Dataset Information

Multibeam bathymetry data of discovery gap in the eastern North Atlantic.

ABSTRACT: We present a multibeam bathymetry dataset of Discovery Gap; a narrow gap in ridge of the Azores-Gibraltar Fracture Zone (eastern North Atlantic). The gap serves as gateway for the exchange of deep water between the Madeira and Iberian abyssal plains. A bathymetric survey was carried out during the 43rd cruise of the research vessel (R/V) Akademik Nikolaj Strakhov in October 2019. The data were collected with the acquisition system RESON SeaBat 7150 (12.5 kHz), using the integrated navigation system Applanix POS MV, and processed using QINSy software. The multibeam bathymetry data are presented in tabular format (ASCII-table (*.txt), spreadsheets (*.xlsx)), and as a digital elevation model (DEM) of the bottom relief. The DEM was created using ArcGIS software in ESRI-ASCII-grid (*.asc) and GeoTIFF raster (*.tif) formats with a spatial resolution of 100 m. We provide processed data that can be used directly for further studies. The dataset is available with the article.

SUBMITTER: Dudkov I

PROVIDER: S-EPMC7260289 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.

Dataset Information

Multibeam bathymetry data of discovery gap in the eastern North Atlantic.

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