• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.365-379
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• 2021

Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1201-1208
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• 2020

A mass of seawater with similar properties in the ocean is called a water mass, and the front is a sea area where two masses of different properties meet. The gradient algorithm is a method of extracting where the sea water temperature pixel changes rapidly assuming that the slope is large, and the place with the large slope is assumed to be a front. This method is able to process large amounts of satellite data at once. Therefore, in this study, we tried to find the front lines in the sea area around the Korean Peninsula by using a gradient algorithm. The study data used gridded sea surface temperature satellite data. The resolution was 1/4°, and the monthly average data from January 1993 to December 2018 were used. There were major five fronts representatively, China Coastal Front, South Sea Coastal Front, Kuroshio Front/ Kuroshio Extension Front, Subpolar Front and the Subarctic Front. As a result of comparing the distribution of front by season, more types of front were distributed in winter and spring than in summer and autumn, and the distribution range was wider.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.91-101
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• 2021

This study attempted to conduct the study from a macro perspective more specifically through the temporal and spatial analysis of Minbuk villages according to a change in a Civilian Control Line, such as a social and spatial distribution and a change of the existing Minbuk villages. To this end, this study conducted the spatial analysis for the change in the Minbuk villages according to the adjustment of the Civilian Control Line in time series by using a map of the Armistice Agreement Vol. 2, Google Earth, a digital cadastral map, an administrative district map, and the like are used as spatial data, and summarizing and constructing, as attribute data, a statistical yearbook, Ministry of Defense and Cheorwon-Gun notification data, a Land Use Regulation Information System, and cadastral map attribute information. After the enactment of the Military Facility Protection Act, the analysis was performed on a 20-year basis based on the 1976 statistical yearbook of which the Civilian Control Line was drawn. As a result, the total area of the Civilian Control Zone in Cheorwon from 1975 to 2015 decreased by 105.8 km², and 9 of 14 Minbuk villages were released and only 6 villages existed. The unoccupied villages were analyzed as 14 villages, 10 fewer than the existing surveyed or statistical villages. The movement of the Civilian Control Line to the north may disappear the unique characteristics of the Minbuk villages but should be done carefully as it is closely related to the lives of the current residents, and policies should be established in terms of sustainable development and conservation of the villages. This study is significant in conducting the temporal and spatial analysis, which is the basis of the Minbuk regions and the Minbuk villages, and may be used as basic data necessary for subsequent analysis study.

• 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.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1721-1732
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• 2020

Elementary school students spend a lot of time in a school and are more exposed to indoor air pollution. Also the students are physically growing and have a relatively high respiratory rate per unit weight compared to adults, so it is known that there is a high sensitivity to indoor air pollution. Therefore, indoor air quality in a school is becoming an important factor for the student's health. In this study, a correlation analysis using levels of indoor and outdoor Particulate Matter (PM) measured from five elementary schools in Seoul was performed to evaluate the effect of outdoor PM on indoor PM. PM ratio and indoor/outdoor (I/O) ratio were also analyzed to investigate the actual condition of indoor air quality and effect of outdoor PM on indoor PM. As a result, the correlation between indoor and outdoor PM in elementary school was more significant in PM2.5 and PM1 than PM10. In the case of I/O ratio, the I/O ratio of PM10 was higher than that of 1 in four elementary schools except SD (BB:2.21, NS: 1.67, IS: 1.73, SI: 1.17). This indicates that the activity of students has a great effect on the concentration of indoor PM10.

• Journal of the Korean Geosynthetics Society
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• v.21 no.1
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• pp.1-10
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• 2022

In this study, a model parameter evaluation method using relative density was proposed to utilize applicable UBC3D-PLM for liquefaction behavior. In addition, dynamic effective stress analysis, that is, liquefaction analysis, was performed on the case of the liquefaction occurrence region where acceleration and pore water pressure were measured, and compared with the actual measurement and the existing Finn analysis results. Through this study, it was found that the proposed method can easily evaluate the necessary parameters required by the related model and predict the pore water pressure behavior in the region where liquefaction occurs. In addition, in the case of the study area, both measurements and numerical analysis showed that liquefaction occurred when a certain amount of time elapsed after the earthquake acceleration reached the maximum value. In the case of UBC3D-PLM applied in this study, the excess pore water pressure behavior similar to the actual measurement was predicted, and the occurrence of liquefaction was evaluated in the same way as the actual measurement. In particular, although the excess pore water pressure in the sand layer was greater, the phenomenon in which liquefaction occurred in the silt layer was accurately realized. It is expected that the proposed model parameter evaluation method and finite element analysis applying UBC3D-PLM can be used to select the liquefaction reinforcement region in the future seismic design and reinforcement by evaluating the liquefaction occurrence region similarly to the real one.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.4
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• pp.291-306
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• 2021

From the climate change scenario experiments of 21 models participating in Coupled Climate Model Inter-comparison Project Phase 6, future changes of sea surface temperature (SST) and Kuroshio in the Northwest Pacific were analyzed. The spatial feature of SST change was found to be related to the change of the current speed and spatial distribution of Kuroshio. To investigate the relationship between the change in latitude of the Kuroshio extension region, which flows along the boundary between the subtropical gyre and the subarctic gyre in the North Pacific, and the large-scale atmospheric circulation due to global warming, the zero-windstress curl line for each climate change experiment from 9 out of 21 models were compared. As the atmospheric radiative forcing increases due to the increase of greenhouse gases, it was confirmed that the zero-windstress curl line moves northward, which is consistent with the observation. These results indicate that as the Hadley Circulation expands to the north due to global warming, the warming of the mid-latitudes to which the Korean Peninsula belongs may be accelerated. The volume transport and temperature of the Tsushima Warm Current flowing into the East Sea through the Korea Strait also increased as the atmospheric radiative forcing increased.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1329-1341
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• 2022

Destroying the marine environment and marine ecosystem and causing marine accidents, marine debris is generated every year, and among them, submerged marine debris is difficult to identify and collect because it is on the seabed. Therefore, deep-learning-based semantic segmentation was experimented on waste fish nets and waste ropes using underwater images to identify efficient collection and distribution. For segmentation, a high-resolution network (HRNet), a state-of-the-art deep learning technique, was used, and the performance of each optimizer was compared. In the segmentation result fish net, F1 score=(86.46%, 86.20%, 85.29%), IoU=(76.15%, 75.74%, 74.36%), For the rope F1 score=(80.49%, 80.48%, 77.86%), IoU=(67.35%, 67.33%, 63.75%) in the order of adaptive moment estimation (Adam), Momentum, and stochastic gradient descent (SGD). Adam's results were the highest in both fish net and rope. Through the research results, the evaluation of segmentation performance for each optimizer and the possibility of segmentation of marine debris in the latest deep learning technique were confirmed. Accordingly, it is judged that by applying the latest deep learning technique to the identification of submerged marine debris through underwater images, it will be helpful in estimating the distribution of marine sedimentation debris through more accurate and efficient identification than identification through the naked eye.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1257-1271
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• 2022

Recently, many Earth observation optical satellites have been developed, as their demands were increasing. Therefore, a rapid preprocessing of satellites became one of the most important problem for an active utilization of satellite images. Satellite image matching is a technique in which two images are transformed and represented in one specific coordinate system. This technique is used for aligning different bands or correcting of relative positions error between two satellite images. In this paper, we propose an automatic image matching method among satellite images with different ground sampling distances (GSDs). Our method is based on improved feature matching and robust estimation of transformation between satellite images. The proposed method consists of five processes: calculation of overlapping area, improved feature detection, feature matching, robust estimation of transformation, and image resampling. For feature detection, we extract overlapping areas and resample them to equalize their GSDs. For feature matching, we used Oriented FAST and rotated BRIEF (ORB) to improve matching performance. We performed image registration experiments with images KOMPSAT-3A and RapidEye. The performance verification of the proposed method was checked in qualitative and quantitative methods. The reprojection errors of image matching were in the range of 1.277 to 1.608 pixels accuracy with respect to the GSD of RapidEye images. Finally, we confirmed the possibility of satellite image matching with heterogeneous GSDs through the proposed method.