• 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.

• Journal of Korea Water Resources Association
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• v.56 no.3
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• pp.211-224
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• 2023

In this study, a soil moisture estimation was performed using the Water Cloud Model (WCM), a backscatter model that considers vegetation based on SAR (Synthetic Aperture Radar). Sentinel-1 SAR and Sentinel-2 MSI (Multi-Spectral Instrument) images of a 40 × 50 km² area including the Yongdam Dam watershed of the Geum River were collected for this study. As vegetation descriptor of WCM, Sentinel-1 based vegetation index RVI (Radar Vegetation Index), depolarization ratio (DR), and Sentinel-2 based NDVI (Normalized Difference Vegetation Index) were used, respectively. Forward modeling of WCM was performed by 3 groups, which were divided by the characteristics between backscattering coefficient and soil moisture. The clearer the linear relationship between soil moisture and the backscattering coefficient, the higher the simulation performance. To estimate the soil moisture, the simulated backscattering coefficient was inverted. The simulation performance was proportional to the forward modeling result. The WCM simulation error showed an increasing pattern from about -12dB based on the observed backscattering coefficient.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.28-28
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• 2022

본 연구는 용담댐유역을 포함한 금강 유역 상류 지역을 대상으로 Sentinel-1 SAR (Synthetic Aperture Radar) 위성영상을 기반으로 한 토양수분 산정을 목적으로 하였다. Sentinel-1 영상은 2019년에 대해 12일 간격으로 수집하였고, 영상의 전처리는 SNAP (SentiNel Application Platform)을 활용하여 기하 보정, 방사 보정 및 Speckle 보정을 수행하여 VH (Vertical transmit-Horizontal receive) 및 VV (Vertical transmit-Vertical receive) 편파 후방산란계수로 변환하였다. 토양수분 산정에는 Water Cloud Model (WCM)이 활용되었으며, 모형의 식생 서술자(Vegetation descriptor)는 RVI (Radar Vegetation Index)와 NDVI (Normalized Difference Vegetation Index)를 활용하였다. RVI는 Sentinel-1 영상의 VH 및 VV 편파자료를 이용해 산정하였으며, NDVI는 동기간에 대해 10일 간격으로 수집된 Sentinel-2 MSI (MultiSpectral Instrument) 위성영상을 활용하여 산정하였다. WCM의 검정 및 보정은 한국수자원공사에서 제공하는 10 cm 깊이의 TDR (Time Domain Reflectometry) 센서에서 실측된 6개 지점의 토양수분 자료를 수집하여 수행하였으며, 매개변수의 최적화는 비선형 최소제곱(Non-linear least square) 및 PSO (Particle Swarm Optimization) 알고리즘을 활용하였다. WCM을 통해 산정된 토양수분은 피어슨 상관계수(Pearson's correlation coefficient)와 평균제곱근오차(Root mean square error)를 활용하여 검증을 수행할 예정이다.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.52-63
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• 2021

In previous research, the coastal wetlands were detected by using the vegetation indices or land cover classification maps derived from the multispectral bands of the satellite or aerial imagery, and this approach caused the various limitations for detecting the coastal wetlands with high accuracy due to the difficulty of acquiring both land cover and topographic information by using the single remote sensing data. This research suggested the efficient methodology for detecting the coastal wetlands using the sentinel-2 satellite image and SRTM(Shuttle Radar Topography Mission) DEM (Digital Elevation Model) acquired in Gomsoman Bay, west coasts of South Korea through the following steps. First, the NDWI(Normalized Difference Water Index) image was generated using the green and near-infrared bands of the given Sentinel-2 satellite image. Then, the binary image that separating lands and waters was generated from the NDWI image based on the pixel intensity value 0.2 as the threshold and the other binary image that separating the upper sea level areas and the under sea level areas was generated from the SRTM DEM based on the pixel intensity value 0 as the threshold. Finally, the coastal wetland map was generated by overlaying analysis of these binary images. The generated coastal wetland map had the 94% overall accuracy. In addition, the other types of wetlands such as inland wetlands or mountain wetlands were not detected in the generated coastal wetland map, which means that the generated coastal wetland map can be used for the coastal wetland management tasks.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.70-82
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• 2020

Recently, a lot of damages have been caused by urban flooding, and heavy rainfall that temporarily occur are the main causes of these phenomenons. The damages caused by urban flooding are identified as the change in the water balance in urban areas. To indirectly identify it, this research analyzed the change in the remote sensing indices on each land cover before and after heavy rainfall by utilizing daily precipitation data and multi-temporal Sentinel-2 satellite imagery. Cases of heavy rain advisory and warning were selected based on the daily precipitation data. And statistical fluctuation were compared by acquiring Sentinel-2 satellite images during the corresponding period and producing them as NDVI, NDWI and NDMI images about each land cover with a radius of 1,000 m based on the Seoul Weather Station. As a result of analyzing the maximum value, minimum value, mean and fluctuation of the pixels that were calculated in each remote sensing index image, there was no significant changes in the remote sensing indices in urban areas before and after heavy rainfall.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.2
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• pp.88-99
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• 2022

The river network is one of the essential topographical characteristics in river management. The river network which as previously constructed by the ground surveying method has recently begun to be efficiently constructed using the remote sensing datasets. Since it is difficult to remove these obstacles such as bridges in the urban rivers, it is rare to construct the urban river networks with the various obstacles. In this study, the Sentinel-2 satellite imagery was used to develop the automatic method for detecting the urban river networks without the obstacles and with the preserved boundaries as follows. First, the normalized difference water index image was generated using the multispectral bands of the given Sentinel-2 satellite imagery, and the binary image that could classify the water body and other regions was generated. Next, the morphological operations were employed for detecting the complete river networks with the obstacles removed and the boundaries preserved. As a result of applying the proposed methodology to Han River in Seoul, the complete river networks with the obstacles removed and the boundaries preserved were well constructed.