| Title | Merging AUV-based multibeam and image data to map the small-scale heterogeneity of Mn-nodule distribution |
| Download | Download (whole publication) |
| |
| Licence | Please note the adoption of the Open Government Licence - Canada
supersedes any previous licences. |
| Author | Alevizos, E; Schoenning, T; Köser, K; Snellen, M; Greinert, J |
| Source | Program 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, https://doi.org/10.4095/305404  Open Access |
| Links | GeoHab 2017
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| Year | 2017 |
| Publisher | Natural Resources Canada |
| Meeting | 2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017 |
| Document | open file |
| Lang. | English |
| Media | on-line; digital |
| Related | This publication is contained in Program and abstracts: 2017
GeoHab Conference, Dartmouth, Nova Scotia, Canada |
| File format | pdf |
| Subjects | marine 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 |
| Program | Offshore Geoscience |
| Released | 2017 09 26 |
| Abstract | AUVs 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. |
| Summary | (Plain Language Summary, not published)
The sixteenth annual GeoHab Conference was held this year (2017) at the Waterfront Campus of the Nova Scotia Community College in Dartmouth, Nova Scotia,
Canada. |
| GEOSCAN ID | 305404 |
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