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• W2133469759 abstract "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. The unfiltered classes which showed agreement with the ground-truth data were then compared to the WCDE results. Comparison of surface areas reveals the overall percentage agreement is relatively constant with depth (67.0–70.0%), with Kappa coefficient increasing from k = 0.17–0.35 as depth (and surface area) increases. The results have demonstrated that both methods were more effective at detecting the presence of Laminaria sp. (82.6–77.3%) than Agarum cribrosum, (66.6–30.4%), and that the efficiency of prediction decreased with depth. Canopy volume derived from the WCDE analysis was between 1.21 × 106 m3 at" @default.
• W2133469759 created "2016-06-24" @default.
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• W2133469759 date "2011-01-01" @default.
• W2133469759 modified "2023-10-03" @default.
• W2133469759 title "Detection of deep water benthic macroalgae using image-based classification techniques on multibeam backscatter at Cashes Ledge, Gulf of Maine, USA" @default.
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• W2133469759 doi "https://doi.org/10.1016/j.ecss.2010.10.016" @default.
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