eCite Digital Repository
Do marine substrates 'look' and 'sound' the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images
Citation
Lucieer, VL and Hill, Nicole and Barrett, NS and Nichol, S, Do marine substrates 'look' and 'sound' the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images, Estuarine, Coastal and Shelf Science, 117 pp. 94-106. ISSN 0272-7714 (2013) [Refereed Article]
Copyright Statement
Crown Copyright 2012
DOI: doi:10.1016/j.ecss.2012.11.001
Abstract
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 Details
Item Type: | Refereed Article |
---|---|
Keywords: | multibeam acoustic data, autonomous underwater vehicles, substrate prediction, image |
Research Division: | Agricultural, Veterinary and Food Sciences |
Research Group: | Fisheries sciences |
Research Field: | Fisheries management |
Objective Division: | Environmental Management |
Objective Group: | Terrestrial systems and management |
Objective Field: | Assessment and management of terrestrial ecosystems |
UTAS Author: | Lucieer, VL (Dr Vanessa Lucieer) |
UTAS Author: | Hill, Nicole (Dr Nicole Hill) |
UTAS Author: | Barrett, NS (Associate Professor Neville Barrett) |
ID Code: | 80950 |
Year Published: | 2013 |
Web of Science® Times Cited: | 88 |
Deposited By: | Sustainable Marine Research Collaboration |
Deposited On: | 2012-11-19 |
Last Modified: | 2017-10-24 |
Downloads: | 1 View Download Statistics |
Repository Staff Only: item control page