• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.315-324
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• 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
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• v.28 no.2B
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• pp.177-185
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• 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.

• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.845-854
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• 2006

Establishment of snowmelt factors is necessary to simulate stream flow using snowmelt models during snowmelt periods. 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. The objective of this study was to extract snowmelt factors using RS, GIS technique and meteorological data. Snow cover maps were derived from NOAA/AVHRR images for the winter seasons from 1997 to 2003. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation station. Depletion curves of snowmelt area were described from the linear regression equations of each year between the average temperature and snow cover area in Soyanggang-dam and chungju-dam watershed.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.33-43
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• 2011

This study is to make snowfall distribution map for the 4 heavy snowfall events of January 2001, March of 2004, December of 2005 and January of 2010, and compare the results for three cases of construction methods. The cases are to generate the map by applying IDW(Inverse Distance Weighting) interpolation to 76 ground measured snowfall point data (Snow Depth Map; SDM), mask out the SDM with the MODIS snow cover area (MODIS SCA) of Terra MODIS (MODerate resolution Imaging Spectroradiometer) (SDM+MODIS SCA; SDM_M), and consider the snowdepth lapse rate of snowfall by elevation (Digital Elevation Model; DEM) to the second case (SDM_M+DEM; SDM_MD). By applying the MODIS SCA, the SCA of 4 events was 62.9%, 44.1%, 52.0%, and 69.0% for the area of South Korea. For the average snow depth, the SDM_M decreased 0.9cm, 1.9cm, 0.8cm, and 1.5cm compared to SDM and the SDM_MD increased 1.3cm, 0.9cm, 0.4cm, and 1.2cm respectively.

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

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.1-12
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• 2015

This study is to analyze the heavy snowfall vulnerable area of snow load design criteria for greenhouse, cattle shed and building using ground measured snow depth data and Terra MODIS snow cover area(SCA). To analyze the heavy snowfall vulnerable area, Terra MODIS satellite images for 12 years(2001-2012) were used to obtain the characteristics of snow depth and snow cover areas respectively. By comparing the snow load design criteria for greenhouse(cm), cattle shed($kg/m^2$), and building structure($kN/m^2$) with the snow depth distribution results by Terra MODIS satellite images, the facilities located in Jeolla-do, Chungcheong-do, and Gangwon-do areas were more vulnerable to exceed the current design criteria.

• KCID journal
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• v.18 no.2
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• pp.111-121
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• 2011

This study is to estimate the possible damage area of greenhouse by heavy snowfall event using terra MODIS snow cover area (SCA) and the ground measured snowfall data (GMSD). For the 4 heavy snowfall events of January 2001, March 2004, December 2005 and January 2010, the areas exceeding the design criteria of snowfall depth for greenhouse breaking were extracted by coupling the MODIS SCA and GMSD. The main damaged regions were estimated as Gangwon province in 2001, Chungbuk and part of Gyeongbuk province in 2004, Jeonbuk and Jeonnam province in 2005, and Gangwon and part of Gyeonggi province in 2010 respectively. Comparing with the investigated number of greenhouse damaged data, the estimated areas reflected the statistical data except 2001. The 2001 greenhouse damages were caused by the high wind speed (35.7m/sec) together with snowfall. The results of this study can be improved if the design criteria of wind speed is added.