• International Journal of Contents
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• v.16 no.4
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• pp.16-25
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• 2020

The size of observation data in astronomy has been increasing exponentially with the advents of wide-field optical telescopes. This means the needs of changes to the way used for large-scale astronomy data analysis. The complexity of analysis tools and the lack of extensibility of computing environments, however, lead to the difficulty and inefficiency of dealing with the huge observation data. To address this problem, this paper proposes a workflow execution system for analyzing large-scale astronomy data efficiently. The proposed system is composed of two parts: 1) a workflow execution manager and its RESTful endpoints that can automate and control data analysis tasks based on workflow templates and 2) an elastic resource manager as an underlying mechanism that can dynamically add/remove virtualized computing resources (i.e., virtual machines) according to the analysis requests. To realize our workflow execution system, we implement it on a testbed using OpenStack IaaS (Infrastructure as a Service) toolkit and HTCondor workload manager. We also exhaustively perform a broad range of experiments with different resource allocation patterns, system loads, etc. to show the effectiveness of the proposed system. The results show that the resource allocation mechanism works properly according to the number of queued and running tasks, resulting in improving resource utilization, and the workflow execution manager can handle more than 1,000 concurrent requests within a second with reasonable average response times. We finally describe a case study of data reduction system as an example application of our workflow execution system.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.3-6
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• 2002

In this study, we investigate the atmospheric environment changes in the aspect of optical remote sensing using surface observation data from 1971 to 2000 of Korea Meteorological Administration. Visibility, spatially averaged over Korean peninsula, is systematically reduced from about 28km to 18km during the last 30 years. It means that atmospheric conditions for the optical remote sensing over Korean peninsula are growing worse and worse due to the degradation of air quality. The 30-year average of cloud amount shows a strong seasonal variation, maximum(75%) in summer and minimum (35%) in autumn. Precipitation also shows a very similar variation pattern with cloud. The temperature and sea level pressure show a opposite seasonal change pattern, maximum(minimum in SLP) in summer and minimum(maximum in SLP) in winter, respectively. Relative humidiy(RH) is one of the variables mostly affected by urbanization or urban heat island. As a results, annual mean RH is decreased from 73% to 68% during last 30 years. When we take into account the favorable and unfavorable factors all together, summer and autumn are the worst and the best season for optical remote sensing in Korea.

• Proceedings of the KSRS Conference
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• v.2
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• pp.655-659
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• 2006

Surface Solar Insolation is important for vegetation productivity, hydrology, crop growth, etc. In this study, Surface Solar Insolation is estimated using Multi-functional Transport Satellite (MTSAT-1R) in clear and cloudy conditions. For the Cloudy sky cases, the surface solar insolation is estimated by taking into account the cloud transmittance and multiple scattering between cloud and surface. This model integrated Kawamura's model and SMAC code computes surface solar insolation with a 5km ${\times}$ 5km spatial resolution in hourly basis. The daily value is derived from the available hourly Surface Solar Insolation, independently for every pixel. To validation, this study uses ground truth data recorded from the pyranometer installed by the Korea Meteorological Agency (KMA). The validation of estimated value is performed through a match-up with ground truth. Various match-up with ground truth. Various match-up window sizes are tested with 3${\times}$3, 5${\times}$5, 7${\times}$7, 9${\times}$9, 10${\times}$10, 11${\times}$11, 13${\times}$13 pixels to define the spatial representativity of pyranometer measurement, and to consider drifting clouds from adjacent pixels across the ground station during the averaging interval of 1 hour are taken into account.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.389-396
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• 2006

Surface Solar Insolation is important for vegetation productivity, hydrology, crop growth, etc. In this study, Surface Solar Insolation is estimated using Multi-functional Transport Satellite (MTSAT-1R) in clear and cloudy conditions. For the Cloudy sky cases, the surface solar insolation is estimated by taking into account the cloud transmittance and multiple scattering between cloud and surface. This model integrated Kawamura's model and SMAC code computes surface solar insolation with a $5\;km{\times}5\;km$ spatial resolution in hourly basis. The daily value is derived from the available hourly Surface Solar Insolation, independently for every pixel. To validation, this study uses ground truth data recorded from the pyranometer installed by the Korea Meteorological Agency (KMA). The validation of estimated value is performed through a match-up with ground truth. Various match-up with ground truth. Various match-up window sizes are tested with $3{\times}3,\;5{\times}5,\;7{\times}7,\;9{\times}9,\;10{\times}10,\;11{\times}11,\;13{\times}pixels to define the spatial representativity of pyranometer measurement, and to consider drifting clouds from adjacent pixels across the ground station during the averaging interval of 1 hour are taken into account.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.62-77
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• 2019

Open Data Cube(ODC) has been emerging and developing as the open source platform in the Committee on Earth Observation Satellites(CEOS) for the Global Earth Observation System of Systems(GEOSS) deployed by the Group on Earth Observations (GEO), ODC can be applied to the deployment of scalable and large amounts of free and open satellite images in a cloud computing environment, and ODC-based country or regional application services have been provided for public users on the high performance. This study first summarizes the status of ODC, and then presents concepts and some considering points for linking this platform with Korea Multi-Purpose Satellite (KOMPSAT) images. For the reference, the main contents of ODC with the Google Earth Engine(GEE) were compared. Application procedures of KOMPSAT satellite image to implement ODC service were explained, and an intermediate process related to data ingestion using actual data was demonstrated. As well, it suggested some practical schemes to utilize KOMPSAT satellite images for the ODC application service from the perspective of open data licensing. Policy and technical products for KOMPSAT images to ODC are expected to provide important references for GEOSS in GEO to apply new satellite images of other countries and organizations in the future.

• Journal of the Korean earth science society
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• v.31 no.7
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• pp.720-737
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• 2010

This study is to analyze the temporal and spatial distributions of solar radiation in South Korea. Solar radiation data is observed every minute at 22 KMA (Korea Meteorological Administration) stations using pyranometer from January 2000 to August 2007. These data were calibrated using intensive comparative observation and solar radiation model. Intensive comparative observations are accomplished at 22 KMA stations between KNU (Kangnung (Gangneung-Wonju) National University) standard and station instruments during the month of August 2007. The solar radiation of a clear sky mainly is affected by precipitable water, solar altitude and geological height. Also old (raw) data is corrected by the solar radiation model only about clear day and is revised based on the temporal trend of instrument's sensitivity decrease. At all periods and all stations, differences between raw data (13.31 MJ/day) and corrected data (13.75 MJ/day) are 0.44 MJ/ day. So, the spatial distribution of solar radiation is calculated with seasonal and annual mean, and is the relationship with cloud amount is analyzed. The corrected data show a better consistency with the cloud amount than the old data.

• Publications of The Korean Astronomical Society
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• v.12 no.1
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• pp.63-84
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• 1997

We carried out high-resolution(FWHM=3' .3) HI 21 cm observations of the supernova remnant(SNR) PKS0607+17 and HII region S261 using Arecibo 305-m telescope. The observation was to investigate whether the high-velocity(HV) gas detected in the southern area of PKS0607+17 by Koo & Heiles(1991) is physically associated with the SNR or not. The velocity of the HV gas ranges from +64 km/s to +87 km/s, which is difficult to result from the Galactic rotation. The HV gas could be the gas accelerated by supernova blast wave. However, because the observation of Koo and Heiles(1991) was carried out using Hat Creek radio telescope(FWHM $\simeq$ 36'), the association of the HV gas with the SNR could not be investigated. Using the Arecibo HI 21cm data, we have found that the HV gas appears m the southern part of the SNR and its velocity ranges from +61 km/s to +77 km/s. But the HV gas is scattered m the whole field, not only toward PKS0607+17 but also outside the SNR Accordingly the HV gas is probably not associated with the SNR, but is accidentally aligned along the same line of sight toward the SNR. Instead we have found that HI clouds at low velocities could be possibly associated with the SNR. In Arecibo HI 21cm channel maps the HI gas seems to surround the southern boundary of the SNR at $V_{LSR}$ = +19.6 ~ +40.2 km/s. But because the region of the Arecibo HI 21cm observation is not wide enough to examine the HI gas distribution, we investigated this area using the Berkely low-latitude HI survey data(Weaver & Williams 1974) too. There we found HI gas surrounding the radio continuum boundary of PKS0607+17 at $V_{LSR}$ = +21.6 ~ +258 km/s. It is possible that this HI gas is associated with the SNR, in which case, the velocity of the SNR $V_o$ $\simeq$ +26 km/s, its distance d $\simeq$ 12.5 kpc and its radius R $\simeq$ 145 pc. If we assume that the expansion velocity is ~10 km/s, then the age of the SNR is $\sim4.4\times10^6$ years. PKS0607+17 could be one of the oldest SNRs in the Galaxy. We also studied HI propertities of the HII region S261, which is $\sim1^{\circ}$ away from PKS0607+17. There has been no high-resolution m 21 cm observational study on S261. We discovered HI cloud located at the north-eastern part of S261 at $V_{LSR}$ = +5 km/s ~ +10 km/s, which is possibly associated with the HII region. The central velocity of the HI cloud $V_{LSR}$ = +7.2 km/s and the corresponding distance d = 1.5 kpc. This velocity is comparable to the radio recombination line velocities.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.79-89
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• 2008

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.861-871
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• 2019

Landsat satellite images have been increasingly used for disaster damage analysis and disaster monitoring because they can be used for periodic and broad observation of disaster damage area. However, periodic disaster monitoring has limitation because of areas having missing data due to clouds as a characteristic of optical satellite images. Therefore, a study needs to be conducted for restoration of missing areas. This study detected and removed clouds and cloud shadows by using the quality assessment (QA) band provided when acquiring Landsat-8 images, and performed image restoration of removed areas through a spatial and temporal adaptive reflectance fusion (STARFM) algorithm. The restored image by the proposed method is compared with the restored image by conventional image restoration method throught MLC method. As a results, the restoration method by STARFM showed an overall accuracy of 89.40%, and it is confirmed that the restoration method is more efficient than the conventional image restoration method. Therefore, the results of this study are expected to increase the utilization of disaster analysis using Landsat satellite images.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.583-598
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• 2015

This paper presents derivation of background temperature from geostationary satellite and its validation based on ground measurements and Geographic Information System (GIS) for future use in weather and surface heat variability. This study only focuses on daily and monthly brightness temperature in 2012. From the analysis of COMS Meteorological Data Processing System (CMDPS) data, we have found an error in cloud distribution of model, which used as a background temperature field, and in examining the spatial homogeneity. Excessive cloudy pixels were reconstructed by statistical reanalysis based on consistency of temperature measurement. The derived Brightness temperature has correlation of 0.95, bias of 0.66 K and RMSE of 4.88 K with ground station measurements. The relation between brightness temperature and both elevation and vegetated land cover were highly anti-correlated during warm season and daytime, but marginally correlated during cold season and nighttime. This result suggests that time varying emissivity data is required to derive land surface temperature.