• Korean Journal of Heritage: History & Science
• /
• v.56 no.4
• /
• pp.54-70
• /
• 2023

Surveys and studies on cultural assets using LiDAR measurement are already active overseas. Recently, awareness of the advantages and availability of LiDAR measurement has increased in Korea, and cases of using it for surveys of cultural assets are gradually increasing. However, it is usually restricted to surveys of mountain fortresses and is not actively used for surveys of ancient tombs yet. Therefore, this study intends to emphasize the need to secure fundamental data from LiDAR measurement for the era from the Three Kingdoms to Unified Silla in which recovery, maintenance, etc., in addition to the actual surveys, are unfulfilled due to the sites being mainly distributed in mountainous areas. For this, LiDAR measurement was executed for the area of Jangsan Ancient Tombs and Chunghyo-dong Ancient Tombs in Seoak-dong, Gyeongju, to review the distribution and geographical conditions of ancient tombs. As a result, in the Jangsan Ancient Tombs, in which a precision archaeological (measurement) survey was already executed, detailed geographic information and distribution conditions could be additionally identified, which could not be known only with the layout indicated by the topographic map of the existing report. Also, in the Chunghyo-dong Ancient Tombs, in which an additional survey was not conducted after 10 tombs were found during the Japanese colonial period, the location of the ancient tombs initially excavated was accurately identified, and the status and additional information was acquired, such as on the conditions of ancient tombs not surveyed. Such information may also be used as fundamental data for the preservation and maintenance of future ancient tombs in addition to the survey and study of the ancient tombs themselves. LiDAR measurement is most effective for identifying the condition of ancient tombs in mountainous areas where observation is difficult or access is limited due to the forest zone. It may be executed before on-site surveys, such as archaeological surveys, to secure data with high availability as prior surveys or pre-surveys. Therefore, it is necessary to secure fundamental data from LiDAR measurement in future surveys of ancient tombs and to establish a survey and maintenance/utilization plan based on this. To establish survey/study and preservation/maintenance measures for ancient tombs located in mountainous areas, a precision archaeological survey is currently executed to draw up a distribution chart of ancient tombs. If LiDAR measurement data is secured before this and used, a more effective and accurate distribution chart can be drawn up, and the actual conditions can be identified. Also, most omissions or errors in information can be prevented in on-site surveys of large regions. Therefore, it is necessary to accumulate fundamental data by actively using LiDAR measurement in future surveys of ancient tombs.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.50-63
• /
• 2018

The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.137-154
• /
• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Journal of Environmental Health Sciences
• /
• v.46 no.4
• /
• pp.376-387
• /
• 2020

Objectives: This study is aimed at offering basic data for making plans for offensive odor management after researching offensive odor occurrence and characteristics in Gangwon Province. Methods: The data used in the study is based on offensive odor data analyzed by the Gangwon Institute of Health and Environment from 2012 to 2019. The data were reclassified by year, month, facility, and region to identify characteristics of occurrence. Finally, a distribution map of offensive odors was created using ArcGIS. Results: The highest monthly frequency of offensive odor occurrence falls in June, August, and July, and the summer season and third quarter are the highest. According to the latest eight-year data for Gangwon Province, complaints about offensive odors in county areas are more frequent than those in city areas. There are many offensive odor complaints in Wonju, Cheorwon, and Heongsung. The main offensive odor emission facilities are livestock and waste treatment (recycling) facilities. Complaints about offensive odors are relatively lower the Yeongdong area than Yeongseo area, which is considered to be the result of characteristics of land-sea breezes and geographical factors. Offensive odors from livestock facilities count for an average of 53.9% of the total, and the inadequacy rate of livestock facilities averages 36.9%. Conclusions: To maintain a clean environment in Gangwon Province, it is strongly recommended that an offensive odor reduction plan for livestock facilities be established. Areas with a high density of offensive odor occurrence should be identified and systematically managed with short- and mid-term measures. If offensive odors is managed using GIS, it is possible to identify the characteristics of occurrence by time and space and also by facility. In addition, since systematic data management is possible, it is believed that a rapid response to offensive odors, prediction of their spread, and efficient management are possible.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.1
• /
• pp.81-89
• /
• 2011

The previous land gravity data in Korea showed locally biased irregular distribution. Especially, this problem was more serious in the mountainous area where the data density was significantly low. The same problem appeared in GPS/Levelling data thus the precision of the geoid could not be improved. From 2008, new gravity and GPS/Levelling data has been collected by the unified control point and survey on the benchmark project which were funded by the national geographic information institute. The newly obtained data has much better distribution and precision so that it could be used for update precision of geoid model. In this study, the new precision geoid has been calculated based old and new gravity data and this model showed 5.29cm of precision compared to 927 points of GPS/Levelling data. And the degree of fit and precision of hybrid geoid has been calculated 2.99cm and 3.67cm. The new gravimetric geoid has been updated about 27% over whole country. And it showed 42% of precision update due to collection of new gravity data on the Kangwon/Kyeongsang area which showed quite low distribution. In 2010, about 4,000 points of gravity and 300 points of GPS/Levelling data has been obtained by unified control and survey on benchmark project. We expect that new data will contribute to updating geoid precision and veri tying precision more objectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.1
• /
• pp.87-96
• /
• 2012

The U.S. Geological Survey (USGS) announced that an earthquake of 9.0 magnitude had occurred near the east coast of Japan on March 11, 2011, resulting in a displacement of the crust of about 2.4 meters. The Korean peninsula is located on the Eurasian tectonic plate that stretches out to Japan; therefore, there is a high possibility of being affected by an earthquake. The Korean GPS CORS network operated by the National Geographic Information Institute (NGII) was processed for ten days before and after the earthquake. Both static and kinematic baseline processing were tested for the determination of crustal deformation. The static baseline processing was performed in two scenarios: 1) fixing three IGS stations in China, Mongolia and Russia; 2) fixing SUWN, one of the CORS networks in Korea, in order to effectively verify crustal deformation. All data processing was carried out using Bernese V5.0. The test results show that most of the parts of the Korean peninsula have moved to the east, ranging 1.2 to 5.6 cm, compared to the final solution of the day before the earthquake. The stations, such as DOKD and ULLE that are established on the islands closer to the epicenter, have clearly moved the largest amounts. Furthermore, the station CHJU, located on the southwestern part of Korea, presents relatively small changes. The relative positioning between CORS confirms the fact that there were internal distortions of the Korean peninsula to some extent. In addition, the 30-second interval kinematic processing of CORS data gives an indication of earthquake signals with some delays depending on the distance from the epicenter.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.1
• /
• pp.39-48
• /
• 2012

Filtering process that separates ground and non-ground points from LIDAR data is important in order to create the digital elevation model (DEM) or extract objects on the ground. The purpose of this research is to select the most effective filtering algorithm through qualitative and quantitative analysis for the existing filtering method used to extract ground points from LIDAR data. For this, four filtering methods including Adaptive TIN(ATIN), Perspective Center-based filtering method(PC), Elevation Threshold with Expand Window(ETEW) and Progressive Morphology(PM) were applied to mountain area, urban area and the area where building and mountains exist together. Then the characteristics for each method were analyzed. For the qualitative comparison of four filtering methods used for the research, visual method was applied after creating shaded relief image. For the quantitative comparison, an absolute comparison was conducted by using control points observed by GPS and a relative comparison was conducted by the digital elevation model of the National Geographic Information Institute. Through the filtering experiment of the LIDAR data, the Adaptive TIN algorithm extracted the ground points in mountain area and urban area most effectively. In the area where buildings and mountains coexist, progressive morphology algorithm generated the best result. In addition, as a result of qualitative and quantitative comparisons, the applicable filtering algorithm regardless of topographic characteristics appeared to be ATIN algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.31 no.3
• /
• pp.239-247
• /
• 2013

The global gravitational model is essential for precision geoid model construction. Also, it would be used as basic scientific data in geophysical and oceanographic fields. In Korea, EGM2008 has been used from the late 2000s. After publishing EGM2008, new gravitational models such as GOCO02S, GOCO03S, EIGEN-6C, EIGEN-6C2 based on GOCE data were developed. Therefore, we need to verify recent models to select optimal one for geoid computation in Korea. In this study, we compared new models generated based on the GOCE data to EGM2008 and verified the precision of models by comparing with NGII(National Geographic Information Institute) GPS/Leveling data. When comparing EIGEN models to EGM2008, the difference is about 8cm. On the other h and, about 70cm of difference between GOCO models and EGM2008 has been calculated. The reason for this is because GOCO models have been developed using only satellite data while EGM2008 has been used gravity and altimeter data as well as satellite data. When comparing global gravitational model to GPS/Leveling data, EGM2008 showed the best precision of 6.1cm over whole Korean peninsula. The new global gravitational model using additional GOCE data will be published consistently, so the precision verification of new model should be continued.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.3
• /
• pp.237-247
• /
• 2011

As of 2011, 72 Permanent GPS Stations are installed to control DGPS reference points by the National Geographic Information Institute in South Korea. As the center of the Earth's mass continues to move, the coordinates of the permanent GPS stations become inconsistent over time. Thus, a reference frame using a set of coordinates and their velocities of a global network of stations at a specific period has been used to solve the inconsistency. However, the relative movement of the permanent GPS stations can lower the accuracy of GPS relative positioning. In this research, we first analyzed the data collected daily during the past 30 months at the 40 permanent GPS stations within South Korea and the 5 IGS permanent GPS stations around the Korean Peninsula using a global network adjustment. We then calculated the absolute and relative amount of movement of the GPS permanent stations. We also identified the optimum renewal period of the permanent GPS stations considering the accuracy of relative GPS surveying. Finally, we developed a Korean a Korean crustal movement model that can be used to improvement of accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.3
• /
• pp.293-301
• /
• 2011

The ionospheric delay is the largest error source in GPS positioning after the SA effect has been turned off in May, 2000. In this study, we used 44 permanent GPS stations being operated by National Geographic Information Institute (NGII) to estimate Total Electron Content (TEC) based on pseudorange measurements phase-leveled by a linear combination with carrier phases. The Differential Code Bias (DCB) of GPS satellites and receivers was estimated and applied for an accurate estimation of the TEC. To validate our estimates of DCB, changes of TEC values after DCB application were investigated. As a result, the RMS error went down by about an order of magnitude; from 35~45 to 3~4 TECU. After the DCB correction, ionospheric TEC maps were produced at a spatial resolution of $1^{\circ}{\times}1^{\circ}$. To analyze the effect of the number of sites used for map generation on the accuracy of TEC values, we tried 10, 20, 30, and 44 stations and the RMS error was computed with the Global Ionosphere Map as the truth. While the RMS error was 5.3 TECU when 10 sites are used, the error reduced to 3.9 TECU for the case of 44 stations.