• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.537-547
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• 2010

The importance of vegetation in studies of global climate and biogeochemical cycles is well recognized. Especially. the FPAR (fraction of photosynthetically active radiation) is one of the important parameters in ecosystem productivity and carbon budget models. Therefore, accurate estimates of vegetation parameters are increasingly important in environmental impact assessment studies. In this study, optical FPAR using the Terra MODIS (MODerate resolution Imaging Spectroradiometer), SPOT VEGETATION and ECOCLIMAP data reproduced on the Korean peninsula. We applied the empirical method which is usually estimated as a linear or nonlinear function of vegetation indices. As results, we estimated the accurate expression which is 0.9039 of $R^2$ in cropland and 0.7901 of $R^2$ in forest. Finally, this study could be demonstrated to calibrate that produced FPAR while the overall pattern and random noise through the comparative analysis of FPAR on the reference data. Optimal use of input parameter on the Korean peninsula should be helping the accuracy of output as well as the improved quality of research.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.403-409
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• 2003

Since SPOT4 satellite contained VEGETATION 1 sensor launched, the noise in VEGETATION data was occasionally arisen a difficulty for the data traitement. Blind line noise types were studied in VEGETATION-l short wave infrared channel(SWIR). In order to provide a precis product, the procedure for removing this noise is strongly recommended. In the case that the blind values are clearly distinguished from contamination-free values a simple threshold method was applied, while a changeable threshold method was used for the blind value mixed with contamination-free values. New algorithm presented in this study is consists of two method for each type of SWIR blind. After removing blind line, there were again some residual pixels of blind, because the threshold is not determinated sufficiently low. Lower threshold could remove the blind line as well as the contamination-free pixels. Nevertheless, the results showed a good qualitative improvement as compared with other algorithm.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.235-244
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• 2004

This study focuses on the assessment for proposed algorithm to discriminate cloudy pixels from snowy pixels through use of visible, near infrared, and short wave infrared channel data in VEGETATION-1 sensor embarked on SPOT-4 satellite. Traditional threshold algorithms for cloud and snow masks did not show very good accuracy. Instead of these independent masking procedures, K-Means clustering scheme is employed for cloud/snow discrimination in this study. The pixels used in clustering were selected through an integration of two threshold algorithms, which group ensemble the snow and cloud pixels. This may give a opportunity to simplify the clustering procedure and to improve the accuracy as compared with full image clustering. This paper also compared the results with threshold methods of snow cover and clouds, and assesses discrimination capability in VEGETATION channels. The quality of the cloud and snow mask even more improved when present algorithm is implemented. The discrimination errors were considerably reduced by 19.4% and 9.7% for cloud mask and snow mask as compared with traditional methods, respectively.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.101-104
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• 2009

기후 시스템에서 지구온난화는 세계적으로 매우 중요한 문제이고 이는 기후변화, 이상기온, 폭우, 가뭄 등의 문제를 초래한다. 특히 사막화는 전 세계적으로 10억 명 이상의 사람들에게 영향을 미치고 있다. 건조한 상태의 식생은 사막화되기 쉽기 때문에 식생의 수분상태는 사막화의 중요한 지표이다. 본 논문에서는 중국과 몽골 사막 주변영역에 대해 식생의 수분상태를 탐지하였다. 식생의 수분상태를 탐지하기 위해 1999년부터 2006년까지의 SPOT/VEGETATION 위성 이미지를 이용하여 정규화 수분지수(NDWI: Normalized Difference Water Index)를 산출하였다. 그 결과 1999년부터 2006년까지의 NDWI는 사막주변영역에서 감소하는 경향을 보였고, 그 영역은 몽골 고비사막 북동지역과 중국 타클라마칸 사막의 남동지역에 위치해 있었다.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.2
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• pp.28-37
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• 2008

In this study, we perform land surface monitoring of NDVI (Normalized Difference Vegetation Index) variation by using remote sensing data during 1999-2005 over North Korea, which can't easily access to measure directly land surface characteristics due to one of the world's most closed societies. North Korea forest region has most abundant forest vegetation - so called Lungs of Korea in the Korea peninsula. NDVI represents vegetation activity used in many similar studies. In this study, we detect vegetation variation and analysis factors of the change over North Korea. By using variation of NDVI, we can infer that effect of drought over North Korea, and reduced vegetation indices by typhoon in North Korea. Land surface type except barren ground with decreased NDVI value is considered as when North Korea region was suffering from drought and typhoon effects, which show lower than mean of 7-year NDVI value. Especially, in recently, the food production of North Korea with political and economical issues can be inferred indirectly these trends by using estimated output data from this study.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.94-100
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• 2008

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.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.175-184
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• 2009

This study attempts to analyze the difference among the variations of ecological distribution in Gobi desert and Manchuria through satellite based land cover classification. This was motivated by two well-known facts: 1) Gobi desert, which is an old source region, had been gradually expanded eastward; 2) Manchuria, which is located in east of Gobi desert, was observed as a new source region of yellow dust. An unsupervised classification called ISODATA clustering method was employed to detect the land cover change and to characterize the status of desertification and its expanding trends using NDVI (Normalized Difference Vegetation Index) derived from VEGETATION sensor onboard the SPOT satellite for 1999 and 2007. We analyzed NDVI annual variation pattern for every classes and divide into 5 level according to their vegetation's density level based on NDVI. As results, Gobi desert is showed positive variation: a decrease $78,066km^2$ in central Gobi desert and out skirts of Gobi desert (level-0) but Manchuria area is worse than previous time: an increase $25,744km^2$.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.214-217
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• 2006

전 지구영역에 대해 1km 의 해상도로 제공되는 새로운 개념으로 완성된 지표 변수데이터 베이스인 Ecoclimap-1이 소개 된 바 있다. 이것은 기상과 기후의 모델에서 토양과 식물과 대기 사이의 이동 모식을 설명하기 위해 고안 되었으며, 이러한 Ecoclimap-1은 토지피복지도와 기후기도를 중첩하여 얻어진 Ecosystem 지도를 기본 주제도로 사용한다. 하지만 중첩한 결과 같은 class 에 포함되더라도 내부 class 에서의 식생의 시간 변동성은 여전히 높게 나타난다. 이와 달리 Ecoclimap-2는 새로운 방법으로, SPOT/VEGETATION S10 NDVI 자료를 이용하여 토지피복을 보다 동질의 하위 집단으로 분류 한다. 본 연구 에서는 이러한 Ecoclimap-2의 일환으로 동북 아시아 영역의 생태기후지도를 제공하고자 한다. 이러한 Ecosystem map 은 앞으로의 기후 모델 연구에 있어 중요한 base map 으로 사용 되어질 것으로 보이며 또한 생태계모델, 기상 모델의 중요한 input 자료로서 쓰여 질 것으로 사료 된다.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.18-20
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• 2003

Land cover change detection is one of the most important trends in which remote sensing data could be used to assist strategists and the planners to decide the best land use policy. Two images of NOAA-AVHRR and SPOT vegetation acquired in November 1992 and 2002 were used to assess the changes of Agricultural lands in Egypt. A supervised classification together with two change images derived from classification result and NDVI were used to evaluate the trend and form of the change. It was found that agricultural areas increased by about 14.3 % during the study period in particular around the River Nile Delta and near the Northern Lakes of Egypt. The new cultivated lands were extracted mainly from the desert and from the salt marches areas. At the same time, parts of the agricultural lands were turned into non-cultivated land because of the urban expansion and soil degradation.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.582-584
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• 2003

This paper explores two such indexes----the Aerosol Free Vegetation Index (AFRI) and the Atmospherically Resistant Vegetation Index (ARVI). Comparisons were made with the NDVI (normalized vegetation index) to see if they indeed performed better. In general, the results showed that the AFRI and ARVI (with gamma=1) did indeed perform better than their NDVI counterpart study with the related channels were employed.