• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.193-203
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• 2009

Snow cover is one of the important parameters since it determines surface energy balance and its variation. To classify snow and cloud from satellite data is very important process when inferring land surface information. Generally, misclassified cloud and snow pixel can lead directly to error factor for retrieval of surface products from satellite data. Therefore, in this study, we perform algorithm for detecting snow cover area with remote sensing data. We just utilize visible reflectance, and infrared channels rather than using NDSI (Normalized Difference Snow Index) which is one of optimized methods to detect snow cover. Because COMS MI (Meteorological Imager) channels doesn't include near infra-red, which is used to produce NDSI. Detecting snow cover with visible channel is well performed over clear sky area, but it is difficult to discriminate snow cover from mixed cloudy pixels. To improve those detecting abilities, brightness temperature difference (BTD) between 11 and 3.7 is used for snow detection. BTD method shows improved results than using only visible channel.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.792-797
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• 2013

4D film is just a film that made by adding some physical effects to 3D film or general film. In order to provide physical effects to the audience, the data that make the physical effect must be added to each frames. In this paper, we proposed a video detection system that can efficiently provide physical effects by assessing the present situation such as explosion scene, snowing scene. The proposed video detection system contains an algorithm for fire detection by using R color and $C_r$ value, and also an algorithm for snow detection by using RGB color model. The system constitutes in a MCU that from 8051 family. In the performance evaluations, the result shows that 91% of detection rate in case of fire and 25% of false detection rate in case of snow. Also the system is capable of providing physical effects automatically.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.287-299
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• 2017

The Southern Volcanic Zone (SVZ) of Chile consists of many volcanoes, including the Mt.Villarrica and Mt.Llaima, and the two volcanoes are covered with snow at the top of Mountain. The purpose of this study is to analyze the relationship between the ice caps and the volcanic activity of the two volcanoes for 25 years by using the satellite image data are available in a time series. A total of 60 Landsat-5 TM and Landsat-7 ETM + data were used for the study from September 1986 to February 2011. Using NDSI (Normalized Difference Snow Index) algorithm and SRTM DEM, snow cover and snowline were extracted. Finally, the snow cover area, lower-snowline, and upper-snowline, which are quantitative indicators of snow cover change, were directly or indirectly affected by volcanic activity, were extracted from the satellite images. The results show that the volcanic activity of Villarrica volcano is more than 55% when the snow cover is less than 20 and the lower-snowline is 1,880 m in Llaima volcano. In addition, when the upper-snowline of the two volcanoes is below -170m, it can be confirmed that the volcano is differentiated with a probability of about 90%. Therefore, the changes in volcanic snowfall are closely correlated with volcanic activity, and it is possible to indirectly deduce volcanic activity by monitoring the snow.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.286-289
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• 2007

This study is to develop a cloud detection algorit1un for COMS and it is currently tested by using MODIS level 2B and MTSAT-1R satellite radiance data. Unlike many existing cloud detection schemes which use a threshold method and traditional statistical methods, in this study a feed-forward neural network method with back-propagation algorit1un is used. MODIS level 2B products are matched with feature information of five-band MTSAT 1R image data to form the training dataset. The neural network is trained over the global region for the period of January to December in 2006 with 5 km spatial resolution. The main results show that this model is capable to detect complex cloud phenomena. And when it is applied to seasonal images, it shows reliable results to reflect seasonal characteristics except for snow cover of winter. The cloud detection by the neural network method shows 90% accuracy compared to the MODIS products.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.135-143
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• 2012

Remote sensing which observes repeatedly the whole Earth and GIS-based decision-making technology have been utilized widely in disaster management such as early warning monitoring, damage investigation, emergent rescue and response, rapid recovery etc. In addition, various countermeasures of national level to collect timely satellite imagery in emergency have been considered through the operation of a satellite with onboard multiple sensors as well as the practical joint use of satellite imagery by collaboration with space agencies of the world. In order to respond heavy snowfall disaster occurred on the east coast of the Korean Peninsula in February 2011, snow-covered regions were analyzed and detected in this study through NDSI(Normalized Difference Snow Index) considering reflectance of wavelength for MODIS sensor and change detection algorithm using satellite imagery collected from International Charter. We present the application case of National Disaster Management Institute(NDMI) which supported timely decision-making through GIS spatial analysis with various spatial data and snow cover map.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.139-147
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• 2012

Mongolia's solar-meteorological resources map has been developed using satellite data and reanalysis data. Solar radiation was calculated using solar radiation model, in which the input data were satellite data from SRTM, TERA, AQUA, AURA and MTSAT-1R satellites and the reanalysis data from NCEP/NCAR. The calculated results are validated by the DSWRF (Downward Short-Wave Radiation Flux) from NCEP/NCAR reanalysis. Mongolia is composed of mountainous region in the western area and desert or semi-arid region in middle and southern parts of the country. South-central area comprises inside the continent with a clear day and less rainfall, and irradiation is higher than other regions on the same latitude. The western mountain region is reached a lot of solar energy due to high elevation but the area is covered with snow (high albedo) throughout the year. The snow cover is a cause of false detection from the cloud detection algorithm of satellite data. Eventually clearness index and solar radiation are underestimated. And southern region has high total precipitable water and aerosol optical depth, but high solar radiation reaches the surface as it is located on the relatively lower latitude. When calculated solar radiation is validated by DSWRF from NCEP/NCAR reanalysis, monthly mean solar radiation is 547.59 MJ which is approximately 2.89 MJ higher than DSWRF. The correlation coefficient between calculation and reanalysis data is 0.99 and the RMSE (Root Mean Square Error) is 6.17 MJ. It turned out to be highest correlation (r=0.94) in October, and lowest correlation (r=0.62) in March considering the error of cloud detection with melting and yellow sand.