Abstract
National mapping programs (e.g., INFOMAR and MAREANO) and global efforts (Seabed 2030) acquire large volumes of multibeam echosounder data to map large areas of the seafloor. Developing an objective, automated and repeatable approach to extract meaningful information from such vast quantities of data is now essential. Many automated or semi-automated approaches have been defined to achieve this goal. However, such efforts have resulted in classification schemes that are isolated or bespoke, and therefore it is necessary to form a standardised classification method. Sediment wave fields are the ideal platform for this as they maintain consistent morphologies across various spatial scales and influence the distribution of biological assemblages. Here, we apply an object-based image analysis (OBIA) workflow to multibeam bathymetry to compare the accuracy of four classifiers (two multilayer perceptrons, support vector machine, and voting ensemble) in identifying seabed sediment waves across three separate study sites. The classifiers are trained on high-spatial-resolution (0.5 m) multibeam bathymetric data from Cork Harbour, Ireland and are then applied to lower-spatial-resolution EMODnet data (25 m) from the Hemptons Turbot Bank SAC and offshore of County Wexford, Ireland. A stratified 10-fold cross-validation was enacted to assess overfitting to the sample data. Samples were taken from the lower-resolution sites and examined separately to determine the efficacy of classification. Results showed that the voting ensemble classifier achieved the most consistent accuracy scores across the high-resolution and low-resolution sites. This is the first object-based image analysis classification of bathymetric data able to cope with significant disparity in spatial resolution. Applications for this approach include benthic current speed assessments, a geomorphological classification framework for benthic biota, and a baseline for monitoring of marine protected areas.
Highlights
IntroductionVast amounts of high-resolution multibeam echosounder (MBES) data have been acquired and are further proposed in support of national (e.g., Ireland’s INFOMAR [1]and Norway’s MAREANO [2] projects) and global mapping efforts (e.g., the NipponFoundation-GEBCO Project Seabed 2030 [3])
Vast amounts of high-resolution multibeam echosounder (MBES) data have been acquired and are further proposed in support of national (e.g., Ireland’s INFOMAR [1]and Norway’s MAREANO [2] projects) and global mapping efforts
The model with the highest mean test accuracy and K-score was achieved by the Voting Ensemble (VE) and MLP2 classifiers, followed by the MLP1 algorithm
Summary
Vast amounts of high-resolution multibeam echosounder (MBES) data have been acquired and are further proposed in support of national (e.g., Ireland’s INFOMAR [1]and Norway’s MAREANO [2] projects) and global mapping efforts (e.g., the NipponFoundation-GEBCO Project Seabed 2030 [3]). 2021, 13, 2317 geomorphological features that affect the distribution of marine biological communities [8]. These features have a consistent morphology across a variety of spatial scales and are depicted across several scales of spatial resolution of data [9]. As such, they offer an ideal platform for the development of a scalable automated classification scheme [10,11,12]
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