Characterisation of seafloor substrate using advanced processing of multibeam bathymetry, backscatter, and sidescan sonar in Table Bay, South Africa

Abstract

A method to map seafloor substrates using machine learning, based on geophysical data including multibeam bathymetry, backscatter, and side-scan sonar, is currently being developed. Results from a case study in Table Bay, southwestern South Africa, are presented here, showing a method of physical seafloor classification that uses a number of statistical algorithms and software programs. In the first step of the process, a customised tool was created within ArcGIS using python scripting language to classify seafloor bathymetry, which can be applied to areas beyond South Africa. The tool created by the authors was based on pioneering work done by the National Oceanic and Atmospheric Administration on a benthic terrain modelling toolbox. In step two, multibeam bathymetry, backscatter and side-scan sonar data processed using Qimera, Fledermaus Geocoder Toolbox, and Navlog processing software, were classified using machine learning techniques including Decision Trees, Random Forests, and k-means clustering computer algorithms. The results from these algorithms were compared to classify the seafloor substrate distribution. Our results have allowed a comparison of advantages and disadvantages of each machine learning technique and we found that the k-means clustering techniques were the simplest to implement and understand and worked best based on their seafloor segmentation capabilities in Table Bay, with all three data sets (multibeam bathymetry, backscatter and side-scan sonar). In future research, ground-truthing methods (for example underwater video and grab samples) will be used to validate the interpreted data to create accurate seafloor substrate maps. This work provides the initial steps to develop a holistic predictive tool that classifies geophysical data into substrate maps using machine-learning techniques. The maps can be used to model biological communities and to produce benthic habitat maps for use in marine science and management.