• Journal of Korean Society of Forest Science
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• v.103 no.2
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• pp.264-269
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• 2014

The aim of this study was to determine the current distribution area of Robinia pseudoacacia habitat and to estimate its stand yield as well as its carbon stocks. In order to do so, the area of R. pseudoacacia distribution is obtained based on the large-scaled forest type map (1:5,000). Also, Weibull diameter distribution model is used to predict the yield of R. pseudoacacia stands. In addition, carbon emission factor is applied to calculate carbon stocks and removals. To obtain the stand yield of R. pseudoacacia, we developed estimation equation considering growth factors of the stand, e.g. mean diameter, the basal area, maximum and minimun diameter and etc. and tested it to ensure accuracy. Consequently, estimation equation derived from all growth factors have shown significance that could also be used for analysis. Site index was also established to determine the productivity of the forestland that later turned out to be ranging from 16 to 22. Based on these results, stand yield tables were drawn up. R. pseudoacacia is widely distributed in inland areas of Gyeongsang, Chungcheong and Gyeonggi provinces which covers total area of 26,770 ha. And when it is converted into carbon stocks, it amounts to 2,517,598tC with annual carbon uptake of 3.76tC/ha which is comparable to Querqus species that is known to storer large amounts of carbon. Therefore, R. pseudoacacia is also expected to serve as a viable carbon pool that would contribute to the mitigation of climate change. Furthermore, stand yield tables, an outcome of this survey would assist not only in proper management but also in sustainable management policy of R. pseudoacacia.

• Smart Media Journal
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• v.11 no.5
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• pp.75-81
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• 2022

In order to respond to carbon neutrality and climate change in agriculture, agricultural machinery, which has been developed centered on internal combustion engines, needs to be converted to an electric-based technology that does not emit greenhouse gases. In this study, simulations for electric UTV suspension design were performed to reduce vibration and shock of electric UTV for agricultural use and to improve driving stability and control performance of the vehicle. The simulation was performed by dividing the tolerance load of the vehicle body and the loaded load state. The range of motion of the suspension spring of UTV is within 30% of the range of motion under condition B under tolerance, the displacement of the UTV suspension with full load is reduced from 264mm to 121mm, and the damping speed is 260mm/s to 300mm/s that it can be seen that the range of motion is within 60%. Suspension design of electric UTV for multi-purpose agricultural work is a very important factor for maintaining agricultural work ability in towing work such as tillage as well as driving and terrain adaptation. The results of this study can be usefully used to determine the spring parameters with the appropriate damping range so that the electric UTV can be used for various agricultural tasks.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.43-65
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• 2023

Recently, with the increasing international interest on environmental issues, efforts have been made to reduce greenhouse gas emissions due to ship fuel, however, the dependence on fossil fuel is expected to continue for a while. Since fuel costs account for a high portion of the total operating cost of a ship, it is necessary to analyze the influence of oil prices on the shipping markets. The purpose of this study is to evaluate the relationship between the international oil prices and the four major shipping markets for bulk carriers. This study employed WTI as the oil price variable while monthly data from 2017 to 2020 from the four major shipping markets by classifying freight rates, charter rates, newbuilding prices, and secondhand prices were also considered in multiple ship sizes of capesize, panamax, supramax, and handysize. Firstly, the results of the correlation analysis using the VAR model indicate that changes in international oil prices have a statistically positive (+) significant effect on BCIS only in the second time lag, on BSIS at all lags, and on BHIS only in the first staggered period. Secondly, as a result of correlation analysis using the VECM model, in the case of BPIC, BHIC, BCIN, and BHIR, the cointegration coefficient value has a negative (-) significant effect at the 5% significance level in the cointegration relationship with international oil prices. Further, in the case of the dynamic correlation, the increase in oil price in the first period of the lag leads to a decrease in the BCIN newbuilding prices while the increase in the oil price in the first and second period in the lag leads to a decrease in the BHIR used ship prices.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1449-1463
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• 2020

As recent global warming and climate changes become more serious, the importance of CO2 absorption by forests is increasing to cope with the greenhouse gas issues. According to the UN Framework Convention on Climate Change, it is required to calculate national CO2 absorptions at the local level in a more scientific and rigorous manner. This paper presents the gridded expansion of forest flux observations and mapping of daily CO2 absorption by the forests in Korea using numerical weather prediction data and satellite images. To consider the sensitive daily changes of plant photosynthesis, we built a machine learning model to retrieve the daily RACA (reference amount of CO2 absorption) by referring to the climax forest in Gwangneung and adopted the NIFoS (National Institute of Forest Science) lookup table for the CO2 absorption by forest type and age to produce the daily AACA (actual amount of CO2 absorption) raster data with the spatial variation of the forests in Korea. In the experiment for the 1,095 days between Jan 1, 2013 and Dec 31, 2015, our RACA retrieval model showed high accuracy with a correlation coefficient of 0.948. To achieve the tier 3 daily statistics for AACA, long-term and detailed forest surveying should be combined with the model in the future.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.526-535
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• 2021

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.