• Journal of Korea Water Resources Association
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• v.39 no.5 s.166
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• pp.453-466
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• 2006

The objective of this study was to analyze the changes in the hydrological environment in Soyanggang-dam watershed due to climate change results (in yews 2050 and 2100) which were simulated using CCCma CGCM2 based on SRES A2 and B2. The SRES A2 and B2 were used to estimate NDVI values for selected land use using the relation of NDVI-Temperature using linear regression of observed data (in years 1998$\sim$2002). Land use change based on SRES A2 and B2 was estimated every 5- and 10-year period using the CA-Markov technique based on the 1985, 1990, 1995 and 2000 land cover map classified by Landsat TM satellite images. As a result, the trend in land use change in each land class was reflected. When land use changes in years 2050 and 2100 were simulated using the CA-Markov method, the forest class area declined while the urban, bareground and grassland classes increased. When simulation was done further for future scenarios, the transition change converged and no increasing trend was reflected. The impact assessment of evapotranspiration was conducted by comparing the observed data with the computed results based on three cases supposition scenarios of meteorological data (temperature, global radiation and wind speed) using the FAO Penman-Monteith method. The results showed that the runoff was reduced by about 50% compared with the present hydrologic condition when each SRES and periods were compared. If there was no land use change, the runoff would decline further to about 3$\sim$5%.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.4
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• pp.100-108
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• 2016

The climatic index for tourism(CIT) has recently been advanced, which includes complete human energy balance models such as physiological equivalent temperature(PET) and universal thermal climate index(UTCI). This study investigated human thermal sensation and comfort at Woljung-ri Beach, Jeju, Republic of Korea, in spring and summer 2015 for landscape planning and design in beach areas. Microclimatic data measurements and human thermal sensation/comfort surveys from ISO 10551 were conducted together. There were 869 adults that participated. As a result, perceptual and thermal preference that consider only physiological aspects had high coefficients of determination($r^2$) with PET in linear regression analyses: 92.8% and 87.6%, respectively. However, affective evaluation, personal acceptability and personal tolerance, which consider both physiological and psychological aspects, had low $r^2s$: 60.0%, 21.1% and 46.4%, respectively. However, the correlations between them and PET were all significant at the 0.01 level. The neutral PET range in perceptual for human thermal sensation was $25{\sim}27^{\circ}C$, but a PET range less or equal to 20% dissatisfaction, which was recommended by ASHRAE Standard 55, could not be achieved in perceptual. Only PET ranges in affective evaluation and personal tolerance affected by both aspects were qualified for the recommendation as $21{\sim}32^{\circ}C$ and $17{\sim}37^{\circ}C$, respectively. Therefore, the PET range of $21{\sim}32^{\circ}C$ is recommended to be used for the human thermal comfort zone of beach areas in landscape planning and design as well as tourism and recreational planning. PET heat stress level ranges on the beach were $2{\sim}5^{\circ}C$ higher than those in inland urban areas of the Republic of Korea. Also, they were similar to high results of tropical areas such as Taiwan and Nigeria, and higher than those of western and middle Europe and Tel Aviv, Israel.

• Journal of Nutrition and Health
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• v.52 no.2
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• pp.206-216
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• 2019

Purpose: Management of the metabolic risk factors in diabetes patients is essential for preventing or delaying diabetic complications. This study compared the levels of the metabolic risk factors in diabetes patients according to the income levels, and examined the secular trends in recent decades. Methods: The data from the Korea National Health and Nutrition Examination Survey 1998 ~ 2014 were used. The diabetes patients were divided into three groups based on their household income levels. General information was obtained through self-administered questionnaires, and the blood biomarkers and blood pressure data were obtained from a health examination. Multivariable linear regression models were used to compare the metabolic biomarker levels according to the household income levels, adjusting for potential confounding factors. Results: The fasting blood glucose, hemoglobin A1c, and blood lipid (total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride) levels were similar in the three groups. During the survey period of 16 years, the blood pressure showed a significant decreasing trend with time in all groups (p < 0.001). In contrast, the fasting blood glucose (p = 0.004), total cholesterol (p < 0.001), and LDL-cholesterol levels (p = 0.007) decreased significantly, and the HDL-cholesterol level (p < 0.001) increased significantly in the highest-income groups. In the lowest-income group, the fasting blood glucose (p = 0.02), total cholesterol (p < 0.001), and triglyceride (p = 0.003) levels showed a significant decreasing trend over time. On the other hand, the middle-income group showed no significant change in any of the metabolic risk factors except for blood pressure. Conclusion: The level of management of metabolic risk factors according to the income level of Korean diabetes patients was similar. On the other hand, the highest- and lowest-income groups showed positive trends of management of these factors during 16 years of observation, whereas the middle-income group did not show any improvement.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.