• Atmosphere
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• v.18 no.4
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• pp.317-338
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• 2008

Analyses of observational data and numerical simulations were performed to understand the mechanism of MCSs (Mesoscale Convective Systems) occurred on 13-14 July 2004 over Jindo area of the Korean Peninsula. Observations indicated that synoptic environment was favorable for the occurrence of heavy rainfall. This heavy rainfall appeared to have been enhanced by convergence around the Changma front and synoptic scale lifting. From the analyses of storm environment using Haenam upper-air observation data, it was confirmed that strong convective instability was present around the Jindo area. Instability indices such as K-index, SSI-index showed favorable condition for strong convection. In addition, warm advection in the lower troposphere and cold advection in the middle troposphere were detected from wind profiler data. The size of storm, that produced heavy rainfall over Jindo area, was smaller than $50{\times}50km^2$ according to radar observation. The storm developed more than 10 km in height, but high reflectivity (rain rate 30 mm/hr) was limited under 6 km. It can be judged that convection cells, which form cloud clusters, occurred on the inflow area of the Changma front. In numerical simulation, high CAPE (Convective Available Potential Energy) was found in the southwest of the Korean Peninsula. However, heavy rainfall was restricted to the Jindo area with high CIN (Convective INhibition) and high CAPE. From the observations of vertical drop size distribution from MRR (Micro Rain Radar) and the analyses of numerically simulated hydrometeors such as graupel etc., it can be inferred that melted graupels enhanced collision and coalescence process of heavy precipitation systems.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.157-163
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• 2010

It is well known that weather conditions are closely related with the number and severity of traffic accidents. At present, installation of safety countermeasures including systems is common approach to reduce the damage of traffic accidents at expressways. In this study, the differences of causation factors to influence traffic accidents considering road alignment characteristics and weather conditions. In order to identify the relationship between road and weather conditions, discriminant analysis has been performed with 500 traffic accident data at expressways. Weather conditions are divided into several categories such as snow, sunny, rain, fog, and cloud. Also, road conditions such as types of pavements, grades are analyzed. As the results, major impacting road conditions to traffic accidents are concrete pavement and 3% or more down grades. In these road conditions, visible distance will be reduced and actual braking distances will be increased. This study shows that the expressway sections under concrete pavement and down grades should be more cautious than other sections. It also shows that fog condition is the mose dangerous situation in terms of traffic accidents.

• Spatial Information Research
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• v.19 no.1
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• pp.29-40
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• 2011

GOCI the world first Ocean Color Imager in Geostationary Orbit, which could obtain total 8 images of the same region a day, however, its spatial resolution(500m) is not enough to use for the accurate land application, Super Resolution(SR), reconstructing the high resolution(HR) image from multiple low resolution(LR) images introduced by computer vision field. could be applied to the time-series remotely sensed images such as GOCI data, and the higher resolution image could be reconstructed from multiple images by the SR, and also the cloud masked area of images could be recovered. As the precedent study for developing the efficient SR method for GOCI images, on this research, it reproduced the simulated data under the acquisition process of the remote sensed data, and then the simulated images arc applied to the proposed algorithm. From the proposed algorithm result of the simulated data, it turned out that low resolution(LR) images could be registered in sub-pixel accuracy, and the reconstructed HR image including RMSE, PSNR, SSIM Index value compared with original HR image were 0.5763, 52.9183 db, 0.9486, could be obtained.

• Journal of the Korean Solar Energy Society
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• v.30 no.2
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• pp.72-80
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• 2010

The main thrust of this paper is to investigate a practical way of generating the monthly averaged daily horizontal solar radiation in Korea. For estimating the horizontal solar radiation, the clearness index($K_T$) and the clearness number($C_N$) which are required for the use of Liu and Jordan's model and ASHRAE Clear Sky model were derived based on the measured weather data. Third-order polynomials returning $K_T$ and��$C_N$ for a given location were derived as a function of cloud amount, month, date, latitude and longitude. The predicted monthly averaged daily horizontal solar radiation values were compared with those acquired from the established design weather data. The MBE(Mean Bias Error) and RMSE (Root Mean Squares for Error) between the predicted values and the measured data were near zero. It means that the suggested third-order polynomials for $K_T$ and $C_N$ have good applicability to Liu and Jordan's model and ASHRAE Clear Sky model.

• Journal of Korean Society on Water Environment
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• v.35 no.1
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• pp.1-8
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• 2019

The purpose of this study is to evaluate the nutritional status of Lake Jinyang using Landsat 8 satellite image band correlated with chlorophyll-a, which is also related to algae proliferation. We selected 20 Landsat 8 images dating from 2013 to 2017, taken close to water quality measurement date when the cloud cover was less than 20 %. Based on the results of the previous studies, analyzing the correlation between chlorophyll-a, and Landsat 8 satellite image band, we selected near infrared wavelength, band 5 which is closely related to the population of algae. The nutritional status was classified using the Aizaki trophic state index (TSIm). The results of the regression equation between band 5 and the observed chlorophyll-a data was used to calculate chlorophyll-a for the image data from 2013 to 2017. The concentration of chlorophyll-a ranged from 3 to $16.1mg/m^3$. To illustrate the spatial distribution of chlorophyll-a within the lake, the chlorophyll-a concentration was divided into five grades. The images on October 14, 2014 and April 10, 2016 showed relatively high value of chlorophyll-a, while January 18, 2015 and December 6, 2016 chlorophyll-a value were below 5. The images on October 14, 2014 and April 10, 2016 were rated as eutrophic status in most areas. The results of simulating water quality for the day when the water quality was not measured resulted to an approximate value for the Panmun station while the Naedong station needed some corrections.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.59.2-59.2
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• 2020

Disks around protostars are the birthplace of planets. The first step toward planet formation is grain growth from ㎛-sized grains to mm/cm-sized grains in a disk, particularly in dense regions. In order to study whether grains grow and segregate at the protostellar stage, we investigate the ALMA Band 3 (3.1 mm) and 7 (0.87 mm) dust continuum observations of the protostellar disk WL 17 in ρ Ophiuchus L1688 cloud. As reported in a previous study, the Band 3 image shows substructures: a narrow ring and a large central hole. On the other hand, the Band 7 image shows different substructures: a non-axisymmetric ring and an off-center hole. The two-band observations provide a mean spectral index of 2.3, which suggests the presence of mm/cm-sized large grains. Its non-axisymmetric distribution may imply dust segregation between small and large grains. We perform radiative transfer modeling to examine the size and spatial distributions of dust grains in the WL 17 disk. The best-fit model suggests that large grains (>1 cm) exist in the disk, settling down toward the midplane, whereas small grains (~10 ㎛) well mixed with gas are distributed off-center and non-axisymmetrically in a thick layer. The low spectral index and the modeling results suggest that grains rapidly grow at the protostellar stage and that grains differently distribute depending on sizes, resulting in substructures varying with observed wavelengths. To understand the differential grain distributions and substructures, we discuss the effects of the protoplanet(s) expected inside the large hole and the possibility of gravitational instability.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.179-190
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• 2024

This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.

• 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 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.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.63.1-63.1
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• 2010

Since the solar energy resource is the main input for sizing any solar photovoltaic system and solar thermal power system, it is essential to utilize the solar radiation data as a application and development of solar energy system increase. It will be necessary to understand and evaluate the insolation data. The Korea Institute of Energy Research(KIER) has begun collecting horizontal global insolation data since May, 1982 at 16 different locations in Korea and for the more detailed analysis, Images taken by geostationary satellite may be used to estimate solar irradiance fluxes at earth's surface. It is based on the empirical correlation between a satellite derived cloud index and the irradiance at the ground. From the results, the measured data has been collected at 16 different stations and estimated using satellite at 23 different stations over the South Korea from 1982 to 2009. The Result of analysis shows that the annual-average daily global radiation on the horizontal surface is $3.56kWh/m^2/day$.