• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.765-779
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• 2022

This study evaluates the accuracy in identifying the burned area in South Korea using multi-temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.505-510
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• 2014

Normalized Difference Vegetation Index (NDVI) is a major indicator for monitoring climate change and detecting vegetation coverage. In order to retrieve NDVI, it is preprocessed using cloud masking and atmospheric correction. However, the preprocessed NDVI still has abnormally low values known as noise which appears in the long-term time series due to rainfall, snow and incomplete cloud masking. An existing method of using polynomial regression has some problems such as overestimation and noise detectability. Thereby, this study suggests a simple method using amoving average approach for correcting NDVI noises using SPOT/VEGETATION S10 Product. The results of the moving average method were compared with those of the polynomial regression. The results showed that the moving average method is better than the former approach in correcting NDVI noise.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.91-91
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• 2001

현재 진행되고 있다고 여겨지는 지구변화 (Global Change)의 연구는 환경/지구과학의 초미의 관심사로 떠오르고 있다. 특히 온실가스의 분출로 인한 지구 온난화 (Global Warming)는 지구환경에 부정적인 효과가 초래될 것으로 우려되는바, 여러 지구환경 인자들의 변화를 초래할 것으로 예측되고 있다. 가장 직접적인 인자는 대기온도이고 아울러 해수온도/해류, 바람속도/방향, 대기화학 조성, 식생분포, 구름량, 얼음분포 등이 간접적인 인자들이다. 본 연구에서는 EOS 위성군 중 고층대기 연구를 위한 UARS 위성의 HRDI 센서의 자료를 분석하였다. HRDI는 대기성분 중 산소 $O_2$ 발광선의 도플러 변이를 측정하여 바람속도를 측정한다. 이 자료의 분석을 통하여 50~100 km 상공의 바람속도 변화를 지상에서의 OH 발광선 관측치와 비교하였다. 본 연구는 초기 연구로서 정략적이고 보편적인 결과 도출보다는 향후 연구를 위한 기반연구로서의 성격을 갖는다. 지구온난화는 대기의 온도를 상승시키고, 이는 대기 중 에너지의 증가를 불러와 필연적으로 고층대기의 교란 현상이 있을 것으로 예상된다. 앞으로 지구전체 대기의 풍속/풍향의 고도변화가 분석되면 지구온난화에 의한 고층대기 변화가 탐지될 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1615-1633
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• 2023

Sargassum horneri is one of the floating algae in the sea, which breeds in large quantities in the Yellow Sea and East China Sea and then flows into the coast of Republic of Korea, causing various problems such as destroying the environment and damaging fish farms. In order to effectively prevent damage and preserve the coastal environment, the development of Sargassum horneri detection algorithms using satellite-based remote sensing technology has been actively developed. However, incorrect detection information causes an increase in the moving distance of ships collecting Sargassum horneri and confusion in the response of related local governments or institutions,so it is very important to minimize false detections when producing Sargassum horneri spatial information. This study applied technology to automatically remove false detection results using the GOCI-II-based Sargassum horneri detection algorithm of the National Ocean Satellite Center (NOSC) of the Korea Hydrographic and Oceanography Agency (KHOA). Based on the results of analyzing the causes of major false detection results, it includes a process of removing linear and sporadic false detections and green algae that occurs in large quantities along the coast of China in spring and summer by considering them as false detections. The technology to automatically remove false detection was applied to the dates when Sargassum horneri occurred from February 24 to June 25, 2022. Visual assessment results were generated using mid-resolution satellite images, qualitative and quantitative evaluations were performed. Linear false detection results were completely removed, and most of the sporadic and green algae false detection results that affected the distribution were removed. Even after the automatic false detection removal process, it was possible to confirm the distribution area of Sargassum horneri compared to the visual assessment results, and the accuracy and precision calculated using the binary classification model averaged 97.73% and 95.4%, respectively. Recall value was very low at 29.03%, which is presumed to be due to the effect of Sargassum horneri movement due to the observation time discrepancy between GOCI-II and mid-resolution satellite images, differences in spatial resolution, location deviation by orthocorrection, and cloud masking. The results of this study's removal of false detections of Sargassum horneri can determine the spatial distribution status in near real-time, but there are limitations in accurately estimating biomass. Therefore, continuous research on upgrading the Sargassum horneri monitoring system must be conducted to use it as data for establishing future Sargassum horneri response plans.

• Journal of Astronomy and Space Sciences
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• v.24 no.4
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• pp.309-314
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• 2007

Originating from the Oort cloud, some comets disappear to impact against the Sun or to split up by strong gravitational force. Then they don't go back to the Oort cloud. They are called sungrazing comets. The comets are detected by sublimation of ices and ejection of gas and dust through solar heat close to the Sun. There exists the charge transfer from heavy ions in the solar wind to neutral atoms in the cometary atmosphere by interaction with the solar wind. Cometary atoms would be excited to high electronic levels and their do-excitation would result in X-ray emission, or it would be scattering of solar X-ray emission by very small cometary grains. We calculated the X-ray emission applying the model suggested by Mendis & Flammer (1984) and Cravens (1997). In our estimation, the sungrazing comet whose nucleus size is about 1 km in radius might be detectable within a distance of 3 solar radius from the sun on soft X-ray solar camera.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.571-581
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• 2008

The sea fog occurs frequently in the west coast of Korea in spring and summer. This study focused on the detection of sea fog using MODIS satellite images. We presented a method for sea fog detection based on the homogeneity level between low stratus and sea fog, which was that the top surface of sea fog had a homogeneous aspect while that of low stratus had a heterogenous aspect. The results showed that the both homogeneity of $11{\mu}m$ brightness temperature (BT) and brightness temperature difference (BTD, $BT_{3.7{\mu}m}-BT_{11{\mu}m}$) were available to discriminate sea fog from low stratus. The frequency of difference between BT in fog/stratus area and BT in clear area provided reasonable result. In addition, the threshold values of standard deviations of BT and BTD in the fog/stratus area were applicable to differentiate fog from low stratus.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• The Optical Journal
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• s.158
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• pp.55-57
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• 2015

여기저기서 드론이 뜨고 있다. 아마존이 날아오른 드론으로 고객의 문 앞까지 배달하는 모습은 일대 장관이었다. 이제 웬만한 방송에서 하늘 높이 오른 드론으로 내려다본 모습을 전송하는 것은 그저 일상일 뿐이다. 뿐만 아니라, 사람이 직접 닿을 수 없는 곳에서 드론으로 사람을 찾는다거나, 드론을 통해 고층건물의 안전도를 검사한다거나, 정찰을 하는 등 다양한 활용도가 돋보인다. 라스베가스의 세계가전전시회(CES)에서 바르셀로나의 모바일월드콩그레스(MWC)에서 그리고 뉴욕의 장난감전시회 에서도 드론은 스타로 부상했다. 이제 드론은 대중화와 상업적 성공의 기로에 서 있다. 이를 위해서는 기계적 성능이상으로 중요한 것이 드론과 사용자간의 상호작용을 통해 이루어내는 사용자 경험이다. 즉 드론을 얼마나 쉽고 편하고 정확하고 안전하게 조종할 수 있도록 만드는가가 차별화와 경쟁력의 시작이다. 만약 드론이 지금처럼 조종하기 어렵고 심지어 인명과 재산을 위협한다고 인식되면 산업적 잠재성은 그저 한여름 밤의 꿈으로 사그러들 수밖에 없다. 몇 가지 사례를 보자. 지난 2월 미국 Fox TV 생방송에서 Popular Science 잡지 편집장 Dave Mosher는 드론의 안전성에 관해서 말하고 있었다. 그 때 데모를 위해 날던 드론이 갑자기 균형을 잃으면서 추락하였다. 이 사고로 인해 드론이 안전하지 않을 수 있다는 인식이 퍼지게 되었다. 경미한 사고지만 심각한 위협감을 일으키기도 한다. 레이더에 탐지되지 않던 드론이 미국 백악관 앞마당에 추락한 것이 그런 예이다. 어떤 사용자는 재미삼아 드론을 구름 위까지 날려 보냈다. 그러더니 드론이 제어력을 상실하였고, 결국 추락하고 말았다. 다행히도 누군가의 머리 위로 떨어지지는 않았다.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.239-251
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• 1999

The elimination process of cloud-contaminated pixels is one of important steps before obtaining the accurate parameters of land and ocean surface from AVHRR imagery. We developed a 6step threshold method to detect the cloud-contaminated pixels from NOAA-14/AVHRR datime imagery over land using different combination of channels. This algorithm has two phases : the first is to make a cloud-free characteristic data of land surface using compositing techniques from channel 1 and 5 imagery and a dynamic threshold of brightness temperature, and the second is to identify the each pixel as a cloud-free or cloudy one through 4-step threshold tests. The merits of this method are its simplicity in input data and automation in determining threshold values. The threshold of infrared data is calculated through the combination of brightness temperature of land surface obtained from AVHRR imagery, spatial variance of them and temporal variance of observed land surface temperature. The method detected the could-comtaminated pixels successfully embedded inthe NOAA-14/AVHRR daytime imagery for the August 1 to November 30, 1996 and March 1 to July 30, 1997. This method was evaluated through the comparison with ground-based cloud observations and with the enhanced visible and infrared imagery.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.53-66
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• 2018

The information of surface reflectance ($R_{sfc}$) is important for the heat balance and the environmental/climate monitoring. The $R_{sfc}$ sensitivity to error-induced variables for the Geostationary Environment Monitoring Spectrometer (GEMS) retrieval from geostationary-orbit satellite observations at 300-500 nm was investigated, utilizing polar-orbit satellite data of the MODerate resolution Imaging Spectroradiometer (MODIS) and Ozone Mapping Instrument (OMI), and the radiative transfer model (RTM) experiment. The variables in this study can be cloud, Rayleigh-scattering, aerosol, ozone and surface type. The cloud detection in high-resolution MODIS pixels ($1km{\times}1km$) was compared with that in GEMS-scale pixels ($8km{\times}7km$). The GEMS detection was consistent (~79%) with the MODIS result. However, the detection probability in partially-cloudy (${\leq}40%$) GEMS pixels decreased due to other effects (i.e., aerosol and surface type). The Rayleigh-scattering effect in RGB images was noticeable over ocean, based on the RTM calculation. The reflectance at top of atmosphere ($R_{toa}$) increased with aerosol amounts in case of $R_{sfc}$<0.2, but decreased in $R_{sfc}{\geq}0.2$. The $R_{sfc}$ errors due to the aerosol increased with wavelength in the UV, but were constant or slightly decreased in the visible. The ozone absorption was most sensitive at 328 nm in the UV region (328-354 nm). The $R_{sfc}$ error was +0.1 because of negative total ozone anomaly (-100 DU) under the condition of $R_{sfc}=0.15$. This study can be useful to estimate $R_{sfc}$ uncertainties in the GEMS retrieval.