Explore the vast range of titles available.

Although aerial image-based bathymetric mapping can provide, unlike acoustic or LiDAR (Light Detection and Ranging) sensors, both water depth and visual information, water refraction poses significant challenges for accurate depth estimation. In order to tackle this challenge, we propose an image correction methodology, which first exploits recent machine learning procedures that recover depth from image-based dense point clouds and then corrects refraction on the original imaging dataset. This way, the structure from motion (SfM) and multi-view stereo (MVS) processing pipelines are executed on a refraction-free set of aerial datasets, resulting in highly accurate bathymetric maps. Performed experiments and validation were based on datasets acquired during optimal sea state conditions and derived from four different test-sites characterized by excellent sea bottom visibility and textured seabed. Results demonstrated the high potential of our approach, both in terms of bathymetric accuracy, as well as texture and orthoimage quality.

The development of multibeam echosounders (MBES) as a seabed mapping tool has resulted in the widespread uptake of backscatter intensity as an indicator of seabed substrate properties. Though increasingly common, the lack of standard calibration and the characteristics of individual sonars generally produce backscatter measurements that are relative to a given survey, presenting major challenges for seabed mapping in areas that comprise multiple MBES surveys. Here, we explore methods for backscatter dataset harmonization that leverage areas of mutual overlap between surveys for relative statistical calibration—referred to as “bulk shift” approaches. We use three multispectral MBES datasets to simulate the harmonization of backscatter collected over multiple years, and using multiple operating frequencies. Results suggest that relatively simple statistical models are adequate for bulk shift harmonization procedures, and that more flexible approaches may produce inconsistent results that risk statistical overfitting. While harmonizing datasets collected using the same operating frequency from separate surveys is generally feasible given reasonable temporal limitations, results suggest that the success at harmonizing datasets of different operating frequencies partly depends on the extent to which the frequencies differ. We recommend approaches and diagnostics for ensuring the quality of harmonized backscatter mosaics, and provide an R function for implementing the methods presented here.

The upper part of the Krka River estuary and Prokljan Lake are a specific example of a well-stratified estuarine environment in a submerged river canyon. Here, we reconstructed the geomorphological evolution of the area and classified the data gathered in the study, integrating multibeam echosounder data, backscatter echosounder data, side-scan sonar morpho-bathymetric surveys, and acoustic sub-bottom profiling, with the addition of ground-truthing and sediment analyses. This led to the successful classification of the bottom sediments using the object-based image analysis method. Additional inputs to the multibeam echosounder data improved the segmentation of the seafloor classification, geology, and morphology of the surveyed area. This study uncovered and precisely defined distinct geomorphological features, specifically submerged tufa barriers and carbonate mounds active during the Holocene warm periods, analogous to recent tufa barriers that still exist and grow in the upstream part of the Krka River. Fine-grained sediments, classified as estuarine sediments, hold more organic carbon than coarse-grained sediments sampled on barriers. A good correlation of organic carbon with silt sediments allowed the construction of a prediction map for marine sedimentary carbon in this estuarine/lake environment using multibeam echosounder data. Our findings highlight the importance of additional inputs to multibeam echosounder data to achieve the most accurate results.

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.

The anthropogenic impact in the German Exclusive Economic Zone (EEZ) is high due to the presence of manifold industries (e.g., wind farms, shipping, and fishery). Therefore, it is of great importance to evaluate the different impacts of such industries, in order to enable reasonable and sustainable decisions on environmental issues (e.g., nature conservation). Bottom trawling has a significant impact on benthic habitats worldwide. Fishing gear penetrates the seabed and the resulting furrows temporarily remain in the sediment known as trawl marks (TM), which can be recognized in the acoustic signal of side-scan sonars (SSS) and multibeam echo sounders (MBES). However, extensive mapping and precise descriptions of TM from commercial fisheries at far offshore fishing grounds in the German EEZ are not available. To get an insight into the spatial patterns and characteristics of TM, approximately 4800 km2 of high-resolution (1 m) SSS data from three different study sites in the German EEZ were analyzed for changes in TM density as well as for the geometry of individual TM. TM were manually digitalized and their density per square kilometer was calculated. In general, TM density was highest in August and October. Moreover, different gear types could be identified from investigating individual TM in SSS data. Beam trawl marks were observed to have widths of up to 22 m whereas otter board marks showed widths up to 6 m. The persistence of TM was estimated to 2–7 days minimum for all three sites based on the SSS data from 2015–2019. A maximum persistence could be defined at one site (Dogger Bank) and it was five months for the investigation period 2016–2017. Besides the main factors driving TM degradation (wave-base impact, sediment-type), different methods for TM detection (SSS, MBES, under-water video) are discussed. The study provides valuable information on the physical impact of bottom trawling on the seabed and can support existing monitoring strategies.

The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.

Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles. To acquire information from around the vehicle, it is desirable for these sonar systems to insonify a large area; thus, the sonar transmit beampattern should have a wide field of view. In this work, we study the problem of the optimization of wide transmission beampatterns. We consider the conventional phased-array beampattern design problem where all array elements transmit an identical waveform. The complex weight vector is adjusted to create the desired beampattern shape. In our experiments, we consider wide transmission beampatterns (≥20∘) with uniform output power. In this paper, we introduce a new iterative-convex optimization method for narrowband linear phased arrays and compare it to existing approaches for convex and concave–convex optimization. In the iterative-convex method, the phase of the weight parameters is allowed to be complex as in disciplined convex–concave programming (DCCP). Comparing the iterative-convex optimization method and DCCP to the standard convex optimization, we see that the former methods archive optimized beampatterns closer to the desired beampatterns. Furthermore, for the same number of iterations, the proposed iterative-convex method achieves optimized beampatterns, which are closer to the desired beampattern than the beampatterns achieved by optimization with DCCP.

Cold-water corals form substantial biogenic habitats on continental shelves and in deep-sea areas with topographic highs, such as banks and seamounts. In the Atlantic, many reef and mound complexes are engineered by Lophelia pertusa , the dominant framework-forming coral. In this study, a variety of mapping approaches were used at a range of scales to map the distribution of both cold-water coral habitats and individual coral colonies at the Mingulay Reef Complex (west Scotland). The new ArcGIS-based British Geological Survey (BGS) seabed mapping toolbox semi-automatically delineated over 500 Lophelia reef ‘mini-mounds’ from bathymetry data with 2-m resolution. The morphometric and acoustic characteristics of the mini-mounds were also automatically quantified and captured using this toolbox. Coral presence data were derived from high-definition remotely operated vehicle (ROV) records and high-resolution microbathymetry collected by a ROV-mounted multibeam echosounder. With a resolution of 0.35 × 0.35 m, the microbathymetry covers 0.6 km 2 in the centre of the study area and allowed identification of individual live coral colonies in acoustic data for the first time. Maximum water depth, maximum rugosity, mean rugosity, bathymetric positioning index and maximum current speed were identified as the environmental variables that contributed most to the prediction of live coral presence. These variables were used to create a predictive map of the likelihood of presence of live cold-water coral colonies in the area of the Mingulay Reef Complex covered by the 2-m resolution data set. Predictive maps of live corals across the reef will be especially valuable for future long-term monitoring surveys, including those needed to understand the impacts of global climate change. This is the first study using the newly developed BGS seabed mapping toolbox and an ROV-based microbathymetric grid to explore the environmental variables that control coral growth on cold-water coral reefs.

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.

The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas.