Articles | Volume 15, issue 23
Biogeosciences, 15, 7347–7377, 2018
https://doi.org/10.5194/bg-15-7347-2018

Special issue: Assessing environmental impacts of deep-sea mining...

Biogeosciences, 15, 7347–7377, 2018
https://doi.org/10.5194/bg-15-7347-2018

Research article 13 Dec 2018

Research article | 13 Dec 2018

Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

Iason-Zois Gazis et al.

Data sets

Swath sonar multibeam EM122 bathymetry during SONNE cruise SO239 with links to raw data files, PANGAEA,2016 J. Greinert https://doi.org/10.1594/PANGAEA.859456

Seafloor images and raw context data along AUV tracks during SONNE cruises SO239 and SO242/1. GEOMAR - Helmholtz Centre for Ocean Research Kiel, PANGAEA, 2017. J. Greinert, T. Schoening, K. Köser and M. Rothenbeck https://doi.org/10.1594/PANGAEA.882349

Source code for the Compact Morphology-based Nodule Delineation (CoMoNoD) algorithm T. Schoening https://doi.org/10.1594/PANGAEA.875070

Results of nodule detection along AUV tracks during SONNE cruises SO239 and SO242/1 T. Schoening https://doi.org/10.1594/PANGAEA.883838

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Short summary
The use of high-resolution hydroacoustic and optic data acquired by an autonomous underwater vehicle can give us detailed sea bottom topography and valuable information regarding manganese nodules' spatial distribution. Moreover, the combined use of these data sets with a random forest machine learning model can extend this spatial prediction beyond the areas with available photos, providing researchers with a new mapping tool for further investigation and links with other data.
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