Seafloor morphology and substrate mapping in the Gulf of St Lawrence, Canada, using machine learning approaches

Abstract

Detailed maps of seafloor substrata and morphology can act as valuable proxies for predicting and understanding the distributions of benthic communities and are important for guiding conservation initiatives. High resolution acoustic remote sensing data can facilitate the production of detailed seafloor maps, but are cost-prohibitive to collect and not widely available. In the absence of targeted high resolution data, global bathymetric data of a lower resolution, combined with legacy seafloor sampling data, can provide an alternative for generating maps of seafloor substrate and morphology. Here we apply regression random forest to legacy data in the Gulf of St Lawrence, Canada, to generate a map of seabed sediment distribution. We further apply k-means clustering to a principal component analysis output to identify seafloor morphology classes from the GEBCO bathymetric grid. The morphology classification identified most morphological features but could not discriminate valleys and canyons. The random forest results were in line with previous sediment mapping work done in the area, but a large proportion of zero values skewed the explained variance. In both models, improvements may be possible with the introduction of more predictor variables. These models prove useful for generating regional seafloor maps that may be used for future management and conservation applications.