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- W2620807789 abstract "Abstract. Landsat imagery satisfies the characteristics of big data because of its massive data archive since 1972, continuous temporal updates, and various spatial resolutions from different sensors. As a case study of Landsat big data analysis, a total of 776 Landsat scenes were analyzed that cover a part of the Han River in South Korea. A total of eleven sample datasets was taken at the upstream, mid-stream and downstream along the Han River. This research aimed at analyzing locational variance of reflectance, analyzing seasonal difference, finding long-term changes, and modeling algal amount change. There were distinctive reflectance differences among the downstream, mid-stream and upstream areas. Red, green, blue and near-infrared reflectance values decreased significantly toward the upstream. Results also showed that reflectance values are significantly associated with the seasonal factor. In the case of long-term trends, reflectance values have slightly increased in the downstream, while decreased slightly in the mid-stream and upstream. The modeling of chlorophyll-a and Secchi disk depth imply that water clarity has decreased over time while chlorophyll-a amounts have decreased. The decreasing water clarity seems to be attributed to other reasons than chlorophyll-a." @default.
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- W2620807789 date "2017-05-30" @default.
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- W2620807789 title "LANDSAT BIG DATA ANALYSIS FOR DETECTING LONG-TERM WATER QUALITY CHANGES: A CASE STUDY IN THE HAN RIVER, SOUTH KOREA" @default.
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- W2620807789 doi "https://doi.org/10.5194/isprs-annals-iv-1-w1-83-2017" @default.
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