• Journal of Korea Port Economic Association
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• v.36 no.3
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• pp.21-38
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• 2020

Currently, in-port emissions are a serious problem in port cities. However, emissions, especially non-greenhouse gases, from the operation of cargo handling equipment (CHE) have received significant attention from scientific circles. This study estimates the amount of emissions from on-land port diesel-powered CHE in the Port of Incheon. With real-time activity data provided by handling equipment operating companies, this research applies an activity-based approach to capture an up-to-date and reliable diesel-powered CHE emissions inventory during 2017. As a result, 105.6 tons of carbon monoxide (CO), 243.2 tons of nitrogen oxide (NOx), 0.005 tons of sulfur oxide (Sox), 22.8 tons of particulate matter (PM), 26.0 tons of volatile organic compounds (VOCs), and 0.2 tons of ammonia (NH3) were released from the landside CHE operation. CO and NOx emissions are the two primary air pollutants from the CHE operation in the Port of Incheon, contributing 87.71% of the total amount of emissions. Cranes, forklifts, tractors, and loaders are the four major sources of pollution in the Port of Incheon, contributing 84.79% of the total in-port CHE emissions. Backward diesel-powered machines equipped in these CHE are identified as a key cause of pollution. Therefore, this estimation emphasizes the significant contribution of diesel CHE to port air pollution and suggests the following green policies should be applied: (1) replacement of old diesel powered CHE by new liquefied natural gas and electric equipment; (2) the use of NOx reduction after-treatment technologies, such as selective catalytic reduction in local ports. In addition, a systematic official national emission inventory preparation method and consecutive annual in-port CHE emission inventories are recommended to compare and evaluate the effectiveness of green policies conducted in the future.

• Journal of Korean Society of Forest Science
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• v.104 no.2
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• pp.239-247
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• 2015

Since 2006, new national forest inventory in Korea has been restructured to assess current status and and monitor the changes in forest resources based on permanent sample plots. The objective of estimate this study is to assess changes in forest resources such as land use/cover categories and forest stand variables. For this study, permanent plot data were collected between 2006-2008 and 2011-2013 in Chungcheongbuk-do, respectively. In order to produce land use/cover change matrix which plays an important role as an activity data for estimating GreenHouse Gas inventory, permanent plots were classified into six land use/cover categories. Additionally, matrixes for assessing the changes in age class and dominant tree species can provide more detailed information. For forest stand variables(tree density, basal area, growing stock, mean diameter at breath height, and mean height), their growth and change were assessed. The periodic annual growth ratios for tree density and basal area were slightly declined whereas that of growing stock was estimated to be about 3.7%. The uncertainty of changes in forest stand variables is less than 5%, except for tree density (RSE: 58%). The variation of tree density is relatively high compared to the other variables.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.502-510
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• 2022

In the national greenhouse gas inventory, a settlements category has never been included owing to the lack of activity data. Therefore, this study was conducted to obtain basic data for estimating biomass carbon storage in settlements. Nondestructive stem analysis with a laser dendrometer was performed on four major street tree species (Metasequoia glyptostroboides, Prunus armeniaca, Ginkgo biloba, and Acer buergerianum) in Wonju city, South Korea. Allometric equations of the aboveground volume were developed using five models, and allometric equations of crown area were developed with diameter at breast height (DBH) as an independent variable. The best performing allometric equations were aD²+bD+c for M.glyptostroboides and G. biloba, aD+bD² for P. armeniaca, and a+bD² for A. buergerianum. Regarding the allometric equations of crown area with DBH as an independent variable, G. biloba and A. buergerianum exhibited low coefficients of determination (R²), i.e., < 0.364, whereas M. glyptostroboides and P. armeniaca exhibited satisfactory R² values, i.e., > 0.767, probably due to different street tree management practices. The allometricequations in this study will support the carbon inventory of settlements and urban tree monitoring in management practices.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1721-1730
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• 2023

As awareness of the problem of global warming emerges around the world, the role of carbon sinks in settlement is increasingly emphasized to achieve carbon neutrality in urban areas. In order to manage carbon sinks in settlement, it is necessary to identify the current status of carbon sinks. Identifying the status of carbon sinks requires a lot of manpower and time and a corresponding budget. Therefore, in this study, a map predicting the location of trees was created using already established tree location information and Sentinel-2 satellite images targeting Seoul. To this end, after constructing a tree presence/absence dataset, structured data was generated using 16 types of vegetation indices information constructed from satellite images. After learning this by applying the Extreme Gradient Boosting (XGBoost) model, a tree prediction map was created. Afterward, the correlation between independent and dependent variables was investigated in model learning using the Shapely value of Shapley Additive exPlanations(SHAP). A comparative analysis was performed between maps produced for local parts of Seoul and sub-categorized land cover maps. In the case of the tree prediction model produced in this study, it was confirmed that even hard-to-detect street trees around the main street were predicted as trees.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.1
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• pp.88-100
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• 2018

Methane emissions from rice paddies are the largest source of greenhouse gases in the agricultural sector, but there are significant regional differences depending on the surrounding conditions and cultivation practices. To visualize these differences and to analyze their causes and characteristics, the methane emissions from each administrative district in South Korea were calculated according to the IPCC guidelines using the data from the 2010 Agriculture, Forestry and Fisheries Census, and then the results were mapped by using the ArcGIS. The nationwide average of methane emissions per unit area was $380{\pm}74kg\;CH_4\;ha^{-1}\;yr^{-1}$. The western region showed a trend toward higher values than the eastern region. One of the major causes resulting in such regional differences was the $SF_o$ (scaling factor associated with the application of organic matter), where the number of cultivation days played an important role to either offset or deepen the differences. Comparison of our results against the actual methane emissions data observed by eddy covariance flux measurement in the three KoFlux rice paddy sites in Gimje, Haenam and Cheorwon showed some differences but encouraging results with a difference of 10 % or less depending on the sites and years. Using the updated GWP (global warming potential) value of 28, the national total methane emission in 2010 was estimated to be $8,742,000tons\;CO_2eq$ - 13% lower than that of the National Greenhouse Gas Inventory Report (i.e., $10,048,000tons\;CO_2eq$). The administrative districts-based map of methane emissions developed in this study can help identify the regional differences, and the analysis of their key controlling factors will provide important scientific basis for the practical policy makings for methane mitigation.

• Korean Journal of Environmental Agriculture
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• v.32 no.3
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• pp.179-184
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• 2013

BACKGROUND: Accurate estimates of total direct $CH_4$ emissions from croplands on a country scale are important for global budgets of anthropogenic sources of $CH_4$ emissions and for the development of effective mitigation strategies. Methane production resulted by the anaerobic decomposition of organic compounds where $CO_2$ acts as inorganic electron acceptor. This process could be affected by the addition of rice straw, water management and rice variety itself. METHODS AND RESULTS: Rice (Oryza sativa L. Japonica type, var Samkwangbyeo) was cultivated in four plots: (1) Nitrogen-Phosphorus-Potassium (NPK) ($N-P_2O_5-K_2O$:90-45-57 kg/ha); (2) NPK plus 3 Mg/ha rice straw (RS3); (3) NPK plus 5 Mg/ha rice straw (RS5); (4) NPK plus 7 Mg/ha rice straw (RS7) for 3 years (2010-2012) and the rice straw incorporated in fall (Nov.) in Gyeonggi-do Hwaseong-si. Gas samples were collected using the closed static chamber which were installed in each treated plot of $152.9m^2$. According to application of 3, 5, 7 Mg/ha of rice straw, methane emission increased by 46, 101, 190%, respectively, compared to that of the NPK plot. CONCLUSION(S): We obtained a quantitative relationship between $CH_4$ emission and the amount of rice straw applied from rice fields which could be described by polynomial regression of order 2. The emission scaling factor estimated by the relationship were in the range of IPCC GPG (2000).

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.892-897
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• 2010

LCA (Life Cycle assessment) was carried out to estimate on carbon footprint and to establish of LCI (Life Cycle Inventory) database of sweetpotato production system. Based on collecting the data for operating LCI, it was shown that input of organic fertilizer was value of 3.26E-01 kg $kg^{-1}$ and it of mineral fertilizer was 1.02E-01 kg $kg^{-1}$ for sweetpotato production. It was the highest value among input for sweetpotato production. And direct field emission was 2.47E-02 kg $kg^{-1}$ during sweetpotato cropping. The result of LCI analysis focussed on greenhouse gas (GHG) was showed that carbon footprint was 4.05E-01 kg $CO_2$-eq. $kg^{-1}$ sweetpotato. Especially $CO_2$ for 71% of the GHG emission and the value was 2.88E-01 kg $CO_2$-eq. $kg^{-1}$ sweetpotato. Of the GHG emission $CH_4$, and $N_2O$ were estimated to be 18% and 11%, respectively. It might be due to emit from mainly fertilizer production (32%) and sweetpotato cultivation (28%) for sweetpotato production system. $N_2O$ emitted from sweetpotato cultivation for 90% of the GHG emission. With LCIA (Life Cycle Impact Assessment) for sweetpotato production system, it was observed that the process of fertilizer production might be contributed to approximately 90% of GWP (global warming potential). Characterization value of GWP and POCP were 4.05E-01 $CO_2$-eq. $kg^{-1}$ and 5.08E-05 kg $C_2H_4$-eq. $kg^{-1}$, respectively.