Skip to main content

IMIS

A new integrated search interface will become available in the next phase of marineinfo.org.
For the time being, please use IMIS to search available data

 

[ report an error in this record ]basket (0): add | show Print this page

Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning
Gazis, I.-Z.; Schoening, T.; Alevizos, E.; Greinert, J. (2018). Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning. Biogeosciences 15(23): 7347-7377. https://dx.doi.org/10.5194/bg-15-7347-2018
In: Gattuso, J.P.; Kesselmeier, J. (Ed.) Biogeosciences. Copernicus Publications: Göttingen. ISSN 1726-4170; e-ISSN 1726-4189, more
Peer reviewed article  

Available in  Authors 

Keyword
    Marine/Coastal

Authors  Top 
  • Gazis, I.-Z.
  • Schoening, T.
  • Alevizos, E.
  • Greinert, J., more

Abstract
    In this study, high-resolution bathymetric multibeam and optical image data, both obtained within the Belgian manganese (Mn) nodule mining license area by the autonomous underwater vehicle (AUV) Abyss, were combined in order to create a predictive random forests (RF) machine learning model. AUV bathymetry reveals small-scale terrain variations, allowing slope estimations and calculation of bathymetric derivatives such as slope, curvature, and ruggedness. Optical AUV imagery provides quantitative information regarding the distribution (number and median size) of Mn nodules. Within the area considered in this study, Mn nodules show a heterogeneous and spatially clustered pattern, and their number per square meter is negatively correlated with their median size. A prediction of the number of Mn nodules was achieved by combining information derived from the acoustic and optical data using a RF model. This model was tuned by examining the influence of the training set size, the number of growing trees (ntree), and the number of predictor variables to be randomly selected at each node (mtry) on the RF prediction accuracy. The use of larger training data sets with higher ntree and mtry values increases the accuracy. To estimate the Mn-nodule abundance, these predictions were linked to ground-truth data acquired by box coring. Linking optical and hydroacoustic data revealed a nonlinear relationship between the Mn-nodule distribution and topographic characteristics. This highlights the importance of a detailed terrain reconstruction for a predictive modeling of Mn-nodule abundance. In addition, this study underlines the necessity of a sufficient spatial distribution of the optical data to provide reliable modeling input for the RF.

All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors