• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.449-457
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• 2022

The Port of Busan is currently ranked as the seventh largest container port worldwide in terms of cargo throughput. However, port competition in the Far-East region is fierce. The growth rate of container throughput handled by the port of Busan has recently slowed down. In this study, we analyzed how economic conditions and multiple external shocks could influence cargo throughput and identified potential implications for port business. The aim of this study was to build a model to accurately forecast port throughput using the ARIMA model, which could incorporate external socio-economic shocks, and the VEC model considering causal variables having long-term effects on transshipment cargo. Findings of this study suggest that there are three main areas affecting container throughput in the port of Busan, namely the Russia-Ukraine war, the increased competition for transshipment cargo of Chinese ports, and the weaker growth rate of the Korean economy. Based on the forecast, in order for the Port of the Port of Busan to continue to grow as a logistics hub in Northeast-Asia, policy intervention is necessary to diversify the demand for transshipment cargo and maximize benefits of planned infrastructural investments.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.913-922
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• 2022

The purpose of this research is to propose the measurement of improving DEM by using the water surface range of SAR image analysis for river corridors and to suggest the construction of satellite-based 3D river spatial information of inaccessible regions such as North Korea. For this research, it has been progressed from the accessible area, watershed of Namgang river, the branch of Nakdonggang river. The satellite image was collected from SAR satellite image data for a year in 2021 which was provided by ESA from Sentinel-1A/B data and extracted from the seasonal water surface area. Ground gauge water level was collected from hourly intervals data by WAMIS. The DEM was improved by analysis of the river altitude of water surface area change by the combination of the ground water level of minimum to maximum water surface area data extracted from SAR image analysis. After the improvement of DEM, the altitude of the river varied that it is defined to comprise more natural form of river DEM than the existing DEM. The correction validation of improvement DEM was necessary in field survey elevation data; however, the correction validation was not progressed due to the absence of the data. Although, the purpose of this research is to provide the improvement of DEM by using the analyzed water surface by existing DEM data and SAR image analysis. After the progression of additional correction validation research, we would plan to examine the application in other places and to progress the additional methodological research to apply in inaccessible and unmeasured area including the North Korea.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.543-557
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• 2021

Because aerosols have different spectral characteristics according to the size and composition of the particle and to the satellite sensors, a comparative analysis of aerosol products from various satellite sensors is required. In South Korea, however, a comprehensive study for the comparison of various official satellite AOD (Aerosol Optical Depth) products for a long period is not easily found. In this paper, we aimed to assess the performance of the AOD products from MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), Himawari-8, and Sentinel-3 by referring to the AERONET (Aerosol Robotic Network) sun photometer observations for the period between January 2015 and December 2019. Seasonal and geographical characteristics of the accuracy of satellite AOD were also analyzed. The MODIS products, which were accumulated for a long time and optimized by the new MAIAC (Multiangle Implementation of Atmospheric Correction) algorithm, showed the best accuracy (CC=0.836) and were followed by the products from VIIRS and Himawari-8. On the other hand, Sentinel-3 AOD did not appear to have a good quality because it was recently launched and not sufficiently optimized yet, according to ESA (European Space Agency). The AOD of MODIS, VIIRS, and Himawari-8 did not show a significant difference in accuracy according to season and to urban vs. non-urban regions, but the mixed pixel problem was partly found in a few coastal regions. Because AOD is an essential component for atmospheric correction, the result of this study can be a reference to the future work for the atmospheric correction for the Korean CAS (Compact Advanced Satellite) series.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.171-183
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• 2016

Land Surface Temperature (LST) retrieved from Landsat 8 measured from 2013 to 2014 and it is corrected by surface temperature observed from ground. LST maps are retrieved from Landsat 8 calculate using the linear regression function between raw Landsat 8 LST and ground surface temperature. Seasonal and annual LST maps developed an average LST from season to annual, respectively. While the higher LSTs distribute on the industrial and commercial area in urban, lower LSTs locate in surrounding rural, sea, river and high altitude mountain area over Seoul and surrounding area. In order to correct the LST, linear regression function calculate between Landsat 8 LST and ground surface temperature observed 3 Korea Meteorological Administration (KMA) synoptic stations (Seoul(ID: 108), Incheon(ID: 112) and Suwon(ID: 119)) on the Seoul and surrounding area. The slopes of regression function are 0.78 with all data and 0.88 with clear sky except 5 cloudy pixel data. And the original Landsat 8 LST have a correlation coefficient with 0.88 and Root Mean Square Error (RMSE) with $5.33^{\circ}C$. After LST correction, the LST have correlation coefficient with 0.98 and RMSE with $2.34^{\circ}C$ and the slope of regression equation improve the 0.95. Seasonal and annual LST maps represent from urban to rural area and from commercial to industrial region clearly. As a result, the Landsat 8 LST is more similar to the real state when corrected by surface temperature observed ground.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.2
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• pp.4116-4120
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• 1976

With the use of many rivers increased nearly to the capacity, the need for information concerning daily quantities of water and the total annual or seasonal runoff has became increased. A systematic record of the flow of a river is commonly made in terms of the mean daily discharge Since. a single observation of stage is converted into discharge by means of rating curve, it is essential that the stage discharge relations shall be accurately established. All rating curves have the looping effect due chiefly to channel storage and variation in surface slope. Loop rating curves are most characteristic on streams with somewhat flatter gradients and more constricted channels. The great majority of gauge readings are taken by unskilled observers once a day without any indication of whether the stage is rising or falling. Therefore, normal rating curves shall show one discharge for one gauge height, regardless of falling or rising stage. The above reasons call for the correction of the discharge measurements taken on either side of flood waves to the theoretical steady-state condition. The correction of the discharge measurement is to consider channel storage and variation in surface slope. (1) Channel storage As the surface elevation of a river rises, water is temporarily stored in the river channel. There fore, the actual discharge at the control section can be attained by substracting the rate of change of storage from the measured discharge. (2) Variation in surface slope From the Manning equation, the steady state discharge Q in a channel of given roughness and cross-section, is given as {{{{Q PROPTO SQRT { 1} }}}} When the slope is not equal, the actual discharge will be {{{{ { Q}_{r CDOT f } PROPTO SQRT { 1 +- TRIANGLE I} CDOT TRIANGLE I }}}} may be expressed in the form of {{{{ TRIANGLE I= { dh/dt} over {c } }}}} and the celerity is approximately equal to 1.3 times the mean watrr velocity. Therefore, The steady-state discharge can be estimated from the following equation. {{{{Q= { { Q}_{r CDOT f } } over { SQRT { (1 +- { A CDOT dh/dt} over {1.3 { Q}_{r CDOT f }I } )} } }}}} If a sufficient number of observations are available, an alternative procedure can be applied. A rating curve may be drawn as a median line through the uncorrected values. The values of {{{{ { 1} over {cI } }}}} can be yielded from the measured quantities of Qr$.$f and dh/dt by use of Eq. (7) and (8). From the 1/cI v. stage relationship, new vlues of 1/cI are obtained and inserted in Eq. (7) and (8) to yield the steady-state discharge Q. The new values of Q are then plotted against stage as the corrected steadystate curve.

• Journal of Navigation and Port Research
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• v.44 no.1
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• pp.44-52
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• 2020

More accurate forecasting of port cargo in the global long-term recession is critical for the implementation of port policy. In this study, the Busan Port container volume (export cargo and transshipment cargo) was estimated using the Vector Autoregressive (VAR) model and the vector error correction (VEC) model considering the causal relationship between the economic scale (GDP) of Korea, China, and the U.S. as well as ARIMA, a single volume model. The measurement data was the monthly volume of container shipments at the Busan port J anuary 2014-August 2019. According to the analysis, the time series of import and export volume was estimated by VAR because it was relatively stable, and transshipment cargo was non-stationary, but it has cointegration relationship (long-term equilibrium) with economic scale, interest rate, and economic fluctuation, so estimated by the VEC model. The estimation results show that ARIMA is superior in the stationary time-series data (local cargo) and transshipment cargo with a trend are more predictable in estimating by the multivariate model, the VEC model. Import-export cargo, in particular, is closely related to the size of our country's economy, and transshipment cargo is closely related to the size of the Chinese and American economies. It also suggests a strategy to increase transshipment cargo as the size of China's economy appears to be closer than that of the U.S.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.51-64
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• 2015

We improved the MODerate resolution Imaging Spectroradiometer (MODIS) land cover map over the Asia-Oceania region through the reclassification of the misclassified pixels. The misclassified pixels are defined where the number of land cover types are greater than 3 from the 12 years of MODIS land cover map. The ratio of misclassified pixels in this region amounts to 17.53%. The MODIS Normalized Difference Vegetation Index (NDVI) time series over the correctly classified pixels showed that continuous variation with time without noises. However, there are so many unreasonable fluctuations in the NDVI time series for the misclassified pixels. To improve the quality of input data for the reclassification, we corrected the MODIS NDVI using Correction based on Spatial and Temporal Continuity (CSaTC) developed by Cho and Suh (2013). Iterative Self-Organizing Data Analysis (ISODATA) was used for the clustering of NDVI data over the misclassified pixels and land cover types was determined based on the seasonal variation pattern of NDVI. The final land cover map was generated through the merging of correctly classified MODIS land cover map and reclassified land cover map. The validation results using the 138 ground truth data showed that the overall accuracy of classification is improved from 68% of original MODIS land cover map to 74% of reclassified land cover map.

• Plant Journal
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• v.11 no.2
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• pp.45-51
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• 2015

In this paper, studied condenser operating management which is affecting power plants efficiency considering the cost of poor quality. Sea water temperature and condenser pressure have clear correlation in S power plants. As the sea water temperature changes, condenser pressure changed -1.7~+20 mmHg from design condenser pressure(38.1 mmHg). Use the heat rate correction curve from manufactory company, realized that efficiency and cost of poor quality changed 0.0201%, 12,830 won/h at Unit #1,2 but 0.0155%, 9,832 won/h when condenser pressure 1 mmHg rise. Also, checked that it is changed depend on seasonal corresponding operation, plant ageing and the point of preventive maintenance like overhaul maintenance. This study said if we considered complying with management range and planning overhaul maintenance, then it could help reducing operating maintenance losses minimum 2.5 billion won per 1 year (case : Unit #1, forty days maintenance).