Acoustic detection of a scallop bed from a single-beam echosounder in the St. Lawrence

• Published in: ICES Journal of Marine Science
• Main Authors: Hutin, Estelle, Simard, Yvan, Archambault, Philippe
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2005
• Subjects: Ecology; Aquatic Science; Ecology, Evolution, Behavior and Systematics; Oceanography; Iceland
• Online Access: http://dx.doi.org/10.1016/j.icesjms.2005.03.007; http://academic.oup.com/icesjms/article-pdf/62/5/966/29151549/62-5-966.pdf

Abstract 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.