the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantification of the fine-scale distribution of Mn-nodules: insights from AUV multi-beam and optical imagery data fusion
Evangelos Alevizos
Timm Schoening
Kevin Koeser
Mirjam Snellen
Jens Greinert
Abstract. Autonomous underwater vehicles (AUVs) offer unique possibilities for exploring the deep seafloor in high resolution over large areas. We highlight the results from AUV-based multibeam echosounder (MBES) bathymetry / backscatter and digital optical imagery from the DISCOL area acquired during research cruise SO242 in 2015. AUV bathymetry reveals a morphologically complex seafloor with rough terrain in seamount areas and low-relief variations in sedimentary abyssal plains which are covered in Mn-nodules. 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 m−2 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 backscatter mosaics.
In total, two unsupervised methods and one machine learning approach were utilized for backscatter classification and Mn-nodule predictive mapping. Bayesian statistical analysis was applied to the raw backscatter values resulting in six acoustic classes. In addition, Iterative Self-Organizing Data Analysis (ISODATA) clustering was applied to the backscatter mosaic and its statistics (mean, mode, 10th, and 90th quantiles) suggesting an optimum of six clusters as well. Part of the nodule metrics data was combined with bathymetry, bathymetric derivatives and backscatter statistics for predictive mapping of the Mn-nodule density using a Random Forest classifier. Results indicate that acoustic classes, predictions from Random Forest model and image-based nodule metrics show very similar spatial distribution patterns with acoustic classes hence capturing most of the fine-scale Mn-nodule variability. Backscatter classes reflect areas with homogeneous nodule density. A strong influence of mean backscatter, fine scale BPI and concavity of the bathymetry on nodule prediction is seen. These observations imply that nodule densities are generally affected by local micro-bathymetry in a way that is not yet fully understood. However, it can be concluded that the spatial occurrence of Mn-covered areas can be sufficiently analysed by means of acoustic classification and multivariate predictive mapping allowing to determine the spatial nodule density in a much more robust way than previously possible.
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Evangelos Alevizos et al.


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RC1: 'Revision of BG-2018-60, Alevizos et al.', Anonymous Referee #1, 06 Mar 2018
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AC1: 'Reply to review comments of referee #1', Evangelos Alevizos, 29 Mar 2018
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AC1: 'Reply to review comments of referee #1', Evangelos Alevizos, 29 Mar 2018
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RC2: 'Reviewer #2 - A very interesting paper, in need of a few technical clarifications', Philippe Blondel, 09 Apr 2018
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AC2: 'Reply to review points of reviewer 2', Evangelos Alevizos, 29 Apr 2018
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AC2: 'Reply to review points of reviewer 2', Evangelos Alevizos, 29 Apr 2018


-
RC1: 'Revision of BG-2018-60, Alevizos et al.', Anonymous Referee #1, 06 Mar 2018
-
AC1: 'Reply to review comments of referee #1', Evangelos Alevizos, 29 Mar 2018
-
AC1: 'Reply to review comments of referee #1', Evangelos Alevizos, 29 Mar 2018
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RC2: 'Reviewer #2 - A very interesting paper, in need of a few technical clarifications', Philippe Blondel, 09 Apr 2018
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AC2: 'Reply to review points of reviewer 2', Evangelos Alevizos, 29 Apr 2018
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AC2: 'Reply to review points of reviewer 2', Evangelos Alevizos, 29 Apr 2018
Evangelos Alevizos et al.
Evangelos Alevizos et al.
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Cited
3 citations as recorded by crossref.
- Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning I. Gazis et al. 10.5194/bg-15-7347-2018
- Importance of Spatial Autocorrelation in Machine Learning Modeling of Polymetallic Nodules, Model Uncertainty and Transferability at Local Scale I. Gazis & J. Greinert 10.3390/min11111172
- Seabed Mapping Using Shipboard Multibeam Acoustic Data for Assessing the Spatial Distribution of Ferromanganese Crusts on Seamounts in the Western Pacific J. Joo et al. 10.3390/min10020155