A bottom-up methodology for integrating underwater video and acoustic mapping for seafloor substrate classification

A method that links acoustic mapping data to underwater video observations of seafloor substrate is described for use in defining fish habitat. Three study areas in the Aleutian Islands were acoustically mapped using sidescan and multibeam sonar. The sidescan sonar data were used to compute average reflectivity (hardness) and seafloor complexity. The multibeam depth data were used to determine local slope, rugosity (seafloor roughness) and relative height. Underwater video was collected from three to four transects in each of the three study areas. The underwater video was used to classify the seafloor into nine observed primary and secondary substrate classes. A statistical relationship between the observed (video) and the remotely sensed (acoustic) seafloor characteristics was estimated using a classification tree. The best classification tree utilized rugosity, reflectivity and complexity data and produced misclassification rates of less than 25% overall. Mean grain size of sediment samples was not strongly related to the acoustic data. Error rates were highest for those substrate classes with the smallest number of data points. The results highlight the need for adequate sample sizes and coverage of all potential substrate types when groundtruthing acoustic maps.