• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.201-203
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• 2003

Two MODIS instruments on board the Terra and Aqua Satellites are operational for global remote sensing of the land, ocean and atmosphere. Atmospheric sounding data with a high spatial resolution from MODIS will provide a wealth of useful information. The vertical air temperature and moisture data were retrieved using the MODIS data, and compared with the radiosonde data obtained in the Korean Peninsula. The correlation coefficient are 0.99 and 0.89 for air temperature and moisture cases, respectively. Air temperature data were relatively good agreement, but the moisture data from MODIS were underestimated.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.410-414
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• 2004

The MODIS Land Rapid Response System (RRS) has been developed to provide rapid access to MODIS data globally, with initial emphasis on 250 m color composite imagery and active fire data. Fire detection is based on a contextual algorithm that exploits the strong emission of mid-infrared radiation from fires. This algorithm examines each pixel of the MODIS swath, and ultimately assigns to each one of the following classes: missing data, cloud, water, non-fire, fire, or unknown. In this paper, we introduce the MODIS Rapid Response System established at the Korea Aerospace Research Institute (KARI) and present some application results for Korea using the direct broadcast data acquired at KARI ground station.

• Journal of Environmental Impact Assessment
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• v.20 no.2
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• pp.199-205
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• 2011

The Saemangeum embankment construction have changed the flowing on the topography of the coastal marine environment. However, the variety of ecological factors are changing from outside of Saemangeum embankment area. The ecosystem of various marine organisms have led to changes by sea surface temperature. The aim of this study is to monitoring of sea surface temperature(SST) changes were measured by using thermal infrared satellite imagery, MODIS and Landsat. The MODIS data have the high temporal resolution and Landsat satellite data with high spatial resolution was used for time series monitoring. The extracted informations from sea surface temperature changes were compared with the dyke to allow them inside and outside of Saemangeum embankment. The spatial extent of the spread of sea water were analyzed by SST using MODIS and Landsat thermal channel data. The difference of sea surface temperature between inland and offshore waters of Saemangeum embankment have changed by seasonal flow and residence time of sea water in dyke.

• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.104-104
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• 2010

지난 2008년 가을부터 시작되어 2009년 봄까지 발생했던 전국적인 극한 가뭄을 계기로 가뭄모니터링의 필요성은 증대되었다. 본 연구는 우리나라에서 가뭄 모니터링을 위한 MODIS 위성영상 자료의 활용을 제안하였다. MODIS 영상은 임의의 지역의 시 공간적 특성을 관찰할 수 있는 해상도를 보유하고 있으며, MODIS에서 제공하는 MOD11(LST: Land Surface Temperature)은 가뭄 발생의 판별에는 유효하나 가뭄 심도와 지속기간을 판단하기 위해서는 기준이 되는 강우량 및 가뭄지수와의 비교가 필요하다고 알려져 있다. 본 연구에서는 MOD11(LST) 위성자료와 EDI(Effective Drought Index) 가뭄지수의 상관성을 고려하여 한반도 가뭄모니터링을 위한 MODIS 위성영상의 활용성을 평가하였다.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.151-154
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• 2009

MODIS Aqua에 의한 한반도 주변 해역의 관측은 엽록소의 변동을 관측하여 해양환경변화를 관측하는데 주로 연구되었다. 그러나 황해는 Case II 해역으로 해양의 엽록소 관측에 오차가 크기 때문에 동해와 남해, 동중국해역을 관측한 연구가 발표되어 왔다. 특히 적조와 같은 이상변동을 관측하는데 해색자료의 활용은 매우 활발한 연구가 이루어졌다. 하지만 K490(Diffuse attenuation coefficient for downwelling irradiance at 490nm)은 OCTS, SeaWiFS, MODIS, MERIS 등의 해색자료에 의해 많은 자료가 생산되고 있으나 이를 이용한 한반도 주변 해역의 해양환경변동에 관한 연구는 부족한 실정이다. 본 연구에서는 2008년 한반도 주변해역에서 관측된 MODIS 자료를 처리하여 K490에 의한 시-공간적 변화를 분석하였다. 특히 K490에 의한 월 변화(monthly)와 통계처리 결과를 분석하여 기후변화의 주요 분석인자인 해양의 일차생산력을 판단하는데 중요한 영향을 미치는 K490의 공간 특성을 제시하였다. 한반도 주변 해역의 K490에 의한 시-공간적 특성은 육상의 주요 하천에서 기인하는 부유사와 서남해역의 재부유 물질에 의한 영향이 시-공간적으로 뚜렷하게 나타나는 것을 파악할 수 있었다.

• 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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• 2002.10a
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• pp.679-684
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• 2002

MODIS LAI has been one of key products for analyzing the quantitative aspects ofterrestrial ecology. This study was designed to validate MODIS LAS product by using the reference LAI map that was derived from both ground measurements and ETM＋ surface reflectance. The study area, the Kwangneung Experiment Forest in Korea, covers mixed species of deciduous and conifers of temperate forest. Throughout the growing season of 2002, we have measured LAS at the 30 sample plots using an optical device. Ground measured LAS data were then correlated to the ETM＋ reflectance to produce a continuous map of LAS over the study area. Preliminary comparison between MODIS LAI and the reference LAI map showed the lower LAI values from the MODIS product.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1039-1041
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• 2003

Ocean color products derived from remote sensing satellite data are useful for monitoring the sea water quality such as the concentrations of chlorophyll, sediments and dissolved organic matter. Currently, ocean color products derived from MODIS data can be requested from NASA over the internet. However, due to the bandwidth limitation of most users in this region, and the time delay in data delivery, the products cannot be use for near-real time monitoring of sea water chlorophyll. CRISP operates a MODIS data receiving station for environmental monitoring purposes. MODIS data have been routinely received and processed to level 1B. We have adapted the higher level processing algorithms from the Institutional Algorithms provided by NASA to run in a standalone environment. The implemented algorithms include the MODIS ocean color algorithms. Seasonal chlorophyll concentration composite can be compiled for the region. By comparing the near-real time chlorophyll product with the seasonal composite, anomaly in chlorophyll concentration can be detected.

• Atmosphere
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• v.20 no.4
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• pp.415-426
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• 2010

A Land cover map over East Asian region (Kongju national university Land Cover map: KLC) is classified by using support vector machine (SVM) and evaluated with ground truth data. The basic input data are the recent three years (2006-2008) of MODIS (MODerate Imaging Spectriradiometer) NDVI (normalized difference vegetation index) data. The spatial resolution and temporal frequency of MODIS NDVI are 1km and 16 days, respectively. To minimize the number of cloud contaminated pixels in the MODIS NDVI data, the maximum value composite is applied to the 16 days data. And correction of cloud contaminated pixels based on the spatiotemporal continuity assumption are applied to the monthly NDVI data. To reduce the dataset and improve the classification quality, 9 phenological data, such as, NDVI maximum, amplitude, average, and others, derived from the corrected monthly NDVI data. The 3 types of land cover maps (International Geosphere Biosphere Programme: IGBP, University of Maryland: UMd, and MODIS) were used to build up a "quasi" ground truth data set, which were composed of pixels where the three land cover maps classified as the same land cover type. The classification results show that the fractions of broadleaf trees and grasslands are greater, but those of the croplands and needleleaf trees are smaller compared to those of the IGBP or UMd. The validation results using in-situ observation database show that the percentages of pixels in agreement with the observations are 80%, 77%, 63%, 57% in MODIS, KLC, IGBP, UMd land cover data, respectively. The significant differences in land cover types among the MODIS, IGBP, UMd and KLC are mainly occurred at the southern China and Manchuria, where most of pixels are contaminated by cloud and snow during summer and winter, respectively. It shows that the quality of raw data is one of the most important factors in land cover classification.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.6
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• pp.1-12
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• 2006

Remote sensing cannot provide a direct measurement of vegetation index (VI) but it can provide a reasonably good estimate of vegetation index, defined as the ratio of satellite bands. The monitoring of vegetation in nearby urban regions is made difficult by the low spatial resolution and temporal resolution image captures. In this study, enhancing spatial resolution method is adapted as to improve a low spatial resolution. Recent studies have successfully estimated normalized difference vegetation index (NDVI) using improved resolution method such as from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra satellite. Image enhancing spatial resolution is an important tool in remote sensing, as many Earth observation satellites provide both high-resolution and low-resolution multi-spectral images. Examples of enhancement of a MODIS multi-spectral image and a MODIS NDVI image of Cheongju using a Landsat TM high-resolution multi-spectral image are presented. The results are compared with that of the IHS technique is presented for enhancing spatial resolution of multi-spectral bands using a higher resolution data set. To provide a continuous monitoring capability for NDVI, in situ measurements of NDVI from paddy field was carried out in 2004 for comparison with remotely sensed MODIS data. We compare and discuss NDVI estimates from MODIS sensors and in-situ spectroradiometer data over Ochang plain region. These results indicate that the MODIS NDVI is underestimated by approximately 50%.