JongCheol Pyo

Chlorophyll-a concentration estimation using three difference bio-optical algorithms, including a correction for the low-concentration range: the case of the Yiam reservoir, Korea

ABSTRACT Bio-optical algorithms have been applied to monitor water quality in surface water systems. Empirical algorithms have been applied to estimate the chlorophyll-a (chl-a) concentrations. However, the performance of each algorithm severely degrades at concentrations notably lower than 10 mg m−3. This could be attributed to the chl-a specific absorption coefficient that became less consistent at low chl-a concentrations. Nonetheless, no effort has been made in previous studies to correct existing algorithms. In this study, we propose a correction approach to improve their performance for chl-a estimation in Yiam reservoir, Korea. Estimated chl-a concentrations of the algorithms improved after applying the correction process proposed in this study; Nash–Sutcliffe efficiency values increased from 53% to 65% and root mean square error decreased from 39% to 43%, respectively. Further research is needed to verify the correction approaches for different years or study sites.

Coastal chlorophyll-a concentrations monitoring in complex coastal region using machine learning techniques (Conference Presentation)

Frequency and intensity of the harmful algal blooms (HABs) increased globally since 1970s. The increase in HABs have negatively affected aquatic ecosystem and aquaculture industry. The economic losses were about

Effect of atmospheric correction performance on quantify cyanobacteria concentration using hyperspectral imager sensing (Conference Presentation)

1 billion in Europe,

Application of CE-QUAL-W2 Model and Scenario Analysis for Predicting Water Quality Constituents in Sayeun Reservoir

100 million in USA and

Modeling seasonal variability of fecal coliform in natural surface waters using the modified SWAT

121 billion in Korea per year. There were various field monitoring campaigns for ecological and biological researches. However, traditional HABs monitoring has limitations on both spatial and temporal coverage. In these days, multispectral remote sensing methods using satellite sensors have been widely used to monitor HABs in ocean and coastal areas. However, the satellite systems used in ocean and coastal research, such as MODIS, SeaWiFS and etc. have limitations in study on complex coastline, because of their coarse spatial resolution (~ few km). In this research, we conducted two-year intensive monitoring on the South Sea of Korea from 2016 to 2017 at 62 sampling station and used landsat-8 operational land imager (OLI) satellite that has 30m spatial resolution. We used 4 band (band 1 to 4), 4-band ratio (band 1 over band 3 and 4, and band 2 over band 3 and 4) and mixed dataset of 4 band and 4-band ratio. The empirical OC algorithms showed poor performances, under 0.25 of r-squared. The machine learning techniques, i.e., artificial neural network (ANN) and support vector machine (SVM) were applied to enhance performance of estimating chl-a on landsat-8 application. Parameters for developing ANN and SVM model were optimized using a pattern search algorithm in MATLAB toolbox. All dataset were divided into 80 % of training and 20 % of validation data. In the training step, mixed dataset showed the best performance in both ANN and SVM models, whereas 4-band ratio and 4 band dataset in the validation step showed the best performance in ANN and SVM, respectively. The ANN model showed poor performance in low chl-a concentrations but SVM had more accurate performance in low and mid concentrations. Both models under-estimated chl-a in mid to high concentration range. For the mapping results, the ANN model using 4 band dataset showed very low concentration of chl-a in most of research area, whereas SVM showed high concentration of chl-a in coastal area and bay. The result using 4-band ratio dataset showed similar chl-a distribution in ANN and SVM. For mixed dataset results the ANN model estimated over 8 mg m-3 of chl-a at some of coastal, almost zero in near coastal area and over 2 mg m-3 chl-a concentration for off-shore area. In case of SVM, all region showed approximately 2 mg m-3 of chl-a concentration. Landsat-8 OLI was not proper system for OC algorithms. Machine learning techniques were effective tools for enhancing ocean chl-a estimation performance using landsat-8 OLI. Thus, this study showed potential of landsat-8 OLI application to coastal HAB monitoring.

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Remote sensing is useful technique to not only detect harmful algal bloom but also quantify the concentration of the harmful algae by using surface reflectance data. The hyperspectral image allows to investigate detail spatial information of harmful algae. Atmospheric correction is critical process in order to achieve an accurate optical signal in water surface. However, the influence of atmospheric correction performance on the estimation of PC concentration has rarely been studied. Thus, this study investigated the influence of three atmospheric correction method on the spatial distribution and concentration of phycocyanin (PC) concentration from estimation of the bio-optical algorithms. Atmospheric correction is an important image processing technique of hyperspectral image because the result of the correction is expected to influence bio-optical algorithms for quantifying phycocyanin (PC) concentration. From August to October, four field and airborne monitoring campaigns in the Baekje Weir in South Korea were implemented to measure water surface reflectance. PC concentrations in the surface water were analyzed using freezing and thawing method. The two band ratio algorithm, the three band ratio algorithm, the Li algorithm, and the Simis algorithm were utilized to estimate PC concentration. And, atmospheric correction of hyperspectral image was conducted by MODTRAN 6, ATCOR 4, and an artificial neural network (ANN). The ANN model utilized atmospheric parameters which generated from MODTRAN 6 to simulate water surface reflectance. Bio-optical algorithms were applied to the atmospherically corrected image for generating PC distribution map. Even though the atmospheric correction result from the ANN showed Nash-Sutcliffe efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively, the PC concentration from the bio-optical algorithms showed NSE values ranged from 0.17 to 0.57. The ANN model was turned out to be required having large quantities of input data in order to have precise simulation performances. Atmospheric correction from MODTRAN 6 showed an NSE value over 0.8, whereas the correction from ATCOR 4 had a negative NSE value. However, the accuracy in certain regions of the reflectance spectra (λ 700 nm) was relatively low compared to the other spectra region because of insufficient atmospheric observation data during monitoring period. The relationship between atmospheric correction and bio-optical algorithm performance for MODTRAN 6, ATCOR 4, and ANN simulation showed different estimation of PC concentration in the atmospherically corrected images. The precise atmospheric correction by MODTRAN 6 enabled an accurate bio-optical algorithm performance and thus was critical to generating a satisfactory spatial distribution map of PC. In addition, the Li and Simis algorithms were revealed to be much more sensitive to the performance of the atmospheric correction than the other algorithms. Therefore, this study provides a useful tool for understanding importance of atmospheric correction influence of hyperspectral images on quantification of harmful algal blooms.

Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea

This study estimated water quality constituents especially in CBOD, TN, TP, and Chlorophyll-a in Sayeun reservoir by using CE-QUAL-W2 model. With water quality data in surface, middle, and bottom of water body, the model calibration was implemented by changing water quality parameters in the model. Using the calibrated model, we performed scenario analysis to investigate the variation of water quality in respond to different elevations. CBOD, TN, and Chlorophyll-a concentration predicted by the model showed a good agreement with the measured the trend of the concentrations. However, TP estimated was relatively low tendency by the model. We found that water quality (i.e., CBOD, TN, TP, and chlorophyll-a) at both water surface and middle layer was degraded in respond to the decrease of water level by 6.8m. Although results of CBOD in both surface and middle layers decreased to about 2% and 1%, respectively, in the surface layer, TN, TP, and Chlorophyll-a increased to about 4%, 3%, and 51% and, in the middle, TN and TP increased up to 12% and 3%, respectively. In the middle layer, especially, water quality degraded mainly due to increased organic matter from growth, mortality, decay, and sedimentation of algae and anaerobic release of nutrients from sediment. This study demonstrated that water quality could be influenced by controlling water surface elevation, implying that there is a need to control nutrient inflow and re-suspension from sediment in the reservoir.