• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.1003-1006
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• 2006

Frontal regions in midlatitude storms exhibit a wide range of behavior, which can be observed by remote sensors. These include decay, strengthening, rotating, and sometimes spawning of new cyclones. Here we refine and apply recent theories of front and frontal wave development to a case of a front clearly observed and analyzed in remote sensing data. By applying innovative analysis techniques to the data we assess the respective roles of ageostrophy, background deformation, and Boundary Layer processes in determining the evolution of the surface front. Our analysis comprises of diagnosis of the terms appearing in the vorticity and divergence equations using remotely sensed observations.

• 대한원격탐사학회지
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• 제10권1호
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• pp.15-29
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• 1994

원격탐사자료의 픽셀값 변이 중 (위성에 의하여) 주사되는 물체 또는 전경등의 자체에 의한 것이 아닌 기타 변이에 대해서는 핏셀의 방사값을 보정하게 된다. 이러한 핏셀값 변이의 원 인에 대해서는 감지방치의 성능차이 및 작동불량, 또는 대기에 의한 간섭효과등을 들 수 있다. 감 지장치들의 성능차에 의하여(원치 않는) 줄이 그어진 화상자료를, 그리고 작동불량으로 값이 零 (영)인 픽셀들을 얻게 되며, 또한 빛의 산란에 기인하는 대기의 편기등이 발생하게 되는 데, 이러 한 변이들에 대하여는 그 방사값을 보정한다. 본고에서는 이러한 방사값 보정법으로 일부 주요원 칙에 대하여 예제와 함께 고찰하였다.

• 대한원격탐사학회지
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• 제10권2호
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• pp.63-82
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• 1994

Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. A reconstruction system was developed to increase the discrimination capability for imagery that has been modified by residual dffects resulting from imperfect sensing of the target and by atmospheric attenuation of the signal. Utilizing temporal information based on an adaptive timporal filter, it recovers missing measurements resulting from cloud cover and sensor noise and enhances the imagery. The temporal filter effectively tracks a systematic trend in remote sensing data by using a polynomial model. The reconstruction system were applied to the AVHRR data collected over Korean Peninsula. The results show that missing measurements are typically recovered successfully and the temporal trend in vegetation change is exposed clearly in the reconstructed series.

• Journal of Ecology and Environment
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• 제34권2호
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• pp.149-156
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• 2011

Phenological variables derived from remote sensing are useful in determining the seasonal cycles of ecosystems in a changing climate. Satellite remote sensing imagery is useful for the spatial continuous monitoring of vegetation phenology across broad regions; however, its applications are substantially constrained by atmospheric disturbances such as clouds, dusts, and aerosols. By way of contrast, a tower-based ground remote sensing approach at the canopy level can provide continuous information on canopy phenology at finer spatial and temporal scales, regardless of atmospheric conditions. In this study, a tower-based ground remote sensing system, called the "Phenological Eyes Network (PEN)", which was installed at the Gwangneung Deciduous KoFlux (GDK) flux tower site in Korea was introduced, and daily phenological progressions at the canopy level were assessed using ratios of red, green, and blue (RGB) spectral reflectances obtained by the PEN system. The PEN system at the GDK site consists of an automatic-capturing digital fisheye camera and a hemi-spherical spectroradiometer, and monitors stand canopy phenology on an hourly basis. RGB data analyses conducted between late March and early December in 2009 revealed that the 2G_RB (i.e., 2G - R - B) index was lower than the G/R (i.e., G divided by R) index during the off-growing season, owing to the effects of surface reflectance, including soil and snow effects. The results of comparisons between the daily PEN-obtained RGB ratios and daily moderate-resolution imaging spectroradiometer (MODIS)-driven vegetation indices demonstrate that ground remote sensing data, including the PEN data, can help to improve cloud-contaminated satellite remote sensing imagery.

• 대한원격탐사학회지
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• 제29권6호
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• pp.663-670
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• 2013

Total Vertical Column Density (VCD) of $NO_2$, a key component in air quality and tropospheric chemistry was measured using a ground-based instrument, Pandora, in Seoul from March 2012 to October 2013. The $NO_2$ measurements using Pandora were compared with those obtained by satellite remote sensing from Ozone Monitoring Instrument (OMI) where the intercomparison characteristics were analyzed as a function of measurement geometry, cloud amount and aerosol loading. The negative biases of the OMI $NO_2$ VCD were larger when cloud amount and Aerosol Optical Depth (AOD) were higher. The correlation coefficient between $NO_2$ VCDs from Pandora and OMI was 0.53 for the entire measurement period, whereas the correlation coefficient between the two was 0.74 when the cloud amount and AOD were low (cloud amount<3, AOD<0.4). The low bias of OMI data was associated with the shielding effect of the cloud and the aerosols.

• 대한원격탐사학회지
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• 제29권6호
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• pp.657-662
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• 2013

The comparison between $CO_2$ volume mixing ratios observed by GOSAT and TCCON from September 2009 through November 2012 was performed at Tsukuba and Saga, two downwind sites in East Asia. The temporal trends of $CO_2$ values obtained from GOSAT show good agreement with those observed by TCCON at these two by the TCCON, showing a coefficient of determination ($R^2$) of 0.65. The regression slop we obtained was 0.92, showing a small bias of GOSAT $CO_2$ values compared to those observed by TCCON. However, we found the higher correlation in fall and winter than that in spring and summer. The $CO_2$ volume mixing ratios observ sites. The $CO_2$ volume mixing ratios observed by GOSAT are also in good agreement with those measured ed by GOSAT are in good agreement with those measured by the TCCON at those two sites in fall and winter, showing a coefficient of determination ($R^2$) of 0.66 where as the correlation of determination obtained between GOSAT and TCCON was only 0.27 in spring and summer.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1021-1023
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• 2003

Atmospheric water vapor is a key variable in numerical weather prediction (NWP) models, but it is a crucial factor to limit the accuracy of high-precision GPS positioning technique. For both issues, knowledge about the amount of water vapor is extremely important. In this study, we perform a simulation study to utilize GPS signals through a developed tomographic scheme to retrieve 3D structure of atmospheric wet refractivity, which may be assimilated into NWP models for advancing forecasting or position calculation for improving GPS positioning accuracy. For the purpose of knowing the absolute accuracy of the developed tomographic method, a well-defined temporal and spatial varying state of atmospheric profile is utilized. Under such circumstance, several factors that may influence the retrievals can be easily examined and their impacts may be clearly quantified. They include the values of the positional dilution of precision (PDOP) factors of the GPS signals, ... etc. Based upon the use of a variety spectrum of adjustable factors, many interesting findings are obtained. For example, the more is the number of the observed GPS signals the better becomes the retrievals as expected. Also, the smaller is the PDOP value the better becomes the retrievals.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.582-584
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• 2003

This paper explores two such indexes----the Aerosol Free Vegetation Index (AFRI) and the Atmospherically Resistant Vegetation Index (ARVI). Comparisons were made with the NDVI (normalized vegetation index) to see if they indeed performed better. In general, the results showed that the AFRI and ARVI (with gamma=1) did indeed perform better than their NDVI counterpart study with the related channels were employed.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.383-385
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• 2006

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.594-596
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• 2003

Due to the fact that the composition and variability of aerosols is considered rather complex, it is difficult to employ a simple and straightforward physical model in calculating the aerosol size distribution in the absence of actual data. This complicates the already difficult retrieval of various atmospheric parameters from remotely sensed data. Thus, the main purpose of this study is trying to find an effective aerosol size coefficient that is stable, and can depict the particle size distribution. This paper also attempts to construct an 'effective aerosol size coefficient' database for each respective season, where it can quickly and effectively supply pertinent information of the atmosphere's opacity.