Mapping marine benthic biotopes using acoustic ground discrimination systems

Foster-Smith, R.L.; Sotheran, Ian

doi:10.1080/0143116031000066323a

Cited by 40 publications

(15 citation statements)

References 7 publications

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“…Increasing the range of habitats or biotopes will surely enlarge the possibility of overlapping signatures and consequently reduce the discrimination. The classification accuracy may be improved by grouping habitats or biotopes in broader categories, but with the risk of missing smallscale but significant units (Foster-Smith and Sotheran, 2003). Nevertheless, the information extracted by the discriminant analysis for the three scallop-density classes, with a percentage of correct classification of 91% for the areas that hold the greatest scallop densities, is significant for the objectives of this study.…”

Section: Discussionmentioning

confidence: 98%

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

Hutin

Simard

Archambault

2005

ICES Journal of Marine Science

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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.

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Section: Discussionmentioning

confidence: 98%

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

Hutin

Simard

Archambault

2005

ICES Journal of Marine Science

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“…The relative phase and phase delay between the four receivers is used for determining the angle of returning echo, which combined with the elapsed time, permits calculation of the distance to the scattering location at the seabed. The amplitude of the signal provides backscatter information, which is a measure of the relative texture and hardness of the seabed (Courtney and Shaw, 2000;Foster-Smith and Sotheran, 2003;Collier and Brown, 2005;Wienberg and Bartholomä , 2005). The across track sampling density of the GeoSwath system is 1.5 cm, and vertical resolution is estimated to be AE<2 cm (GeoAcoustics, 2004).…”

Section: Interferometric Sonarmentioning

confidence: 99%

Monitoring the volume and lateral spread of disposed sediments by acoustic methods, Oslo Harbor, Norway

Lepland

Bøe

Lepland

et al. 2009

Journal of Environmental Management

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“…The SeaBed Mapper 4.0 data files were filtered for erroneous points, which could be recognized by spurious depth data or missing substrate values. Spurious data records may be caused by very slow vessel speeds or sharp movements, aeration in the water column, interruption or failure in the hardware or by presence of fish [6,8]. The data were saved as .csv files, imported into ArcView 3.2 (ESRI), and converted to a point coverage.…”

Section: Post-processingmentioning

confidence: 99%

“…Single beam AGDS surveys produce a continuous string of points along a cruise track [5][6][7] which in shallow coastal waters (0-50 m depth) can be assigned a position (lat/long), depth, a hardness value (E1) and roughness value (E2) [8]. This can be carried out either in real time or after, during post-processing.…”

Section: Introductionmentioning

confidence: 99%

“…Many habitat mapping programs have adopted raster data models and employ algorithmic interpolation techniques such as inverse distance weighting [6,17,18] or kriging [6,19]. Interpolation procedures such as kriging are best suited to point data where fuzzy boundaries exist and classifications change by degrees across gradients, although this technique makes many assumptions about the spatial nature of the data that are hard to verify [8].…”

Section: Introductionmentioning

confidence: 99%

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Thematic accuracy assessment of acoustic seabed data for shallow benthic habitat mapping

Halley

Bruce

2007

International Journal of Environmental Studies

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A single beam acoustic ground discrimination system (AGDS) was used to survey 1.25 km 2 of shallow (< 20 m depth) seabed on the northeast coast of Tasmania, Australia. This paper investigates the uncertainties associated with the qualitative interpretation of real time and post-processed acoustic signal, and the effect of track spacing on the mapping of rocky reef distribution. The survey was repeated with different track spacing (50 m, 100 m, 200 m and 50 × 200 m shore normal) to investigate the influence of data density and 'knowledge based interpolation' validated against direct measurements made with an underwater video camera. Habitat area calculations varied substantially only with the 50 × 200 m transect. These results have important implications for the qualitative assessment and application of AGDS technology in shallow water marine habitat mapping.

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Mapping marine benthic biotopes using acoustic ground discrimination systems

Cited by 40 publications

References 7 publications

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

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

Monitoring the volume and lateral spread of disposed sediments by acoustic methods, Oslo Harbor, Norway

Thematic accuracy assessment of acoustic seabed data for shallow benthic habitat mapping

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