• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.285-295
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• 2001

We developed a CASE-II water model that will enable the simulation of remote sensing reflectance($R_{rs}$) at the coastal waters for the retrieval of suspended sediments (SS) concentrations from satellite imagery. The model has six components which are: water, chlorophyll, dissolved organic matter (DOM), non-chlorophyllous particles (NC), heterotrophic microorganisms and an unknown component, possibly represented by bubbles or other particulates unrelated to the five first components. We measured $R_{rs}$, concentration of SS and chlorophyll, and absorption of DOM during our field campaigns in Korea. In addition, we generated $R_{rs}$ from different concentrations of SS and chlorophyll, and various absorptions of DOM by random number functions to create a large database to test the model. We assimilated both the computer generated parameters as well as the in-situ measurements in order to reconstruct the reflectance spectra. We validated the model by comparing model-reconstructed spectra with observed spectra. The estimated $R_{rs}$ spectra were used to (1) evaluate the performance of four wavelengths and wavelengths ratios for accurate retrieval of SS. 2) identify the optimum band for SS retrieval, and 3) assess the influence of the SS on the chlorophyll algorithm. The results indicate that single bands at longer wavelengths in visible better results than commonly used channel ratios. The wavelength of 625nm is suggested as a new and optimal wavelength for SS retrieval. Because this wavelength is not available from SeaWiFS, 555nm is offered as an alternative. The presence of SS in coastal areas can lead to overestimation chlorophyll concentrations greater than 20-500%.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.417-424
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• 2021

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

• Proceedings of KOSOMES biannual meeting
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• 2009.06a
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• pp.53-54
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• 2009

The influence of atmospheric aerosol on satellite ocean color data were evaluated using SeaWiFS monthly standard mapped image products. The atmospheric optical thickness (AOT) was increased in spring and summer, and it showed the strong positive correlation with remote sensing reflectance, normalized waterleaving radiance /solar irradiance, at 555 nm (Rrs555) which is a component of the satellite chlorophyll estimation. Such the high AOT and high Rrs555 pixels showed overestimation of satellite chlorophyll in spring, especially in the area which showed large phytoplankton absorption which 1s expressed by low remote sensing reflectance at 443, 490 and 510 nm (Rrs 443, Rrs490 and Rrs510).

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

• Korean Journal of Remote Sensing
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• v.16 no.3
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• pp.243-260
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• 2000

Ocean remote sensing reflectance of just above water level was modeled using inherent optical properties of seawater contents, total absorption (a) and backscattering(bb) coefficients ($R_{rs}$=0.046 $b_b$/(a+$b_b$). This modeling was based on the specific absorption and backscattering coefficients of 5 optically active seawater components； phytoplankton pigments, non-chlorophyllous suspended particles, dissolved organic matters, heterotrophic microorganisms, and the other unknown particle components. Simulated remote sensing reflectance($R_{rs}$) and water leaving radiance(Lw) spectra were well agreed with in-situ measurements obtained using a bi-directional fields remote spectrometer in coastal waters and open ocean. $R_{rs}$ values in SeaWiFS bands from the model were analyzed to develop 2-band ratio ocean color chlorophyll with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The model algorithms were examined and compared with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The remote reflectance model will be very helpful to understand the variation of water leaving radiances caused by the various components in the seawater, and to develop new ocean color algorithm for CASE-II water using neural network method or other analytical method, and in the model of fine atmospheric signal correction.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.700-702
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• 2003

A new method for automatic detection of absorption features is proposed. This method is based on the modulus maximum of the scale-space image calculated by continuous wavelet transform. This method is computationally efficient as compared to traditional methods. The continuum removal algorithm is than implemented on the detected absorption features to reduce some additive factors caused by other absorbing of materials. The results show that the chlorophyll absorption features are detected exactly.

• Korean Journal of Ecology and Environment
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• v.41 no.spc
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• pp.11-20
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• 2008

In this study, we applied a Particle Swarm Optimization (PSO) algorithm to predict the changes of chlorophyll-${\alpha}$ related to environmental factors in agricultural reservoirs in Korean national scale. Data were obtained from water quality monitoring networks of reservoirs operated by the Ministry of Agriculture and Forestry and the Ministry of Environment of Korea. From the database of the monitoring networks, 290 reservoirs were chosen with variables such as chlorophyll-${\alpha}$ and 13 environmental factors (COD, TN, TP, Altitude, Bank height, etc.) measured in 2002. Based on Carlson's trophic status index, reservoirs were divided into five groups, and most agricultural reservoirs $(TSI_{CHL}\;64.1%,\;TSI_{TP}\;75.5%)$ were in the eutrophic states. The groups were discriminated with environmental variables, showing that COD, DO, and TP were important factors to determine the trophic states. MLP-PSO (Multilayer perceptron (MLP) with PSO for the optimization) was applied for the prediction of chlorophyll-${\alpha}$ with environment factors, and showed high predictability (r=0.83, p<0.001). Additionally, the sensitivity analysis of the MLP-PSO model showed that COD had the strongest positive effects on the concentration of chlorophyll-${\alpha}$, and followed by TP, TN, DO, whereas altitude and bank height had negative effects on the concentration of chlorophyll-${\alpha}$.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.445-449
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• 2005

Various methods to detect the phytoplankton/red tide blooms in the oceanic waters have been developed and tested on satellite ocean color imagery since the last two and half decades, but accurate detection of blooms with these methods remains challenging in optically complex turbid waters, mainly because of the eventual interference of absorbing and scattering properties of dissolved organic and particulate inorganic matters with these methods. The present study introduces a new method called Red tide Index (Rl), providing indices which behave as a good measure of detecting red tide algal blooms in high scattering and absorbing waters of the Korean South Sea and Yellow Sea. The effectiveness of this method in identifying and locating red tides is compared with the standard Ocean Chlorophyll 4 (OC4) bio-optical algorithm applied to SeaWiFS ocean imagery, acquired during two bloom episodes on 27 March 2002 and 28 September 2003. The result revealed that OC4 bio-optical algorithm falsely identifies red tide blooms in areas abundance in colored dissolved organic and particulate inorganic matter constituents associated with coastal areas, estuaries and river mouths, whereas red tide index provides improved capability of detecting, predicting and monitoring of these blooms in both clear and turbid waters.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.4
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• pp.277-288
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• 2020

In this study, we performed algorithms to predict algae of Chlorophyll-a (Chl-a). Water quality and quantity data of the middle Nakdong River area were used. At first, the correlation analysis between Chl-a and water quality and quantity data was studied. We extracted ten factors of high importance for water quality and quantity data about the two weirs. Algorithms predicted how ten factors affected Chl-a occurrence. We performed algorithms about decision tree, random forest, elastic net, gradient boosting with Python. The root mean square error (RMSE) value was used to evaluate excellent algorithms. The gradient boosting showed 10.55 of RMSE value for the Gangjeonggoryeong (GG) site and 11.43 of RMSE value for the Dalsung (DS) site. The gradient boosting algorithm showed excellent results for GG and DS sites. Prediction value for the four algorithms was also evaluated through the Receiver operating characteristic (ROC) curve and Area under curve (AUC). As a result of the evaluation, the AUC value was 0.877 at GG site and the AUC value was 0.951 at DS site. So the algorithm's ability to interpret seemed to be excellent.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.101-101
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• 2017

근래에 대한민국 담수계에 조류 대발생으로 인한 수질악화 문재가 대두되고 있다. 또한 독성물질을 생성하는 남조류종이 우점하는 현상으로인해 수질문제와더불에 생태계와 인간의 건강도 잠재적인 위험을 받고있는 실정이다. 이와같은 조류 대발생으로인한 피해를 최소화하기위해 효과적인 수질관리가 필수적이다. 원격탐사기술은 조류의 공간적인 분포를 해석하고 농도를 정량화하기위해 이용되고 있다. 현재까지 많은 분광알고리즘들이 개발되어 담수유역에 적용이 되고 있다. 수체마다 다른 분광특성 때문에 알고리즘내의 파라미터 및 분광밴드 조정이 필수적이다. 하지만 대부분의 연구에선 파라미터와 밴드의 변경에 따른 결과향상에만 초점이 맞춰지고 있어 분광알고리즘내의 파라미터와 분광밴드사이의 관계 이해 뿐만아니라 알고리즘 최종 산출물에 대한 영향에 관한 설명이 전무한 실정이다. 본 연구에선, 대한민국 백제보를 대상으로 현장모니터링 및 조류추출 실험을 진행하였고, 이를 기반으로 5가지 클로로필a 알고리즘과 2가지 피코시아닌 알고리즘을 구축하였다. 알고리즘내에서 변수들의 관계와 영향을 알아보기위해 민감도 분석을 실시하였다. 민감도 분석 조건을 기반으로 one-objective 최적화 및 multi-objective 최적화를 실시하여 백제보수계를 대표할 수 있는 최적 변수들을 모의하였다. 민감도 분석결과 후방산란계수에 영향을 미치는 파라미터와 조류 생체량에 영향을 미치는 파라미터가 다른 변수들 및 알고리즘 농도산정결과에 가장 민감한 것으로 나타났다. multi-objective 최적화 결과가 one-objective 결과 및 reference 결과보다 대부분 정확도가 향상되었고 흡광도 계수를 함께 고려할 수 있기 때문에 백제보 수계의 분광특성을 함께 고려하여 대표할 수 있는 장점을 가지는 것으로 나타났다. 따라서, 본 연구는 민감도 분석을 활용하여 분광알고리즘 내의 변수들의 이해를 도모하였고, 최적화 기법 중, multi-objective 최적화 기법이 백제보의 분광특성을 대변하는 최적변수를 제시할 수 있음과 동시에 보다 나은 정확성을 제고할 수 있음을 확인하였다.