• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.375-375
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• 2023

본 연구는 농지를 대상으로 토양수분 및 가뭄 현황을 분석하는 데 그 목적이 있다. 토양수분을 파악하기 위해 Terra MODIS(Moderate Resolution Imaging Spectroradiometer) 위성영상기반의 토양수분 산정모형을 개발하였다. 해당 모형은 MODIS LST(Land Surface Temperature) 및 NDVI(Normalized Difference Deficit Index)를 기반으로 SCS-CN(Soil Conservation Service-Curve Number) 방법에서 착안한 수문학적 개념 5일 선행강우 및 무강우일수를 입력자료로 하며, 토양 종류 및 계절에 따른 토양수분의 특성을 고려하였다. 모형의 개발을 위해 MODIS LST 및 NDVI 영상을 2013년부터 2022년까지 각각 일별 및 16일 단위로 구축하였으며, 동 기간에 대해 전국 88개소의 기상청 종관기상관측소의 강수량 및 LST 자료를 수집하였다. MODIS LST는 실측 LST 자료를 활용해 조건부합성기법을 적용하여 상세화하였고, 수집된 강수량자료는 역거리가중법을 활용해 공간 보간을 수행하였다. 토양특성의 구분은 농촌진흥청에서 정밀토양도를 수집하여 활용하였다. 공간 분포된 토양수분에서 농지에 해당하는 토양수분을 추출하기 위해 스마트팜맵을 구축하고, 농지 속성에 해당하는 위치 정보를 조회 후 이를 시군구별로 평균하여 일별 평균 토양수분값을 산정하였다. 토양수분 기반의 가뭄 현황 분석을 위해 구축된 정밀토양도에서 작물 생장과 관련된 영구위조점 및 포장용수량을 활용해 5단계(정상, 관심, 주의, 경계, 심각)의 가뭄 위험도를 산정하였으며, 실제 가뭄 현황과의 비교를 통해 토양수분기반의 가뭄 위험도의 실효성을 검증하고자 한다.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.639-645
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• 2016

Yeongjong-do is seventh largest island in the west coast of Korea which is 4.8 km away in the direction of south-west from Incheon. The mudflat area around the Yeongjong-do has variable dimension according to tide level. In addition, Yeongjong-do is important area with high environmental value as wintering sites for migratory birds. The mudflat of Yeongjong-do is also meaningful region because it is used as place of education and tourist attraction. But, there are increasing concerns about preservation of mudflat area caused by artificial development such as land reclamation project and Incheon airport construction. In this paper, mudflat area was detected using Landsat 7 ETM+ images that United States Geological Survey (USGS) is providing the data in 16 days period. The false color composite was made from band 7, 5, and 3 that could dividing boundary between water and land for the purpose of appearance of boundary line in mudflat region. This area was calculated using results of classification based on false color composite images and was digitized through repetitive algorithm during research of period. Therefore, the change of northeastern area in Yeongjong-do was detected according to tide level during 16 years from 2000 to 2015 on the basis of providing period at tide station. This paper will expect as indicator for range of area in same tide level prior to the start of the research for preservation of mudflat. It will be also scientific grounds about change of mudflat area caused by artificial development.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.143-154
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• 2004

The researchers, who seek geological and environmental information, depend on the remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, the adverse weather conditions and the expensive equipment can restrict that the researcher can collect their data anywhere and any time. To allow for better flexibility, we have developed a compact, a multi-spectral automatic Aerial photographic system(PKNU 2). This system's Multi-spectral camera can catch the visible(RGB) and infrared(NIR) bands($3032{\times}2008$ pixels) image. Visible and infrared bands images were obtained from each camera respectively and produced Color-infrared composite images to be analyzed in the purpose of the environment monitor but that was not very good data. Moreover, it has a demerit that the stereoscopic overlap area is not satisfied with 60% due to the 12s storage time of each data, while it was possible that PKNU 2 system photographed photos of great capacity. Therefore, we have been developing the advanced PKNU 2(PKNU 3) that consists of color-infrared spectral camera can photograph the visible and near infrared bands data using one sensor at once, thermal infrared camera, two of 40 G computers to store images, and MPEG board to compress and transfer data to the computer at the real time and can attach and detach itself to a helicopter. Verification and calibration of each sensor(REDLAKE MS 4000, Raytheon IRPro) were conducted before we took the aerial photographs for obtaining more valuable data. Corrections for the spectral characteristics and radial lens distortions of sensor were carried out.

• 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.

• Journal of the Korean earth science society
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• v.40 no.5
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• pp.447-463
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• 2019

Sea surface wind is a fundamental element for understanding the oceanic phenomena and for analyzing changes of the Earth environment caused by global warming. Global research institutes have developed and operated scatterometers to accurately and continuously observe the sea surface wind, with the accuracy of approximately ${\pm}20^{\circ}$ for wind direction and ${\pm}2m\;s^{-1}$ for wind speed. Given that the spatial resolution of the scatterometer is 12.5-25.0 km, the applicability of the data to the coastal area is limited due to complicated coastal lines and many islands around the Korean Peninsula. In contrast, Synthetic Aperture Radar (SAR), one of microwave sensors, is an all-weather instrument, which enables us to retrieve sea surface wind with high resolution (<1 km) and compensate the sparse resolution of the scatterometer. In this study, we investigated the Geophysical Model Functions (GMF), which are the algorithms for retrieval of sea surface wind speed from the SAR data depending on each band such as C-, L-, or X-band radar. We reviewed in the simulation of the backscattering coefficients for relative wind direction, incidence angle, and wind speed by applying LMOD, CMOD, and XMOD model functions, and analyzed the characteristics of each GMF. We investigated previous studies about the validation of wind speed from the SAR data using these GMFs. The accuracy of sea surface wind from SAR data changed with respect to observation mode, GMF type, reference data for validation, preprocessing method, and the method for calculation of relative wind direction. It is expected that this study contributes to the potential users of SAR images who retrieve wind speeds from SAR data at the coastal region around the Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.997-1008
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• 2023

Ship detection at sea can be performed in various ways. In particular, satellites can provide wide-area surveillance, and Synthetic Aperture Radar (SAR) imagery can be utilized day and night and in all weather conditions. To propose an efficient ship detection method from SAR images, this study aimed to apply the You Only Look Once Version 5 (YOLOv5) model to Sentinel-1 images and to analyze the difference between individual vs. integrated models and the accuracy characteristics by polarization. YOLOv5s, which has fewer and lighter parameters, and YOLOv5x, which has more parameters but higher accuracy, were used for the performance tests (1) by dividing each polarization into HH, HV, VH, and VV, and (2) by using images from all polarizations. All four experiments showed very similar and high accuracy of 0.977 ≤ AP@0.5 ≤ 0.998. This result suggests that the polarization integration model using lightweight YOLO models can be the most effective in terms of real-time system deployment. 19,582 images were used in this experiment. However, if other SAR images,such as Capella and ICEYE, are included in addition to Sentinel-1 images, a more flexible and accurate model for ship detection can be built.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.875-884
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• 2000

Sea fog/stratus is very difficult to detect because of the characteristics of air-sea interaction and locality ,and the scantiness of the observed data from the oceans such as ships or ocean buoys. The aim of our study develops new algorism for sea fog detection by using Geostational Meteorological Satellite-5(GMS-5) and suggests the technics of its continuous detection. In this study, atmospheric synoptic patterns on sea fog day of May, 1999 are classified; cold air advection type(OOUTC, May 10, 1999) and warm air advection type(OOUTC, May 12, 1999), respectively, and we collected two case days in order to analyze variations of water vapor at Osan observation station during May 9-10, 1999.So as to detect daytime sea fog/stratus(OOUTC, May 10, 1999), composite image, visible accumulated histogram method and surface albedo method are used. The characteristic value during day showed A(min) .20% and DA < 10% when visible accumulated histogram method was applied. And the sea fog region which is detected is similar in composite image analysis and surface albedo method. Inland observation which visibility and relative humidity is beneath 1Km and 80%, respectively, at OOUTC, May 10,1999; Poryoung for visble accumulated histogram method and Poryoung, Mokp'o and Kangnung for surface albedo method. In case of nighttime sea fog(18UTC, May 10, 1999), IR accumulated histogram method and Maximum brightness temperature method are used, respectively. Maxium brightness temperature method dectected sea fog better than IR accumulated histogram method with the charateristic value that is T_max < T_max_trs, and then T_max is beneath 700hPa temperature of GDAPS(Global Data Assimilation and Prediction System). Sea fog region which is detected by Maxium brighness temperature method was similar to the result of National Oceanic and Atmosheric Administratio/Advanced Very High Resolution Radiometer (NOAA/AVHRR) DCD(Dual Channel Difference), but usually visibility and relative humidity are not agreed well in inland.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.9
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• pp.979-986
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• 2010

This paper presents an algorithm for retrieving precise radar cross sections(RCS) of various trihedral corner reflectors (TCR) which are external calibrators of synthetic aperture radar(SAR) systems. The theoretical RCSs of the TCRs are computed based on the physical optics(PO), geometrical optics(GO), and physical theory of diffraction(PTD) techniques; that is, the RCS computation includes the single reflections(PO), double reflections(GO-PO), triple reflections(GO-GO-PO), and edge diffractions(PTD) from the TCR. At first, we acquire an SAR image of the area that five TCRs installed in, and then extract the RCS of the TCRs. The RCSs of the TCRs are extracted accurately from the SAR image by adding up the power spill, which is generated due to the radar IRF(Impulse Response Function), using a square window. We compare the extracted RCSs with the theoretical RCSs and analyze the difference between the theoretical and experimental RCSs of the TCR for various window sizes and various backscattering coefficient levels of the adjacent area. Finally, we propose the minimum size of the integration area and the maximum level of the backscattering coefficients for the adjacent area.