This study was conducted to verify the role of forest roads in the extinction of large forest fires in Korea. The study area was the forest fire-damaged area of Gangneung City, Gangwon Special Self-Governing Province, in April 2023, which is one of the areas with the highest road density among the major forest fires that have occurred so far. The scope of the forest fire damage area was confirmed through on-site survey, and the intensity of the fire was carried out through Sentinel-2 satellite imagery analysis. After that, the relationship between the damage range and intensity and the forest road was examined. About 59.6 km of roads were built within 50 m from the boundary of the forest fire damage area, which can easily access the entire 149.1 ha of forest fire damaged area. The road density is as high as 168.9 m/ha. All forests that were fragmented by roads were fragmented into 83 places, and all of these forests could be judged to have spread by spotting fire. As a result of analyzing the distribution of damage intensity by distance from the road to see the extent of damage according to the ease of access of fire extinguishing vehicles, it was confirmed that the proportion of areas with low-intensity damage has increased sharply even from 75 m or more away from the road. The results of analyzing the distribution of damage intensity by altitude to see the extent of damage according to the ease of access of fire extinguishing showed that the proportion of areas with low-intensity damage increased as the altitude increased, while the proportion of areas with damage of more than strong intensity decreased as the altitude increased. It was confirmed that there is no data that roads inside or adjacent to forests in the forest fire area of Gangneung City are effective in extinguishing forest fires. These results are contrary to the logic that increasing the road density in forests is effective in extinguishing forest fires. In the case of this fire area in Gangneung City, the road density is 43 times higher than the current road density in Korea claimed by the Korea Forest Service of 3.9 m/ha. This study suggests that roads can be a hindrance to extinguishing forest fires.
The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.
Waterbody change detection using satellite images has recently been carried out in various regions in South Korea, utilizing multiple types of sensors. This study utilizes optical satellite images from Landsat and Sentinel-2 based on Google Earth Engine (GEE) to analyze long-term surface water area changes in four monitored small and medium-sized water supply dams and agricultural reservoirs in South Korea. The analysis covers 19 years for the water supply dams and 27 years for the agricultural reservoirs. By employing image analysis methods such as normalized difference water index, Canny Edge Detection, and Otsu'sthresholding for waterbody detection, the study reliably extracted water surface areas, allowing for clear annual changes in waterbodies to be observed. When comparing the time series data of surface water areas derived from satellite images to actual measured water levels, a high correlation coefficient above 0.8 was found for the water supply dams. However, the agricultural reservoirs showed a lower correlation, between 0.5 and 0.7, attributed to the characteristics of agricultural reservoir management and the inadequacy of comparative data rather than the satellite image analysis itself. The analysis also revealed several inconsistencies in the results for smaller reservoirs, indicating the need for further studies on these reservoirs. The changes in surface water area, calculated using GEE, provide valuable spatial information on waterbody changes across the entire watershed, which cannot be identified solely by measuring water levels. This highlights the usefulness of efficiently processing extensive long-term satellite imagery data. Based on these findings, it is expected that future research could apply this method to a larger number of dam reservoirs with varying sizes,shapes, and monitoring statuses, potentially yielding additional insights into different reservoir groups.
Journal of the Korean Association of Geographic Information Studies
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v.23
no.3
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pp.26-67
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2020
Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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v.37
no.6
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pp.445-452
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2019
In this study, parallel processing codes of k-means clustering algorithm were developed and implemented in a PC-cluster for unsupervised classification of large satellite images. We implemented intra-node code using multicores of CPU (Central Processing Unit) based on OpenMP (Open Multi-Processing), inter-nodes code using a PC-cluster based on message passing interface, and hybrid code using both. The PC-cluster consists of one master node and eight slave nodes, and each node is equipped with eight multicores. Two operating systems, Microsoft Windows and Canonical Ubuntu, were installed in the PC-cluster in turn and tested to compare parallel processing performance. Two multispectral satellite images were tested, which are a medium-capacity LANDSAT 8 OLI (Operational Land Imager) image and a high-capacity Sentinel 2A image. To evaluate the performance of parallel processing, speedup and efficiency were measured. Overall, the speedup was over N / 2 and the efficiency was over 0.5. From the comparison of the two operating systems, the Ubuntu system showed two to three times faster performance. To confirm that the results of the sequential and parallel processing coincide with the other, the center value of each band and the number of classified pixels were compared, and result images were examined by pixel to pixel comparison. It was found that care should be taken to avoid false sharing of OpenMP in intra-node implementation. To process large satellite images in a PC-cluster, code and hardware should be designed to reduce performance degradation caused by file I / O. Also, it was found that performance can differ depending on the operating system installed in a PC-cluster.
Ship monitoring using satellite synthetic aperture radar (SAR) images consists of ship detection, ship discrimination, and ship classification. A large number of methods have been proposed to improve the detection and discrimination capabilities, while only a few studies exist for ship classification. Thus, many studies for the ship classification are needed to construct ship monitoring system having high performance. Note that constructing database (DB), which contains both SAR images and labels of various ships, is important for research on the ship classification. In the airborne SAR classification, many methods have been developed using moving and stationary target acquisition and recognition (MSTAR) DB. However, there has been no publicly available DB for research on the ship classification using satellite SAR images. Recently, Shanghai Key Laboratory has constructed OpenSARShip DB using both SAR images of various ships generated from Sentinel-1 satellite of European Space Agency (ESA) and automatic identification system (AIS) information. Thus, the applicability of OpenSARShip DB for ship classification should be investigated by using the concepts of airborne SAR classification which have shown high performances. In this study, ship classification using satellite SAR images are conducted by applying the concepts of airborne SAR classification to OpenSARShip DB, and then the applicability of OpenSARShip DB is investigated by analyzing the classification performances.
Kim, Daesun;Kim, Eun-Sook;Lim, Jong-Hwan;Lee, Yangwon
Korean Journal of Remote Sensing
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v.36
no.5_1
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pp.835-846
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2020
Recent severe climate changes and extreme weather events have caused the uncommon types of forest ecosystem disturbances such as hails and gypsy moths. This paper describes the analysis of the forest ecosystem disturbances using ISODATA (Iterative Self-organizing Data Analysis Technique Algorithm) with the RapidEye and Sentinel-2 images, regarding the cases of the hail damages in Hwasun in 2017 and the gypsy moth damages in the Chiak Mountain in 2020. In the case of hail damages, the comparison of the June image of this study and the July field survey of the previous study showed that the damage severity increased from June to July as the drought overlapped after the trees were injured by the hails. In the case of gypsy moths, significant leaf damages were found from the image of June, and the damages were mainly distributed at the low-altitude slope near Wonju City. We made sure that satellite remote sensing is a very effective method to detect various and unusual forest ecosystem disturbances caused by climate change. Also, it is expected that the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024 can be actively utilized to monitor such forest ecosystem disturbances.
Recent global warming and anthropogenic activities have caused more frequent and massive wildfires with longer durations and more significant damages. MODIS has been monitoring global wildfires for almost 20 years, and GK2A and Himawari-8 are observing the wildfires in East Asia 144 times a day. However, the spatial resolution of 1 to 2 km is not sufficient for the detection of small and medium-size active fires, and therefore the studies on the active fire detection using high-resolution images are essential. However, there is no official product for the high-resolution active fire detection. Hence, we implemented the active fire detection algorithm of Landsat 8 and carried out a high-resolution-based detection of active fires in Australia in 2019, followed by the comparisons with the products of Himawari-8 and MODIS. Regarding the intense fires, the three satellites showed similar results, whereas the weak igniting and extinguishing fires or the fires in narrow areas were detected by only Landsat 8 with a 30m resolution. Small-sized fires, which are the majority in Korea, can be detected by the high-resolution satellites such as Landsat 8, Sentinel-2, Kompsat-3A, and the forthcoming Kompsat-7. Also, a comprehensive analysis together with the geostationary satellites in East Asia such as GK2A, Himawari-8, and Fengyun-3 will help the interoperability and the improvement of spatial and temporal resolutions.
Son, Byung Ho;Dominici, Laura S;Aydogan, Fatih;Shulman, Lawrence N;Ahn, Sei Hyn;Cho, Ja Young;Coopey, Suzanne B;Kim, Sung Bae;Min, H Elise;Valero, Monica;Wang, Jiping;Caragacianu, Diana;Gong, Gyung-yub;Hevelone, Nathanael D;Baek, Seunghee;Golshan, Mehra
Asian Pacific Journal of Cancer Prevention
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v.16
no.6
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pp.2531-2535
/
2015
Background: Breast cancer diagnosed in young women may be more aggressive, with higher rates of local and distant recurrence compared to the disease in older women. Epidemiologic evidence suggests that Korean women have a lower incidence of breast cancer than women in the United States, but that they present at a younger age than their American counterparts. We sought to compare risk factors and management of young women with breast cancer in Boston, Massachusetts (US) with those in Seoul, South Korea (KR). Materials and Methods: A retrospective review was performed of consecutive patients less than 35 years old with a diagnosis of breast cancer at academic cancer centers in the US and KR from 2000-2005. Patient data were obtained by chart review. Demographic, tumor and treatment characteristics were compared utilizing Pearson's chisquare or Wilcoxon rank-sum tests where appropriate. All differences were assessed as significant at the 0.05 level. Results: 205 patients from the US and 309 from KR were analyzed. Patients in US were more likely to have hormone receptor positive breast cancer, while patients in KR had a higher rate of triple negative lesions. Patients in US had a higher mean body mass index and more often reported use of birth control pills, while those in the KR were less likely to have a sentinel node procedure performed or to receive post mastectomy radiation. Conclusions: Patients under 35 diagnosed with breast cancer in the US and KR differ with respect to demographics, tumor characteristics and management. Although rates of breast conservation and mastectomy were similar, US patients were more likely to receive post mastectomy radiation. The lower use of sentinel node biopsy is explained by the later adoption of the technique in KR. Further evaluation is necessary to evaluate recurrence rates and survival in the setting of differing disease subtypes in these patients.
The purpose of this paper is to suggest to tackle the problem of poor cooperation in space activities, by re-examining the nature of the competitive political environment, and by building up a normative overarching framework, One of the most acute problems that hampers regional cooperation is the U.S. influence as represented in the MTCR, a supplier's cartel, as was evidenced in the ill-fate of the 2001 launch contract between China and Korea the next year. Notably China, the third space power in the world, has not been allowed to join the MTCR despite her application in June 2004. A possible reconciliation between China and the MTCR over her application for a partnership would set a cornerstone in building up a cooperative environment in the Northeast Asia. Just as the Helsinki process was an overarching norm building framework, comprising human rights, security and environmental issues, it would be desirable that a future peace framework in Northeast Asia dealing with the pending issues of Korean peninsula should also comprise of such broad issues as one relating to cooperation in space activities in the region. South Korea could tap expertise from her neighbor China. When South Korea become an independent space power either with her own technology or otherwise, she would be in a better position to play a role as a balancer in coordinating between the two neighboring space giants. It is remarkable that the Japanese led APRSAT has contributed much in establishing Sentinel Asia as a part of the Disaster Management Scheme, in that each participant, whether it be a state agency, or a private entity like a university or a research institute, can tap the common data to contribute to the common good of safety.
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