Remote sensing of habitat characteristics using echo metrics and image-based seabed classes

Abstract

Abstract The seabed can be classified using data from vertical, split-beam echosounders. This was demonstrated recently using a model parameterized with acoustic estimates of slope, roughness, normal-incidence backscattering strength, and variation of backscattering strength by frequency and incidence angle. These seabed classifications were interpreted and validated using published surficial geology maps, but the acoustic data indicated greater spatial variability. Here, classifications of sediment grain or feature size are ascribed to areas ∼10 m2. First, images of the seabed in the study area are ascribed, based on per cent coverage, to seven primary classes ranging from mud through high-relief rock, and 25 primary–secondary classes. Then, a refined seabed classifier, based on the acoustic model parameters is trained, using a nearest-neighbours algorithm, on a subset of the class data. The classifier accurately predicts 96% of the primary classes, and 93% of the primary–secondary classes from an independent data subset. These methods should be useful for characterizing, mapping, and quantifying potential seabed habitat domains of demersal fish and benthic invertebrates.