• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.58-63
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• 1998

The aim of the present study is to define and tentatively to interpret the distribution of polluted water released from Lake Sihwa into Yellow Sea using Landsat TM. Since the region is an extreme case 2 water, empirical algorithms for chlorophyll-a and suspended sediments have limitations. This work focuses on the use of multi-temporal Landsat TM. We applied PCA to detect evolution of spatial feature of polluted water after release from the lake. The PCA results were compared with in situ data, such as chlorophyll-a, suspended sediments, Secchi disk depth (SDD), surface temperature, radiance reflectance at six bands. The in situ remote sensing reflectance was analysed with PCA. On the basis of these In situ data we found good correlation between first Principal Component and Secchi disk depth ($R^2$=0.7631), although other variables did not result in such a good correlation. The problems in applying PCA techniques to multi-spectral remote sensed data are also discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.3
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• pp.71-80
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• 1999

The aim of the present study is to define and tentatively to interpret the distribution of polluted water released from Lake Sihwa into the Yellow Sea using Landsat TM. Since the region is an extreme Case 2 water, empirical algorithms for detecting concentration of chlorophyll-a and suspended sediments have limitations. This work focuses on the use of multi-temporal Landsat TM data. We applied PCA to detect evolution of spatial feature of polluted water after release from the lake Sihwa. The PCA results were compared with in situ data, such as chlorophyll-a, suspended sediments, Secchi disk depth(SDD), surface temperature, remote sensing reflectance at six channel of SeaWiFS. Also, the in situ remote sensing reflectance obtained by PRR-600(Profiling Reflectance Radiometer) was compared with PCA results of Landsat TM data sets to find good correlation between first Principal Component and Secchi disk depth($R^2$=0.7631), although other variables did not result in such a good correlation. Therefore, Problems in applying PCA techniques to multi-spectral remotely sensed data were also discussed in this paper.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.1
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• pp.27-33
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• 2001

SDD(Secchi disk depth) algorithm were composed of SeaWiFS bands combination using in-water optical data sets obtained Lake Sihwa, Kyungki Bay, Chunsu Bay, and Chinhae Bay. SDD algorithm were compared with in-situ data. Reflectance band ratio, $R_{rs}$(490/665) had the highest correlation($R^2$=0.8188) with in-situ data. For in-water algorithm applied to satellite data, reflectance band ratios of Landsat TM data were calculated. However, the results of applied Landsat TM had the low correlation, these reason were discussed in this paper.

• Korean Journal of Ecology and Environment
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• v.39 no.3 s.117
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• pp.296-309
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• 2006

This study aimed to analyze water quality (temperature, secchi depth, DO, $NH_4$, $NO_3$, $PO_4$, TN, TP, chlorophyll a) and trophic state index during the period of year 2000 ${\sim}$ 2004 in Lake Juam, Lake Dongbok and Lake Youngsan. Lakes Juam and Dongbok except Lake Youngsan were stratified during warm seasons. Water turbidity estimated by secchi disk depth was the highest in Lake Youngsan compared with other lakes. DO concentrations were low in the bottom water when chlorophyll a was high in Lake Juam and Dongbok. Nutrient concentrations were higher in Lake Youngsan than other lakes whereas chlorophyll a was highest in Lake Dongbok. Lake Youngsan was the most eutrophic compared to other two lakes based on the Trophic State Idex (TP) and TSI (SD), The TSI (CHL) was high but the TSI (TP) were low in Lake Juam and Dongbok. These results suggest phytoplankton may be limited by phosphates (P) in Lake Juam and Dongbok whereas light availability in the water column may affect growth of phytoplankton in Lake Youngsan.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.221-234
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• 2016

In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.5
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• pp.474-483
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• 2015

In order to understand the seasonal change of phytoplankton as well as the effect of water physico-chemical parameters, we investigated 18 stations in coastal areas of the East Sea in February, May, August and November in 2009. The taxa of phytoplankton observed in this study were classified as 37 Bacillariophyceae, 22 Dinophyceae, 1 Euglenophyceae, 3 Dictyophyceae and 1 Cryptophyceae. Phytoplankton abundance ranged from $1.2{\times}10^3cells/L$ to $246.6{\times}10^3cells/L$(with a mean value of $24.8{\times}10^3cells/L$), the highest biomass was observed in May. The dominant species were Leptocylindrus danicus, Chaetoceros affinis, Pseudo-nitzschia pungens, Thalassionema nitzschioides and etc. Pearson's correlation co-efficient between phytoplankton abundance and other water parameters showed the positive relationships with pH, DO, Secchi-disk depth, and SS, the negative relationships with $SiO_2-Si$. Seasonal patterns of phytoplankton dominant species were affected by the characteristics of water masses based on T-S diagram analysis. In particular, phytoplankton distributional patterns were related with water temperature in May and salinity in August, respectively. According to the result of MDS(Multi-dimensional scaling) using the phytoplankton abundance and species composition, the spatial distribution of phytoplankton were characterized with Ganwon(Group A) and Gyeongbuk(Group B) at the coastal areas of Jukbyeon or Uljin.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.959-974
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• 2021

Forel-Ule Index (FUI) is an index which classifies the colors of inland and seawater exist in nature into 21 gradesranging from indigo blue to cola brown. FUI has been analyzed in connection with the eutrophication, water quality, and light characteristics of water systems in many studies, and the possibility as a new water quality index which simultaneously contains optical information of water quality parameters has been suggested. In thisstudy, Ocean Colour-Climate Change Initiative (OC-CCI) based 4 km FUI was spatially downscaled to the resolution of 500 m using the Geostationary Ocean Color Imager (GOCI) data and Random Forest (RF) machine learning. Then, the RF-derived FUI was examined in terms of its correlation with various water quality parameters measured in coastal areas and its spatial distribution and seasonal characteristics. The results showed that the RF-derived FUI resulted in higher accuracy (Coefficient of Determination (R²)=0.81, Root Mean Square Error (RMSE)=0.7784) than GOCI-derived FUI estimated by Pitarch's OC-CCI FUI algorithm (R²=0.72, RMSE=0.9708). RF-derived FUI showed a high correlation with five water quality parameters including Total Nitrogen, Total Phosphorus, Chlorophyll-a, Total Suspended Solids, Transparency with the correlation coefficients of 0.87, 0.88, 0.97, 0.65, and -0.98, respectively. The temporal pattern of the RF-derived FUI well reflected the physical relationship with various water quality parameters with a strong seasonality. The research findingssuggested the potential of the high resolution FUI in coastal water quality management in the Korean Peninsula.