• The Korean Journal of Nuclear Medicine
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• v.34 no.6
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• pp.478-486
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• 2000

Purpose: The sentinel lymph node is defined as the first draining node from a primary tumor and reflects the histologic feature of the remainder of the lymphatic basin status. The aim of this study was to evaluate the usefulness of lymphoscintigraphy and intraoperative radioguided gamma probe for identification and removal of sentinel lymph node in breast cancer. Materials and Methods: Lymphoscintigraphy was performed preoperatively in 15 patients with biopsy proven primary breast cancer. Tc-99m antimony sulfide colloid was injected intradermally at four points around the tumor. Imaging acquisition included dynamic imaging, followed by early and late static images at 2 hours. The sentinel lymph node criteria on lymphoscintigraphy is the first node of the highest uptake in early and late static images. We tagged the node emitting the highest activity both in vivo and ex vivo. Histologic study for sentinel and axillary lymph node investigation was done by Hematoxylin-Eosin staining. Results: On lymphoscintigraphy, three of 15 patients had clear lymphatic vessels in dynamic images, and 11 of 15 patients showed sentinel lymph node in early static image and three in late static 2 hours image. Mean detection time of sentinel lymph node on lymphoscintigraphy was $33.5{\pm}48.4$ minutes. The sentinel lymph node localization and removal by lymphoscintigraphy and intraoperative gamma probe were successful in 14 of 15 patients (detection rate: 93.3%). On lymphoscintigraphy, 14 of 15 patients showed $2.47{\pm}2.00$ sentinel lymph nodes. On intraoperative gamma probe, $2.36{\pm}1.96$ sentinel lymph nodes were detected. In 7 patients with positive results of sentinel lymph node metastasis, 5 patients showed positive results of axillary lymph node (sensitivity: 72%) but two did not. In 7 patients with negative results of sentinel lymph node metastasis, all axillary nodes were free of disease (specificity: 100%). Conclusion: Sentinel lymph node biopsy with lymphoscintigraphy and intraoperative gamma probe is a reliable method to predict axillary lymph node metastasis in breast cancer, and unnecessary axillary lymph node dissection can be avoided.

• 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.5_1
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• pp.1187-1198
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• 2021

Sea wind isrecently drawing attraction as one of the sources of renewable energy. Thisstudy describes a new method to produce a 10 m resolution sea wind field using Sentinel-1 images and low-resolution NWP (Numerical Weather Prediction) data with artificial intelligence technique. The experiment for the South East coast in Korea, 2015-2020,showed a 40% decreased MAE (Mean Absolute Error) than the generic CMOD (C-band Model) function, and the CC (correlation coefficient) of our method was 0.901 and 0.826, respectively, for the U and V wind components. We created 10m resolution sea wind maps for the study area, which showed a typical trend of wind distribution and a spatially detailed wind pattern as well. The proposed method can be applied to surveying for wind power and information service for coastal disaster prevention and leisure activities.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.285-293
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• 2024

This study explores the use of Sentinel-1 Synthetic Aperture Radar (SAR), processed through Google Earth Engine (GEE), to monitor changes in the areas of Antarctic ice shelves. Focusing on the Campbell Glacier Tongue (CGT) and Drygalski Ice Tongue (DIT),the research utilizes GEE's cloud computing capabilities to handle and analyze large datasets. The study employs Otsu's method for image binarization to distinguish ice shelves from the ocean and mitigates detection errors by averaging monthly images and extracting main regions. Results indicate that the CGT area decreased by approximately 26% from January 2016 to January 2024, primarily due to calving events,while DIT showed a slight increase overall,with notable reduction in recent years. Validation against Sentinel-2 optical images demonstrates high accuracy,underscoring the effectiveness of SAR and GEE for continuous, long-term monitoring of Antarctic ice shelves.

• 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.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1747-1761
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• 2023

Wetlands, defined as lands periodically inundated or exposed during the year, are crucial for sustaining biodiversity and filtering environmental pollutants. The importance of mapping and monitoring their topographical changes is therefore paramount. This study focuses on the topographical variations at the Incheon River estuary wetland post-restoration, noting a lack of adequate prior measurements. Using a multi-temporal Sentinel-1 dataset from October 2014 to March 2023, we mapped long-term variations in water bodies and detected topographical change anomalies using a pixel-frequency approach. Our analysis, based on 196 Sentinel-1 acquisitions from an ascending orbit, revealed significant topography changes. Since 2020, employing the pixel-frequency technique, we observed area increases of +0.0195, 0.0016, 0.0075, and 0.0163 km² in water level sections at depths of 2-3 m, 1-2 m, 0-1 m, and less than 0 m, respectively. These findings underscore the effectiveness of the wetland restoration efforts in the area.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.609-620
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• 2023

In this study, we used Sentinel-1 C-band synthetic aperture radar to calculate the surface state factor (SSF) for distinguishing the frozen-thawed state of soil. The accuracy of SSF classification was analyzed through comparison with air temperature (AT), grass temperature (GT), and underground temperature (UT). For the analysis, 116 Sentinel-1B Descending nodes observed over a period of 4 years from 2017 to 2020 were established for the central region of South Korea. AT, GT, and UT data were obtained from 23 soil moisture observation points of the Rural Development Administration during the same period, and analyzed using the 06:00 am data adjacent to the shooting time of the Sentinel-1B images. The average accuracy and F1-score for all stations were 0.63 and 0.47 for AT, 0.63 and 0.48 for GT, and 0.57 and 0.21 for UT, respectively. For winter (December-February) data, the average accuracy and F1-score were 0.66 and 0.76 for AT, 0.67 and 0.76 for GT, and 0.47 and 0.44 for UT, respectively. The increase in accuracy during winter data may be attributed to the fact that errors occurring in other seasons are not included.

• Journal of the Korean earth science society
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• v.41 no.1
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• pp.31-39
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• 2020

Cheonji, the largest caldera lake in Asia, is located at the summit of Baekdu Mountain. Cheonji is covered with snow and ice for about six months of the year due to its high altitude and its surrounding environment. Since most of the sources of water are from groundwater, the water temperature is closely related to the volcanic activity. However, in the 2000s, many volcanic activities have been monitored on the mountain. In this study, we analyzed the dimension of ice produced during winter in Baekdu Mountain using Sentinel-1 satellite image data provided by the European Space Agency (ESA). In order to calculate the dimension of ice from the backscatter image of the Sentinel-1 satellite, 20 Gray-Level Co-occurrence Matrix (GLCM) layers were generated from two polarization images using texture analysis. The method used in calculating the area was utilized with the Support Vector Machine (SVM) algorithm to classify the GLCM layer which is to calculate the dimension of ice in the image. Also, the calculated area was correlated with temperature data obtained from Samjiyeon weather station. This study could be used as a basis for suggesting an alternative to the new method of calculating the area of ice before using a long-term time series analysis on a full scale.