• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 2005.10a
• /
• pp.254-259
• /
• 2005

The shape of a streamflow hydrograph is very much controlled by the area and depth of snow cover in mountain area. The purpose of this study is to suggest extraction methods for snow cover area and depth using NOAA/AVHRR images in Soyanggang watershed. Snow cover area maps ware derived form channel 1, 3, 4 images of NOAA/AVHRR based on threshold value. In order to extract snow cover depth, snow cover area maps were overlaid daily snow depth data form 7 meteorological observation stations. Snow cover area and depth was mapped for period of Dec. 2002 and Mar. 2003. For evaluating snowmelt changes, depletion curve was created using daily snow cover area in the same period. It is necessary to compare these results with observed data and check the applicability of the suggested method in snowmelt simulation.

• KCID journal
• /
• v.14 no.2
• /
• pp.225-235
• /
• 2007

The shape of streamflow hydrograph during the early period of spring is very much controlled by the area and depth of snow cover especially in mountainous area. When we simulate the streamfolw of a watershed snowmelt, we need some information for snow cover extent and depth distribution as parameters and input data in the hydrological models. The purpose of this study is to suggest an extraction method of snow cover area and snow depth distribution using Terra MODIS image. Snow cover extent for South Korea was extracted for the period of December 2000 and April 2006. For the snow cover area, the snow depth was interpolated using the snow depth data from 69 meteorological observation stations. With these data, it is necessary to run a hydrological model considering the snow-related data and compare the simulated streamflow with the observed data and check the applicability for the snowmelt simulation.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_1
• /
• pp.1025-1032
• /
• 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.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1289-1291
• /
• 2003

Climatic characteristic and changes of snow cover over the Northern Hemisphere, Eurasian, North America and the Tibet Plateau are studied based on data of the Northern Hemisphere snow cover area observed by Satellite remote sensing from 1966 to 2003. Meanwhile, the seasons' distribution charts of the Asian snow cover are made, they provide the climatic background of snow cover. In addition, the influences of snow cover on the climate, especially on the monsoon are analysed briefly, and some significance results are concluded.

• Korean Journal of Remote Sensing
• /
• v.23 no.2
• /
• pp.119-124
• /
• 2007

The few observed data related snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) building for 5 major watersheds in South Korea. Especially SDC is important parameter of snowmelt model.

• Korean Journal of Remote Sensing
• /
• v.30 no.3
• /
• pp.341-350
• /
• 2014

Landsat images can monitor the snow-covered Earth surface variations with the ground resolution of 30m and the multi-spectral bands in the visible, NIR, SWIR and TIR spectral regions for the last 30 years. The Southern Volcanic Zone (SVZ) of Chile consists of many volcanoes, and all of the volcanoes are covered with snow at the top of mountain. Snow cover area in southern province of the SVZ of Chile (37 to $46^{\circ}S$) have been influenced by significant frontal retreats as well as eruptive activities. In this study, we have investigated the changes of the snow-cover area and snow-line elevation at Mt. Villarrica and Mt. Llaima, Chile from three Landsat images acquired on Feb. 1990, 2005 and 2011. The snow-cover areas are 13.42, 26.75 and $21.60km^2$ at Mt. Villarrica in 1990, 2005 and 2011, respectively, and 3.82, 25.12 and $8.89km^2$ at Mt. Llaima in 1990, 2005 and 2011, respectively. The snow-line elevations are 1871, 1738 and 1826m at Mt. Villarrica in 1990, 2005 and 2011, respectively, and 2007, 1822 and 1818m at Mt. Llaima in 1990, 2005 and 2011, respectively. The results indicate that both of the snow-cover and snow-line changes are strongly related with the volcanic activity change. The results demonstrate that the snow-cover area and snow-line elevation changes can be used as an indicator of the volcanic activity at Mt. Villarrica and Mt. Llaima, Chile.

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

• Korean Journal of Remote Sensing
• /
• v.29 no.3
• /
• pp.315-324
• /
• 2013

Spatial information of snow cover and depth distribution is a key component for snowmelt runoff modeling. Wide snow cover areas can be extracted from NOAA AVHRR or Terra MODIS satellite images. In this study eight sets of annual snow cover data (1997-2006) in two mountainous watersheds (A: Chungju-Dam and B: Soyanggang-Dam) were extracted using NOAA AVHRR images. The distribution of snow depth within the Snow Cover Area (SCA) was generated using snowfall data from ground meteorological observation stations. Snow depletion characteristics for the two watersheds were analyzed snow distribution time series data. The decreased pattern of SCA can be expressed as a logarithmic function; the determination coefficients were 0.62 and 0.68 for the A and B watersheds, respectively. The SCA decreased over 70% within 10 days from the time of maximum SCA.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.2B
• /
• pp.177-185
• /
• 2008

Accurate monitoring of snow cover is a key component for studying climate and global as well as for daily weather forecasting and snowmelt runoff modelling. The few observed data related to snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) were built for 7 major watersheds in South Korea. The decrease pattern of SCA for time (day) was expressed as exponentially decay function, and the determination coefficient was ranged from 0.46 to 0.88. The SCA decreased 70% to 100% from the maximum SCA when 10 days passed.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.75-77
• /
• 2003

The spatial distribution of snow cover area is a crucial input to models of hydrology and climate in alpine and other seasonally snow covered areas.The objective in our study is to develop a rapidly automatic and high accuracy snow cover mapping algorithm applicable for the Tibetan Plateau which is the most sensitive about climatic change. Monitoring regional snow extent reqires higher temoral frequency-moderate spatial resolution imagery.Our algorithm is based AVHRR and MODIS data and will provide long-term fraction snow cover area map.We present here a technique is based on the multiple endmembers approach and by taking advantages of current approaches, we developed a technique for automatic selection of local reference spectral endmembers.