Mr. Si-Young Oh Profile

Si-Young Oh

at Chungbuk National Univ

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Publications (2)

Proceedings Article | 21 November 2012 Paper

Landsat imagery-based water turbidity monitoring in Lake Paldang, Korea

Sang-il Na, Jin-ki Park, Shin-chul Baek, Si-young Oh, Jong-hwa Park

Proceedings Volume 8524, 85242H (2012) https://doi.org/10.1117/12.977323

KEYWORDS: Landsat, Principal component analysis, Water, Satellite imaging, Earth observing sensors, Satellites, Remote sensing, Agriculture, Image processing, Infrared imaging

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Turbid water of agricultural reservoir and downstream is getting worse and worse because the soil flows out from current residential land development and road construction. Sediment loads, which fill the water bodies (lakes, agricultural reservoir, dams, and aquatic ecosystems) are one of the most important environmental problems throughout the world. Water turbidity is a commonly used index of the factors that determine light penetration in the water column. Consistent estimation of water turbidity is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean. Traditional methods monitoring fixed geographical locations at fixed intervals may not be representative of the mean water turbidity in estuaries between intervals, and can be expensive and time consuming. Although remote sensing offers a good solution to this limitation, it is still not widely used due in part to required complex processing of imagery. The aims of this study were two folds: to map water turbidity and estimate water turbidity level based on Landsat imagery. Based on field measurements and principal component analysis (PCA), was examined the spatial variability of water turbidity in Lake Paldang by using the Landsat satellite imagery collected on 2001~2007. The result of this study is that when we carried out PCA using Landsat imagery, water turbidity had contributed at PC 2 which was similar to the in-situ data. A correlation formula (water turbidity = 0.3169 × PC2 – 21.477, R² = 0.6319) between the in-situ data and PC2. And we can now use formula to map the water turbidity distribution from the synchronously acquired Landsat imagery, and continue the discussion on the inverse water turbidity results of the Landsat imagery. Because results from this type of study vary with season and time of day, it is necessary to monitor continuously in-situ data as well as radiance feature of reflectance in order to determine accurately the environmental factors of water quality.

Proceedings Article | 8 November 2012 Paper

Evaluating the spatial and temporal solar energy potential in South Korea

Si-Young Oh, Jin-Ki Park, Jong-Hwa Park

Proceedings Volume 8529, 85290I (2012) https://doi.org/10.1117/12.977388

KEYWORDS: Solar radiation, Meteorology, Solar radiation models, Solar energy, Atmospheric modeling, Data modeling, Climatology, Climate change, Renewable energy, Statistical analysis

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