials to sink into the seabed after deployment, preventing the formation of an effective range and height, and rendering the area unsuitable for AFR construction. These findings provided valuable scientific guidance for AFR construction in the study area and other similar coastal regions. Full article

Observed longitudinal water-surface profiles are commonly used to calibrate river hydrodynamic models, relying on assumptions of lateral uniformity in water surface elevation and velocity distribution. While suitable for 1D models, this approach has limitations in regard to 2D model calibration. When 2D flow measurements are available, a more robust quantitative evaluation is necessary to assess model accuracy. This study introduces a novel methodology to improve 2D model calibration and evaluate performance. High-resolution bathymetric and hydrodynamic data collected with a multibeam echosounder (MBES) and acoustic Doppler current profiler (ADCP) were aligned to compare observed and simulated flow velocities at matching spatial locations. Statistical metrics, including relative mean absolute error and root-mean-square error, were employed to assess hydrodynamic modeling. The methodology was tested using MBES and ADCP measurements alongside TELEMAC-2D simulations of a dynamic neck cutoff on the White River, Arkansas, USA. This approach provides a 2D calibration process, enhancing model accuracy and informing parameter selection, such as channel boundary roughness and downstream boundary water surface elevation. Full article

Kelps are important habitat-forming species in shallow marine environments, providing critical habitat, structure, and productivity for temperate reef ecosystems worldwide. Many kelp species are currently endangered by myriad pressures, including changing water temperatures, invasive species, and anthropogenic threats. This situation necessitates advanced methods to detect kelp density, which would allow tracking density changes, understanding ecosystem dynamics, and informing evidence-based management strategies. This study introduces an innovative approach to detect kelp density with multibeam echosounder water column data. First, these data are filtered into a point cloud. Then, a range of variables are derived from these point cloud data, including average acoustic energy, volume, and point density. Finally, these variables are used as input to a Random Forest model in combination with bathymetric variables to classify sand, bare rock, sparse kelp, and dense kelp habitats. At 5 m resolution, we achieved an overall accuracy of 72.5% with an overall Area Under the Curve of 0.874. Notably, our method achieved high accuracy across the entire multibeam swath, with only a 1 percent point decrease in model accuracy for data falling within the part of the multibeam water column data impacted by sidelobe artefact noise, which significantly expands the potential of this data type for wide-scale monitoring of threatened kelp ecosystems. Full article

Multibeam echosounder (MBES) backscatter data are influenced by underwater sound absorption, which is dependent on environmental parameters such as temperature, salinity, and depth. This study leverages CTD datasets from the Korea Oceanographic Data Center (KODC) to analyze and visualize the spatiotemporal variations in absorption parameters in the East Sea of Korea, which are subject to pronounced variability over time and space. The legacy MBES backscatter data, originally processed using generalized absorption parameters that neglected spatiotemporal variations, were compared with the calibrated data. The calibration process included inverse calculation of water temperature with depth-specific average salinity values from the nearest KODC stations. This calibration revealed discrepancies of up to 2.1 dB in backscatter intensity across survey lines, highlighting the potential misrepresentation of legacy MBES backscatter data due to site-specific absorption variability having been overlooked. By addressing these discrepancies, this study underscores the importance of incorporating spatiotemporal absorption variability into MBES calibration workflows. This integrated approach not only enhances the reliability of legacy MBES data but also provides valuable insights for marine resource management, seafloor mapping, and environmental monitoring in highly dynamic marine environments such as the East Sea of Korea. Full article

The ocean, covering 71% of Earth’s surface, remains largely unexplored due to the challenges of the marine environment. This study focuses on the Kuźnica Deep in the Baltic Sea, aiming to develop an automatic seabed mapping methodology using multibeam echosounders (MBESs) and machine learning. The research integrates various scientific fields to enhance understanding of the Kuźnica Deep’s underwater landscape, addressing sediment composition, backscatter intensity, and geomorphometric features. Advances in remote sensing, particularly, object-based image analysis (OBIA) and machine learning, have significantly improved geospatial data analysis for underwater landscapes. The study highlights the importance of using a reduced set of relevant features for training models, as identified by the Boruta algorithm, to improve accuracy and robustness. Key geomorphometric features were crucial for seafloor composition mapping, while textural features were less significant. The study found that models with fewer, carefully selected features performed better, reducing overfitting and computational complexity. The findings support hydrographic, ecological, and geological research by providing reliable seabed composition maps and enhancing decision-making and hypothesis generation. Full article

This paper proposes a method for visualizing three-dimensional non-uniformly sampled data from multibeam echosounder systems (MBESs), aimed at addressing the requirements of monitoring complex and dynamic underwater flow fields. To tackle the challenges associated with spatially non-uniform sampling, the proposed method employs linear interpolation along the radial direction and arc length weighted interpolation in the beam direction. This approach ensures consistent resolution of three-dimensional data across the same dimension. Additionally, an opacity transfer function is generated to enhance the visualization performance of the ray casting algorithm. This function leverages data values and gradient information, including the first and second directional derivatives, to suppress the rendering of background and non-interest regions while emphasizing target areas and boundary features. The simulation and experimental results demonstrate that, compared to conventional two-dimensional beam images and three-dimensional images, the proposed algorithm provides a more intuitive and accurate representation of three-dimensional data, offering significant support for the observation and analysis of spatial flow field characteristics. Full article

Biogenic reefs support marine biodiversity and play a key role in a healthy marine environment. Protecting and enhancing reef-building species, such as Sabellaria spinulosa, require mapping and monitoring strategies. A multi-scale and multi-sensor mapping campaign, including a multi-beam echosounder, side-scan sonar (SSS), box corer and ROV with an attached video camera, has been carried out in the northern Brown Bank (Dutch Continental Shelf) in August 2023. A semi-automated classification workflow, based on a support vector machine (machine learning), was developed to map Sabellaria reefs using SSS and video data. Elevated Sabellaria reefs were classified with a precision and sensitivity of 52% and 49%, respectively. The classified SSS images were merged into full-coverage percentage maps of Sabellaria reef coverage. Located between the swales of the tidal ridges, it was estimated that the reefs cover an area of 3.8 to 5.7% within the surveyed areas. The maps indicate (1) on the large-scale a preference of Sabellaria spinulosa for settlement to the east of the deepest part of the swale and (2) on the small-scale a preference for the troughs towards the stoss side of the megaripples. The employed survey strategy and the developed classification workflow can be extended to other environmental areas and further developed into a standard monitoring procedure. Full article

The main objective of this paper is to investigate the performance of the Network Real-time Kinematic (NRTK) technique in hydrographic surveying and check whether it meets the International Hydrography Organization (IHO) minimum bathymetry standards for the safety of navigation hydrographic surveys. To this end, the KAU-Hydrography 2 vessel was used to conduct a hydrographic survey session at Sharm Obhur. NRTK corrections were streamed in real time from the KSA-CORS NTRIP server and GNSS data were collected at the same time at the base station using a Trimble SPS855 GNSS receiver. Multibeam records were collected using a Teledyne RESON SeaBat T50-P multibeam echosounder in addition to Valeport’s sound velocity profiler records and Applanix POSMV data. Applanix POSPac MMS 8.3 software was used to process the GNSS data of the base station along with the POSMV data to obtain the Smoothed Best Estimate of Trajectory (SBET) file, which is used as a reference solution. The NRTK solution is then compared with the reference solution. It is shown that the Total Horizontal Uncertainty (THU) and the Total Vertical Uncertainty (TVU) of the NRTK solution are 6.38 cm and 3.10 cm, respectively. Statistical analysis of the differences between the seabed surface generated using the NRTK solution and the seabed surface generated using the Post-Processed Kinematic (PPK) technique showed an average of −0.19 cm and a standard deviation of 2.4 cm. From these results, we can conclude that the KSA-CORS NRTK solution successfully meets IHO minimum bathymetry standards for the safety of navigation hydrographic surveys at a 95% confidence level for all orders of hydrographic surveys. Full article