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

Abstract

P. 2005. Acoustic detection of a scallop bed from a single-beam echosounder in the St. Lawrence. e ICES Journal of Marine Science, 62: 966e983.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 volumebackscattering strength (S v ) 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 S v 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 S v PCA C K-means approach presented similar depth-dependence, but, the PCA C K-means on the S v 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 S v signature of the scallop bed is presented, and the most discriminant part of the acoustic signal is identified.Published by Elsevier Ltd on behalf of International Council for the Exploration of the Sea.

Mapping the Shallow Water Seabed Habitat With the SHOALS

“…Within this outlook, it will be relevant to find out specific sets of extracted variables that could be used to correctly and significantly identify the nature of the habitat surveyed and the influence of species aerial percentages on SHOALS signatures. Depth variation has been shown in the literature to affect our capacity to detect and differentiate remote-sensed signatures [9], [10], [13]. The depth regression that considers the variance due to other parameters than depth obviously generated some interesting classifications.…”

“…This approach is not only time consuming and costly but also highly disturbing for the benthic biotopes (i.e., habitats and their associated communities), and only provides scattered discrete data across the study area. Recent improvements in single-beam echosounders, sidescan sonar, and signal processing now provide effective tools to explore the seabed as a complement to the physical sampling methods traditionally used to carry out benthic surveys [9], [10].…”

Seabed habitat mapping techniques: an overview of the performance of various systems

“…More recently acoustic ground-discrimination systems (AGDS) have been developed to detect the acoustic-reflectance properties of the seabed. Indeed, the nature of bottom echoes is influenced not only by basic sediment grainsize parameters, sediment sorting, microtopography, sediment density and porosity, but also by the presence, concentration and type of benthic fauna and flora (TSEMAHMAN & COLLINS, 1997;COLLINS & GALLOWAY, 1998;BORNHOLD et al, 1999;HAMILTON et al, 1999;KLOSER et al, 2001;ANDERSON et al, 2002;HUTIN et al, 2005). RoxAnnì (CHIVERS et al, 1990) and QTC VIEWì (COLLINS et al, 1996) are two widely used AGDSs to extract shape, energy or both features contained in the bottom acoustic signals.…”

A comparison of multi-class support vector machine and classification tree methods for hydroacoustic classification of fish-schools in Chile

“…Several methods have been compared (Haralabous and Georgakarakos, 1996;Simmonds et al, 1996;Woodd-Walker et al, 2003;Hutin et al, 2005;Robotham et al, 2010), however, the performances of classification trees and support vector machine methods have not. Classification trees have been used in applications related to ecology, botany and medical diagnosis (Breiman et al, 1984;Ripley, 1996;De'Ath and Fabricius, 2000) and only recently for species identification of fish-school echotraces (Fernandes, 2009).…”