dc.citation.conferencePlace |
IO |
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dc.citation.conferencePlace |
Bali; Indonesia |
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dc.citation.endPage |
2295 |
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dc.citation.startPage |
2293 |
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dc.citation.title |
34th Asian Conference on Remote Sensing 2013, ACRS 2013 |
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dc.citation.volume |
3 |
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dc.contributor.author |
Im, Jungho |
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dc.contributor.author |
Lee, Junghee |
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dc.contributor.author |
Kim, Miae |
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dc.contributor.author |
Park, Youngje |
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dc.date.accessioned |
2023-12-20T00:37:52Z |
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dc.date.available |
2023-12-20T00:37:52Z |
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dc.date.created |
2014-07-30 |
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dc.date.issued |
2013-10-20 |
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dc.description.abstract |
This study proposes a method to assess water quality using Geostationary Ocean Color Imager (GOCI) satellite data. Three basic GOCI products including chlorophyll concentration, total suspended sediment, and dissolved organic materials are used to develop an ocean surface water quality index. Machine learning approaches such as random forest and Cubist (i.e., modified regression trees) are used to estimate water quality and develop the ocean surface water quality index. |
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dc.identifier.bibliographicCitation |
34th Asian Conference on Remote Sensing 2013, ACRS 2013, v.3, pp.2293 - 2295 |
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dc.identifier.isbn |
978-162993910-0 |
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dc.identifier.scopusid |
2-s2.0-84903438766 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/46759 |
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dc.language |
영어 |
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dc.publisher |
34th Asian Conference on Remote Sensing 2013, ACRS 2013 |
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dc.title |
Development of coastal surface water quality index using geostationary ocean color imager (GOCI) satellite products |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2013-10-20 |
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