• Journal of Environmental Impact Assessment
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• v.23 no.3
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• pp.220-226
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• 2014

This study investigated temporal-spatial and variations in Geostationary Ocean Color Imager (GOCI) products of parameters total suspended solids (TSS) and chlorophyll-a in the North Marine Areas of East China Sea.GOCI data were collected daily from February 2012 to December 2012. The higher chlorophyll-a values were observed during the investigation period. The relatively large increase in TSS and chlorophyll-a at the sampling stations coupled with typhoon events during the summer rainy period. The abnormal chlorophyll-a concentration was mainly driven by meteorological factors such as typhoon and rainfall in the coastal areas of Jeju and the North Marine Areas of East China Sea. The abnormal high chlorophyll-a concentration at the majority of the coastal stations indicate eutrophication of coastal waters, especially Red tide. The events such as eutrophication and abnormal high chlorophyll-a concentration may potentially influence outbreak of Red Tide, detected with GOCI parameters.

• Journal of Forest and Environmental Science
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• v.32 no.2
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• pp.205-211
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• 2016

Leaf chlorophyll content provides valuable information about physiological status of plants. However, fewer studies have investigated the difference in chlorophyll concentration in leaves of tropical afforestation tree species. Therefore, this study examines the difference in foliar chlorophyll contents of six tropical afforestation tree species namely: Tectona grandis, Pentaclethra macrophylla, Piptademiastrum africanum, Azadirachta indica, Brachystegia eurycoma and Gmelina arborea found in the relict forest in Umudike, South east, Nigeria. A single factor experiment in a completely randomised design in three replicates was employed to analyse the rate of leaf chlorophyll contents. Fisher's least significant different was used to test for significance in mean difference in foliar chlorophyll contents between tree species at 95% confidence interval using analysis of variance. The results of this study showed a significant difference in foliar chlorophyll concentration between the tree species with Tectona grandis having a higher chlorophyll concentration than other trees this could be as a result of its higher vegetative activity which increases its primary productivity followed by Pentaclethra macrophylla while Azadirachta indica having least the chlorophyll concentration. The study further revealed that other indigenous tree species like Piptademiastrum africanum and Brachystegia eurycoma have higher chlorophyll concentration. Further studies should be carry out to examine factors that have contributed informed the differences in the chlorophyll concentration of these trees species, thus this would broaden the understanding of their physiological status and equally encourage there conservation.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.635-651
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• 2012

To produce a level-3 monthly composite image from daily level-2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-a concentration data set in the East Sea, we applied four average methods such as the simple average method, the geometric mean method, the maximum likelihood average method, and the weighted averaging method. Prior to performing each averaging method, we classified all pixels into normal pixels and abnormal speckles with anomalously high chlorophyll-a concentrations to eliminate speckles from the following procedure for composite methods. As a result, all composite maps did not contain the erratic effect of speckles. The geometric mean method tended to underestimate chlorophyll-a concentration values all the time as compared with other methods. The weighted averaging method was quite similar to the simple average method, however, it had a tendency to be overestimated at high-value range of chlorophyll-a concentration. Maximum likelihood method was almost similar to the simple average method by demonstrating small variance and high correlation (r=0.9962) of the differences between the two. However, it still had the disadvantage that it was very sensitive in the presence of speckles within a bin. The geometric mean was most significantly deviated from the remaining methods regardless of the magnitude of chlorophyll-a concentration values. Its bias error tended to be large when the standard deviation within a bin increased with less uniformity. It was more biased when data uniformity became small. All the methods exhibited large errors as chlorophyll-a concentration values dominantly scatter in terms of time and space. This study emphasizes the importance of the speckle removal process and proper selection of average methods to reduce composite errors for diverse scientific applications of satellite-derived chlorophyll-a concentration data.

• Ocean and Polar Research
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• v.30 no.3
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• pp.277-287
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• 2008

We collected information on seasonal and interannual variability of surface chlorophyll a concentration between 1997-2007 from the Northwest Pacific Ocean. Satellite data were used to acquire chlorophyll a and sea surface temperature from six regions: East Sea/Ulleung Basin, East China Sea, Philippin Sea, Warm Pool region, Warm Pool North region, and Warm Pool East region. Mixed layer depth (MLD) was calculated from temperature profiles of ARGO floats data in four of the six regions during 2002-2007. In the East Sea/Ulleung Basin, seasonal variability of chlorophyll a concentration was attributed to seasonal change of MLD, while there was no significant relationship between chlorophyll a concentration and MLD in the Warm Pool region. Interannual anomaly in sea surface temperature were similar among the East Sea, East China Sea, Philippin Sea, and Warm Pool North region. The anomaly pattern was reversed in the Warm Pool East region. However, the anomaly pattern in the Warm Pool region was intermediate of the two patterns. In relation to chlorophyll a, there was a reversed interannual anomaly pattern between Warm Pool North and Warm Pool East, while the anomaly pattern in the Warm Pool region was similar to that of Warm Pool North except for the El $Ni\tilde{n}o$ years (1997/1998, 2002/2003, 2006/2007). However, there was no distinct relationship among other seas. Interestingly, in the Warm Pool and Warm Pool East regions, sea surface temperature showed a pronounced inverse pattern with chlorophyll a. This indicates a strong interrelationship among sea surface temperature-MLD-chlorophyll a in the regions. In the Warm Pool and Warm Pool East, zonal distribution of chlorophyll a concentration within the past 10 years has shown a good relationship with sea surface temperature which reflects ENSO variability.

• Korean Journal of Environmental Biology
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• v.29 no.4
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• pp.312-320
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• 2011

In order to estimate a better methodological factor to understand phytoplankton ecology between abundance and bio-volume of phytoplankton, each 1,160 phytoplankton data, including abundance, classification and chlorophyll $a$ concentration were collected in Korean coastal waters of Incheon (Yellow sea), Tongyeong (South sea), and Ulsan (East sea). Based on these data, phytoplankton bio-volume can be calculated through a geometric model. The correlation coefficient between abundance and chlorophyll $a$ concentration was higher than the coefficient between biovolume and chlorophyll $a$ concentration, because a small size phytoplankton has relatively dense chlorophyll contents compared with the proportion of chlorophyll in a large size phytoplankton. Thus, the interpretation using abundance to understand phytoplankton ecology in Korean coastal waters may be more effective than that using bio-volume.

• Journal of the Korean Society of Tobacco Science
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• v.26 no.2 s.52
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• pp.186-192
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• 2004

This study was carried out to analyze the organic acids, polyphenols, pigments and fiber materials concentration with a different stalk position and grade of korean leaf tobaccos. Eight kinds of flue-cured leaf tobaccos which were different stalk position and grade were used for this study. Three kinds of major organic acids(citric, malic and oxalic), 2 kinds of polyphenols(chlorogenic acid and rutin), 3 kinds of pigments($\beta$-carotene, chlorophyll-a and chlorophyll-b), and 2 kinds of fiber components(pectin and lignin) were analyzed. All of these chemical components were changed with a different stalk position. When the citric acid, malic acid, $\beta-carotene$, chlorophyll-a, and lignin concentration were low in the middle stalk position and high in both bottom and upper position, oxalic acid and chlorogenic acid show the highest concentration in the middle stalk position. All of these chemical components also changed with a different grade of leaf tobaccos. As the citric acid, malic acid, $\beta-carotene$, chlorophyll-b, and lignin concentration decreased as the grade ascended, the oxalic acid and chlorogenic acid concentration increased as the grade ascended. This results assumed that the quality of korean leaf tobacco was directly proportional to oxalic acid and chlorogenic acid concentration but it was inversely proportional to citric acid, malic acid, $\beta-carotene$, chlorophyll-b and lignin concentration.

• Environmental Engineering Research
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• v.25 no.4
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• pp.515-521
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• 2020

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1($\frac{{\mu}g}{l}$), however, the four-layer NNs proved superior in terms of R² and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.

• Korean Journal of Remote Sensing
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• v.18 no.3
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• pp.141-146
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• 2002

Chlorophyll-a concentration maps of Yongdam reservoir in September and October, 2001 were produced using Landsat ETM imagery and the in-situ water quality measurement data. In-situ water samples were collected on 16th September and 18th October during the satellite overpass. The correlations between the DN values of the imagery and the values of chlorophyll-a concentration were analyzed. The visible bands(band 1, 2, 3) and the near infrared band(band 4) data of September image showed the correlation coefficient values higher than 0.9. The October image showed correlation coefficient values of about 0.7 due to the low variations of chlorophyll-a concentration. Regression models between the DN values of the Landsat ETM image and the chlorophyll-a concentration have been developed for each image. The developed regression models were then applied to each image, and finally the chlorophyll-a distribution maps of Yongdam reservoir were produced. The produced maps showed the spatial distribution of the chlorophyll-a in Yongdam reservoir in a synoptic way so that the tropic state could be easily monitored and analysed in the spatial domain.

• Proceedings of the KSRS Conference
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• v.1
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• pp.446-449
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• 2006

It is backscattering of solar radiation by water body that makes ocean color observable from above, either by airplanes or satellites. Given the very low direct contribution to backscattering by phytoplankton cells, it is curious why the retrieval of phytoplankton concentration from remotely observed ocean color is evidently successful. From semianalytical bio-optical models, a dataset is created of spectral absorption, scattering and backscattering coefficients as a function of chlorophyll concentration. Four scenarios are considered, 1) only molecular and no particle scattering, 2) random particle backscattering uncorrelated with chlorophyll concentration, 3) constrained random particle scattering with known backscattering ratio, and 4) constrained random scattering with random backscattering ratio. Scenario 1 only introduces moderate errors of -20% - 90%. And for scenarios 3 and 4, the errors are largely within 30% and 100%. Scenario 2 introduces the largest errors, with the retrieved chlorophyll concentration virtually uncorrelated with the true values, implying the backscattering must somehow be related to the trophic state. The results of the study suggested These 3 cases confirmed that while it is the absorption by phytoplankton that in large part decides the accuracy of chlorophyll concentration retrieval, for the success of monitoring of global ocean primary productivity we have to improve our knowledge on particle backscattering.

• Ocean Science Journal
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• v.42 no.1
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• pp.49-59
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• 2007

Besides empirical algorithms with the blue-green ratio, the algorithms based on fluorescence are also important and valid methods for retrieving chlorophyll-a concentration in the ocean waters, especially for Case II waters and the sea with algal blooming. This study reviews the history of initial cognitions, investigations and detailed approaches towards chlorophyll fluorescence, and then introduces the biological mechanism of fluorescence remote sensing and main spectral characteristics such as the positive correlation between fluorescence and chlorophyll concentration, the red shift phenomena. Meanwhile, there exist many influence factors that increase complexity of fluorescence remote sensing, such as fluorescence quantum yield, physiological status of various algae, substances with related optical property in the ocean, atmospheric absorption etc. Based on these cognitions, scientists have found two ways to calculate the amount of fluorescence detected by ocean color sensors: fluorescence line height and reflectance ratio. These two ways are currently the foundation for retrieval of chlorophyll-a concentration in the ocean. As the in-situ measurements and synchronous satellite data are continuously being accumulated, the fluorescence remote sensing of chlorophyll-a concentration in Case II waters should be recognized more thoroughly and new algorithms could be expected.