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  • AUV  (2)
  • Autonomous underwater vehicle (AUV)  (1)
  • Benthic organisms  (1)
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  • 1
    Publication Date: 2022-05-25
    Description: Author Posting. © The Authors, 2005. This is the author's version of the work. It is posted here by permission of Elsevier B. V. for personal use, not for redistribution. The definitive version was published in Continental Shelf Research 26 (2006): 194-205, doi:10.1016/j.csr.2005.10.004.
    Description: The benthic communities of the deep insular shelf at the Hind Bank Marine Conservation District (MCD), an important spawning grouper aggregation site, were studied with the Seabed autonomous underwater vehicle (AUV) at depths between 32 to 54 m. Four digital phototransects provided data on benthic species composition and abundance of the insular shelf off St. Thomas, U.S. Virgin Islands. Within the western side of the MCD, well developed coral reefs with 43% mean living coral cover were found. The Montastrea annularis complex was dominant at all four sites between 33 to 47 m, the depth range where reefs were present. Maximum coral cover found was 70% at depths of 38 to 40 m. Quantitative determinations of sessile-benthic populations, as well as the presence of motile-megabenthic invertebrates and algae were obtained. The Seabed AUV provided new quantitative and descriptive information of a unique coral reef habitat found within this deeper insular shelf area.
    Description: Funding was provided in part by the CenSSIS ERC of the National Science Foundation under grant EEC-9986821 and by the Caribbean Fishery Management Council. University of the Virgin Islands staff time was supported by a grant from Sea Grant (R-101-1-02) to R. Nemeth.
    Keywords: Atlantic ; U.S. Virgin Islands ; Coral reef ; Deep hermatypic corals ; AUV
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
    Format: 907575 bytes
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  • 2
    Publication Date: 2022-05-25
    Description: Recent advances in underwater robotics and imaging technology now enable the rapid acquisition of large datasets of near-bottom high-resolution digital imagery. These images provide the potential for developing a non-invasive technique for fisheries data acquisition that reveals the organisms in their natural habitat and can be used to identify important habitat characteristics. Using these large datasets effectively, however, requires the development of computer-based techniques that increase the efficiency of data analysis. This document describes one such tool, FISH_ROCK, which was developed for a group of fisheries researchers using the SeaBED AUV during a research cruise in October 2005. FISH_ROCK is a graphical user interface (GUI) that is executed within Matlab, and allows users digitally generate a database that includes organism identification, quantity, size and distribution as well as details about their habitat. Further development of this GUI will enable its use in different oceanographic environments including the deep sea, and will include modules that perform data analysis.
    Description: Funding was provided by the National Oceanic and Atmospheric Administration under Grant No. AB133F05SE5828.
    Keywords: Benthic organisms ; Bottom photographs ; Database
    Repository Name: Woods Hole Open Access Server
    Type: Technical Report
    Format: 2300110 bytes
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  • 3
    Publication Date: 2022-05-25
    Description: Author Posting. © Elsevier B.V., 2007. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Earth and Planetary Science Letters 267 (2008): 341-352, doi:10.1016/j.epsl.2007.11.053.
    Description: Detailed near-bottom investigation of a series of giant, kilometer scale, elongate pockmarks along the edge of the mid-Atlantic continental shelf confirms that methane is actively venting at the site. Dissolved methane concentrations, which were measured with a commercially available methane sensor (METS) designed by Franatech GmbH mounted on an autonomous underwater vehicle (AUV), are as high as 100 nM. These values are well above expected background levels (1-4 nM) for the open ocean. Sediment pore water geochemistry gives further evidence of methane advection through the seafloor. Isotopically light carbon in the dissolved methane samples indicates a primarily biogenic source. The spatial distribution of the near-bottom methane anomalies (concentrations above open ocean background), combined with water column salinity and temperature vertical profiles, indicate that methane-rich water is not present across the entire width of the pockmarks, but is laterally restricted to their edges. We suggest that venting is primarily along the top of the pockmark walls with some advection and dispersion due to local currents. The highest methane concentrations observed with the METS sensor occur at a small, circular pockmark at the southern end of the study area. This observation is compatible with a scenario where the larger, elongate pockmarks evolve through coalescing smaller pockmarks.
    Description: This work was supported by NSF grants OCE- 0242426, OCE-0242804 and OCDE-0242449 and ONR grant N00014-02-1-0691.
    Keywords: Pockmarks ; Seafloor venting ; Methane ; AUV
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
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  • 4
    Publication Date: 2022-05-25
    Description: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Field Robotics 35 (2018): 705-716, doi:10.1002/rob.21771.
    Description: For robots to succeed in complex missions, they must be reliable in the face of subsystem failures and environmental challenges. In this paper, we focus on autonomous underwater vehicle (AUV) autonomy as it pertains to self‐perception and health monitoring, and we argue that automatic classification of state‐sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to AUV sensor data in order to automatically characterize its performance patterns, then demonstrate how in combination with operator‐supplied semantic labels these patterns can be used for fault detection and diagnosis by means of a nearest‐neighbor classifier. The method is evaluated using data collected by the Monterey Bay Aquarium Research Institute's Tethys long‐range AUV in three separate field deployments. Our results show that the proposed method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults at a high rate of correct detection with a very low false detection rate.
    Description: Office of Naval Research Grant Number: N00014‐14‐1‐0199; David and Lucile Packard Foundation
    Keywords: Autonomous underwater vehicle (AUV) ; Autonomy ; Fault detection and diagnosis ; Topic modeling
    Repository Name: Woods Hole Open Access Server
    Type: Article
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