Comprehensive deep seafloor monitoring system in JAMSTEC

Fujiwara, Noriyuki; Momma, H.; Kawaguchi, Katsuyoshi; Iwase, Ryoichi; Kinoshita, Hajimu

doi:10.1109/ut.1998.670132

Cited by 11 publications

(4 citation statements)

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“…The above assumptions involving N and a are important and merit further discussion. With regard to selectivity (a), the results of Row et al (9) and Fujiwara et al (11,12) clearly illustrate that for a given surfactant and neutral solutes (the solutes of interest in the present study), the selectivity is virtually independent of k', i.e., the surfactant concentration.…”

Section: Introductionsupporting

confidence: 50%

“…For direct separation, either chemically bonded chiral stationary phases (CSP's) or conventional stationary phases with chiral mobile phase additives are usually used. Reviews on the direct chromatographic separation of enantiomers using these two approaches are available (9)(10)(11)(12)(13)(14). Most of the work on direct chiral separation has been focused on analytes in a simple sample matrix; little has been published on the direct separation of drug enantiomers in biological fluids (15)(16)(17)(18).…”

Section: Literature Citedmentioning

confidence: 99%

“…Likewise, a number of effects on the selectivity, a, in MEC have been examined, such as applied voltage (10), pH (11,12), organic modifier content (8,13), surfactant identity (4,10, 14), and surfactant concentration (9,11,12). Since for a given surfactant the selectivity for neutral solutes in MEC is determined almost exclusively by the water/micelle partition coefficients (vide infra), it is not surprising that all the variables except surfactant identity had very little, if any, effect.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of micellar electrokinetic chromatography

Foley¹

1990

Anal. Chem.

158

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Section: Introductionsupporting

confidence: 50%

Section: Literature Citedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of micellar electrokinetic chromatography

Foley¹

1990

Anal. Chem.

158

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“…Such acoustic-based methods have also been previously developed by established institutions e.g. MBARI's integrated mapping system 16 , IFREMER's software for multibeam echosounder and side scan sonar mapping (CARAIBES) 17 and JAMSTEC's deep seafloor monitoring system 18 . The limiting characteristic of these hydro-acoustic based workflows is that their footprint size makes them unsuitable for detecting small objects on the seafloor.…”

mentioning

confidence: 99%

Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean

Mbani

Schoening

Gazis

et al. 2022

Sci Rep

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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.

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Comprehensive deep seafloor monitoring system in JAMSTEC

Cited by 11 publications

References 3 publications

Optimization of micellar electrokinetic chromatography

Optimization of micellar electrokinetic chromatography

Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean

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