A multivariate analytical method to characterize sediment attributes from high-frequency acoustic backscatter and ground-truthing data (Jade Bay, German North Sea coast)

One of the burning issues on the topic of acoustic seabed classification is the lack of solid, repeatable, statistical procedures that can support the verification of acoustic variability in relation to seabed properties. Acoustic sediment classification schemes often lead to biased and subjective interpretation, as they ultimately aim at an oversimplified categorization of the seabed based on conventionally defined sediment types. However, grain size variability alone cannot be accounted for acoustic diversity, which will be ultimately affected by multiple physical processes, scale of heterogeneity, instrument settings, data quality, image processing and segmentation performances. Understanding and assessing the weight of all of these factors on backscatter is a difficult task, due to the spatially limited and fragmentary knowledge of the seabed from of direct observations (e.g. grab samples, cores, videos). In particular, large-scale mapping requires an enormous availability of ground-truthing data that is often obtained from heterogeneous and multidisciplinary sources, resulting into a further chance of misclassification. Independently from all of these limitations, acoustic segments still contain signals for seabed changes that, if appropriate procedures are established, can be translated into meaningful knowledge. In this study we design a simple, repeatable method, based on multivariate procedures, with the scope to classify a 100 km(2), high-frequency (450 kHz) sidescan sonar mosaic acquired in the year 2012 in the shallow upper-mesotidal inlet of the Jade Bay (German North Sea coast). The tool used for the automated classification of the backscatter mosaic is the QTC SWATHVIEW (TM) software. The ground-truthing database included grab sample data from multiple sources (2009-2011). The method was designed to extrapolate quantitative descriptors for acoustic backscatter and model their spatial changes in relation to grain size distribution and morphology. The modelled relationships were used to: 1) asses the automated segmentation performance, 2) obtain a ranking of most discriminant seabed attributes responsible for acoustic diversity, 3) select the best-fit ground-truthing information to characterize each acoustic class. Using a supervised Linear Discriminant Analysis (LDA), relationships between seabed parameters and acoustic classes discrimination were modelled, and acoustic classes for each data point were predicted. The model predicted a success rate of 63.5%. An unsupervised LDA was used to model relationships between acoustic variables and clustered seabed categories with the scope of identifying misrepresentative ground-truthing data points. The model prediction scored a success rate of 50.8%. Misclassified data points were disregarded for final classification. Analyses led to clearer, more accurate appreciation of relationship patterns and improved understanding of site-specific processes affecting the acoustic signal. Value to the qualitative classification output was added by comparing the latter with a more recent set of acoustic and ground-truthing information (2014). Classification resulted in the first acoustic sediment map ever produced in the area and offered valuable knowledge for detailed sediment variability. The method proved to be a simple, repeatable strategy that may be applied to similar work and environments.