In this study we outline the techniques used to transform multibeam acoustic data into spatial layers that can be used for predictive habitat modeling. The results allow us to identify multibeam attributes which may act as potential surrogates for environmental variables that influence biodiversity and define which variables may be reliable for predicting the distribution of species in temperate waters. We explore a method for analyzing the spatially coincident multibeam bathymetric and backscatter data from shallow coastal waters to generate spatial data products that relate to the classes derived from fine-scale visual imagery obtained using an autonomous underwater vehicle (AUV). Classifications of the multibeam data are performed for substrate, rugosity and sponge cover. Overall classification accuracies for the classes associated with substratum, rugosity and sponge structure were acceptable for biodiversity assessment applications. Accuracies were highest for rugosity classes at 65%, followed by substratum classes at 64% and then sponge structure classes at 57%. Random forest classifiers at a segmentation scale of 30 performed best in classifying substratum and rugosity, while K-nearest neighbour classifiers performed best for sponge structure classes, with no difference in accuracy between scale 30 and 60. Incorporating backscatter variables using segmentation improved the overall accuracy achieved by the best performing model by between 1% (rugosity) and 9 % (substratum) above using topographic variables only in the grid-based analyses. Results suggest that imagebased backscatter classification show considerable promise for the interpretation of multibeam sonar data for the production of substrate maps. A particular outcome of this research is to provide appropriate and sufficiently fine-scale physical covariates from the multibeam acoustic data to adequately inform models predicting the distribution of biodiversity on benthic reef habitats.

Item Type:	Refereed Article
Keywords:	multibeam acoustic data, autonomous underwater vehicles, substrate prediction, image
Research Division:	Agricultural and Veterinary Sciences
Research Group:	Fisheries Sciences
Research Field:	Fisheries Management
Objective Division:	Environment
Objective Group:	Ecosystem Assessment and Management
Objective Field:	Ecosystem Assessment and Management of Marine Environments
Author:	Lucieer, VL (Dr Vanessa Lucieer)
Author:	Hill, Nicole (Dr Nicole Hill)
Author:	Barrett, NS (Associate Professor Neville Barrett)
ID Code:	80950
Year Published:	2013
Web of Science® Times Cited:	42
Deposited By:	Sustainable Marine Research Collaboration
Deposited On:	2012-11-19
Last Modified:	2017-10-24
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