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  • Alevizos, Evangelos  (5)
  • 1
    Online Resource
    Online Resource
    MDPI AG ; 2018
    In:  Geosciences Vol. 8, No. 12 ( 2018-11-30), p. 446-
    In: Geosciences, MDPI AG, Vol. 8, No. 12 ( 2018-11-30), p. 446-
    Abstract: This study presents a novel approach, based on high-dimensionality hydro-acoustic data, for improving the performance of angular response analysis (ARA) on multibeam backscatter data in terms of acoustic class separation and spatial resolution. This approach is based on the hyper-angular cube (HAC) data structure which offers the possibility to extract one angular response from each cell of the cube. The HAC consists of a finite number of backscatter layers, each representing backscatter values corresponding to single-incidence angle ensonifications. The construction of the HAC layers can be achieved either by interpolating dense soundings from highly overlapping multibeam echo-sounder (MBES) surveys (interpolated HAC, iHAC) or by producing several backscatter mosaics, each being normalized at a different incidence angle (synthetic HAC, sHAC). The latter approach can be applied to multibeam data with standard overlap, thus minimizing the cost for data acquisition. The sHAC is as efficient as the iHAC produced by actual soundings, providing distinct angular responses for each seafloor type. The HAC data structure increases acoustic class separability between different acoustic features. Moreover, the results of angular response analysis are applied on a fine spatial scale (cell dimensions) offering more detailed acoustic maps of the seafloor. Considering that angular information is expressed through high-dimensional backscatter layers, we further applied three machine learning algorithms (random forest, support vector machine, and artificial neural network) and one pattern recognition method (sum of absolute differences) for supervised classification of the HAC, using a limited amount of ground truth data (one sample per seafloor type). Results from supervised classification were compared with results from an unsupervised method for inter-comparison of the supervised algorithms. It was found that all algorithms (regarding both the iHAC and the sHAC) produced very similar results with good agreement ( 〉 0.5 kappa) with the unsupervised classification. Only the artificial neural network required the total amount of ground truth data for producing comparable results with the remaining algorithms.
    Type of Medium: Online Resource
    ISSN: 2076-3263
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655946-8
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  • 2
    In: Marine Geology, Elsevier BV, Vol. 370 ( 2015-12), p. 31-42
    Type of Medium: Online Resource
    ISSN: 0025-3227
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 1500648-7
    detail.hit.zdb_id: 2181-7
    SSG: 13
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  • 3
    In: Biogeosciences, Copernicus GmbH, Vol. 15, No. 8 ( 2018-04-27), p. 2525-2549
    Abstract: Abstract. In this study, ship- and autonomous underwater vehicle (AUV)-based multibeam data from the German ferromanganese-nodule (Mn-nodule) license area in the Clarion–Clipperton Zone (CCZ; eastern Pacific) are linked to ground-truth data from optical imaging. Photographs obtained by an AUV enable semi-quantitative assessments of nodule coverage at a spatial resolution in the range of meters. Together with high-resolution AUV bathymetry, this revealed a correlation of small-scale terrain variations (〈 5 m horizontally, 〈 1 m vertically) with nodule coverage. In the presented data set, increased nodule coverage could be correlated with slopes 〉 1.8∘ and concave terrain. On a more regional scale, factors such as the geological setting (existence of horst and graben structures, sediment thickness, outcropping basement) and influence of bottom currents seem to play an essential role for the spatial variation of nodule coverage and the related hard substrate habitat. AUV imagery was also successfully employed to map the distribution of resettled sediment following a disturbance and sediment cloud generation during a sampling deployment of an epibenthic sledge. Data from before and after the “disturbance” allow a direct assessment of the impact. Automated image processing analyzed the nodule coverage at the seafloor, revealing nodule blanketing by resettling of suspended sediment within 16 h after the disturbance. The visually detectable impact was spatially limited to a maximum of 100 m distance from the disturbance track, downstream of the bottom water current. A correlation with high-resolution AUV bathymetry reveals that the blanketing pattern varies in extent by tens of meters, strictly following the bathymetry, even in areas of only slightly undulating seafloor (〈1 m vertical change). These results highlight the importance of detailed terrain knowledge when engaging in resource assessment studies for nodule abundance estimates and defining mineable areas. At the same time, it shows the importance of high-resolution mapping for detailed benthic habitat studies that show a heterogeneity at scales of 10 to 100 m. Terrain knowledge is also needed to determine the scale of the impact by seafloor sediment blanketing during mining operations.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2158181-2
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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2018
    In:  Marine Geophysical Research Vol. 39, No. 1-2 ( 2018-6), p. 289-306
    In: Marine Geophysical Research, Springer Science and Business Media LLC, Vol. 39, No. 1-2 ( 2018-6), p. 289-306
    Type of Medium: Online Resource
    ISSN: 0025-3235 , 1573-0581
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 414196-9
    detail.hit.zdb_id: 1478200-5
    SSG: 16,13
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  • 5
    In: Biogeosciences, Copernicus GmbH, Vol. 15, No. 23 ( 2018-12-13), p. 7347-7377
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2158181-2
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