• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.571-585
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• 2017

The number of spaceborne optical sensors including red-edge band has been increasing since red-edge band is known to be effective to enhance the information content on biophysical characteristics of vegetation. Considering that the Agriculture and Forestry Satellite is planning to carry an imaging sensor having red-edge band, we tried to analyze the current status and potential of red-edge band. As a case study, we analyzed the effect of using red-edge band and tried to find the optimum band width and wavelength region of the red-edge band to estimate leaf area index (LAI) of very dense tree canopy. Field spectral measurements were conducted from April to October over two tree species (white oak and pitch pine) having high LAI. Using the spectral measurement data, total 355 red-edge bands reflectance were simulated by varying five band width (10 nm, 20 nm, 30 nm, 40 nm, 50 nm) and 71 central wavelength. Two red-edge based spectral indices(NDRE, CIRE) were derived using the simulated red-edge band and compared with the LAI of two tree species. Both NDRE and CIRE showed higher correlation coefficients with the LAI than NDVI. This would be an alternative to overcome the limitation of the NDVI saturation problem that NDVI has not been effective to estimate LAI over very dense canopy situation. There was no significant difference among five band widths of red-edge band in relation to LAI. The highest correlation coefficients were obtained at the red-edge band of center wavelength near the 720 nm for the white oak and 710 nm for the pitch pine. To select the optimum band width and wavelength region of the red-edge band, further studies are necessary to examine the relationship with other biophysical variables, such as chlorophyll, nitrogen, water content, and biomass.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.409-420
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• 2012

Since the Industrial Revolution, CO2 levels have been increasing with climate change. In this study, Analyze time-series changes in snow cover quantitatively and predict the vanishing point of snow cover statistically using remote sensing. The study area is Mt. Kilimanjaro, Tanzania. 23 image data of Landsat-5 TM and Landsat-7 ETM+, spanning the 27 years from June 1984 to July 2011, were acquired. For this study, first, atmospheric correction was performed on each image using the COST atmospheric correction model. Second, the snow cover area was extracted using the NDSI (Normalized Difference Snow Index) algorithm. Third, the minimum height of snow cover was determined using SRTM DEM. Finally, the vanishing point of snow cover was predicted using the trend line of a linear function. Analysis was divided using a total of 23 images and 17 images during the dry season. Results show that snow cover area decreased by approximately $6.47km^2$ from $9.01km^2$ to $2.54km^2$, equivalent to a 73% reduction. The minimum height of snow cover increased by approximately 290 m, from 4,603 m to 4,893 m. Using the trend line result shows that the snow cover area decreased by approximately $0.342km^2$ in the dry season and $0.421km^2$ overall each year. In contrast, the annual increase in the minimum height of snow cover was approximately 9.848 m in the dry season and 11.251 m overall. Based on this analysis of vanishing point, there will be no snow cover 2020 at 95% confidence interval. This study can be used to monitor global climate change by providing the change in snow cover area and reference data when studying this area or similar areas in future research.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.5
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• pp.435-444
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• 2001

Daily time series of longshore wind at 8 stations, sea surface temperature (SST) at 11 stations in the eastern coast of the Korean peninsula during $1983\~1997$ and the NOAA/AVHRR satellite data during $1990\~1998$ were used in order to study the temporal and spatial variations of the upwelling cold water which occurred in the summer season. The cold water occurred frequently in the eastern coastal waters of Korea such as Soimal, Kijang, Ulgi, Kampo, Pohang, Youngduk, Chukbyun, Chumunjin and Sokcho, During the upwelling cold water phenomenon, SST came down more than $-5^{\circ}C$ in a day. The maximum of the averaged RMS amplitude of daily SST was $5.8^{\circ}C$ along the eastern coast of Korea on Julian day 212 from $1983\~1997$. The cross correlation coefficients were higher than 0.5 between Sokcho and Chumunjin in the northern part of the East Sea, and along Soimal, Kijang, Ulgi, Kampo and Pohang in the southern part of the East Sea. In late July, 1995 the cold water occurred at Ulgi coastal area and extended to Ullung island which is located 250 km off the Ulgi coast. Even though the distance between Soimal and the Ulgi coast area is more than 120 km, the cross correlation coefficient related to the anomalies of SST due to upwelling cold water was the highest (0.7) in the southeastern coastal area of the Korean peninsula. This connection may be due to the cyclonic circulation of the Tsushima Current in this area and the topography of the ocean rather than the local south wind which induced the coastal upwelling.

• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.3
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• pp.166-178
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• 2015

We investigated marine environmental characteristics of Goheung coastal areas in August where is known to be the first outbreak site of Cochlodinium polykrikoides (hereafter C. polykrikoides) blooms, based on the oceanographic data observed from 1993 to 2013 around the Korean southern coastal waters including Eastern China Sea by National Fisheries Research and Development Institute (NFRDI). The data of NOAA/NGSST satellite images as well as numerical simulation results by Seo et al. [2013] were also used for analysis. Water temperatures at the surface and bottom layers in Goheung coast, i.e. Narodo, were $25.0^{\circ}C$ and $23.7^{\circ}C$ so that they were higher than $23.8^{\circ}C$ and $19.4^{\circ}C$ in Geoje coast where is a reference site, respectively. In addition, salinities at the surface and bottom layers in Goheung coast were 31.78 psu and 31.98 psu so that they were a little higher than 31.54 psu at the surface but a little lower than 32.79 psu at the bottom in Geoje coast, respectively. That is, the differences in water temperature or salinity between the surface and bottom layers in Goheung coast in August were not large compared to Geoje coast. This suggests that stratification in Goheung coast in August is fairly weak or may not be established. In addition, the concentrations of DIN and DIP at the surface layer were 0.068 mg/L ($4.86{\mu}M$) and 0.015 mg/L ($5.14{\mu}M$) in Goheung coast while 0.072 mg/L ($5.14{\mu}M$) and 0.01 mg/L ($0.32{\mu}M$) in Geoje coast, so they did not indicate a meaningful difference. On the other hand, when C. polykrikoides blooms, water temperature and salinity in August at the station 317-22 ($31.5^{\circ}N$, $124^{\circ}E$) of the East China Sea, where is near the mouth of Yangtze River, were $27.8^{\circ}C$ and 31.61 psu, respectively. Thus, water temperature was much higher whereas salinity was almost similar compared to Goheung coast. Furthermore, concentrations of $NO_3-N$ and $PO_4-P$ in the East China Sea in August were remarkably high compared to Goheung coast. When C. polykrikoides blooms, according to not only the image data of satellites NOAA/NGSST but also numerical experiment results by Seo et al.[2013], the freshwater out of Yangtze River was judged to clearly affect the Korean southern coastal waters. Therefore, the supply of nutrients in terms of Yangtze River may greatly contribute to the outbreak of C. polykrikoides blooms in Goheung coast in summer.

• Korean Journal of Environment and Ecology
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• v.36 no.4
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• pp.433-441
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• 2022

Light pollution is one of the factors that disturb coastal and island ecosystems. This study examined the factors causing light pollution in the islands in Daedohaehaesang National Park using nighttime satellite images. This study selected 101 islands with an area of 100,000 m² or more in Daedohaehaesang National Park, and measured the levels of light pollution of the selected islands by calculating mean nighttime radiance recorded in VIIRS DNB monthly images for January, April, August, and October 2019. Of seven districts of the park, The highest mean nighttime radiance was recorded in Geumodo district (17,666nW/m²/sr), followed by Geonumdo·Baekdo, Narodo, Soando·Cheongsando districts. By season, mean nighttime radiance in October was the highest at 9,509nW/m²/sr, followed by August, January, and April. Regression analyses show that the total floor area and the number of lighthouses in a 5 km buffer area had a statistically significant effect on mean nighttime radiance at all times, but those within the island did not, indicating that light pollution in islands in a national park where land development is strictly restricted is influenced by artificial lights in nearby areas. However, the total floor area of an island significantly affected mean nighttime radiance only in August, which appears to be attributed to the impact of intensive use of artificial light by visitors during summer vacation. The size of an island had a negative (-) effect on nighttime radiance. This negative effect suggests that light pollution is a type of ecological edge effect, i.e., the smaller island is more likely to have a relatively larger proportion of edge area that is affected by light emitted from the neighboring areas. The results of this study indicate that managing artificial lights in nearby areas is necessary to mitigate light pollution in islands in marine and coastal national parks.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.847-860
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• 2020

The 2-pass DInSAR (Differential Interferometric SAR) processing steps for DEM generation consist of the co-registration of SAR image pair, interferogram generation, phase unwrapping, calculation of DEM errors, and geocoding, etc. It requires complicated steps, and the accuracy of data processing at each step affects the performance of the finally generated DEM. In this study, we developed an improved method for enhancing the performance of the DEM generation method based on the 2-pass DInSAR technique of TanDEM-X bistatic SAR images was developed. The developed DEM generation method is a method that can significantly reduce both the DEM error in the unwrapped phase image and that may occur during geocoding step. The performance analysis of the developed algorithm was performed by comparing the vertical accuracy (Root Mean Square Error, RMSE) between the existing method and the newly proposed method using the ground control point (GCP) generated from GPS survey. The vertical accuracy of the DInSAR-based DEM generated without correction for the unwrapped phase error and geocoding error is 39.617 m. However, the vertical accuracy of the DEM generated through the proposed method is 2.346 m. It was confirmed that the DEM accuracy was improved through the proposed correction method. Through the proposed 2-pass DInSAR-based DEM generation method, the SRTM DEM error observed by DInSAR was compensated for the SRTM 30 m DEM (vertical accuracy 5.567 m) used as a reference. Through this, it was possible to finally create a DEM with improved spatial resolution of about 5 times and vertical accuracy of about 2.4 times. In addition, the spatial resolution of the DEM generated through the proposed method was matched with the SRTM 30 m DEM and the TanDEM-X 90m DEM, and the vertical accuracy was compared. As a result, it was confirmed that the vertical accuracy was improved by about 1.7 and 1.6 times, respectively, and more accurate DEM generation was possible with the proposed method. If the method derived in this study is used to continuously update the DEM for regions with frequent morphological changes, it will be possible to update the DEM effectively in a short time at low cost.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.2
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• pp.1-9
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• 2005

This study was performed to analyze time series landuse pattern of urban areas and the change of the areas by using remotely sensed multiple sensors. The results were as follows. First, according to the result of time series analysis, most agricultural land has been changed into built-up areas by development work such as the land development or land readjustment project, arrangement of science parks or military facilities, and location of public establishment like government buildings. Second, if the expansion of built-up areas maintains the present scale and speed, it seems that a lot of parts of land would be changed into built-up areas, especially centering around agricultural land, so it is necessary to establish the plan for urban space. Third, I have synthetically collected the data of the project of urban development and systematically monitored the process of in expansion the built-up areas up to now (from the past). I hereby could lay the foundation that makes us scientifically forecast the direction of expansion in the built-up areas by the urban development in the future.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.170.1-170.1
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• 2015

안티모니 (Sb)를 기반으로 한 제2형 초격자 (Type II superlattice, T2SL)구조 적외선 검출기 연구는 2000년대 들어 Sb 계열의 화합물 반도체 성장 기술이 발전함에 따라 HgCdTe (MCT), InSb, 양자우물 적외선 검출기 (QWIP)를 대체할 수 있는 고성능의 양자형 적외선 검출 소재로 부상하였으며, 현재 전 세계적으로 활발한 연구가 진행되고 있다. 특히, 기존의 양자형 적외선 검출소자에 비해 전자의 유효질량이 상대적으로 커서 밴드 간의 투과전류가 줄어들 뿐만 아니라, 전자와 정공이 서로 다른 물질 영역에 분포하여 Auger 재결합률을 효과적으로 줄일 수 있어 상온 동작이 가능한 소재로 주목을 받고 있다. 또한, T2SL 구조는 초격자를 구성하는 물질의 두께나 조성 변화를 통한 밴드갭 변조가 용이하여 단파장에서 장파장 적외선에 이르는 광범위한 파장 대역에서 동작이 가능할 뿐만 아니라 구조적 변화를 통해 이중 대역을 동시에 검출 할 수 있는 차세대 적외선 열영상 소자로 알려져 있다. 본 연구에서는 분자선 에피택시(MBE)법을 이용하여 300 주기의 InAs/GaSb (10/10 ML) 제2형 초격자 구조를 성장하여 적외선 검출소자를 제작하였다. 제2형 초격자 구조를 구성하는 물질계에 p-type dopant인 Be을 이용하여 각각 도핑 농도가 다른 시료를 성장하였다. 이때 p-type 도핑 농도는 각각 $1/5/10{\times}10^{15}cm^{-3}$로 변화를 주었다. 성장된 시료의 구조적 특성 분석을 위해 고분해능 X선 회절 (High resolution X-ray diffraction, HRXRD)법을 이용하였으며, 초격자 한 주기의 두께가 6.2~6.4 nm 로 설계된 구조와 동일하게 성장됨을 확인 하였으며, 1차 위성피크의 반치폭은 30~80 arcsec로 우수한 결정성을 가짐을 확인하였다. 적외선 검출을 위한 $410{\times}410{\mu}m^2$ 크기의 단위 소자 공정을 진행하였으며 이때 적외선의 전면 입사를 위해 소자 위에 $300{\mu}m$의 윈도우 창을 제작하였다. 단위 소자의 측벽에는 표면 누설 전류가 흐르는데 이를 방지하기 위해서 표면보호막을 증착하였다. 적외선 검출 소자의 전기적 특성 평가를 위해 각각의 시료의 암전류 (dark current)와 파장별 반응 (spectral response)을 온도별로 측정하여 비교 및 분석하였다.