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The calibration of multibeam echosounders for backscatter measurements can be conducted efficiently and accurately using data from surveys over a reference natural area, implying appropriate measurements of the local absolute values of backscatter. Such a shallow area (20-m mean depth) has been defined and qualified in the Bay of Brest (France), and chosen as a reference area for multibeam systems operating at 200 and 300 kHz. The absolute reflectivity over the area was measured using a calibrated single-beam fishery echosounder (Simrad EK60) tilted at incidence angles varying between 0° and 60° with a step of 3°. This reference backscatter level is then compared to the average backscatter values obtained by a multibeam echosounder (here a Kongsberg EM 2040-D) at a close frequency and measured as a function of angle; the difference gives the angular bias applicable to the multibeam system for recorded level calibration. The method is validated by checking the single- and multibeam data obtained on other areas with sediment types different from the reference area.

Habitat mapping data are increasingly being recognised for their importance in underpinning marine spatial planning. The ability to collect ultra-high resolution (cm) multibeam echosounder (MBES) data in shallow waters has facilitated understanding of the fine-scale distribution of benthic habitats in these areas that are often prone to human disturbance. Developing quantitative and objective approaches to integrate MBES data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for habitat mapping products. Whilst supervised classification approaches are becoming more common, users are often faced with a decision whether to implement a pixel based (PB) or an object based (OB) image analysis approach, with often limited understanding of the potential influence of that decision on final map products and relative importance of data inputs to patterns observed. In this study, we apply an ensemble learning approach capable of integrating PB and OB Image Analysis from ultra-high resolution MBES bathymetry and backscatter data for mapping benthic habitats in Refuge Cove, a temperate coastal embayment in south-east Australia. We demonstrate the relative importance of PB and OB seafloor derivatives for the five broad benthic habitats that dominate the site. We found that OB and PB approaches performed well with differences in classification accuracy but not discernible statistically. However, a model incorporating elements of both approaches proved to be significantly more accurate than OB or PB methods alone and demonstrate the benefits of using MBES bathymetry and backscatter combined for class discrimination.

Target strength (TS) is an acoustic backscatter measurement that reflects fish size, whereas fish density is an important indicator of aquatic ecosystem conditions. This study aimed to describe the distribution patterns of TS and fish density in Kapota Atoll, Wakatobi Waters, using hydroacoustic technology with a single-beam chosounder, Simrad EK-15. The results showed that TS values ranged from -51.8 dB to -37.6 dB, with an average of -42.5 dB, and tended to increase with depth, indicating the presence of larger fish. The spatial distribution of fish density varied significantly both horizontally and vertically. The highest density was detected at depths of 21–25 m, with a maximum of 45,602 individuals/1000m³ and minimum of 2 individuals/1000m³. An average density of 514 individuals/1000m³ reflects a good environmental carrying capacity. The lagoon area tended to be the center of fish aggregation because of its more complex habitat, whereas depths beyond 33 m showed a significant decrease in density. The negative relationship between TS and fish density indicates that larger fish are more commonly found in deeper layers but in lower numbers. These findings provide a scientific basis for sustainable fisheries management in the Kapota Atoll.

Recently, the rapid development of the seabed mapping industry has allowed researchers to collect hydroacoustic data in shallow, nearshore environments. Progress in marine habitat mapping has also helped to distinguish the seafloor areas of varied acoustic properties. As a result of these new developments, we have collected a multi-frequency, multibeam echosounder dataset from the valuable nearshore environment of the southern Baltic Sea using two frequencies: 150 kHz and 400 kHz. Despite its small size, the Rowy area is characterized by diverse habitat conditions and the presence of red algae, unique on the Polish coast of the Baltic Sea. This study focused on the utilization of multibeam bathymetry and multi-frequency backscatter data to create reliable maps of the seafloor. Our approach consisted of the extraction of 70 secondary features of bathymetric and backscatter data, including statistic and textural attributes of different scales. Based on ground-truth samples, we have identified six habitat classes and selected the most relevant features of the bathymetric and backscatter data. Additionally, five types of image processing pixel-based and object-based classifiers were tested. We also evaluated the performance of algorithms using an accuracy assessment based on the validation subset of the ground-truth samples. Our best results reached 93% overall accuracy and a kappa coefficient of 0.90, confirming that nearshore seabed habitats can be accurately distinguished based on multi-frequency, multibeam echosounder measurements. Our predictive habitat mapping of shallow euphotic zones creates a new scientific perspective and provides relevant data for the management of natural resources. Object-based approaches previously used in various environments and areas suggest that methodology presented in this study may be scalable.

The distribution of zooplankton biomass in the upwelling system of La Guajira (Caribbean Sea), was evaluated using more than 800 linear km of acoustic data obtained from two samplings conducted in 2018 during a period of weak (May) and intense (December) upwelling. Modeling of the spatial variability and interpolation of zooplankton acoustic biomass was performed using a geostatistical method and its relationship with environmental variables through generalized additive models. It was found that the spatial distribution of zooplankton acoustic biomass was different in the two periods evaluated. In May it was concentrated on the edge of the continental shelf and in December it was associated with upwelling foci or areas with high fluvial inputs from the Magdalena River (Gulf of Salamanca). In the weak upwelling period, zooplankton biomass was higher and was associated with high dissolved oxygen concentrations and low chlorophyll-a levels. The seasonal variability of zooplankton biomass does not show a linear relationship with upwelling intensity, probably as a consequence of air-ocean-river interaction processes affecting the region. La distribución de la biomasa de zooplancton en el sistema de surgencia de La Guajira (Mar Caribe), se evaluó utilizando más de 800 km lineales de datos acústicos obtenidos de dos muestreos realizados en 2018 durante un período de surgencia débil (mayo) e intensa (diciembre). La modelación de la variabilidad espacial e interpolación de la biomasa acústica del zooplancton se realizó con un método geoestadístico y su relación con variables ambientales a través de modelos aditivos generalizados. Se encontró que la distribución espacial de la biomasa acústica del zooplancton fue diferente en los dos períodos evaluados. Para mayo se concentró en el borde de la plataforma continental y en diciembre se asoció con focos de surgencias o zonas con altos aportes fluviales del río Magdalena (Golfo de Salamanca). En el período de afloramiento débil, la biomasa de zooplancton fue mayor y se asoció con altas concentraciones de oxígeno disuelto y niveles bajos de clorofila-a. La variabilidad estacional de la biomasa de zooplancton no muestra una relación lineal con la intensidad de los afloramientos, probablemente como consecuencia de procesos de interacción aire-océano-río que afectan la región.

Acoustic seafloor measurements with multibeam echosounders (MBESs) are currently often used for submarine habitat mapping, but the MBESs are usually not acoustically calibrated for backscattering strength (BBS) and cannot be used to infer absolute seafloor angular dependence. We present a study outlining the calibration and showing absolute backscattering strength values measured at a frequency of 150 kHz at around 10–20 m water depth. After recording bathymetry, the co-registered backscattering strength was corrected for true incidence and footprint reverberation area on a rough and tilted seafloor. Finally, absolute backscattering strength angular response curves (ARCs) for several seafloor types were constructed after applying sonar backscattering strength calibration and specific water column absorption for 150 kHz correction. Thus, we inferred specific 150 kHz angular backscattering responses that can discriminate among very fine sand, sandy gravel, and gravelly sand, as well as between bare boulders and boulders partially overgrown by red algae, which was validated by video ground-truthing. In addition, we provide backscatter mosaics using our algorithm (BBS-Coder) to correct the angle varying gain (AVG). The results of the work are compared and discussed with the published results of BBS measurements in the 100–400 kHz frequency range. The presented results are valuable in extending the very sparse angular response curves gathered so far and could contribute to a better understanding of the dependence of backscattering on the type of bottom habitat and improve their acoustic classification.

The ocean floor, its species and habitats are under pressure from various human activities. Marine spatial planning and nature conservation aim to address these threats but require sufficiently detailed and accurate maps of the distribution of seabed substrates and habitats. Benthic habitat mapping has markedly evolved as a discipline over the last decade, but important challenges remain. To test the adequacy of current data products and classification approaches, we carried out a comparative study based on a common dataset of multibeam echosounder bathymetry and backscatter data, supplemented with groundtruth observations. The task was to predict the spatial distribution of five substrate classes (coarse sediments, mixed sediments, mud, sand, and rock) in a highly heterogeneous area of the south-western continental shelf of the United Kingdom. Five different supervised classification methods were employed, and their accuracy estimated with a set of samples that were withheld. We found that all methods achieved overall accuracies of around 50%. Errors of commission and omission were acceptable for rocky substrates, but high for all sediment types. We predominantly attribute the low map accuracy regardless of mapping approach to inadequacies of the selected classification system, which is required to fit gradually changing substrate types into a rigid scheme, low discriminatory power of the available predictors, and high spatial complexity of the site relative to the positioning accuracy of the groundtruth equipment. Some of these issues might be alleviated by creating an ensemble map that aggregates the individual outputs into one map showing the modal substrate class and its associated confidence or by adopting a quantitative approach that models the spatial distribution of sediment fractions. We conclude that further incremental improvements to the collection, processing and analysis of remote sensing and sample data are required to improve map accuracy. To assess the progress in benthic habitat mapping we propose the creation of benchmark datasets.

Plankton is an important component of the food web in coastal reef ecosystems. Ocean currents subsidise local production by supplying plankton to resident and reef-associated planktivorous fishes. Measuring the fine-scale distribution of these schooling fishes provides insight into their habitat use and how they balance risk and reward while foraging for plankton. Maintaining their proximity to benthic structure can provide refuge from predation but may also limit foraging opportunities. We used a portable multibeam echosounder to survey schooling fish at 5 natural and 3 artificial reefs, during day and night and under different current conditions. We isolated midwater acoustic targets and used generalised linear models to explain the distribution of schools as a function of current exposure, distance from structure and seafloor complexity. We also isolated individual schools and used generalised least squares to model how school characteristics differed between night and day, using spatial metrics of school area, perimeter length and height above the seafloor. Modelling revealed that the occurrence of schools was almost twice as likely upstream versus downstream of artificial reefs, although distance to reef structure was the main influence. School occurrence was also more likely on artificial versus natural reefs. Schools at artificial reefs exhibited greater volume and areal coverage at night, and during the day they rose higher in the water column while aggregating more closely around the reef. These findings suggest that artificial and natural reefs featuring enhanced vertical relief and direct exposure to the prevailing current are preferred habitats for planktivorous fish.

Multibeam echosounders are becoming widespread for the purposes of seafloor bathymetry mapping, but the acquisition and the use of seafloor backscatter measurements, acquired simultaneously with the bathymetric data, are still insufficiently understood, controlled and standardized. This presents an obstacle to well-accepted, standardized analysis and application by end users. The Marine Geological and Biological Habitat Mapping group (Geohab.org) has long recognized the need for better coherence and common agreement on acquisition, processing and interpretation of seafloor backscatter data, and established the Backscatter Working Group (BSWG) in May 2013. This paper presents an overview of this initiative, the mandate, structure and program of the working group, and a synopsis of the BSWG Guidelines and Recommendations to date. The paper includes (1) an overview of the current status in sensors and techniques available in seafloor backscatter data from multibeam sonars; (2) the presentation of the BSWG structure and results; (3) recommendations to operators, end-users, sonar manufacturers, and software developers using sonar backscatter for seafloor-mapping applications, for best practice methods and approaches for data acquisition and processing; and (4) a discussion on the development needs for future systems and data processing. We propose for the first time a nomenclature of backscatter processing levels that affords a means to accurately and efficiently describe the data processing status, and to facilitate comparisons of final products from various origins.

Generating aerial shoreline segmentation masks can be a daunting task, often requiring manual labeling or correction. This is further problematic because neural segmentation models require decent and abundant data for training, requiring even more manpower to automate the process. In this paper, we propose utilizing Unmanned Surface Vehicles (USVs) in an automated shoreline segmentation system on satellite imagery. The remotely controlled vessel first collects above- and underwater shoreline information using light detection and ranging (LiDAR) and multibeam echosounder (MBES) measuring instruments, resulting in a geo-referenced 3D point cloud. After cleaning and processing these data, the system integrates the projected map with an aerial image of the region. Based on the height values of the mapped points, the image is segmented. Finally, post-processing methods and the k-NN algorithm are introduced, resulting in a complete binary shoreline segmentation mask. The obtained data were used for training U-Net-type segmentation models with pre-trained backbones. The InceptionV3-based model achieved an accuracy of 96% and a dice coefficient score of 93%, demonstrating the effectiveness of the proposed system as a source of data acquisition for training deep neural networks.