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A 1:30,000 substratum map for an area off the north coast of Ireland is presented. The study area is bounded in the south by the Causeway coastline and in the north by the following coordinates: top left corner (6°43'36''W, 55°17'N) and top right corner (6°27'W, 55°17'N). This mapping has been made possible through the availability of full seafloor coverage multibeam swath bathymetry and backscatter data (both gridded to 1 m), together with ground-truthing data collected over the past 40 years. Bathymetry data were used to generate terrain indices such as slope, rugosity, aspect, fine- and broad-scale Benthic Position Index, whilst the backscatter data were interpreted visually, subjected to an unsupervised classification process using QTC Multiview, and combined with the bathymetry-derived parameters into a clustermap in ArcGIS. The resulting maps allowed us to divide the seabed into 10 distinct acoustic classes, which, linked to sediment samples, diver surveys, underwater video-tows and remotely operated vehicle surveys, were converted into a substratum map. This is the most accurate seafloor substratum map to date for the north coast of Ireland and could form the basis for more in-depth geological, biological and hydrodynamic studies of this highly dynamic coastline. [ABSTRACT FROM AUTHOR]

McGonigle, C., Brown, C. J., and Quinn, R. 2010. Insonification orientation and its relevance for image-based classification of multibeam backscatter. – ICES Journal of Marine Science, 67: 1010–1023.The use of multibeam echosounders (MBES) for mapping benthic habitat has gained widespread acceptability. Multibeam backscatter imagery provides an objective tool for scientists and managers to chronicle the extent and condition of the benthic resource. However, there are no standardized methods describing how best to process backscatter data to derive meaningful segmentations, although several acquisition parameters have been identified as having the capacity to affect the classification result. This research attempts to determine how the orientation at which a feature is insonified can affect classification outcome using commercially available software (QTC-Multiview), and to evaluate this significance related to vessel speed as a proxy for data density. A complex 2-km2 area of Stanton Banks, UK, was selected as the test site for the study. The area was insonified using a Kongsberg Simrad EM1002 MBES at perpendicularly opposing orientations, at two different vessel speeds within the same 24-h period. The classifications displayed 53% (k = 0.396) similarity at 4 m s−1 and 49% (k = 0.342) at 2 m s−1 from opposing orientations. Common orientations at different speeds were 68% (k = 0.583) similar (east–west) and 53% (k = 0.384; north–south). Most of the variation was in topographically complex areas, which coincided with shallow depths (<60 m). Meteorological and oceanographic conditions at the time the data were collected were evaluated as having had the potential to influence the outcome of the classifications. Interpretation of the results suggests that the orientation at which insonification occurs has a greater ability to influence the classification result than vessel speed using an image-based technique. [ABSTRACT FROM PUBLISHER]

Efforts to develop a procedurally robust method for automated classification of multibeam backscatter have taken a variety of approaches (e.g., image-based, textural, angular range analysis). For image-based classification, little research has focused on the roles of operational parameters of vessel and sonar system in affecting the final classification. Repeat multibeam surveys (2005 and 2006) conducted at the same area with different sounding densities were classified using QTC-Multiview. Comparison of class areas revealed 78% agreement between classifications derived from the two surveys. Cross-tabulation of ground truth video and class demonstrate 71% agreement in the low-density survey and 77% for the high-density. Differences between classifications are primarily attributed to variation in along track data density, errors in the compensation process, and/ or insufficient quality control of the input data. Natural change detection at the scales observed was determined not to be practically discernable from the errors associated with the classification process. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, efforts have increased to develop quantitative, computer-directed methods for segmentation of multibeam (MBES) backscatter data. This study utilises MBES backscatter data acquired at Stanton Banks (UK) and subsequently processed through the QTC-Multiview software environment in a bid to evaluate the program''s ability to perform unsupervised classification. Statistical comparison with ground-truth data (grab, stills and video) enabled cross validation of acoustic segmentation and biological assemblages observed at the site. 132 unspecified variables were extracted from user-specified rectangular patches of the backscatter image, reduced to three vectors by PCA, then clustered and classified by the software. Multivariate analyses of ground-truth data were conducted on 75 stills images and 51 grab samples. Video footage coincident with the stills was divided into 30s segments and coded by dominant substrate and species. Cross tabulation determined the interrelationship between software classifications, multivariate analysis of the biological assemblages and coded video segments. Multiview optimally identified 19 classes using the automated clustering engine. These were revised to 6 habitats a posteriori, using combined analysis of ground-truth data and Multiview data products. These habitats broadly correspond to major physiographic provinces within the region. Multivariate statistical analysis reveals low levels of assemblage similarity (<35%) for samples occurring within Multiview classes, irrespective of the mode of acquisition. Coded video data is more spatially appropriate than the other methods of ground-truthing investigated, although it is less well suited to the extraction of truly quantitative data. Multivariate analysis indicates assemblages within physiographically distinct Multiview classes have a low degree of biological similarity, supporting the notion that abiotic proxies may be contraindicative of benthic assemblage variations. QTC-Multiview performs well as a mechanism for computer-assisted segmentation of MBES backscatter imagery into acoustic provinces; however a degree of caution is required prior to ascribing ecological significance to these classifications. [Copyright &y& Elsevier]

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 <24 m water depth, 1.82 × 106 m3 at <30 m and 2.45 × 106 m3 at <40 m. These results suggest that the presence of benthic macrophytes has a significant capacity to affect image-based classification of acoustic data, and highlights the fact that multibeam backscatter and image-based classification have significant potential for benthic macroalgal research. This is beneficial to help refine segmentations of substrates, adding valuable contextual information about biological characteristics of infaunal and epifaunal benthic communities. [Copyright &y& Elsevier]