• Journal of Bio-Environment Control
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• v.13 no.4
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• pp.209-216
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• 2004

This study was aimed to assess the effects of microclimate factors on lettuce chlorophyll fluorescent responses and to develop an environment control system for plant growth by adopting a simple genetic algorithm. The photosynthetic responses measurements were repeated by changing one factor among six climatic factors at a time. The maximum Fv'/Fm' resulted when the ambient temperature was $21^{\circ}C,\;CO_2$ concentration range of 1,200 to 1,400 ppm, relative humidity of $68\%$, air current speed of $1.4m{\cdot}s^{-1}$, and the temperature of nutrient solution of $20^{\circ}C$. In PPF greater than $140{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, Fv'/Fm' values were decreased. To estimate the effects of combined microclimate factors on plant growth, a photosynthesis efficiency model was developed using principle component analysis for six microclimate factors. Predicted Fv'/Fm' values showed a good agreement to measured ones with an average error of $2.5\%$. In this study, a simple genetic algorithm was applied to the photosynthesis efficiency model for optimal environmental condition for lettuce growth. Air emperature of $22^{\circ}C$, root zone temperature of $19^{\circ}C,\;CO_2$ concentration of 1,400 ppm, air current speed of $1.0m{\cdot}s^{-1}$, PPF of $430{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, and relative humidity of $65\%$ were obtained. It is feasible to control plant environment optimally in response to microclimate changes by using photosynthesis efficiency model combined with genetic algorithm.

• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.331-335
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• 2013

This study detected red tide areas using the existing Moderate-Resolution Imaging Spectroradiometer(MODIS) and Geostationary Ocean Color Imager(GOCI), and then compared the results between results of two sensors. The coasts of Jeollanam-do in the South Sea of Korea were set as the study area based on the red tide data which occurred on Aug. 26th, 2012. This study compared the results of sensors to detect red tides by using a satellite. In the results of analyzing MODIS by limiting it as chlorophyll concentration and the sea surface temperature which is considered to have red tides by the existing researches, it was possible to delete considerable amount of errors compared to the case of detecting red tides by using only chlorophyll while still there were differences from the range of red tides actually observed. In the results of GOCI by using empirical algorithm for detecting red tides, currently used by Korea Institute of Ocean Science & Technology(KIOST), it was possible to obtain more detailed results than MODIS. However, there was an area misjudged as red tides due to the influence of clouds. Also both MODIS and GOCI extracted red tides were not actually occurring, which might be because they were not able to perfectly distinguish red tides from turbid water in coastal areas with high turbidity.

• Proceedings of the KSRS Conference
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• v.1
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• pp.467-470
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• 2006

Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meteorological Satellite (COMS) is scheduled for launch in 2008. GOCI includes the eight visible-to-near-infrared (NIR) bands, 0.5km pixel resolution, and a coverage region of 2500 ${\times}$ 2500km centered at 36N and 130E. GOCI has had the scope of its objectives broadened to understand the role of the oceans and ocean productivity in the climate system, biogeochemical variables, geological and biological response to physical dynamics and to detect and monitor toxic algal blooms of notable extension through observations of ocean color. To achieve these mission objectives, it is necessary to develop an atmospheric correction technique which is capable of delivering geophysical products, particularly for highly turbid coastal regions that are often dominated by strongly absorbing aerosols from the adjacent continental/desert areas. In this paper, we present a more realistic and cost-effective atmospheric correction method which takes into account the contribution of NIR radiances and include specialized models for strongly absorbing aerosols. This method was tested extensively on SeaWiFS ocean color imagery acquired over the Northwest Pacific waters. While the standard SeaWiFS atmospheric correction algorithm showed a pronounced overcorrection in the violet/blue or a complete failure in the presence of strongly absorbing aerosols (Asian dust or Yellow dust) over these regions, the new method was able to retrieve the water-leaving radiance and chlorophyll concentrations that were consistent with the in-situ observations. Such comparison demonstrated the efficiency of the new method in terms of removing the effects of highly absorbing aerosols and improving the accuracy of water-leaving radiance and chlorophyll retrievals with SeaWiFS imagery.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.513-521
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• 2015

In the processing of ocean color remote sensing data, spatio-temporal binning is crucial for securing effective observation area. The validity determination for given source data refers to the information in Level-2 flag. For minimizing the stray light contamination, NASA OBPG's standard algorithm suggests the use of large filtering window but it results in the loss of effective observation area. This study is aimed for quality improvement of ocean color remote sensing data by recovering/extending the portion of effective observation area. We analyzed the difference between MODIS/Aqua standard and modified product in terms of chlorophyll-a concentration, spatial and temporal coverage. The recovery fractions in Level-2 swath product, Level-3 daily composite product, 8-day composite product, and monthly composite product were $13.2({\pm}5.2)%$, $30.8({\pm}16.3)%$, $15.8({\pm}9.2)%$, and $6.0({\pm}5.6)%$, respectively. The mean difference between chlorophyll-a concentrations of two products was only 0.012%, which is smaller than the nominal precision of the geophysical parameter estimation. Increase in areal coverage also results in the increase in temporal density of multi-temporal dataset, and this processing gain was most effective in 8-day composite data. The proposed method can contribute for the quality enhancement of ocean color remote sensing data by improving not only the data productivity but also statistical stability from increased number of samples.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.213-230
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• 2017

The satellite-based red tide detection algorithms have been developed for specific occurrence waters and red tide species. However, it is essential to study the whole occurrence waters and various red tide species for quick and accurate surveillance of red tide around the Korean coastal waters. In thisstudy, the comprehensive analysesinvolve the spectral features of red tide areas and the suitability of the satellite-based red tide detection algorithms used with GOCI in the Korean coastal waters. As a result, the spectral characteristics were changed according to the chlorophyll content of red tide species and the turbidity of the waters where the red tide appeared. In addition, the previous red tide detection algorithm is applied to GOCI, and it is found that there is a limitation to the red tide area extraction as the existing threshold value. To overcome these limitations, red tide species were divided into two groups according to the difference of chlorophyll content and a system for red tide surveillance wassuggested. It is possible to distinguish between red tide and non-red tide area through five steps. As a result of applying to GOCI, the red tide was appropriately extracted from the previous algorithm based on red tide breaking news. If such a red tide surveillance system is used, it will be possible to efficiently monitor red tide by quick and accurate surveillance of the whole occurrence waters around the Korean and various red tide species.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.482-482
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• 2022

하천의 합류부에서는 수질이 다른 유체가 혼합하여 합류 전과 다른 특성을 보인다. 하천의 합류부에서 수질을 효율적으로 관리하기 위해서는 수질의 공간적인 혼합 특성을 규명하는 것이 중요하다. 합류부에서 수질의 공간적인 혼합 특성을 분석하기 위해 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기 조직화 지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하였다. 세 가지 기법을 비교하여 어떤 알고리즘이 합류부의 수질 변화 특성을 더 뚜렷하게 나타내는지 분석하였다. 수질 변화 비교 인자들은 pH, chlorophyll, DO, Turbidity 등이 있고, 수질 인자들은 YSI를 활용해 측정하였다. 자료의 측정 지역은 낙동강과 황강이 합류하는 지역이며, 보트에 YSI 장비를 부착하고 횡단하여 측정하였다. 측정한 데이터를 R 프로그램을 통해 세 가지 기법을 적용시켜 수질 변화 비교를 분석한다. 토폴로지 데이터 분석(topological data analysis, TDA)은 거대하고 복잡한 데이터로부터 유의미한 정보를 추출하는 데 사용하고, 자기조직화지도(Self-Organizing Map, SOM) 기법은 차원 축소와 군집화를 동시에 수행한다. k-평균 알고리즘(K-means clustering algorithm) 기법은 주어진 데이터를 k개의 클러스터로 묶는 머신러닝 비지도학습에 속하는 알고리즘이다. 세 가지 방법들의 주목적은 클러스터링이다. 클러스터 분석(Cluster analysis)이란 주어진 데이터들의 특성을 고려해 동일한 성격을 가진 여러 개의 그룹으로 대상을 분류하는 데이터 마이닝의 한 방법이다. 군집화 방법들인 TDA, SOM, K-means를 이용해 합류 지역의 수질 특성들을 클러스터링하여 수질 패턴들을 분석해 하천 수질 오염을 방지할 수 있을 것이다. 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기조직화지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하여 합류부에서의 수질 특성을 비교하며 어떤 기법이 합류의 특성을 더욱 뚜렷하게 나타내는지 규명했다. 합류의 특성을 군집화 방법을 이용해 알게 된다면, 합류부의 수질 변화 패턴을 다른 합류 지역에서도 적용할 수 있을 것으로 기대된다.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.3
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• pp.124-132
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• 2010

In spite of the global importance of primary production of phytoplankton, some primary production data in Korean coastal waters still need to be better processed. The daily rates of water column primary production is generally estimated by integrating the primary production per unit volume over time and depth, but efforts for time integration algorithm have been conducted insufficiently. In this study a mathematical equation evaluating daily primary production integrated over time of a day is proposed and the effectiveness of the model is tested on Saemangeum Lake. The daily primary productions computed with the proposed equation were nearly the same with the results numerically integrated by substituting solar irradiance data. It was suggested that better estimation of primary production would be obtained by using monthly or weekly means of solar irradiance rather than more variable daily data. Because of the vertically heterogenous distribution of phytoplankton, it's hard to integrate the equation over depth to give the daily rates of primary production per unit area of water surface. However, the problem would be solved if, after the vertical distribution of phytoplankton was classified into several patterns and reduced to mathematical formula, every composite function of time integrated equation and chlorophyll distribution equation was integrated successfully.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.59-68
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• 1999

The study for detecting suspended sediment distribution in Lake Sihwa, which has a large surface area and coastal area, using remote sensing technique was carried out with development of satellite data collected since 1970. The research, however, analysis of spatial distribution and quantity, is not common in domestic study and useful algorithms have not been proposed. In this study, a suspended sediment algorithm was composed with in-situ data obtained in study area and remote sensing reflectance obtained in-water optical instrument, which has SeaWiFS wavelength bands. However, when the algorithm was applied to Landsat TM data, including an in-situ data set, and some problems arose. The composition of the algorithm which was structured with band difference and band ratio showed the correlation of $R^2$=0.7649 with concentration of suspended sediments. And, between calculated and observed concentration of suspended sediments there was a correlation of $R^2$=0.6959. However, remote sensing reflectance obtained from Landsat TM is not good for the estimation of concentration of suspended sediments, because of high concentration of chlorophyll and CDOM(colored dissolved organic matter).

• Proceedings of the KSRS Conference
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• v.2
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• pp.816-819
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• 2006

One of the most difficult parameters to measure in the sea is current speed and direction. Recently, efforts are being made to estimate the ocean current vectors by utilizing sequential satellite imageries. In this study, we attempted to estimated sea surface current vector (sscv) by using satellite ocean color imageries of SeaWifs around the Korean Peninsula. This ocean color image data has 1-day sampling interval and spatial resolution of 1x1 km. Maximum cross-correlation method is employed which is aimed to detect similar patterns between sequential images. The estimated current vectors are compared to the surface geostrophic current vectors obtained from altimeter of sea level height data. In utilizing the color imagery data, some limitations and drawbacks exist so that in warm water region where phytoplankton concentration is relatively lower than in cold water region, estimation of sscv is poor and unreliable. On the other hand, two current vector fields agree reasonably well in the Korean South Sea region where high concentration of chlorophyll-a and weak tide is observed. In the future, with ocean color images of shorter sampling interval by COMS satellite, the algorithm and methodology developed in the study would be useful in providing the information for the ocean current around Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.141-150
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• 2018

Crop type classification is essential for supporting agricultural decisions and resource monitoring. Remote sensing techniques, especially using hyperspectral imagery, have been effective in agricultural applications. Hyperspectral imagery acquires contiguous and narrow spectral bands in a wide range. However, large dimensionality results in unreliable estimates of classifiers and high computational burdens. Therefore, reducing the dimensionality of hyperspectral imagery is necessary. In this study, the Random Forest (RF) classifier was utilized for dimensionality reduction as well as classification purpose. RF is an ensemble-learning algorithm created based on the Classification and Regression Tree (CART), which has gained attention due to its high classification accuracy and fast processing speed. The RF performance for crop classification with airborne hyperspectral imagery was assessed. The study area was the cultivated area in Chogye-myeon, Habcheon-gun, Gyeongsangnam-do, South Korea, where the main crops are garlic, onion, and wheat. Parameter optimization was conducted to maximize the classification accuracy. Then, the dimensionality reduction was conducted based on RF variable importance. The result shows that using the selected bands presents an excellent classification accuracy without using whole datasets. Moreover, a majority of selected bands are concentrated on visible (VIS) region, especially region related to chlorophyll content. Therefore, it can be inferred that the phenological status after the mature stage influences red-edge spectral reflectance.