We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
A Characterisation of Benthic Currents from Seabed Bathymetry: An Object-Based Image Analysis of Cold-Water Coral Mounds
Summary
Researchers used object-based image analysis of multibeam sonar data to characterize seafloor bedforms and infer benthic current directions and speeds around cold-water coral mounds. The approach automatically classifies seafloor features and links them to oceanographic conditions. Better mapping of deep-sea currents helps explain how marine organisms and particles — including potential plastic debris — are transported along the seafloor.
Seabed sedimentary bedforms (SSBs) are strong indicators of current flow (direction and velocity) and can be mapped in high resolution using multibeam echosounders. Many approaches have been designed to automate the classification of such SSBs imaged in multibeam echosounder data. However, these classification systems only apply a geomorphological contextualisation to the data without making direct assertions on the velocities of benthic currents that form these SSBs. Here, we apply an object-based image analysis (OBIA) workflow to derive a geomorphological classification of SSBs in the Moira Mounds area of the Belgica Mound Province, NE Atlantic through k-means clustering. Cold-water coral reefs as sessile filter-feeders benefit from strong currents are often found in close association with sediment wave fields. This OBIA provided the framework to derive SSB wavelength and wave height, these SSB attributes were used as predictor variables for a multiple linear regression to estimate current velocities. Results show a bimodal distribution of current flow directions and current speed. Furthermore, a 5 k-means classification of the SSB geomorphology exhibited an imprinting of current flow consistency which altered throughout the study site due to the interaction of regional, local, and micro scale topographic steering forces. This study is proof-of-concept for an assessment tool applied to vulnerable marine ecosystems but has wider applications for applied seabed appraisals and can inform management and monitoring practice across a variety of spatial and temporal scales. Deriving spatial patterns of hydrodynamic processes from widely available multibeam echosounder maps is pertinent to many avenues of research including scour predictions for offshore structures such as wind turbines, sediment transport modelling, benthic fisheries, e.g., scallops, cable route and pipeline risk assessment and habitat mapping.
Sign in to start a discussion.
More Papers Like This
Quantifying the three‐dimensional stratigraphic expression of cyclic steps by integrating seafloor and deep‐water outcrop observations
This study examined underwater sediment structures formed by fast-moving water currents, called cyclic steps, using 3D modeling. Understanding these deep-sea depositional processes is relevant to tracking where sediment-bound pollutants including microplastics accumulate on the ocean floor.
Highly variable deep-sea currents over tidal and seasonal timescales
Researchers used advanced deep-sea monitoring to study how near-bed ocean currents vary over tidal and seasonal timescales on the continental slope. They found that these currents are far more variable than previously assumed, with implications for how sediment, organic carbon, and pollutants including microplastics are transported across the deep ocean floor. The study improves understanding of the physical processes that control where contaminants accumulate in deep-sea environments.
Global mapping for the occurrence of all-sized microplastics in seafloor sediments
Researchers developed code for extracting ocean surface current and near-bed thermohaline current data to analyze the hydrodynamic driving forces behind global microplastic distribution patterns in seafloor sediments.
Deep-Sea Debris Identification Using Deep Convolutional Neural Networks
Researchers developed a deep convolutional neural network classifier to identify and distinguish deep-sea debris from seafloor imagery, demonstrating that automated AI-based detection can support submersible clean-up operations targeting marine debris in deep-sea environments.
Detection and identification of environmental faunal proxies in digital images and video footage from northern Norwegian fjords and coastal waters using deep learning object detection algorithms
Researchers developed deep learning object detection algorithms to automate the detection and identification of environmental faunal proxies in digital images and video footage from Norwegian fjords and coastal waters, as part of the ICT+ ocean surveying project at UiT The Arctic University of Norway. The preliminary work aimed to automate identification of objects ranging from foraminifera and microplastics at the micrometre scale to boulders and shipwrecks at the metre scale, replacing labour-intensive manual processing.