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  • 1
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    Grid files from two AUV missions in the DISCOL area during the SONNE cruise SO242/1 (2018)
    PANGAEA
    In:  Supplement to: Alevizos, Evangelos; Schoening, Timm; Köser, Kevin; Snellen, Mirjam; Greinert, Jens (in review): Quantification of the fine-scale distribution of Mn-nodules: insights from AUV multi-beam and optical imagery data fusion. Biogeosciences Discussions, 1-29, https://doi.org/10.5194/bg-2018-60
    Details
    Publication Date: 2024-02-16
    Description: The zip file contains grid files in UTM 16S resulted from AUV mutlibeam data processing and a table with descriptions of these grid files. AUV bathymetry data resulted from interpolation of multibeam depth measurements using the IDW algorithm in SAGA GIS. The AUV bathymetric derivatives (Bathymetric Position Index, Concavity, LS factor, and Terrain Ruggedness Index were calculated in SAGA GIS. The slope derivative was calculated in ArcMap. The AUV backscatter statistics (10th quantile, 90th quantile, mean and mode) were calculated in FMGT Geocoder. The Bayesian classification map was created in SAGA GIS using data from Bayesian classification in Matlab. The ISODATA classification map was created in SAGA GIS using the the AUV backscatter statistics and the Random Forest predictive map was created using the MGET toolbox in ArcMap and the AUV bathymetry, bathymetric derivatives and backscatter statistics data.
    Keywords: Autonomous underwater vehicle; AUV; JPI-OCEANS; JPI Oceans - Ecological Aspects of Deep-Sea Mining; JPIO-MiningImpact; SO242/1; SO242/1_47-1; SO242/1_47-1_AUV 6; Sonne_2
    Type: Dataset
    Format: application/zip, 26.7 MBytes
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 2
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    Performance of Multibeam Echosounder Backscatter-Based Classification for Monitoring Sediment Distributions Using Multitemporal Large-Scale Ocean Data Sets (2019)
    IEEE
    In:  IEEE Journal of Oceanic Engineering, 44 (1). pp. 142-155.
    Details
    Publication Date: 2022-01-31
    Description: Obtaining an overview of the spatial and temporal distribution of seabed sediments is of high interest for multiple research disciplines. Multibeam echosounders allow for the mapping of seabed sediments with high area coverage. In this paper, the repeatability of acoustic classification derived from multibeam echosounder backscatter is addressed. To this end, multibeam echosounder backscatter data acquired on the Cleaver Bank (North Sea) during five different surveys is employed using two different classification methods, i.e., a method based on the principal component analyses and the Bayesian technique. Different vessels were used for the different surveys. The comparison of the classification results between the different surveys indicates good repeatability. This repeatability demonstrates the potential of using backscatter for long-term environmental monitoring. However, the use of different classification methods results in somewhat different classification maps. Monitoring, therefore, requires the consistent use of a single method. Furthermore, it is found that the statistical characteristics of backscatter is such that clustering algorithms are less suited to discern the number of sediment types present in the study area. The Bayesian technique accounting for backscatter statistics is therefore recommended. A strong positive correlation between backscatter and median grain size for finer sediments (〈0.5 mm) using a frequency of 300 kHz is observed within the study area, but an ambiguity is found for sediments with median grain sizes 〉0.5 mm. Consequently, for the situation considered a unique assignment of sediment type to acoustic class is not possible for these coarser sediments.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    DOI: 10.1109/JOE.2018.2791878
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - peer-reviewed
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