Articles | Volume 15, issue 23
https://doi.org/10.5194/bg-15-7347-2018
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, Timm Schoening, Evangelos Alevizos, and Jens Greinert

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Cited articles

Alevizos, E., Schoening, T., Koeser, K., Snellen, M., and Greinert, J.: Quantification of the fine-scale distribution of Mn-nodules: insights from AUV multi-beam and optical imagery data fusion, Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-60, in review, 2018. 
Anselin, L.: Local Indicators of Spatial Association – LISA, Geogr. Anal., 27, 93–115, https://doi.org/10.1111/j.1538-4632.1995.tb00338.x, 1995. 
Atmanand, M. A. and Ramadass, G. A.: Concepts of Deep-Sea Mining Technologies, in: Deep-Sea Mining, edited by: Sharma, R., Resource Springer, Cham. Online ISBN 978-3-319-52557-0, https://doi.org/10.1007/978-3-319-52557-0_6, 2017. 
Bellingham, J.: Autonomous underwater vehicles (AUVs), in: Encyclopedia of Ocean Sciences, edited by: Steele, H., Academic Press, San Diego, 212–216, https://doi.org/10.1006/rwos.2001.0303, 2001. 
Bingham, D., Drake, T., Hill, A., and Lott, R.: The Application of Autonomous Underwater Vehicle (AUV) Technology in the Oil Industry – Vision and Experiences. FIG XXII International Congress Washington, DC USA, 19–26 April, 1–13, 2002. 
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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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