• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.261-272
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• 2023

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.

• The Journal of Korean Society for Radiation Therapy
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• v.34
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• pp.13-20
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• 2022

Purpose : IMRT using Tomotherapy during nasopharyngeal cancer radiation therapy irradiate an accurate dose to tumor tissues and is effective to reduce a dose rapidly in normal tissues. However, this has high MU and long Beam On Time. This study aims to analyze differences in tumors, normal tissues and low-dose distributions and the efficiency of Split VMAT after applying Helical IMRT (Tomotherapy), VMAT (Linac : 2Arc) and Split VMAT (Linac : 4Arc) plans. Materials and Methods : This study targeted ten nasopharyngeal cancer patients of this hospital and compared three treatment plans (Helical IMRT, VMAT, Split VMAT). For Helical IMRT planning, Precision^® (Version 1.1.1.1, Accuray, USA) was used, and for VMAT and Split VMAT planning, Pinnacle^3® (Version 9.10, Philips, USA) was used. The total dose applied was 38.4 Gy / 32 Gy (Daily Dose 2.4 Gy (GTV + 0.3 cm) / 2 Gy (CTV + 0.3 cm) 16Fx), and for GTV + 0.3 cm (P_GTV), 95% of V_38.4Gy was prescribed. VMAT with an angle of 360° 2Arc was applied, and for Split VMAT, the field was divided into the right, the left, the top and the bottom and an angle of 360° 4Arc, 6MV was set. For evaluating the quality of the treatment plans, differences in tumors, normal tissues and low-dose area were compared, and Beam On Time was measured to analyze the efficiency. Results : When calculating the mean values of evaluation items of the three treatment plans (Helical IMRT, VMAT, Split VMAT) for the patients, the H.I (Homogeneity Index) of P_GTV was 1.04, 1.11 and 1.1 respectively, and the C.I (Confomity Index) of P_CTV was 1.03, 0.99 and 1.00 respectively. The mean dose of RT Parotid Gland (Gy) was 14.54, 17.06 and 14.76 respectively, the mean dose of LT Parotid Gland (Gy) was 14.32, 17.32 and 15.09 respectively, the maximum dose of P_Cord (Spinal Cord + 0.3 cm) (Gy) was 20.57, 22.59 and 21.06 respectively, and the maximum dose of Brain Stem (Gy) was 22.35, 23.99 and 21.68 respectively. The 50% isodose curve (cc) was 1332, 1132.5 and 1065.2 respectively. Beam On Time (sec) was 373.7, 130.7 and 254.4 respectively. Conclusion : Displaying a similar treatment plan quality to Helical IMRT, which is used a lot for head and neck treatment, Split VMAT reduced the low-dose area and Beam On Time and produced a better result than VMAT. Therefore, it is considered that Split VMAT is effective not only for nasopharyngeal cancer but also for other head and neck cancers.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.779-793
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• 2023

Intensive and long-term aquaculture activities in Korea have generated considerable amounts of organic matter, deteriorating the sedimentary environment and ecosystem. The Korean government enacted the Fishery Management Act to preserve and manage the environment of fish farms. Based on this, a fisheries environment assessment has been conducted on fish cage farms since 2014, necessitating the development of a scientific and objective evaluation method suitable for the domestic environment. Therefore, a benthic health index (BHI) was developed using the relationship between benthic polychaete communities and organic matter, a major source of pollution in fish farms. In this study, the development process and calculation method of the BHI have been introduced. The BHI was calculated by classifying 225 species of polychaetes appearing in domestic coastal and aquaculture areas into four groups by linking the concentration gradient of the total organic carbon in the sediment and the distributional characteristics of each species and assigning differential weights to each group. Using BHI, the benthic fauna communities were assigned to one of the four ecological classes (Grade 1: Normal, Grade 2: Slightly polluted, Grade 3: Moderately polluted, and Grade 4: Heavily polluted). The application of the developed index in the field enabled effective evaluation of the Korean environment, being relatively more accurate and less affected by the season compared with the existing evaluation methods like the diversity index or AZTI's Marine Biotic Index developed overseas. In addition, using BHI will be useful in the environmental management of fish farms, as the environment can be graded in quantified figures.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1591-1604
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• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.4
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• pp.380-389
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• 2007

Stable carbon and nitrogen isotopes were analyzed in suspended particulate organic matter, macroalgae and macrobenthic invertebrates in order to determine the importance of primary organic matter sources in supporting food webs of rocky subtidal and intertidal macroalgal beds in the Korean coasts. Investigations were conducted at the inter tidal sites within Gwangyang bay, a semi-enclosed and eutrophicated bay, and the subtidal sites of the east coast, a relatively oligotrophic and open environment, in May and June 2005. Water-column suspension feeders showed more negative $\delta^{13}C$ values than those of the other feeding guilds, indicating trophic linkage with phytoplankton and thereby association with pelagic food chains. In contrast, animals of the other feeding guilds, including interface suspension feeders, herbivores, deposit feeders, omnivores and predators, displayed relatively less negative $\delta^{13}C$ values than those of the water-column suspension feeders and similar with that of macroalgae, indicating exclusive use of macroalgae-derived organic matter and association with benthic food chains. Most the macrobenthic species were considered to form strong trophic links with benthic food chains. In addition, the distribution of higher $\delta^{15}N$ values in macrobenthic consumers and macroalgae at the intertidal sites of Gwangyang Bay than those at the subtidal sites of the east coast suggests that anthropogenic nutrients may enhance the macroalgal production at the intertidal sites and in turn be incorporated into the particular littoral food web in Gwangyag Bay. These results confirm the dominant role of macroalgae in supporting rocky subtidal and intertidal food webs in the Korean coasts.

• Economic and Environmental Geology
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• v.57 no.3
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• pp.281-292
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• 2024

North Korea relies heavily on coal as the primary energy source, playing an important role in all energy demand sectors except for the transportation sector. Approximately half of the total electricity is generated through coal-fired power plants, and coal is used to produce heat and power for all industrial facilities. Furthermore, coal has been a significant contributor to earning foreign currency through long-term exports to China. Nevertheless, since the 1980s, indiscriminate mining activities have led to rapid depletion of coal production in most coal mines. Aging mine facilities, lack of investment in new equipment, shortages of fuel and electricity, difficulties in material supply, and frequent damage from flooding have collectively contributed to a noticeable decline in coal production since the late 1980s. North Korea's coal deposits are distributed in various geological formations from the Proterozoic to the Cenozoic, but the most critical coal-bearing formations are Ripsok and Sadong formations distributed in the Pyeongnam Basin of the Late Paleozoic from Carboniferous to Permian, which are called as Pyeongnam North and South Coal Fields. Over 90% of North Korea's coal is produced in these coal fields. The classification of coal in North Korea differs from the international classification based on coalification (peat, lignite, sub-bituminous coal, bituminous coal, and anthracite). North Korean classification based on industrial aspect is classified into bituminous coal, anthracite, and low-grade coal (Chomuyeontan). Based on the energy factor, it is classified into high-calorie coal, medium calorie coal, and low-calorie coal. In North Korea, the term "Chomuyeontan" refers to a type of coal that is not classified globally and is unique to North Korea. It is a low-grade coal exclusively used in North Korea and is not found or used in any other country worldwide. This article compares North Korea's coal classification and the international coal classification of coal and provides insights into the geological characteristics, reserves, utilization, and research trends of North Korean coal resources. This study could serve as a guide for preparing scientific and industrial agendas related to coal collaboration between North Korea and South Korea.

• Atmosphere
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• v.24 no.3
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• pp.303-315
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• 2014

Terrestrial ecosystem plays the important role as carbon sink in the global carbon cycle. Understanding of interactions of terrestrial carbon cycle with climate is important for better prediction of future climate change. In this paper, terrestrial carbon cycle is investigated by Hadley Centre Global Environmental Model, version 2, Carbon Cycle (HadGEM2-CC) that considers vegetation dynamics and an interactive carbon cycle with climate. The simulation for future projection is based on the three (8.5/4.5/2.6) representative concentration pathways (RCPs) from 2006 to 2100 and compared with historical land carbon uptake from 1979 to 2005. Projected changes in ecological features such as production, respiration, net ecosystem exchange and climate condition show similar pattern in three RCPs, while the response amplitude in each RCPs are different. For all RCP scenarios, temperature and precipitation increase with rising of the atmospheric $CO_2$. Such climate conditions are favorable for vegetation growth and extension, causing future increase of terrestrial carbon uptakes in all RCPs. At the end of 21st century, the global average of gross and net primary productions and respiration increase in all RCPs and terrestrial ecosystem remains as carbon sink. This enhancement of land $CO_2$ uptake is attributed by the vegetated area expansion, increasing LAI, and early onset of growing season. After mid-21st century, temperature rising leads to excessive increase of soil respiration than net primary production and thus the terrestrial carbon uptake begins to fall since that time. Regionally the NEE average value of East-Asia ($90^{\circ}E-140^{\circ}E$, $20^{\circ}N{\sim}60^{\circ}N$) area is bigger than that of the same latitude band. In the end-$21^{st}$ the NEE mean values in East-Asia area are $-2.09PgC\;yr^{-1}$, $-1.12PgC\;yr^{-1}$, $-0.47PgC\;yr^{-1}$ and zonal mean NEEs of the same latitude region are $-1.12PgC\;yr^{-1}$, $-0.55PgC\;yr^{-1}$, $-0.17PgC\;yr^{-1}$ for RCP 8.5, 4.5, 2.6.