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TitleMerging AUV-based multibeam and image data to map the small-scale heterogeneity of Mn-nodule distribution
DownloadFree download (whole publication) (pdf 2010 KB)
AuthorAlevizos, E; Schoenning, T; Köser, K; Snellen, M; Greinert, J
SourceProgram and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; by Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Geological Survey of Canada, Open File 8295, 2017 p. 34,
PublisherNatural Resources Canada
Meeting2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017
Documentopen file
Mediaon-line; digital
RelatedThis publication is contained in Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; (2017). Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada, Geological Survey of Canada, Open File 8295
File formatpdf
Subjectsmarine geology; surficial geology/geomorphology; geophysics; mapping techniques; oceanography; bathymetry; seafloor topography; bedrock geology; geophysical surveys; acoustic surveys, marine; sonar surveys; side-scan sonar; marine sediments; manganese nodules; seamounts; modelling; geological mapping; geological mapping techniques; ground-truthing
LinksGeoHab 2017
Released2017 09 26
AbstractAUVs offer the unique possibilities for exploring the deep sea seafloor in high resolution over large areas. We highlight the results from AUV-based multibeam echosounder (MBES) bathymetry / backscatter and digital imagery from the DISCOL area acquired during SO242 in 2015. AUV bathymetry reveals a morphologically complex seafloor with rough terrain in seamount areas and low-relief variations in the Mn-nodule covered sedimentary abyssal plain. Backscatter provides valuable information about the seafloor type and particularly about the influence of Mn-nodules on the response of the transmitted acoustic signal. Primarily Mn-nodule abundances were determined by means of automated nodule detection on AUV seafloor imagery and nodule metrics such as nodules/image and nodules/m2 were calculated automatically for each image allowing further spatial analysis within GIS in conjunction with the acoustic data. AUV-based backscatter was clustered using both raw data and corrected mosaics.
In total two unsupervised methods and one machine learning approach were utilized for backscatter classification and Mn-nodule mapping. Bayesian statistical analysis was applied to the raw backscatter values resulting in six acoustic classes. In addition ISODATA clustering was applied to the backscatter mosaic and its statistics (mean, mode, 90th and 10th quartile) suggesting an optimum of six clusters as well. Part of the nodule metrics data was used together with bathymetry, derivatives (slope, rugosity, BPI, concavity) and backscatter statistics for predictive modelling of the Mn-nodule density using random forests. Results show that acoustic classes, predictions from random forest modelling and image-based nodule metrics show very similar spatial distribution patterns with acoustic classes hence capturing most of the local Mn-nodule variability. A strong correlation of nodule occurrence with mean backscatter, fine scale BPI and concavity of the bathymetry can be seen; backscatter classes reveal a gradient of decreasing nodule occurrence in N-S direction which is also evident in AUV imagery. These observations imply that nodule abundances are affected in general terms by local micro-bathymetry in a way that is not yet fully understood. However it can be concluded that nodule abundances can be sufficiently analysed by means of acoustic classification and multivariate predictive mapping which allows predicting the spatial occurrence of Mn-covered areas as important habitat in the deep sea in a much more robust way than previously possible.