• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.339-351
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• 2011

This study investigates the potentials of satellite, ground measurement data, and geo-spatial information within an urban area for the mapping of the Urban Environmental Quality (UEQ) parameters. The UEQ indicates a complex and various parameters resulting from both human and natural factors, which are greenness, climate, air pollution, the urban infrastructure, and etc. Multi-spectral remote sensing data from the Landsat ETM and TM sensors for the mapping of air pollution by the Haze Optimized Transform (HOT) technique, Urban Heat Island (UHO using the emissivity-fusion method in Seoul from 2000 to 2006 in fine resolution (30m) were analyzed for the estimation of UEQ index. Although the UHI values are similar ($8.4^{\circ}C{\sim}9.1^{\circ}C$) during these years, the spatial coverage of "hot" surface temperature (> $24^{\circ}C$) significantly increased from 2000 to 2006 due to the rapid urban development. Furthermore, high correlations between vegetation index and land surface temperature were achieved with a correlation coefficients of 0.85 (2000), 0.81 (2001), 0.84 (2002), and 0.89 (2006), respectively. It was found that the proposed method was successfully analyzed spatial structure of the UEQ and the scenarios of the best and worst areas within the city were also identified. Based on the quantifiable fine resolution satellite image parameters, UEQ can promote the understanding of the complex and dynamic factors controlling urban environment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.68-81
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• 2021

The agricultural ecosystem is one of the major sources of greenhouse gas (GHG) emissions. In order to search for climate change adaptation options which mitigate GHG emissions while maintaining crop yield, it is advantageous to integrate multiple models at a high spatial resolution. The objective of this study was to develop a tool to support integrated assessment of climate change impact b y coupling the DSSAT model and the DNDC model. DNDC Regional Input File Tool(DRIFT) was developed to prepare input data for the regional mode of DNDC model using input data and output data of the DSSAT model. In a case study, GHG emissions under the climate change conditions were simulated using the input data prepared b y the DRIFT. The time to prepare the input data was increased b y increasing the number of grid points. Most of the process took a relatively short time, while it took most of the time to convert the daily flood depth data of the DSSAT model to the flood period of the DNDC model. Still, processing a large amount of data would require a long time, which could be reduced by parallelizing some calculation processes. Expanding the DRIFT to other models would help reduce the time required to prepare input data for the models.

• Journal of Korea Water Resources Association
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• v.43 no.3
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• pp.309-323
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• 2010

As climate changes and abnormal climates have drawn research interest recently, many countries utilize the GCM, which is based on SRES suggested by IPCC, to obtain more accurate forecast for future climate changes. Especially, many research attempts have been made to simulate localized geographical characteristics by using RCM with the high resolution data globally. To evaluate the impacts of climate and landuse change on water resources in the Han-river basin, we carried out the procedure consisting of the CA-Markov Chain, the Multi-Regression equation using two independent variables of temperature and rainfall, the downscaling technique based on the RegCM3 RCM, and SLURP. From the CA-Markov Chain, the future landuse change is forecasted and the future NDVI is predicted by the Multi-Regression equation. Also, RegCM3 RCM 50 sets were generated by the downscaling technique based on the RegCM3 RCM provided by KMA. With them, 90 year runoff scenarios whose period is from 2001 to 2090 are simulated for the Han-river basin by SLURP. Finally, the 90-year simulated monthly runoffs are compared with the historical monthly runoffs for each dam in the basin. At Paldang dam, the runoffs in September show higher increase than the ones in August which is due to the change of rainfall pattern in future. Additionally, after exploring the impact of the climate change on the structure of water circulation, we find that water management will become more difficult by the changes in the water circulation factors such as precipitation, evaporation, transpiration, and runoff in the Han-river basin.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.375-387
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• 2019

The use of drone-based hyperspectral image offers considerable advantages in high resolution remote sensing applications. The primary objective of this study was to select the optimal bands based on hyperspectral image for the estimation yield of the chinese cabbage. The hyperspectral narrow bands were acquired over 403.36 to 995.19 nm using a 3.97 nm wide, 150 bands, drone-based hyperspectral imaging sensor. Fresh weight data were obtained from 2,031 sample for each field survey. Normalized difference vegetation indices were computed using red, red-edge and near-infrared bands and their relationship with quantitative each fresh weights were established and compared. As a result, predominant proportion of fresh weights are best estimated using data from three narrow bands, in order of importance, centered around 697.29 nm (red band), 717.15 nm (red-edge band) and 808.51 nm (near-infrared band). The study determined three spectral bands that provide optimal chinese cabbage productivity in the visible and near-infrared portion of the spectrum.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.471-479
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• 2024

Flood damage is becoming more serious due to the heavy rainfall caused by climate change. Physically based hydrological models have been utilized to predict stream water level variability and provide flood forecasting. Recently, hydrological simulations using machine learning and deep learning algorithms based on nonlinear relationships between hydrological data have been getting attention. In this study, the Long Short-Term Memory (LSTM) algorithm is used to predict the water level of the Seomjin River watershed. In addition, Climate Prediction Center morphing method (CMORPH)-based gridded precipitation data is applied as input data for the algorithm to overcome for the limitations of ground data. The water level prediction results of the LSTM algorithm coupling with the CMORPH data showed that the mean CC was 0.98, RMSE was 0.07 m, and NSE was 0.97. It is expected that deep learning and remote data can be used together to overcome for the shortcomings of ground observation data and to obtain reliable prediction results.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.6
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• pp.838-848
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• 2013

The Community Multiscale Air Quality (CMAQ) model is capable of providing high quality atmospheric chemistry profiles through the utilization of high-resolution meteorology and emissions data. However, it cannot simulate air quality accurately if input data are not appropriate and reliable. One of the most important inputs required by CMAQ is the air pollutants emissions, which determines air pollutants concentrations during the simulation. For the CMAQ simulation of Korean peninsula, we, in general, use the Korean National Emission Inventory data which are estimated by Clean Air Policy Support System (CAPSS). However, since they are not provided by model-ready emission data, we should convert CAPSS emissions into model-ready data. The SMOKE is the emission model we used in this study to generate CMAQ-ready emissions. Because processing the emissions data is very monotonous and tedious work, we have developed CAPSS2SMOKE program to convert CAPSS emissions into SMOKE-ready data with ease and effective. CAPSS2SMOKE program consists of many codes and routines such as source classification code, $PM_{10}$ to $PM_{2.5}$ ratio code, map projection conversion routine, spatial allocation routine, and so on. To verify the CAPSS2SMOKE program, we have run SMOKE using the CAPSS 2009 emissions and found that the SMOKE results inherits CAPSS emissions quite well.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.36-51
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• 2012

The demand for a climatological dataset with a regular spaced grid is increasing in diverse fields such as ecological and hydrological modeling as well as regional climate impact studies. PRISM(Precipitation-Elevation Regressions on Independent Slopes Model) is a useful method to estimate high-altitude precipitation. However, it is not well discussed over the optimization of PRISM parameters and DEM(Digital Elevation Model) resolution in South Korea. This study developed the PRISM and then optimized parameters of the model and DEM resolution for producing a gridded annual average precipitation data of South Korea with 1km spatial resolution during the period 2000-2005. SCE-UA (Shuffled Complex Evolution-University of Arizona) method employed for the optimization. In addition, sensitivity analysis investigates the change in the model output with respect to the parameter and the DEM spatial resolution variations. The study result shows that maximum radius within which station search will be conducted is 67km. Minimum radius within which all stations are included is 31km. Minimum number of stations required for cell precipitation and elevation regression calculation is four. Optimizing DEM resolution is $1{\times}1km$. This study also shows that the PRISM output very sensitive to DEM spatial resolution variations. This study contributes to improving the accuracy of PRISM technique as it applies to South Korea.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.49-61
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• 2021

The repeated shipboard measurements that have been conducted by the National Institute of Fisheries Science (NIFS) for more than a half century, provide the valuable long-term hydrographic data with high spatial-temporal resolution. However, this unprecedent dataset has been rarely used for oceanic climate sciences because of its reliability issue. In this study, temporal variability of salinity error in the NIFS data was quantified by means of extremely small variability of salinity in the deep layer of the south-western East Sea, in order to contribute to studies on long-term variability of the East Sea. The NIFS salinity errors estimated on the isothermal surfaces of 1℃ have a remarkable temporal variation, such as ~0.160 g/kg in the year of 1961~1980, ~0.060 g/kg in 1981~1994,~0.020 g/kg in 1995~2002, and ~0.010 g/kg in 2003~2014 on average, which basically represent bias error. In the recent years, even though the quality of salinity has been improved, there still remain relatively large bias errors in salinity data presumably due to failure of salinity sensor managements, especially in 2011, 2013, and 2014. On the contrary, the salinity in the year of 2012 was very accurate and stable, whose error was estimated as about 0.001 g/kg comparable to the salinity sensor accuracy. Thus, as long as developing proper data quality control procedures and sensor management systems, I expect that the NIFS shipboard hydrographic data could have good enough quality to support various studies on ocean response to climate variabilities. Additionally, a few points to improve the current NIFS shipboard measurements were suggested in the discussion section.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.6
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• pp.767-773
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• 2017

Infrared stealth is an important technology for naval ships. This technology helps improve the anti-detection performance and survivability of naval ships. In general, the infrared signature of naval ships are categorized into internal and external heat source. External signature are generated by ship surface heating by solar flux as well as the complicated heat transfer process with the surrounding weather condition. Modern naval ships are equipped with seawater injection nozzles on the outside for nuclear, biological and, chemical, and these nozzles are used to control external signature. Wide nozzle placement intervals and insufficient injection pressure, however, have reduced seawater dispersion area. To address this problem, nozzle installation standards must be established. In this study, an actual-scale experimental system was implemented to provide the evidence for nozzle installation standards in order to reduce the infrared signature of naval ships. In addition, the environmental conditions of the experiment were set up through computational fluid dynamics considering the ocean climate data and naval ship management conditions of South Korea. The dispersion distance was measured using a high-resolution thermography system. The flow rate, pipe pressure, and dispersion distance were analyzed, and the evidence for the installation of seawater injection nozzles and operation performance standards was suggested.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.229-241
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• 2006

Global air temperature has risen by $0.6^{\circ}C$ over the last one hundred years due to increased atmospheric greenhouse gases. Moreover, this global warming trend is projected to continue in the future. This study was carried out to evaluate spatial variations in rice production areas by simulating rice-growth and development with projected high resolution climate data in Korea far 2011-2100, which was geospatially interpolated from the 25 km gridded data based on the IPCC SRES A2 emission scenario. Satellite remote sensing data were used to pinpoint the rice-growing areas, and corresponding climate data were aggregated to represent the official 'crop reporting county'. For the simulation experiment, we used a CERES-Rice model modified by introducing two equations to calculate the leaf appearance rate based on the effective temperature and existing leaf number and the final number of leaves based on day-length in the photoperiod sensitive phase of rice. We tested the performance of this model using data-sets obtained from transplanting dates and nitrogen fertilization rates experiments over three years (2002 to 2004). The simulation results showed a good performance of this model in heading date prediction [$R^2$=0.9586 for early (Odaebyeo), $R^2$=0.9681 for medium (Hwasungbyeo), and $R^2$=0.9477 for late (Dongjinbyeo) maturity cultivars]. A modified version of CERES-Rice was used to simulate the growth and development of three Japonica varieties, representing early, medium, and late maturity classes, to project crop status for climatological normal years between 2011 and 2100. In order to compare the temporal changes, three sets of data representing 3 climatological years (2011-2040, 2041-2070, and 2071-2100) were successively used to run the model. Simulated growth and yield data of the three Japonica cultivars under the observed climate for 1971-2000 was set as a reference. Compared with the current normal, heading date was accelerated by 7 days for 2011-2040 and 20 days for 2071-2100. Physiological maturity was accelerated by 15 days for 2011-2040 and 30 days for 2071-2100. Rice yield was in general reduced by 6-25%, 3-26%, and 3-25% per 10a in early, medium, and late maturity classes, respectively. However, mid to late maturing varieties showed an increased yield in northern Gyeonggi Province and in most of Kwangwon Province in 2071-2100.