• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.236-238
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-functional Transport Satellite-1 Replacement (MTSAT-1R) data. The algorithm is the hybrid algorithm that has used two methods combined together. The first method used the differential absorption in brightness temperature difference between $11{\mu}m$ and $12{\mu}m$ (BTD1). The radiation at 11 ${\mu}m$ is absorbed more than at 12 ${\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m$ and $11{\mu}m$ (BTD2). The technique would be most sensitive to dust loading during the day when the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. We have applied the three methods to MTSAT-1R for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. As produced Principle Component Image (PCI) through the PCA is the correlation between BTD1 and BTD2, errors of about 10% that have a low correlation are eliminated for aerosol detection. For the region of aerosol detection, aerosol index (AI) is produced to the scale of BTD1 and BTD2 values over land and ocean respectively. AI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between AI and OMI aerosol index (AI) shows remarkable good correlations during daytime and relatively good correlations over the land.

• 대기
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• 제21권3호
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• pp.273-284
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• 2011

This study investigates the characteristics of satellite brightness temperature (TB) and rainfall intensity over the life cycle of convective cells. The convective cells in the three event cases are detected and tracked from the growth stage to the dissipation stage using the half-hourly infrared (IR) images. For each IR images the values of minimum, mean, and variance for the convective cell's TBs and the sizes of convective cells are calculated and also the relationship between TB and rainfall intensity are investigated, which is obtained using the pixel values of satellite TB and the ground rainfall intensity measured by AWS (Automatic Weather Station). At the growth stage of the convective cells, the TB's variance and cloud size consistently increased, whereas TB's minimum and mean consistently decreased. At this stage the empirical relationships between TB and rainfall intensity are statistically significant and their slopes (intercepts) in absolute values are relatively large (small) compared to those at the dissipation stage. At the dissipation stage of the convective cells, the variability of TB distributions shows the opposite trend. The statistical significance of the empirical relationships are relatively weak, but their slopes (intercepts) vary over life cycle. These results indicate that satellite IR images can provide valuable information in identifying the convective cell's maturity stage and in the growth stage, they may be used in providing considerably accurate rainfall estimates.

• 한국수자원학회논문집
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• 제31권2호
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• pp.145-153
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• 1998

이 논문에서는 Washita '92 자료를 이용하여 토양수분의 1차원 및 2차원 통계특성을 추출하였다. 아울러, 토양수분과 토양, 밝기온도(brightness temperature), 식생지수 사이의 상관관계가 어떤지를 선형회귀분석에 근거하여 조사해 보았으며 결과로서 토양수분은 밝기온도와 유의할만한 상관성이 있는 것으로 나타났다. 토양수분의 시간에 대한 감쇠(decay)계수를 각각의 토양군별로 추정하였고, 역으로 이 값을 이용하여 관측전 마지막 강우의 시점을 추정해 본 결과 관측기록과 유사한 20일 정도로 나타났다. 토양수분의 2차원 통계특성 분석은 2차원 상관도와 스페트럼을 도출하고 분석함으로서 수행하였으며 토양과 식생지수에 대한 2차원 분석결과와 비교하였다. 이러한 분석 결과로 토양수분은 공간적으로 매우 높은 상관성을 갖는 토양과 상대적으로 낮은 사오간성을 보이는 식생의 중간적인 2차원 통계특성을 나타냄을 알 수 있었다. 즉, 지형이 완만하여 지형적인 영향이 상대적으로 적다고 알려진 Washita 지역의 경우 공간적으로 높은 상관성을 보이는 토양의 공극에 존재하는 토양수분은 상대적으로 낮은 상관성을 보이는 식생에 의해 교란되고 있음을 파악할 수 있었다. 선형저수지의 개념과 공간적인 확산을 고려한 동역학적 토양수분 모형의 유도과정을 보였고 모형의 매개변수가 토양수분의 1차원 및 2차원 통계특성으로부터 효과적으로 추정될 수 있음을 보였다.

• 대한원격탐사학회지
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• 제33권2호
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• pp.243-248
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• 2017

지구 기후시스템의 중요구성인자인 해빙은 극지방과 고위도에 분포하는 특성상 위성을 통한 탐지가 활발히 수행되고 있다. 위성자료를 이용한 해빙탐지기법은 반사도와 휘도온도자료를 이용하며, 많은 연구에서 휘도온도자료를 통해 산출된 Ice Surface Temperature (IST)를 활용한 기법인 Moderate-Resolution Imaging Spectroradiometer (MODIS)의 해빙탐지기법을 활용하고 있다. 본 연구에서는 IST 산출과정이 생략된 단순하고 효율적인 동적임계값 기법을 활용한 해빙탐지기법을 제시하고자 한다. 동적임계값을 지정하기 위하여 해수의 어는점 이하의 화소를 대상으로 MODIS IST와 MODIS $11{\mu}m$ 채널의 휘도온도, Brightness Temperature Difference ($BTD:T_{11{\mu}m}-T_{12{\mu}m}$)의 상호관계를 분석하였다. 분석 결과, 세수치의 관계가 선형의 특징을 나타내었으며 이를 활용하여 임계값을 지정하였다. 청천역에서 지정한 임계값을 MODIS $11{\mu}m$ 채널에 적용하여 해빙을 탐지하였다. 또한, 본 연구의 해빙탐지기법의 성능을 검증하기 위해 MODIS Sea ice extent를 이용하여 정확도를 분석하였으며 그 결과, Producer Accuracy (PA) 99% 이상의 높은 정확도를 보였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.214-218
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• 1999

The aim of our study is to develop new algorism for sea fog detection by using Geostational Meteorological Satellite-5(GMS-5) and suggest the techniques of its continuous detection. So as to detect daytime sea fog/stratus(00UTC, May 10, 1999), visible accumulated histogram method and surface albedo method are used. The characteristic value during daytime showed A(min) > 20％ and DA < 10％ when visble accumulated histogram method was applied. And the sea fog region which detected is of similarity in composite image and surface albedo method. In case of nighttime sea fog(18UTC, May 10, 1999), infrared accumulated histogram method and maximum brightness temperature method are used, respectively. Maximum brightness temperature method(T_max method) detected sea fog better than IR accumulated histogram method. In case of T_max method, when infrared value is larger than T_max, fog is detected, where T_max is an unique value, maximum infrared value in each pixel during one month. Then T_max is beneath 700hpa temperature of GDAPS(Global Data Assimilation and Prediction System). Sea fog region which detected by T_max method was similar to the result of National Oceanic and Atmosheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) DCD(Dual Channel Difference). But inland visibility and relative humidity didn't always agreed well.

• 조명전기설비학회논문지
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• 제27권9호
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• pp.6-14
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• 2013

The position of the light source, illuminance distribution, and color temperature were evaluated in each lighting environment for the three main activities in a living room - 'watching TV,' 'reading' and 'relaxing.' In 'watching TV', the experiment was done to estimate the degree of comfort felt by the subjects when they watch static video and moving video, respectably, with different ambient brightness, with or without partial lighting above the TV set, and with different color temperatures. In 'reading', the comfortableness was estimated by the illuminance ratio of the ambient lighting to the lighting for reading and by the difference in color temperature. And in 'relaxing', the comfortableness was estimated by means of the ambient brightness, use/no use of a relaxing lamp, and color temperature. This experiment determined the general satisfaction for each visual act and the optimum lighting environment to reduce glare.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.604-609
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• 2002

The passive microwave remote sensing has progressed considerably in recent years. Important earth surface parameters are detected and monitored by airborne and space born radiometers. However the spatial resolution of real aperture measurements is constrained by the antenna aperture size available on orbiting platforms and on the ground. The inverse problem technique is researched in order to improve the spatial resolution of microwave scanning radiometer. We solve a two-dimensional (surface) temperature-imaging problem with a major intention to develop high-resolution methods. In this paper, the scenario for estimation of both radiometer point spread function (PSF) and target configuration is explained. The PSF of the radiometer is assumed to be unknown and estimated from the observations. The configuration and brightness temperature of targets are also estimated. To do this, we deal with the parametric modeling of observation scenario. The performance of developed algorithms is illustrated on two-dimensional experimental data obtained by the water vapor radiometer.

• 대한원격탐사학회지
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• 제9권1호
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• pp.1-19
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• 1993

A simple bispectral threshold technique which reflects the temporal and spatial characteristics of the analysis area has been developed to classify the cloud type and estimate the cloud cover from GMS/S-VISSR(Stretched Visible and Infrared Spin Scan Radiometer) imagery. In this research, we divided the analysis area into land and sea to consider their different optical properties and used the same time observation data to exclude the solar zenith angle effects included in the raw data. Statistical clear sky radiance(CSRs) was constructed using maximum brightness temperature and minimum albedo from the S-VISSR imagery data during consecutive two weeks. The CSR used in the cloud anaysis was updated on the daily basis by using CSRs, the standard deviation of CSRs and present raw data to reflect the daily variation of temperature. Thresholds were applied to classify the cloud type and estimate the cloud cover from GMS/S-VISST imagery. We used a different thresholds according to the earth surface type and the thresholds were enough to resolve the spatial variation of brightness temperature and the noise in raw data. To classify the ambiguous pixels, we used the time series of 2-D histogram and local standard deviation, and the results showed a little improvements. Visual comparisons among the present research results, KMA's manual analysis and observed sea level charts showed a good agreement in quality.

• 대한원격탐사학회지
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• 제15권3호
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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.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.