• Title/Summary/Keyword: sentinel

Search Result 420, Processing Time 0.027 seconds

Oil Spill Monitoring in Norilsk, Russia Using Google Earth Engine and Sentinel-2 Data (Google Earth Engine과 Sentinel-2 위성자료를 이용한 러시아 노릴스크 지역의 기름 유출 모니터링)

  • Minju Kim;Chang-Uk Hyun
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.3
    • /
    • pp.311-323
    • /
    • 2023
  • Oil spill accidents can cause various environmental issues, so it is important to quickly assess the extent and changes in the area and location of the spilled oil. In the case of oil spill detection using satellite imagery, it is possible to detect a wide range of oil spill areas by utilizing the information collected from various sensors equipped on the satellite. Previous studies have analyzed the reflectance of oil at specific wavelengths and have developed an oil spill index using bands within the specific wavelength ranges. When analyzing multiple images before and after an oil spill for monitoring purposes, a significant amount of time and computing resources are consumed due to the large volume of data. By utilizing Google Earth Engine, which allows for the analysis of large volumes of satellite imagery through a web browser, it is possible to efficiently detect oil spills. In this study, we evaluated the applicability of four types of oil spill indices in the area of various land cover using Sentinel-2 MultiSpectral Instrument data and the cloud-based Google Earth Engine platform. We assessed the separability of oil spill areas by comparing the index values for different land covers. The results of this study demonstrated the efficient utilization of Google Earth Engine in oil spill detection research and indicated that the use of oil spill index B ((B3+B4)/B2) and oil spill index C (R: B3/B2, G: (B3+B4)/B2, B: (B6+B7)/B5) can contribute to effective oil spill monitoring in other regions with complex land covers.

Long-term Outcome after Minimally Invasive Treatment for Early Gastric Cancer beyond the Indication of Endoscopic Submucosal Dissection (내시경점막하박리술의 적응증을 넘어선 조기위암의 미세침습 치료 후 장기 추적 결과)

  • Weon Jin Ko;Joo Young Cho
    • Journal of Digestive Cancer Research
    • /
    • v.5 no.1
    • /
    • pp.44-49
    • /
    • 2017
  • Background: Recently, endoscopic submucosal dissection (ESD) with laparoscopic sentinel lymph node dissection, named ESN or endoscopic full-thickness gastric resection with laparoscopic sentinel lymph node dissection, named Hybrid-natural orifice transluminal endoscopic surgery (NOTES) was suggested the possibility of minimally invasive treatment for patients with early gastric cancer (EGC) who were beyond the indication of ESD. This study aimed to evaluate the outcomes of ESN or Hybrid-NOTES. Methods: We retrospectively analyzed patients treated with these therapies from January 2009 to May 2013 in terms of short- and long-term outcomes. Each patient was diagnosed with EGC but was not included in ESD indications and had the high risk of lymph node metastasis (LNM). Results: A total of 42 patients with EGC treated by ESN or Hybrid-NOTES. Of the 21 patients who underwent ESN, a total of 4 patients underwent additional gastrectomy, 1 with LNM, 1 with surgical complication, and 2 with noncurative resection. Of the 21 patients who underwent Hybrid-NOTES, a total of 5 patients underwent additional surgery, 1 with LNM, 2 with surgical complication, and 2 with noncurative resection. Overall survival was 100% over a mean follow-up of 75 months, but 3 patients underwent ESD or gastrectomy with metachronous lesion. And 1 patient who had received ESN was found to have a metastatic lymph node and undergo palliative chemotherapy. Conclusion: ESN or Hybrid-NOTES showed favorable short-and long-term outcomes. These methods may be utilized as a bridge between ESD and gastrectomy in the case of EGC which is more likely to have LNM beyond the ESD indications.

  • PDF

High-Resolution Sentinel-2 Imagery Correction Using BRDF Ensemble Model (BRDF 앙상블 모델을 이용한 고해상도 Sentinel-2 영상 보정)

  • Hyun-Dong Moon;Bo-Kyeong Kim;Kyeong-Min Kim;Subin Choi;Euni Jo;Hoyong Ahn;Jae-Hyun Ryu;Sung-Won Choi;Jaeil Cho
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.6_1
    • /
    • pp.1427-1435
    • /
    • 2023
  • Vegetation indices based on selected wavelength reflectance measurements are used to represent crop growth and physiological conditions. However, the anisotropic properties of the crop canopy surface can govern spectral reflectance and vegetation indices. In this study, we applied an ensemble of bidirectional reflectance distribution function (BRDF) models to high-resolution Sentinel-2 satellite imagery and compared the differences between correction results before and after reflectance. In the red and near-infrared (NIR) band reflectance images, BRDF-corrected outlier values appeared in certain urban and paddy fields of farmland areas and forest shadow areas. These effects were equally observed when calculating the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2). Furthermore, the outlier values in corrected NIR band were shown in pixels shadowed by mountain terrain. These results are expected to contribute to the development and improvement of BRDF models in high-resolution satellite images.

A Study of Development and Application of an Inland Water Body Training Dataset Using Sentinel-1 SAR Images in Korea (Sentinel-1 SAR 영상을 활용한 국내 내륙 수체 학습 데이터셋 구축 및 알고리즘 적용 연구)

  • Eu-Ru Lee;Hyung-Sup Jung
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.6_1
    • /
    • pp.1371-1388
    • /
    • 2023
  • Floods are becoming more severe and frequent due to global warming-induced climate change. Water disasters are rising in Korea due to severe rainfall and wet seasons. This makes preventive climate change measures and efficient water catastrophe responses crucial, and synthetic aperture radar satellite imagery can help. This research created 1,423 water body learning datasets for individual water body regions along the Han and Nakdong waterways to reflect domestic water body properties discovered by Sentinel-1 satellite radar imagery. We created a document with exact data annotation criteria for many situations. After the dataset was processed, U-Net, a deep learning model, analyzed water body detection results. The results from applying the learned model to water body locations not involved in the learning process were studied to validate soil water body monitoring on a national scale. The analysis showed that the created water body area detected water bodies accurately (F1-Score: 0.987, Intersection over Union [IoU]: 0.955). Other domestic water body regions not used for training and evaluation showed similar accuracy (F1-Score: 0.941, IoU: 0.89). Both outcomes showed that the computer accurately spotted water bodies in most areas, however tiny streams and gloomy areas had problems. This work should improve water resource change and disaster damage surveillance. Future studies will likely include more water body attribute datasets. Such databases could help manage and monitor water bodies nationwide and shed light on misclassified regions.

Spatial Analysis of Carbon Storage in Satellite Radar Imagery Utilizing Sentinel-1: A Case Study of the Ungok Wetlands (위성 레이더 영상 중 Sentinel-1을 활용한 탄소 흡수원 공간분석 - 운곡습지를 대상으로 -)

  • Ha-Eun Yu;Young-Il Cho;Shin-Woo Lee;Moung-Jin Lee
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.6_3
    • /
    • pp.1731-1745
    • /
    • 2023
  • Within the framework of the post-2020 climate regime, the Paris Agreement's emphasis on Nationally Determined Contributions and Biennial Transparency Reporting is paramount in addressing its long-term temperature goal. A salient issue is the treatment of wetland ecosystems within the context of Land Use, Land-Use Change, and Forestry, as defined by the Intergovernmental Panel on Climate Change. In the 2019 National Inventory Report, wetlands were recategorized as emission sources due to their designation as inundated areas. This study employs C-band radar imagery to discriminate between inundated and non-inundated regions of wetlands, enabling the quantification of their spatial dynamics. The research capitalizes on 24-period Sentinel-1 satellite data to cover both the inundation and desiccation phases while centering its attention on Ungok Wetland, a Ramsar-designated inland wetland conservation area in Korea. The inundated area is quantitatively assessed through the integration of multi-temporal Sentinel-1 Single-Look Complex (SLC) data, aerial orthophotography, and inland wetland spatial information. Furthermore, the study scrutinizes fluctuations in the maximum and minimum inundated areas, with substantial changes corroborated via drone aerial reconnaissance. The outcomes of this investigation hold the potential to make substantive contributions to the refinement of national greenhouse gas absorption and emission factors, thereby informing the development of comprehensive greenhouse gas inventories. These efforts align directly with the overarching objectives of the Paris Agreement.

Machine Learning-based Atmospheric Correction for Sentinel-2 Images Using 6SV2.1 and GK2A AOD (6SV2.1과 GK2A AOD를 이용한 기계학습 기반의 Sentinel-2 영상 대기보정)

  • Seoyeon Kim;Youjeong Youn;Jonggu Kang;Yemin Jeong;Soyeon Choi;Yungyo Im;Youngmin Seo;Chan-Won Park;Kyung-Do Lee;Sang-Il Na;Ho-Yong Ahn;Jae-Hyun Ryu;Yangwon Lee
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.5_3
    • /
    • pp.1061-1067
    • /
    • 2023
  • In this letter, we simulated an atmospheric correction for Sentinel-2 images, of which spectral bands are similar to Compact Advanced Satellite 500-4 (CAS500-4). Using the second simulation of the satellite signal in the solar spectrum - vector (6SV)2.1 radiation transfer model and random forest (RF), a type of machine learning, we developed an RF-based atmospheric correction model to simulate 6SV2.1. As a result, the similarity between the reflectance calculated by 6SV2.1 and the reflectance predicted by the RF model was very high.

Mapping Burned Forests Using a k-Nearest Neighbors Classifier in Complex Land Cover (k-Nearest Neighbors 분류기를 이용한 복합 지표 산불피해 영역 탐지)

  • Lee, Hanna ;Yun, Konghyun;Kim, Gihong
    • KSCE Journal of Civil and Environmental Engineering Research
    • /
    • v.43 no.6
    • /
    • pp.883-896
    • /
    • 2023
  • As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas.

The Minimal Range of a Lymphadenectomy in Gastric Cancer according to an Analysis of Sentinel Lymph Node and Solitary Lymph Node Metastasis (위암 환자에서 감시 림프절 및 고립 림프절 전이에 근거한 최소 림프절 절제에 대한 재고)

  • Hwang Ho Kyoung;Hyung Woo Jin;Choi Seung Ho;Noh Sung Hoon
    • Journal of Gastric Cancer
    • /
    • v.4 no.4
    • /
    • pp.272-276
    • /
    • 2004
  • Purpose: The incidence of nodal metastases is as low as 2 to $20\%$ in early gastric cancer, so there is a trend to lessen the extent of surgery. In addition, the adequate range for a lymphadenectomy is controversial, especially in early gastric cancer. In this study, we tried to find the minimal range for a lymphadenectomy by analyzing sentinel-node and solitary lymph-node metastases in gastric cancer. Materials and Methods: The total of 78 patients who underwent a curative gastrectomy with a D2 lymphadenectomy for early gastric cancer between 2000 and 2002 in the Department of Surgery, Yonsei University, Seoul, Korea, were included for the evaluation of sentinel-node metastases.. After a laparotomy, 25 mg of indocyanine green was mixed in 5 ml of normal saline, and all the dye was injected into the subserosal layer around the primary tumor. All nodes stained within 5 minutes were marked. In addition, a total of 141 patients, who underwent a curative gastrectomy between 1997 and 2001 at the Department of Surgery, Yonsei University, Seoul, Korea, were analyzed for solitary lymph- node metastases. Results: Among the 78 patients, sentinel nodes were detected in 69 patients ($88.5\%$). The sentinel nodes in 60 cases ($87.0\%$) were located in the perigastric area. However, 9 cases ($13.0\%$) had sentinel nodes in the N2 group. In the 141 cases that had a solitary metastatic node, 125 cases ($88.6\%$) demonstrated the metastatic lymph node in the perigastric area, and 16 cases ($11.4\%$) showed that the metastatic node in the N2 group. Conclusion: Taken together, removal of a perigastric lymph node could miss early metastases in gastric cancer, so a D1 lymphadenectomy should not be the minimal range of dissection if a lymphadenectomy is necessary. (J Korean Gastric Cancer Assoc 2004;4:272-276)

  • PDF

The Variation of Scan Time According to Patient's Breast Size and Body Mass Index in Breast Sentinel lymphangiography (유방암의 감시림프절 검사에서 유방크기와 체질량지수에 따른 검사시간 변화)

  • Lee, Da-Young;Nam-Koong, Hyuk;Cho, Seok-Won;Oh, Shin-Hyun;Im, Han-Sang;Kim, Jae-Sam;Lee, Chang-Ho;Park, Hoon-Hee
    • The Korean Journal of Nuclear Medicine Technology
    • /
    • v.16 no.2
    • /
    • pp.62-67
    • /
    • 2012
  • Purpose : At this time, the sentinel lymph node mapping using radioisotope and blue dye is preceded for breast cancer patient's sentinel lymph node biopsy. But all patients were applied the same protocol without consideration of physical specific character like the breast sizes and body mass indexes. The purpose of this study is search the optimized scan time in breast sentinel lymphangiography by observing how much the body mass index and breast size influence speed of lymphatic flow. Materials and Methods : The Object of this study was 100 breast cancer patients(Female, 100 persons, average age $50.34{\pm}10.26$ years old)at Severance hospital from October 2011 to December 2011. They were scanned breast sentinel lymphangiography before operation. This study was performed on Forte dual heads gamma camera (Philips Medical Systems, Nederland B.V.). All patients were intra-dermal injected $^{99m}Tc$-Phytate 18.5 MBq, 0.5 ml. For 80 patients, we have scanned without limitation of scan time until the lymphatic flow from the lymph node since injection. We measured how long the lymphatic flow time between departures from injects site and arrival to lymph node using stopwatch. After we calculated patient's Body mass Index and classified as 4 groups. And we measured patient's breast size and classified 3 groups. The modified breast lymphangiography that changing scan time according to comparison study's result was performed on 20 patients and was estimated. Results : The mean scan time as breast size was A group 2.48 minutes, B group 7.69 minutes, C group 10.43 minutes. The mean scan time as body mass index was under weight 1.35 minutes, normal weight 2.56 minutes, slightly over 5.62 minutes, over weighted 5.62 minutes. The success rate of modified breast lymphangiography was 85%. Conclusion : As the Body mass index became higher and breast size became bigger, the total scan time is increased. Based on the obtained information, we designed modified breast lymphangiography protocol. At the cases applying that protocol, most of sentinel lymph nodes were visualized as lymphatic pool. In conclusion, we found that the more success rate in modified protocol considering physical individuality than study carrying out in the same protocol.

  • PDF

Estimation of Soil Moisture Using Sentinel-1 SAR Images and Multiple Linear Regression Model Considering Antecedent Precipitations (선행 강우를 고려한 Sentinel-1 SAR 위성영상과 다중선형회귀모형을 활용한 토양수분 산정)

  • Chung, Jeehun;Son, Moobeen;Lee, Yonggwan;Kim, Seongjoon
    • Korean Journal of Remote Sensing
    • /
    • v.37 no.3
    • /
    • pp.515-530
    • /
    • 2021
  • This study is to estimate soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Multiple Linear Regression Model(MLRM) in the Yongdam-Dam watershed of South Korea. Both the Sentinel-1A and -1B images (6 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The geometric, radiometric, and noise corrections were performed using the SNAP (SentiNel Application Platform) software and converted to backscattering coefficient of VV and VH polarization. The in-situ SM data measured at 6 locations using TDR were used to validate the estimated SM results. The 5 days antecedent precipitation data were also collected to overcome the estimation difficulty for the vegetated area not reaching the ground. The MLRM modeling was performed using yearly data and seasonal data set, and correlation analysis was performed according to the number of the independent variable. The estimated SM was verified with observed SM using the coefficient of determination (R2) and the root mean square error (RMSE). As a result of SM modeling using only BSC in the grass area, R2 was 0.13 and RMSE was 4.83%. When 5 days of antecedent precipitation data was used, R2 was 0.37 and RMSE was 4.11%. With the use of dry days and seasonal regression equation to reflect the decrease pattern and seasonal variability of SM, the correlation increased significantly with R2 of 0.69 and RMSE of 2.88%.