© 2005 International Council for the Exploration of the Sea
Acoustic detection of a scallop bed from a single-beam echosounder in the St. Lawrence
a Institut des Sciences de la Mer de Rimouski, Université du Québec à Rimouski 310 allée des Ursulines, Rimouski, Québec G5L 3A1, Canada
b Department of Fisheries and Oceans, Institut Maurice-Lamontagne 850 route de la Mer, Mont-Joli, Québec G5H 3Z4, Canada
*Correspondence to Y. Simard: tel: +1 418 723 1986, ext 1563; fax: +1 418 724 1842. e-mail: yvan_simard{at}uqar.qc.ca.
Single-beam seabed echoes combined with epi-macrobenthos photographs were used to remotely detect a scallop bed and characterize the specific acoustic signal of Iceland scallop (Chlamys islandica). A dense scallop bed was surveyed in 2002, with a QTC VIEW Series IV acoustic ground-discrimination system (AGDS) connected to a 38 kHz, 7° split-beam SIMRAD EK60 scientific echosounder. In 2003, a 50 kHz, 42° single-beam SUZUKI ES-2025 echosounder was connected to a QTC VIEW Series V AGDS. The QTC VIEW data were analysed with QTC IMPACT following the standard procedures and classified into acoustic classes. Several approaches were tested: unsupervised and supervised survey strategies directed to specific benthic communities. The SIMRAD EK60 seabed volume-backscattering strength (Sv) was submitted to a principal component analysis (PCA), before and after removal of a depth trend, and the scores on the first 10 principal components were classed by a K-means cluster analysis. The same seabed Sv data were submitted to stepwise discriminant analysis whose training data sets were defined with the ground-truth photographs using different groupings: biotope types, community types, and finally scallop-density classes. All the QTC AGDS approaches failed to reveal the scallop bed, community structures, or biotopes. The QTC classifications mimicked the bathymetry with a strong correlation of the acoustic classes with depth. The seabed Sv PCA + K-means approach presented similar depth-dependence, but, the PCA + K-means on the Sv residuals revealed the scallop bed. The discriminant analysis was the best solution for the scallop density with a general classification success rate of 75% and up to 91% for the highest density class. The Sv signature of the scallop bed is presented, and the most discriminant part of the acoustic signal is identified.
Keywords: acoustic ground discrimination, benthic biotopes, habitat mapping, QTC VIEW, remote sensing, scallop bed, seabed classification
Received 27 August 2004; accepted 10 March 2005.
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