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  • 1
    Online Resource
    Online Resource
    American Meteorological Society ; 2021
    In:  Journal of Atmospheric and Oceanic Technology Vol. 38, No. 2 ( 2021-02), p. 141-154
    In: Journal of Atmospheric and Oceanic Technology, American Meteorological Society, Vol. 38, No. 2 ( 2021-02), p. 141-154
    Abstract: The cold-water coral Lophelia pertusa builds up bioherms that sustain high biodiversity in the deep ocean worldwide. Photographic monitoring of the polyp activity represents a helpful tool to characterize the health status of the corals and to assess anthropogenic impacts on the microhabitat. Discriminating active polyps from skeletons of white Lophelia pertusa is usually time consuming and error prone due to their similarity in color in common red–green–blue (RGB) camera footage. Acquisition of finer-resolved spectral information might increase the contrast between the segments of polyps and skeletons, and therefore could support automated classification and accurate activity estimation of polyps. For recording the needed footage, underwater multispectral imaging systems can be used, but they are often expensive and bulky. Here we present results of a new, lightweight, compact, and low-cost deep-sea tunable LED-based underwater multispectral imaging system (TuLUMIS) with eight spectral channels. A branch of healthy white Lophelia pertusa was observed under controlled conditions in a laboratory tank. Spectral reflectance signatures were extracted from pixels of polyps and skeletons of the observed coral. Results showed that the polyps can be better distinguished from the skeleton by analysis of the eight-dimensional spectral reflectance signatures compared to three-channel RGB data. During a 72-h monitoring of the coral with a half-hour temporal resolution in the laboratory, the polyp activity was estimated based on the results of the multispectral pixel classification using a support vector machine (SVM) approach. The computational estimated polyp activity was consistent with that of the manual annotation, which yielded a correlation coefficient of 0.957.
    Type of Medium: Online Resource
    ISSN: 0739-0572 , 1520-0426
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2021720-1
    detail.hit.zdb_id: 48441-6
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  • 2
    In: Sensors, MDPI AG, Vol. 16, No. 2 ( 2016-01-28), p. 164-
    Type of Medium: Online Resource
    ISSN: 1424-8220
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
    detail.hit.zdb_id: 2052857-7
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  • 3
    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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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2017
    In:  Scientific Reports Vol. 7, No. 1 ( 2017-10-17)
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-10-17)
    Abstract: Poly-metallic nodules are a marine resource considered for deep sea mining. Assessing nodule abundance is of interest for mining companies and to monitor potential environmental impact. Optical seafloor imaging allows quantifying poly-metallic nodule abundance at spatial scales from centimetres to square kilometres. Towed cameras and diving robots acquire high-resolution imagery that allow detecting individual nodules and measure their sizes. Spatial abundance statistics can be computed from these size measurements, providing e.g. seafloor coverage in percent and the nodule size distribution. Detecting nodules requires segmentation of nodule pixels from pixels showing sediment background. Semi-supervised pattern recognition has been proposed to automate this task. Existing nodule segmentation algorithms employ machine learning that trains a classifier to segment the nodules in a high-dimensional feature space. Here, a rapid nodule segmentation algorithm is presented. It omits computation-intense feature-based classification and employs image processing only. It exploits a nodule compactness heuristic to delineate individual nodules. Complex machine learning methods are avoided to keep the algorithm simple and fast. The algorithm has successfully been applied to different image datasets. These data sets were acquired by different cameras, camera platforms and in varying illumination conditions. Their successful analysis shows the broad applicability of the proposed method.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2615211-3
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  • 5
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-09-12)
    Abstract: Mapping and monitoring of seafloor habitats are key tasks for fully understanding ocean ecosystems and resilience, which contributes towards sustainable use of ocean resources. Habitat mapping relies on seafloor classification typically based on acoustic methods, and ground truthing through direct sampling and optical imaging. With the increasing capabilities to record high-resolution underwater images, manual approaches for analyzing these images to create seafloor classifications are no longer feasible. Automated workflows have been proposed as a solution, in which algorithms assign pre-defined seafloor categories to each image. However, in order to provide consistent and repeatable analysis, these automated workflows need to address e.g., underwater illumination artefacts, variances in resolution and class-imbalances, which could bias the classification. Here, we present a generic implementation of an Automated and Integrated Seafloor Classification Workflow (AI-SCW). The workflow aims to classify the seafloor into habitat categories based on automated analysis of optical underwater images with only minimal amount of human annotations. AI-SCW incorporates laser point detection for scale determination and color normalization. It further includes semi-automatic generation of the training data set for fitting the seafloor classifier. As a case study, we applied the workflow to an example seafloor image dataset from the Belgian and German contract areas for Manganese-nodule exploration in the Pacific Ocean. Based on this, we provide seafloor classifications along the camera deployment tracks, and discuss results in the context of seafloor multibeam bathymetry. Our results show that the seafloor in the Belgian area predominantly comprises densely distributed nodules, which are intermingled with qualitatively larger-sized nodules at local elevations and within depressions. On the other hand, the German area primarily comprises nodules that only partly cover the seabed, and these occur alongside turned-over sediment (artificial seafloor) that were caused by the settling plume following a dredging experiment conducted in the area.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 6
    Online Resource
    Online Resource
    Optica Publishing Group ; 2018
    In:  Optics Express Vol. 26, No. 6 ( 2018-03-19), p. 7811-
    In: Optics Express, Optica Publishing Group, Vol. 26, No. 6 ( 2018-03-19), p. 7811-
    Type of Medium: Online Resource
    ISSN: 1094-4087
    Language: English
    Publisher: Optica Publishing Group
    Publication Date: 2018
    detail.hit.zdb_id: 1491859-6
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  • 7
    In: Progress in Oceanography, Elsevier BV, Vol. 149 ( 2016-12), p. 106-120
    Type of Medium: Online Resource
    ISSN: 0079-6611
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2016
    detail.hit.zdb_id: 1497436-8
    detail.hit.zdb_id: 4062-9
    SSG: 21,3
    SSG: 14
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  • 8
    In: Biogeosciences, Copernicus GmbH, Vol. 17, No. 6 ( 2020-03-23), p. 1463-1493
    Abstract: Abstract. High-resolution optical and hydro-acoustic sea floor data acquired in 2015 enabled the reconstruction and exact localization of disturbance tracks of a past deep-sea recolonization experiment (DISCOL) that was conducted in 1989 in the Peru Basin during a German environmental impact study associated with manganese-nodule mining. Based on this information, the disturbance level of the experiment regarding the direct plough impact and distribution and redeposition of sediment from the evolving sediment plume was assessed qualitatively. The compilation of all available optical and acoustic data sets available from the DISCOL Experimental Area (DEA) and the derived accurate positions of the different plough marks facilitate the analysis of the sedimentary evolution over the last 26 years for a sub-set of the 78 disturbance tracks. The results highlight the remarkable difference between natural sedimentation in the deep sea and sedimentation of a resettled sediment plume; most of the blanketing of the plough tracks happened through the resettling of plume sediment from plough tracks created later. Generally sediment plumes are seen as one of the important impacts associated with potential Mn-nodule mining. For enabling a better evaluation and interpretation of particularly geochemical and microbiological data, a relative age sequence of single plough marks and groups of them was derived and is presented here. This is important as the thickness of resettled sediment differs distinctly between plough marks created earlier and later. Problems in data processing became eminent for data from the late 1980s, at a time when GPS was just invented and underwater navigation was in an infant stage. However, even today the uncertainties of underwater navigation need to be considered if a variety of acoustical and optical sensors with different resolution should be merged to correlate accurately with the absolute geographic position. In this study, the ship-based bathymetric map was used as the absolute geographic reference layer and a workflow was applied for geo-referencing all the other data sets of the DISCOL Experimental Area until the end of 2015. New high-resolution field data were mainly acquired with sensors attached to GEOMAR's AUV Abyss and the 0.5∘ × 1∘ EM122 multibeam system of RV Sonne during cruise SO242-1. Legacy data from the 1980s and 1990s first needed to be found and compiled before they could be digitized and properly geo-referenced for our joined analyses.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2158181-2
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  • 9
    In: Biogeosciences, Copernicus GmbH, Vol. 17, No. 12 ( 2020-06-19), p. 3115-3133
    Abstract: Abstract. With the mining of polymetallic nodules from the deep-sea seafloor once more evoking commercial interest, decisions must be taken on how to most efficiently regulate and monitor physical and community disturbance in these remote ecosystems. Image-based approaches allow non-destructive assessment of the abundance of larger fauna to be derived from survey data, with repeat surveys of areas possible to allow time series data collection. At the time of writing, key underwater imaging platforms commonly used to map seafloor fauna abundances are autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs) and towed camera “ocean floor observation systems” (OFOSs). These systems are highly customisable, with cameras, illumination sources and deployment protocols changing rapidly, even during a survey cruise. In this study, eight image datasets were collected from a discrete area of polymetallic-nodule-rich seafloor by an AUV and several OFOSs deployed at various altitudes above the seafloor. A fauna identification catalogue was used by five annotators to estimate the abundances of 20 fauna categories from the different datasets. Results show that, for many categories of megafauna, differences in image resolution greatly influenced the estimations of fauna abundance determined by the annotators. This is an important finding for the development of future monitoring legislation for these areas. When and if commercial exploitation of these marine resources commences, robust and verifiable standards which incorporate developing technological advances in camera-based monitoring surveys should be key to developing appropriate management regulations for these regions.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2158181-2
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  • 10
    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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