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Cho, Kyung Hwa
Water-Environmental Informatics Lab (WEIL)
Research Interests
  • Water Quality Monitoring and Modeling, Water Treatment Process Modeling

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Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir

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dc.contributor.author Kwon, Yong Sung ko
dc.contributor.author Pyo, JongCheol ko
dc.contributor.author Kwon, Yong-Hwan ko
dc.contributor.author Duan, Hongtao ko
dc.contributor.author Cho, Kyung Hwa ko
dc.contributor.author Park, Yongeun ko
dc.date.available 2019-12-12T09:08:52Z -
dc.date.created 2019-12-04 ko
dc.date.issued 2020-01 ko
dc.identifier.citation REMOTE SENSING OF ENVIRONMENT, v.236, pp.111517 ko
dc.identifier.issn 0034-4257 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/30563 -
dc.description.abstract The remote sensing of algal pigments is essential for understanding the temporal and spatial distribution of harmful algal blooms (HABs). In particular, the vertical distribution of cyanobacterial pigment (e.g., phycocyanin (PC)) is critical factor in remote sensing because the diel vertical migration of cyanobacteria may affect the spectral signals according to observational time. Although numerous studies have been conducted on the remote sensing of algal bloom using pigments, few studies considered the vertical distribution of the pigments for the remote sensing of cyanobacteria in inland waters. In this regard, the objective of this study was to develop an improved bio-optical remote-sensing method using in-situ remote-sensing reflectance (Rrs) at different water depths and cumulative PC and Chlorophyll-a (Chl-a) concentrations, which was cumulated from the surface to a 5-m water depth. The results showed that the bio-optical algorithm using surface Rrs and surface pigment concentration was more accurate than that using the subsurface Rrs and surface pigments. The bio-optical algorithm using subsurface Rrs showed the highest R-squared (R2) values (0.87–0.94) in each regression with the cumulative PC concentration from surface to each depth. The regressions between drone-based surface reflectance and cumulative PC concentration for each depth indicated a better performance than those between the reflectance and surface PC concentration; the highest R2 value of 0.82 was obtained from a bio-optical algorithm using drone-based reflectance and a 1.0-m cumulative PC concentration, which was the best-performing algorithm. The PC maps developed using the best bio-optical algorithm accurately described the spatial and temporal distributions of the PC concentrations in the reservoir. This study demonstrates that the application of vertical cumulative pigment concentration and subsurface Rrs measurement in bio-optical algorithms can improve their performance in estimating pigments, and that drone-based hyperspectral imagery is an efficient tool for the remote sensing of cyanobacterial pigments over a wide area. ko
dc.language 영어 ko
dc.publisher Elsevier BV ko
dc.title Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85074975899 ko
dc.identifier.wosid 000502894400033 ko
dc.type.rims ART ko
dc.identifier.doi 10.1016/j.rse.2019.111517 ko
dc.identifier.url https://www.sciencedirect.com/science/article/pii/S003442571930536X?via%3Dihub ko
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