• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.275-292
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• 2014

The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.2
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• pp.161-173
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• 2019

In this study, the antioxidative and inhibitory activity of tyrosinase and collagenase for the solvent extract and silica column fractions of Artocarpus nitidus were evaluated. The activities were quantified using the 4 parameter logistic. LC/MS analysis showed that the major component of the fractions was polyphenol and the total polyphenol content of the extract was $48.1{\pm}2.6mg\;GAE/g$. The radical scavenging activities ($SC_{50}$) for 1,1-diphenyl-2-picrylhydrazyl of the extract, fraction-1 and fraction-2 were 16.7, 42.0 and $10.1{\mu}g/mL$, respectively. The value for fraction-2 was the closest to ascorbic acid ($1.5{\mu}g/mL$). The tyrosinase inhibitory activity of the extracts and the fractions showed $IC_{50}$ of 64.9, 0.9 and $1.2{\mu}g/mL$, respectively, and overall activity was higher than that of kojic acid ($7.4{\mu}g/mL$) and arbutin ($119.0{\mu}g/mL$). In the experiment by zebrafish embryo, the whitening activity of fraction-2 (27.5%) was higher than that of kojic acid (18.6%), and there was no adverse effect up to $500{\mu}g/mL$ of fraction-2. For the collagenase inhibitory activity, the samples showed $IC_{50}$ of 139.8, 20.6, and $16.8{\mu}g/mL$, respectively, which were competitive to 1, 10-Phenanthroline ($55.4{\mu}g/mL$). The extract and fraction-2 showed $IC_{50}$ of 61.8 and $67.1{\mu}g/mL$ for elastase. These results suggest that A. nitidus extract can be used as a cosmetic material useful for antioxidant, whitening, and prevention of skin aging without adverse effects.

• Journal of agricultural medicine and community health
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• v.34 no.3
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• pp.279-290
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• 2009

Objectives: WHO insisted on that we should study about association between residential environment and health status and make 'health city' concept as practical motto. This study analyzed about that how community environment affected their health. Methods: We surveyed residential environment satisfaction and health status of a apartment complex residents. We transformed Chun's index about housing environment study and social capital index of WHO and used as community health survey. We analyzed the association between health status and related factor by using principal compound analysis and logistic regression analysis. Results: We found out that the perceived health status 1 years ago was highly related to the residential environment and also extracted five residential environment component (APT maintenance, House, APT complex, Neighbor, APT building) by principal component analysis. After residential environment component, demographic and socioeconomic variable were controlled, the high satisfaction group of APT complex and neighbor relationship was in lower risk of perceived health status 1 years ago than the low satisfaction group. Conclusions: Recently, the importance of residential environment and neighborhood is shaped as community capacity. Therefore, social relationship and residential environment should be the core variable for health promotion of community. After all, we should know the relationship of residential environment and perceived health status 1 years ago. This helps the concept of health city clearly.