• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.469-483
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• 2006

An algorithm was developed to estimate stability indices (SI) over the Korean peninsula using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) infrared brightness temperatures (TBs). The SI is defined as the stability of the atmosphere in the hydrostatic equilibrium with respect to the vertical displacements and is used as an index for the potential severe storm development. Using atmosphere temperature and moisture profiles from Regional Data Assimilation and Prediction System (RDAPS) as initial guess data for a nonlinear physical relaxation method, K index (KI), KO Index (KO), lifted index (LI), and maximum buoyancy (MB) were estimated. A fast radiative transfer model, RTTOV-7, is utilized for reducing the computational burden related to the physical relaxation method. The estimated TBs from the radiative transfer simulation are in good agreement with observed MODIS TBs. To test usefulness for the short-term forecast of severe storms, the algorithm is applied to the rapidly developed convective storms. Compared with the SIs from the RDAPS forecasts and NASA products, the MODIS SI obtained in this research predicts the instability better over the pre-convection areas. Thus, it is expected that the nowcasting and short-term forecast can be improved by utilizing the algorithms developed in this study.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1647-1656
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• 2021

In order to reduce the spread of infection due to COVID-19, South Korea has established a four-step social distancing standard and implemented it by changing the steps based on the rate of confirmed cases. The implementation of social distancing brought about a change in the amount of activity of citizens by limiting social contact such as movement and gathering of people. One of the data that can intuitively confirm this is Night Time Light (NTL). NTL is a variable that can measure the size of the national economy measured using lights captured by satellites, and can be used to understand people's social activities during the night. The NTL visible data is obtained via the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. 1023 of Suomi data from 1 January 2019 until 26 October 2021 were collected to generate time series of NTL radiance change over Seoul to analyze the correlation with social distancing policy. The results show that implementing the level of social distancing generally decreased the NTL radiance both in spatial disparities and temporal patterns. The higher level of policy, limiting human activities combined with the low number of people who have been vaccinated and the closure of various facilities. Because of social distancing, the differences in human activities affected the nighttime light during the COVID-19 pandemic, especially in Seoul, South Korea. Therefore, this study can be used as a reference for the government in evaluating and improving policies related to efforts reducing the transmission of COVID-19.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1389-1403
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• 2021

Disaster management using VHR (very high resolution) satellite images supports rapid damage assessment and also offers detailed information of the damages. However, the acquisition of pre-event VHR satellite images is usually limited due to the long revisit time of VHR satellites. The absence of the pre-event data can reduce the accuracy of damage assessment since it is difficult to distinguish the changed region from the unchanged region with only post-event data. To address this limitation, in this study, we conducted the wildfire-induced change detection on national wildfire cases using post-fire VHR satellite images and GIS (Geographic Information System) data. For GIS data, a national land cover map was selected to simulate the pre-fire NIR (near-infrared) images using the spatial information of the pre-fire land cover. Then, the simulated pre-fire NIR images were used to analyze bi-temporal NDVI (Normalized Difference Vegetation Index) correlation for unsupervised change detection. The whole process of change detection was performed on a superpixel basis considering the advantages of superpixels being able to reduce the complexity of the image processing while preserving the details of the VHR images. The proposed method was validated on the 2019 Gangwon wildfire cases and showed a high overall accuracy over 98% and a high F1-score over 0.97 for both study sites.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1519-1529
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• 2018

The volcanic ash can spread out over hundreds of kilometers in case of large volcanic eruption. The deposition of volcanic ash may induce damages in urban area and transportation facilities. In order to respond volcanic hazard, it is necessary to estimate efficiently the diffusion area of volcanic ash. The purpose of this study is to compare in-situ volcanic deposition and satellite images of the volcanic eruption case. In this study, we used Near-Infrared (NIR) channels 7 and 8 of Geostationary Ocean Color Imager (GOCI) images for Mt. Aso eruption in 16:40 (UTC) on October 7, 2016. To estimate deposit area clearly, we applied Principal Component Analysis (PCA) and a series of morphology filtering (Eroded, Opening, Dilation, and Closing), respectively. In addition, we compared the field data from the Japan Meteorological Agency (JMA) report about Aso volcano eruption in 2016. From the results, we could extract volcanic ash deposition area of about $380km^2$. In the traditional method, ash deposition area was estimated by human activity such as direct measurement and hearsay evidence, which are inefficient and time consuming effort. Our results inferred that satellite imagery is one of the powerful tools for surface change mapping in case of large volcanic eruption.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.265-278
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• 2019

Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data help to enable rapid emergency responses through detection of the artificial and natural disasters occurring at night. The DNB data without correction of lunar irradiance effect distributed by Korea Ocean Science Center (KOSC) has advantage for rapid change detection because of direct receiving. In this study, radiance differences according to the phase of the moon was analyzed for urban and mountain areas in Korean Peninsula using the DNB data directly receiving to KOSC. Lunar irradiance correction algorithm was proposed for the change detection. Relative correction was performed by regression analysis between the selected pixels considering the land cover classification in the reference DNB image during the new moon and the input DNB image. As a result of daily difference image analysis, the brightness value change in urban area and mountain area was ${\pm}30$ radiance and below ${\pm}1$ radiance respectively. The object based change detection was performed after the extraction of the main object of interest based on the average image of time series data in order to reduce the matching and geometric error between DNB images. The changes in brightness occurring in mountainous areas were effectively detected after the calibration of lunar irradiance effect, and it showed that the developed technology could be used for real time change detection.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1025-1032
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• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.111-116
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• 2019

The biotic stress of garlic and tobacco infected by bacteria and virus was evaluated using a thermal imaging camera in a growth chamber. The remote sensing technique using the thermal camera detected that garlic leaf temperature increased when the leaves were infected by bacterial soft rot of garlic. Furthermore, the temperature of leaf was relatively high for the leaves where the colony-forming unit per mL was large. Such temperature patterns were detected for tobacco leaves infected by Cucumber Mosaic Virus using thermal images. In addition, the crop water stress index (CWSI) calculated from leaf temperature also increased for the leaves infected by the virus. The event such that CWSI increased by the infection of the virus occurred before visual disease symptom appeared. Our results suggest that the thermal imaging camera would be useful for the development of crop remote sensing technique, which can be applied to a smart farm.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1385-1394
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• 2022

This study was to analyze the change in albedo by level-2 land cover map for 20 years(2002-2021) using MODerate resolution Imaging Spectroradiometer (MODIS) data. Also, the difference from the MODIS data was analyzed using the 10-year (2012-2021) data of Visible Infrared Imaging Radiometer Suite (VIIRS). For the albedo data of MODIS and VIIRS, daily albedo data, MCD43A3 and VNP43IA, of 500 m spatial resolution of sinusoidal tile grid produced by Bidirectional Reflectance Distribution Function (BRDF) model were prepared for the South Korea range. Reprojection was performed using the code written based on Python 3.9, and the nearest neighbor was applied as the resampling method. White sky albedo and black sky albedo of shortwave were used for analysis. As a result of 20-year albedo analysis using MODIS data, the albedo tends to rise in all land use. Compared to the 2000s (2002-2011), the average albedo of the 2010s (2012-2021) showed the most significant increase of 0.0027 in the forest area, followed by the grass increase of 0.0024. As a result of comparing the albedo of VIIRS and MODIS, it was found that the albedo of VIIRS was larger from 0.001 to 0.1, which was considered to be due to differences in the surface reflectivity according to the time of image capture and sensor characteristics.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1945-1953
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• 2022

Korea Aerospace Research Institute produces mosaic images of the Korean Peninsula every year to promote the use of satellite images and provides them to users in the public sector. However, since the pan-sharpening and color balancing methodologies are applied during the mosaic image processing, the original spectral information is distorted. In addition, there is a limit to analyze using mosaic images as mosaic images provide only Red, Green and Blue bands excluding Near Infrared (NIR) band. Therefore, in order to compensate for these limitations, this study applied the Principal Component Analysis (PCA) technique and indices extracted from R, G, B bands together for image classification and compared the classification results. As a result of the analysis, the accuracy of the mosaic image classification result was about 67.51%, while the accuracy of the image classification result using both PCA and RGB indices was about 75.86%, confirming that the accuracy of the image classification result can be improved. As a result of comparing the PCA and the RGB indices, the accuracy of the image classification result was about 64.10% and 74.05% respectively. Through this, it was confirmed that the classification accuracy using the RGB indices was higher among the two techniques, and implications were derived that it was important to use high quality reference or supplementary data. In the future, additional indices and techniques are needed to improve the classification and analysis results of mosaic images, and related research is expected to increase the utilization of images that provide only R, G, B or limited spectral information.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.695-706
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• 2022

Medium and high-resolution optical satellites have proven their effectiveness in detecting wildfire areas. However, smoke plumes generated by wildfire scatter visible light incidents on the surface, thereby interrupting accurate monitoring of the area where wildfire occurs. Therefore, a technology to extract smoke in advance is required. Deep learning technology is expected to improve the accuracy of smoke extraction, but the lack of training datasets limits the application. However, for clouds, which have a similar property of scattering visible light, a large amount of training datasets has been accumulated. The purpose of this study is to develop a smoke extraction technique using deep learning, and the limits due to the lack of datasets were overcome by using a cloud dataset on transfer learning. To check the effectiveness of transfer learning, a small-scale smoke extraction training set was made, and the smoke extraction performance was compared before and after applying transfer learning using a public cloud dataset. As a result, not only the performance in the visible light wavelength band was enhanced but also in the near infrared (NIR) and short-wave infrared (SWIR). Through the results of this study, it is expected that the lack of datasets, which is a critical limit for using deep learning on smoke extraction, can be solved, and therefore, through the advancement of smoke extraction technology, it will be possible to present an advantage in monitoring wildfires.