• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.133-149
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• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.

• 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 Korean Society of Environmental Engineers
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• v.30 no.9
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• pp.955-960
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• 2008

We investigated and estimated at the characteristics of decomposition and mineralization of benomyl using a design of experiment(DOE) based on the general factorial design in an E-beam process, and also the main factors(variables) with benomyl concentration(X$_1$) and E-beam irradiation(X$_2$) which consisted of 5 levels in each factor was set up to estimate the prediction model and the optimization conditions. At frist, the benomyl in all treatment combinations except 17 and 18 trials was almost degraded and the difference in the decomposition of benomyl in the 3 blocks was not significant(p > 0.05, one-way ANOVA). However, the % of benomyl mineralization was 46%(block 1), 36.7%(block 2) and 22%(block 3) and showed the significant difference of the % that between each block(p < 0.05). The linear regression equations of benomyl mineralization in each block were also estimated as followed; block 1(Y$_1$ = 0.024X$_1$ + 34.1(R$^2$ = 0.929)), block 2(Y$_2$ = 0.026X$_2$ + 23.1(R$^2$ = 0.976)) and block 3(Y$_3$ = 0.034X$_3$ + 6.2(R$^2$ = 0.98)). The normality of benomyl mineralization obtained from Anderson-Darling test in all treatment conditions was satisfied(p > 0.05). The results of prediction model and optimization point using the canonical analysis in order to obtain the optimal operation conditions were Y = 39.96 - 9.36X$_1$ + 0.03X$_2$ - 10.67X$_1{^2}$ - 0.001X$_2{^2}$ + 0.011X$_1$X$_2$(R$^2$ = 96.3%, Adjusted R$^2$ = 94.8%) and 57.3% at 0.55 mg/L and 950 Gy, respectively. A Microtox test using V. fischeri showed that the toxicity, expressed as the inhibition(%), was reduced almost completely after an E-beam irradiation, whereas the inhibition(%) for 0.5 mg/L, 1 mg/L and 1.5 mg/L was 10.25%, 20.14% and 26.2% in the initial reactions in the absence of an E-beam illumination.

• Journal of Preventive Medicine and Public Health
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• v.24 no.3 s.35
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• pp.363-372
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• 1991

We used the health screening data of some rural and urban residents to examine the cross-sectional association between leukocyte count and hypertension. The 206 male and 203 female rural residents were selected by multi-stage cluster sampling method in Kyungsan-Kun area of Kyungbuk province in 1985 and 600 urban residents were selected by the same sampling method as the rural residents in Daegu city of the same province in 1986 compatible with age-sex distribution of Daegu city of 1985 census, but of whom 384 actually responded. The rest of 600 were replaced by age and sex with those who were members of the medical insurance plan visiting the health management department of the university hospital to get the biannual preventive medical checkups. Excluded in the analysis were those having hypertensive history, diseases and extreme outlying values of the screening tests, leaving 373 rural and 571 urban residents. Leukocyte count was measured with ELT-8 Laser shadow method and the unit $cells/mm^3$, Blood pressures were determined with an aneroid sphygmomanometer with pre-standardized method and hypertensives were defined as those showing systolic blood pressure more than 140mmHg and/or diastolic blood pressure more than 90mmHg. Total residents pooled (N=944) showed a significant difference between hypertensives and normotensives ($6965.93{\pm}1997.01\;vs\;6490.61{\pm}1941.32,\;P=0.00$) and in rural residents was noted the similar significant difference (P=0.03). None of significant differences were noted in any stratum stratified by residency and sex. Compared to the lowest quintile of WBC, 2/5 quintile showed odds ratio 0.99 (95% Confidence interval, Ci 0.62-1.59), 3/5 quintile 1.41 (95% CI 0.90-2.21), 4/5 quintile 1.76 (95% CI. 1.14-2.72), and highest quintile 1.80 (1.15-2.82) in the total residents. Likelihood ratio test for linear trend for it indicated a significant trend ($X^2_{trend}=5.53,\;df=1,\;P<0.05$). There were no other significant odds ratios compared to the lowest quintile of WBC in strata stratified by residency and sex. The odds ratios in total residents which had showed significant odds ratios became nonsignificant and of reduced magnitude after controlling age, frequency of smoking and drinking with multiple logistic. regression. In each stratum, it changed magnitudes of odds ratios slightly and unstably. None of the trend tests showed any significant trend. These results suggest that the Friedman et al's finding of association between leukocyte count and hypertension may be due to an statistical type I error resulting from the data dredging in an exploratory study, in which more than 800 variables were screened as possible predictors of hypertension.