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이가람 ( Ga Lam Lee ),김영섭 ( Young Seup Kim ),한경수 ( Kyoung Soo Han ),이창석 ( Chang Suk Lee ),염종민 ( Jong Min Yeom ) 한국지리정보학회 2008 한국지리정보학회지 Vol.11 No.4
Recently, global warming for climate system is a crucial issue over the world and it brings about severe climate change, abnormal temperature, a downpour, a drought, and so on. Especially, a drought over the earth surface accelerates desertification which has been advanced over the several years mainly originated from a climatic change. The objective of this study is to detect variation of vegetation water condition around China and Mongolia desert by using satellite data having advantage in observing surface biological system. In this study, we use SPOT/VEGETATION satellite image to calculate NDWI (Normalized Difference Water Index) around study area desert for monitoring of status of vegetation characteristics. The vegetation water status index from remotely sensing data is related to desertification since dry vegetation is apt to desertify. We can infer vegetation water status using NDWI acquired by NIR (Near infrared) and SWIR (Short wave infrared) bands from SPOT/VGT. The consequence is that NDWI decreased around desert from 1999 to 2006. The areas that NDWI was decreased are located in the northeast of Mongolian Gobi desert and the southeast of China Taklamakan desert.
이창석 ( Chang Suk Lee ),한경수 ( Kyung Soo Han ),염종민 ( Jong Min Yeom ),이가람 ( Ga Lam Lee ),송봉근 ( Bong Guen Song ) 한국지리정보학회 2008 한국지리정보학회지 Vol.11 No.4
In this study, we monitor ice cap using calculated NDSI from September to December in 2001, 2003, 2006, 2007 and snow cover area in 2007 decrease by compare with 2001. Global warming is one of the most important issue in this world. Because global-warming is the reason of various meteorological disasters and extreme weather events in these days and snow and glaciers showed that global warming effect most easily. Snow and glaciers play an important role in Earth cooling system because of their high reflectance. The present study has been carried out monitoring ice cap in Himalayas, using MODIS(Moderate Resolution Imaging Spectroradiometer)data. Indicator to monitoring ice cap, NDSI(Normalized Differenced Snow Index) was used in this study. The NDSI is a spectral band ratio that takes advantage of the spectral differences of snow in visible and short-wave infrared domain to detect snow cover area versus non-snow cover area in a scene. This study is quantitative evaluation about effect of global warming for icecap.
염종민 ( Jong Min Yeom ),한경수 ( Kyung Soo Han ),이가람 ( Ga Lam Lee ) 大韓遠隔探査學會 2009 大韓遠隔探査學會誌 Vol.25 No.2
적설은 지표 에너지수지를 결정하는 중요한 변수 중의 하나이다. 위성자료를 이용하여 지면 정보를 산출함에 있어서 적설과 구름을 구분하는 것은 매우 중요한 위성전처리 과정이다. 일반적으로 잘못된 적설과 구름의 분류는 위성자료를 이용한 지면 정보 산출에 있어서 직접적인 오차 요인이 된다. 따라서, 본 연구에서는 원격탐사 자료를 이용하여 적설 지역을 탐지하는 알고리즘에 대해서 연구하고자 한다. 적설역을 탐하지 하기 위해서, 가장 많이 사용되는 정규화 적설 지수(NDSI: Normalized Difference Snow Index)를 사용하지 않고 가시채널과 적외 채널을 이용한 방법을 제시하였다. COMS 기상영상기 (MI: Meteorological Imager) 채널에서는 정규적설 지수 산출 시 요구되는 근적외 채널을 탑재하지 않기 때문이다. 가시 채널을 이용한 적설 탐지는 구름이 혼재되어 있지 않은 지역에서는 잘 탐지하였으나 구름과 혼재되어 있는 지역에서는 어려움이 있다. 이러한 어려움을 보완하기 위해 적외채널 온도차 (11μm-3.7μm)를 이용하는 방법을 수행하였다. 온도차를 이용하는 방법은 가시채널만을 적용했을 때 보다는 향상된 탐지 능력을 보인다. 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.