• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1321-1330
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• 2023

Normalized Difference Vegetation Index (NDVI) is the most widely used remote sensing data in the agricultural field and is currently provided by most optical satellites. In particular, as high-resolution optical satellite images become available, the selection of optimal optical satellite images according to agricultural applications has become a very important issue. In this study, we aim to define the most optimal optical satellite image when monitoring NDVI in rice fields in Korea and derive the resolution-related requirements necessary for this. For this purpose, we compared and analyzed the spatial distribution and time series patterns of the Dangjin rice paddy in Korea from 2019 to 2022 using NDVI images from MOD13, Landsat-8, Sentinel-2A/B, and PlanetScope satellites, which are widely used around the world. Each data is provided with a spatial resolution of 3 m to 250 m and various periods, and the area of the spectral band used to calculate NDVI also has slight differences. As a result of the analysis, Landsat-8 showed the lowest NDVI value and had very low spatial variation. In comparison, the MOD13 NDVI image showed similar spatial distribution and time series patterns as the PlanetScope data but was affected by the area surrounding the rice field due to low spatial resolution. Sentinel-2A/B showed relatively low NDVI values due to the wide near-infrared band area, and this feature was especially noticeable in the early stages of growth. PlanetScope's NDVI provides detailed spatial variation and stable time series patterns, but considering its high purchase price, it is considered to be more useful in small field areas than in spatially uniform rice paddy. Accordingly, for rice field areas, 250 m MOD13 NDVI or 10 m Sentinel-2A/B are considered to be the most efficient, but high-resolution satellite images can be used to estimate detailed physical quantities of individual crops.

• 대한위암학회:학술대회논문집
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• 2005.04a
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• pp.3-3
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• 2005

• 대한위암학회:학술대회논문집
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• 2003.04a
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• pp.24-24
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• 2003

• Journal of Gastric Cancer
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• v.12 no.1
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• pp.1-2
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• 2012

• Water for future
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• v.54 no.6
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• pp.74-77
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• 2021

• The Journal of the Korean dental association
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• v.48 no.8
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• pp.594-606
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• 2010

Lymph node status is the single most important prognostic factor in oral cancer because lymph node involvement decreases overall survival by 50%. Appropriate management of the regional lymphatics, therefore, plays a central role in the treatment of the oral cancer patients. The purposes of this article are to present the history of neck dissections, including current neck dissection classification, describe the technique of the most common neck dissection applicable to oral cavity cancers, and discuss some of the complications associated with neck dissection. Finally, a brief review of elective neck dissection and sentinel lymph node biopsy will be presented. It is necessary that dentists have to be interested in oral cancer and these interest will make it possible to prevent oral cancer, detect it earlier and also improve the prognosis, survival and the quality of life of survivors.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.21 no.3
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• pp.209-213
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• 2018

Most solitary gastrointestinal (GI) polyps in children are either inflammatory or hamartomatous. Solitary hyperplastic polyp, sentinel polyp and solitary adenomatous polyp have been occasionally diagnosed in adults, but very rarely reported in Korean children. We recently came across a case with adenomatous polyp in the colon, a case with hyperplastic polyp beneath the gastroesophageal junction, a case with hyperplastic polyp in the prepyloric area, and a case with sentinel polyp in the distal esophagus, which are unusual pathologic types in children. These mucosal lesions were diagnosed incidentally during elective endoscopic examinations for GI symptoms. Most polyps do not cause significant symptoms, so the diagnosis might be delayed, especially in children, in whom GI endoscopy is not commonly performed for screening purpose as in the adults.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2016.11a
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• pp.357-358
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• 2016

최근 재난에 대한 광역적 탐지 및 피해상황을 예측하는데 위성레이더 영상의 활용방안이 대두되고 있다. 본 논문에서는 SENTINEL-1 위성레이더 영상을 활용하여 지진발생으로 인한 지표변위를 관측하고자 하였다. 차분간섭기법(DinSAR)을 적용하여 최근 발생한 이탈리아 중부 지진과 한반도 경주 지진의 지표변위를 관측하고 피해범위를 예측하였다. 연구결과 규모 6.4 이탈리아 지진에서 최대 20.1cm의 침하를 관측하였으며, 규모 5.8 경주 지진의 경우 발생지역 20km 범위에서 약 3cm의 지표변위를 관측하였다. 향후 지상 SAR 자료를 구축할 예정이며 재난지역의 다각적 관측자료 취득 및 보다 정확한 재난 피해를 파악 할 수 있을 것으로 기대한다.