Detection of deep water benthic macroalgae using image-based classification techniques on multibeam backscatter at Cashes Ledge,Gulf of Maine,USA |
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| Authors: | Chris McGonigle Jonathan H Grabowski Craig J Brown Thomas C Weber Rory Quinn |
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| Institution: | 1. Centre for Coastal and Marine Research, University of Ulster, Coleraine, Co. Derry, Northern Ireland;2. Gulf of Maine Research Institute, 350 Commercial Street, Portland, ME ME04101, USA;3. Center for Coastal and Ocean Mapping, University of New Hampshire, Durham, NH 03824, USA |
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| Abstract: | Benthic macroalgae form an important part of temperate marine ecosystems, exhibiting a complex three-dimensional character which represents a vital foraging and spawning ground for many juvenile fish species. In this research, image-based techniques for classification of multibeam backscatter are explored for the detection of benthic macroalgae at Cashes Ledge in the Gulf of Maine, USA. Two classifications were performed using QTC-Multiview, differentiated by application of a threshold filter, and macroalgal signatures were independently extracted from the raw sonar datagrams in Matlab. All classifications were validated by comparison with video ground-truth data. The unfiltered classification shows a high degree of complexity in the shallowest areas within the study site; the filtered demonstrates markedly less variation by depth. The unfiltered classification shows a positive agreement with the video ground-truth data; 82.6% of observations recording Laminaria sp., 39.1% of Agarum cribrosum and 100.0% (n = 3) of mixed macroalgae occur within the same acoustically distinct group of classes. These are discrete from the 8.1% recorded agreement with absences and nulls (>40 m) of macrophytes (n = 32) from a total of 86 ground-truth locations. The results of the water column data extraction (WCDE) show similar success, accurately predicting 78.3% of Laminaria sp. and 30.4% of A. cribrosum observations. |
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| Keywords: | image-based multibeam sonar backscatter macroalgae kelps classification |
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