Cochlodinium polykrikoides 적조 탐지를 위한 인공신경망 알고리듬

DC Field Value Language
dc.contributor.author 유신재 -
dc.contributor.author 김예슬 -
dc.contributor.author 손영백 -
dc.date.accessioned 2020-07-15T15:33:16Z -
dc.date.available 2020-07-15T15:33:16Z -
dc.date.created 2020-02-11 -
dc.date.issued 2017-05-16 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/23982 -
dc.description.abstract This study is to develop detection algorithms for the blooms of Cochlodinium polykrikoides, which has caused serious HABs for more than two decades in Korean waters. Various algorithms have been proposed to detect HABs. However, most of those algorithms are empirical and therefore limited in their applicability in the coastal areas where bio-optical conditions are highly variable spatially and temporally. In a previous study, we have shown that remote sensing reflectance of Cochlodinium polykrikoides exhibit a distinctive depression in the blue-green wavelength band (Kim et al., Optics Express, 2016) based on a large data set of remote sensing reflectance simulated using HydroLight and IOCCG data. We also showed that Cochlodinium polykrikoides can be clearly separated from unspecified phytoplankton assemblages in the two waveband ratio space (Rrs(555)/Rrs(531), Rrs(488)/Rrs(443)). Based on this, we are developing neural network algorithms for in-water and satellite applications. Our preliminary test using simulated and satellite data shows that the success rate of the in-water algorithm ranges from 71.6% (chl-a > 5 mg m-3) to 89.8% (chl-a > 30 mg m-3). We also compare the performance of neural network algorithms for satellite data.se algorithms are empirical and therefore limited in their applicability in the coastal areas where bio-optical conditions are highly variable spatially and temporally. In a previous study, we have shown that remote sensing reflectance of Cochlodinium polykrikoides exhibit a distinctive depression in the blue-green wavelength band (Kim et al., Optics Express, 2016) based on a large data set of remote sensing reflectance simulated using HydroLight and IOCCG data. We also showed that Cochlodinium polykrikoides can be clearly separated from unspecified phytoplankton assemblages in the two waveband ratio space (Rrs(555)/Rrs(531), Rrs(488)/Rrs(443)). Based on this, we are developing neural network algorithms for in-water and satellite applications. Our preliminary test using simulated and satellite data shows that the success rate of the in-water algorithm ranges from 71.6% (chl-a > 5 mg m-3) to 89.8% (chl-a > 30 mg m-3). We also compare the performance of neural network algorithms for satellite data. -
dc.description.uri 1 -
dc.language English -
dc.publisher IOCCG -
dc.relation.isPartOf International Ocean Colour Science Meeting -
dc.title Cochlodinium polykrikoides 적조 탐지를 위한 인공신경망 알고리듬 -
dc.title.alternative Neural network algorithms for detecting Cochlodinium polykrikoides blooms -
dc.type Conference -
dc.citation.conferencePlace UK -
dc.citation.title International Ocean Colour Science Meeting -
dc.contributor.alternativeName 유신재 -
dc.contributor.alternativeName 김예슬 -
dc.contributor.alternativeName 손영백 -
dc.identifier.bibliographicCitation International Ocean Colour Science Meeting -
dc.description.journalClass 1 -
Appears in Collections:
Jeju Research Institute > Jeju Marine Research Center > 2. Conference Papers
Jeju Research Institute > Tropical & Subtropical Research Center > 2. Conference Papers
Marine Resources & Environment Research Division > Marine Environment Research Department > 2. Conference Papers
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