• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.204-207
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• 2008

The purpose of this study is to identify how much the MODIS NDVI (Normalized Difference Vegetation Index) can explain the soil moisture simulated from SWAT (Soil and Water Assessment Tool) continuous hydrological model. For the application, ChungjuDam watershed (6,661.3 $km^2$) was adopted which covers land uses of 82.2 % forest, 10.3 % paddy field, and 1.8 % upland crop respectively. For the preparation of spatial soil moisture distribution, the SWAT model was calibrated and verified at two locations (watershed outlet and Yeongwol water level gauging station) of the watershed using daily streamflow data of 7 years (2000-2006). The average Nash and Sutcliffe model efficiencies for the verification at two locations were 0.83 and 0.91 respectively. The 16 days spatial correlation between MODIS NDVI and SWAT soil moisture were evaluated especially during the NDVI increasing periods for forest areas.

• Journal of Environmental Impact Assessment
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• v.10 no.3
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• pp.167-174
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• 2001

National Parks in Korea occupy about four percents of South Korean land. This paper aims to prove the potentiality of the application of remotely sensed data for the effective management of National Parks. Different satellite images such as Landsat TM, IRS-1C, Alternative image, and IKONOS image are analyzed for the detection of changes, the extraction of degraded areas, and the comparison of Normalized Difference Vegetation Index (NDVI) in Bukhansan National Park. The artificial structures such as buildings and paved areas are overvalued in relatively higher resolution data. The result showed that the choice of images should be determined according to specific purposes and the combination of different resolution data may be the solution for the effective management of National Park.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1466-1470
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• 2010

토양수분은 지표의 다양한 과정을 통제하는 중요한 수문학적 변수며 이에 신뢰할 수 있는 토양수분의 정보를 습득하는 것은 매우 중요하다. 그러나 정확한 토양수분의 실측자료는 그 설치비용과 인력부족으로 매우 빈약하여 이를 대체할 만한 정보를 획득하기 위한 연구 또한 부족하다. 요즘, 많은 수문모형의 개발로 토양 수분 또한 결과물로써 많이 이용된다. 그러나 모형에서 모의된 토양수분의 신뢰성을 판단할 때는 실측자료를 이용하는 것이 가장 이상적이나, 토양수분의 실측값이 부족하므로, 유역의 토양수분 실측자료 대신 모의된 토양수분을 적용할 필요가 있다. 이에 따라 본 연구에서는 우리나라 소양강댐 유역에 대하여 SWAT(Soil and Water Assessment Tool) 모형을 이용하여 실측 토양수분자료를 최대한 활용함으로써 토양수분을 모의하였다. 또한 모의된 토양수분을 Terra MODIS NDVI(Normalized Difference Vegetation Index)와 LST(Land Surface Temperature)과의 상관성을 계절별, 월별로 분석하여 그 관계식을 도출하고자 하였다.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.1
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• pp.15-24
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• 2004

The purpose of this study is to estimate monthly evapotranspiration (ET) using normalized difference vegetation index (NDVI) obtained from NOAA-AVHRR data sets. Actual evapotranspiration was evaluated by the complementary relationship, and monthly NDVI was obtained by maximum value composite method from daily NDVI images in the Korean peninsula for the year 2001 The monthly actual ETs for each land cover were compared with the monthly NDVIs to determine relationships between actual ET and NDVI for each land cover category, There was a high correlation between monthly NDVI and monthly mean actual ET. This study presents an alternative approach for land surface evapotranspiration based on remote sensing techniques.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.1
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• pp.19-24
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• 2010

The purpose of this study is to estimate monthly Morton evapotranspiration (ET) using normalized difference vegetation index (NDVI) from MODIS satellite images. Morton ET for land surface conditions was evaluated by using daily meteorological data, and the monthly averaged Morton ETs for each land cover were compared with the monthly NDVIs of three years (2000-2002) at Chungjudam Watershed. There was a high correlation between monthly NDVI and Morton ET for the watershed with average coefficient of determination, 0.80. By comparing the MODIS NDVI ET with SLURP Morton ET, the SLURP ET was smaller than the MODIS NDVI ET. This was estimated from the consideration of soil moisture condition for the ET occurrence in the SLURP model, the limited information from the monthly NDVI values, and the errors from the derived regression equations.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1033-1035
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• 2003

This study attempts to analyze the potential of global scale NDVI data archive to monitor regional scale droughts. Ten-days maximum value NDVI composite data of the northeast Asia region were acquired for the growing seasons from 1993 to 2003. Two NDVI-derived drought indices (SVI, VCI), reported from previous studies, were applied to the study area. Although the SVI and VCI are mainly developed for monitoring the drought condition at the agriculture crop and grasslands, it turned out that they were also effective to reveal the drought condition over the temperate mixed forest. The drought symptom lasts at least one or two months even after the normal raining begins.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.244-247
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• 2003

Multi-temporal data is used to determine the rate of deforestation between the years 1976, 1987 and 2000. Three Landsat TM images, for each period, are pre-processed, mosaicked and normalized difference vegetation index (NDVI) values computed. Based on the values, totally non-forested areas are masked out. The forested areas, both partially and wholly, show a very high degree of correlation between all the bands (reflective), thus necessitating application of principal component analysis. The first two principal components and NDVI values (scaled to 0 ? 255) are used in K-means unsupervised classification to distinguish forest from non-forest areas (that appeared as forest at first). Comparison of the resulting thematic maps gives an annual deforestation rate of roughly 15 0000ha. or 2% between any two epochs.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.670-672
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• 2003

The purpose of this study is to estimate monthly evapotranspiration (ET) using normalized difference vegetation index (NDVI) obtained from NOAA/AVHRR data sets. Actual evapotranspiration was evaluated by the complementary relationship (Morton, 1978, Brutsaert and Stricker, 1979), and monthly NDVI was obtained by maximum value composite method from daily NDVI images in the Korean peninsula for the year 2001. The monthly actual ETs for each land cover were compared with the monthly NDVIs to determine relationships between actual ET and NDVI for each land cover category. There was a high correlation between monthly NDVI and monthly averaged actual ET. This study presents an alternative approach for land surface evapotranspiration based on remote sensing techniques.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.381-392
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• 2003

Multi-temporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land-cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. A harmonic model that can represent seasonal variability is characterized by four components: mean level, frequency, phase and amplitude. The trigonometric components of the harmonic function inherently contain temporal information about changes in land-cover characteristics. Using the estimates which are obtained from sequential images through spectral analysis, seasonal periodicity can be incorporates into multi-temporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 ～ 2000 using a dynamic technique. Land-cover types were then classified both with the estimated harmonic components using an unsupervised classification approach based on a hierarchical clustering algorithm. The results of the classification using the harmonic components show that the new approach is potentially very effective for identifying land-cover types by the analysis of its multi-temporal behavior.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.140-140
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• 2019

본 연구에서는 Terra MODIS 위성자료와 Tensorflow를 활용해 1 km 공간 해상도의 토양수분을 산정하는 알고리즘을 개발하고, 국내 관측 자료를 활용해 검증하고자 한다. 토양수분 모의를 위한 입력 자료는 Terra MODIS NDVI(Normalized Difference Vegetation Index)와 LST(Land Surface Temperature), GPM(Global Precipitation Measurement) 강우 자료를 구축하고, 농촌진흥청에서 제공하는 1:25,000 정밀토양도를 기반으로 모의하였다. 여기서, LST와 GPM의 자료는 기상청의 종관기상관측지점의 LST, 강우 자료와 조건부합성(Conditional Merging, CM) 기법을 적용해 결측치를 보간하였고, 모든 위성 자료의 공간해상도를 1 km로 resampling하여 활용하였다. 토양수분 산정 기술은 인공 신경망(Artificial Neural Network) 모형의 딥 러닝(Deep Learning)을 적용, 기계 학습기반의 패턴학습을 사용하였다. 패턴학습에는 Python 라이브러리인 TensorFlow를 사용하였고 학습 자료로는 농촌진흥청 농업기상정보서비스에서 101개 지점의 토양수분 자료(2014 ~ 2016년)를 활용하고, 모의 결과는 2017 ~ 2018년까지의 자료로 검증하고자 한다.