• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1545-1557
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• 2021

Crops show sensitive spectral characteristics according to their species and growth conditions and although frequent observation is required especially in summer, it is difficult to utilize optical satellite images due to the rainy season. To solve this problem, Constrained Cloud-Maximum Normalized difference vegetation index Composite (CC-MNC) algorithm was developed to generate periodic composite images with minimal cloud effect. In thisstudy, using this method, monthly Sentinel-2A/B Normalized Difference Vegetation Index (NDVI) composite images were produced for paddies and high-latitude cabbage fields from 2019 to 2021. In August 2020, which received 200mm more precipitation than other periods, the effect of clouds, was also significant in MODIS NDVI 16-day composite product. Except for this period, the CC-MNC method was able to reduce the cloud ratio of 45.4% of the original daily image to 14.9%. In the case of rice paddy, there was no significant difference between Sentinel-2A/B and MODIS NDVI values. In addition, it was possible to monitor the rice growth cycle well even with a revisit cycle 5 days. In the case of high-latitude cabbage fields, Sentinel-2A/B showed the short growth cycle of cabbage well, but MODIS showed limitations in spatial resolution. In addition, the CC-MNC method showed that cloud pixels were used for compositing at the harvest time, suggesting that the View Zenith Angle (VZA) threshold needsto be adjusted according to the domestic region.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1719-1729
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• 2021

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-mean-square-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.5
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• pp.629-636
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• 2024

The purpose of this study is to monitor changes in the water surface of reservoirs in verifiable areas in Korea using satellite images and to analyze the water surface area and water storage. The target area of this study is the Daecheong dam of the Geumgang(Riv.), which supplies water to some areas in the Chungcheong area. A study was conducted to detect water surface area by using the Sentinel-1(SAR-C) image and the optical image of Sentinel-2(MSI) among the various observation sensors of satellite images. The correlation between the reservoir's water storage volume, which is ground measurement data, and the extracted water surface area was analyzed. As a result of the analysis, the coefficient of determination(R²) between water surface area and daily storage using SAR images was analyzed to be 0.9242, and in the analysis using Sentinel-2's MSI optical image, it was analyzed to be correlated at 0.8995. In addition, it is analyzed that the water storage volume of the water surface area extracted from the image using the relationship between the water storage volume and the water surface area represents a hydrograph similar to the actual water storage volume. This study is a basic study for the use of satellite images in unmeasured/non-access areas such as North Korea, and plans to conduct a study to analyze annual changes and long-term trends in major dam reservoirs in North Korea by reflecting the results obtained through this study.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.435-441
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• 2020

Sentinel-2A satellite imagery provides VNIR (Visible Near InfraRed) and SWIR (ShortWave InfraRed) wavelength bands, and it is known to be effective for land cover classification, cloud detection, and environmental monitoring. Greenhouse is one of the middle classification classes for land cover map provided by the Ministry of Environment of the Republic of Korea. Since greenhouse is a class that has a lot of changes due to natural disasters such as storm and flood damage, there is a limit to updating the greenhouse at a rapid cycle in the land cover map. In the present study, we utilized Sentinel-2A satellite images that provide both VNIR and SWIR bands for the detection of greenhouse. To utilize Sentinel-2A satellite images for the detection of greenhouse, we produced high-resolution SWIR bands applying to the fusion technique performed in two stages and carried out the detection of greenhouse using SVM (Support Vector Machine) supervised classification technique. In order to analyze the applicability of SWIR bands to greenhouse detection, comparative evaluation was performed using the detection results applying only VNIR bands. As a results of quantitative and qualitative evaluation, the result of detection by additionally applying SWIR bands was found to be superior to the result of applying only VNIR bands.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.175-175
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• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.117-117
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• 2021

본 연구에서는 Sentinel-1A/B C-band SAR(Synthetic Aperture Radar) 위성영상을 기반으로 인공신경망(Artificial Neural Network, ANN) 모형을 활용해 금강 유역 상류 40×50 km² 면적에 대한 토양수분을 산정하였다. 10 m 공간 해상도의 Sentinel-1A/B SAR 영상은 8일 간격으로 2015년부터 2019년까지 5년 동안 구축하였고, SNAP(SentiNel Application Platform)을 통해 기하 보정, 방사 보정 및 잡음(Noise) 보정을 수행하고 VV 및 VH 편파 후방산란계수로 변환하였다. ANN 모형 검증자료로 TDR(Time Domain Reflectometry)로 측정된 9개 지점의 실측 토양수분 자료를 구축하였으며, 수문학적 개념인 선행강우를 고려하기 위해 동지점에 대한 강수량 자료를 구축하였다. ANN은 각 지점에 해당하는 토양 속성별로 모델링하고, 전체 기간 및 계절별로 나누어 모의하였으며, 전체 자료의 60%와 40%를 각각 훈련 및 테스트 데이터로 사용하였다. 산정된 토양수분은 상관계수(Correlation Coefficient, R)와 평균제곱근오차(Root Mean Square Error, RMSE)를 활용하여 검증을 수행할 예정이다.

• Journal of Gastric Cancer
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• v.23 no.1
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• pp.159-170
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• 2023

Maintaining the postoperative quality of life (QOL) while ensuring curability without overtreatment is important in the treatment of early gastric cancer. Postoperative QOL is anticipated to be maintained through minimally invasive function-preserving gastrectomy in early gastric cancer. The concept of the sentinel lymph node (SN) basin is essential to maintain the curability of early gastric cancer using minimally invasive function-preserving gastrectomy. However, additional resection after surgery is difficult to perform in gastric cancer. Thus, the SN basin theory is important. Recently, a multicenter randomized phase III trial in South Korea (SENORITA trial) proved that laparoscopic sentinel node navigation surgery (LSNNS) for stomach preservation results in better postoperative QOL compared with standard gastrectomy in patients with early gastric cancer. LSNNS contributes to patients' QOL based on the concept that curability is not impaired. A multicenter nonrandomized phase III trial is ongoing in Japan, and oncologic safety is expected to be demonstrated. LSNNS has been established as a treatment option for selected patients with early gastric cancer, and its application will become widespread in the future.

• Journal of Gastric Cancer
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• v.20 no.3
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• pp.245-255
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• 2020

Purpose: Recently, non-exposure simple suturing endoscopic full-thickness resection (NESS-EFTR) was developed to prevent tumor exposure to the peritoneal cavity. This study aimed to evaluate the feasibility of NESS-EFTR with sentinel basin dissection for early gastric cancer (EGC). Materials and Methods: This was the prospective SENORITA 3 pilot. From July 2017 to January 2018, 20 patients with EGC smaller than 3 cm without an absolute indication for endoscopic submucosal dissection were enrolled. The sentinel basin was detected using Tc^99m-phytate and indocyanine green, and the NESS-EFTR procedure was performed when all sentinel basin nodes were tumor-free on frozen pathologic examination. We evaluated the complete resection and intraoperative perforation rates as well as the incidence of postoperative complications. Results: Among the 20 enrolled patients, one dropped out due to large tumor size, while another underwent conventional laparoscopic gastrectomy due to metastatic sentinel lymph nodes. All NESS-EFTR procedures were performed in 17 of the 18 other patients (94.4%) without conversion, and the complete resection rate was 83.3% (15/18). The intraoperative perforation rate was 27.8% (5/18), and endoscopic clipping or laparoscopic suturing or stapling was performed at the perforation site. There was one case of postoperative complications treated with endoscopic clipping; the others were discharged without any event. Conclusions: NESS-EFTR with sentinel basin dissection is a technically challenging procedure that obtains safe margins, prevents intraoperative perforation, and may be a treatment option for EGC after additional experience.

• Radiation Oncology Journal
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• v.32 no.3
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• pp.132-137
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• 2014

Purpose: To evaluate non-sentinel lymph node (LN) status after sentinel lymph node biopsy (SNB) in patients with breast cancer and to identify the predictive factors for disease failure. Materials and Methods: From January 2006 to December 2007, axillary lymph node (ALN) dissection after SNB was performed for patients with primary invasive breast cancer who had no clinical evidence of LN metastasis. A total of 320 patients were treated with breast-conserving surgery and radiotherapy. Results: The median age of patients was 48 years, and the median follow-up time was 72.8 months. Close resection margin (RM) was observed in 13 patients. The median number of dissected SNB was two, and that of total retrieved ALNs was 11. Sentinel node accuracy was 94.7%, and the overall false negative rate (FNR) was 5.3%. Eleven patients experienced treatment failure. Local recurrence, regional LN recurrence, and distant metastasis were identified in 0.9%, 1.9%, and 2.8% of these patients, respectively. Sentinel LN status were not associated with locoregional recurrence (p > 0.05). Close RM was the only significant factor for disease-free survival (DFS) in univariate and multivariate analysis. The 5-year overall survival, DFS, and locoregional DFS were 100%, 96.8%, and 98.1%, respectively. Conclusion: In this study, SNB was performed with high accuracy and low FNR and high locoregional control was achieved.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.83-101
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• 2022

Accurate atmospheric correction is essential for the analysis of land surface and environmental monitoring. Aerosol optical depth (AOD) information is particularly important in atmospheric correction because the radiation attenuation by Mie scattering makes the differences between the radiation calculated at the satellite sensor and the radiation measured at the land surface. Thus, it is necessary to use high-quality AOD data for an appropriate atmospheric correction of high-resolution satellite images. In this study, we examined the Second Simulation of a Satellite Signal in the Solar Spectrum (6S)-based atmospheric correction results for the Sentinel-2 images in South Korea using raster AOD (MODIS) and single-point AOD (AERONET). The 6S result was overall agreed with the Sentinel-2 level 2 data. Moreover, using raster AOD showed better performance than using single-point AOD. The atmospheric correction using the single-point AOD yielded some inappropriate values for forest and water pixels, where as the atmospheric correction using raster AOD produced stable and natural patterns in accordance with the land cover map. Also, the Sentinel-2 normalized difference vegetation index (NDVI) after the 6S correction had similar patterns to the up scaled drone NDVI, although Sentinel-2 NDVI had relatively low values. Also, the spatial distribution of both images seemed very similar for growing and harvest seasons. Future work will be necessary to make efforts for the gap-filling of AOD data and an accurate bi-directional reflectance distribution function (BRDF) model for high-resolution atmospheric correction. These methods can help improve the land surface monitoring using the future Compact Advanced Satellite 500 in South Korea.