• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.382-385
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• 2008

MODIS images have a great advantage of high temporal resolution to monitor land cover changes in a large area. The moderate and low spatial resolution satellite images are incomparably economic than high resolution satellite images. As diverse satellite images are provided recently, strategies using satellite images are necessary for continuous, effective and long-term land monitoring. This research purposed to use MODIS images to monitor land cover in Korean peninsula for long-term and continuous change detection. To maximize the advantages of high temporal resolution, the change detection was based on the MODIS temporal profiles of the surface reflectance for one year. In this study as the reflectance patterns of year 2005 were compared with the reflectance patterns of year 2007, the changed pixels could be detected during two years. To set up the threshold value for the decision of change, ASTER images with the higher spatial resolution, 15m, were used for this study. The test area covered the suburban area of metropolitan city, Seoul, where the landcover changes have been frequently happened.

• 한국산학기술학회논문지
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• 제14권11호
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• pp.5417-5422
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• 2013

본 논문은 MODIS 위성 영상을 이용하여 논벼 생산량을 추정하는 모형의 적합도 개선 및 추정모형내 적절한 설명변수를 탐색하고자 수행되었다. 또한 이 연구는 한국에서 논벼 생산량 조사를 위해 위성 영상을 사용하는 방안을 검토하기 위해 수행되었다. 미국, 호주, 일본 등 많은 선진국들은 재배면적 및 생산량 조사와 같은 농업통계를 산출하기 위해 위성 영상을 이용하고 있다. 그러나 위성 영상을 이용한 작물 생산량 조사의 정확성은 아직 충분치 않은 수준이다. 본 연구는 위성 영상을 이용한 논벼 생산량 조사의 정확도를 증대시키기 위한 몇 가지 방법을 검토하고 있다. 많은 작물 중 논벼를 연구대상으로 선정한 이유는 논벼가 다른 작물 보다 재배면적과 작황의 영상 분석이 용이하였기 때문이고, 다양한 위성 영상 중 MODIS 영상을 이용한 것은 한국 논벼 생산량 조사 연구를 위해 보다 적절한 영상을 다수 포함하고 있었기 때문이다. 이 연구에서 등온선에 의해 구분된 논벼로부터 도출된 NDVI지수, 논벼 등숙기의 일조시간, 강우량, 온도 등 기상변수를 이용하여 단수함수가 추정되었다. 단수함수 추정결과, 모형의 적합도(R-squared)는 0.768-0.891를 보였다. 이 연구는 연평균 등온선에 의해 구분된 NDVI지수와 (등숙기) 기상변수가 단수함수 추정에 매우 유용하게 이용될 수 있음을 보이고 있다.

• 대한원격탐사학회지
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• 제23권3호
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• pp.161-169
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• 2007

A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images, $R^2$= 0.82 for SS and $R^2$ = 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.

• 한국농공학회논문집
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• 제49권5호
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• pp.67-77
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• 2007

The images of MODerate resolution Imaging Spectroradiometer (MODIS) that provide wider swath and shorter revisit frequency than Land Satellite (Landsat) and Satellite Pour I' Observation de la Terre (SPOT) has been used fer land cover classification with better spatial resolution than National Oceanic and Atmosphere Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR)'s images. Due to the advantages of MODIS, several researches have conducted, however the results for the land cover classification using MODIS images have less accuracy of classification in small areas because of low spatial resolution. In this study, uncertainty of paddy fields classification using MODIS images was conducted in the region of Gyeonggi-do and the relation between this uncertainty of estimating paddy fields and topographical factors was also explained. The accuracy of classified paddy fields was compared with the land cover map of Environmental Geographic Information System (EGIS) in 2001 classified using Landsat images. Uncertainty of paddy fields classification was analyzed about the elevation and slope from the 30m resolution Digital Elevation Model (DEM) provided in EGIS. As a result of paddy classification, user's accuracy was about 41.5% and producer's accuracy was 57.6%. About 59% extracted paddy fields represented over 50 uncertainty in one hundred scale and about 18% extracted paddy fields showed 100 uncertainty. It is considered that several land covers mixed in a MODIS pixel influenced on extracted results and most classified paddy fields were distributed through elevation I, II and slope A region.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.301-304
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• 2008

Since longer wavelength microwave radiation can penetrate clouds, satellite passive microwave sensors can observe sea ice of the entire polar region on a daily basis. Thus, it is becoming popular to derive sea ice motion vectors from a pair of satellite passive microwave sensor images observed at one or few day interval. Usually, the accuracies of derived vectors are validated by comparing with the position data of drifting buoys. However, the number of buoys for validation is always quite limited compared to a large number of vectors derived from satellite images. In this study, the sea ice motion vectors automatically derived from pairs of AMSR-E 89GHz images (IFOV = 3.5 ${\times}$ 5.9km) by an image-to-image cross correlation were validated by comparing with sea ice motion vectors manually derived from pairs of cloudless MODIS images (IFOV=250 ${\times}$ 250m). Since AMSR-E and MODIS are both on the same Aqua satellite of NASA, the observation time of both sensors are the same. The relative errors of AMSR-E vectors against MODIS vectors were calculated. The accuracy validation has been conducted for 5 scenes. If we accept relative error of less than 30% as correct vectors, 75% to 92% of AMSR-E vectors derived from one scene were correct. On the other hand, the percentage of correct sea ice vectors derived from a pair of SSM/I 85GHz images (IFOV = 15 ${\times}$ 13km) observed nearly simultaneously with one of the AMSR-E images was 46%. The difference of the accuracy between AMSR-E and SSM/I is reflecting the difference of IFOV. The accuracies of H and V polarization were different from scene to scene, which may reflect the difference of sea ice distributions and their snow cover of each scene.

• 대한원격탐사학회지
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• 제38권6_1호
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• pp.1035-1046
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• 2022

이 연구에서는 토지 피복 분류를 위한 다중 해상도 자료 융합의 적용성을 평가하였다. 여기서 다중 해상도 자료 융합 모델로는 spatial time-series geostatistical deconvolution/fusion model (STGDFM)을 적용하였다. 연구 지역은 미국 Iowa 주의 일부 농경 지역으로 선정하였으며, 대상 지역의 규모를 고려해 다중 해상도 자료 융합의 입력 자료로 Moderate Resolution Imaging Spectroradiometer (MODIS) 및 Landsat 영상을 사용하였다. 이를 바탕으로 STGDFM 적용해 Landsat 영상이 결측된 시기에서 가상의 Landsat 영상을 생성하였다. 그리고 획득한 Landsat 영상과 함께 STGDFM의 융합 결과를 입력 자료로 사용해 토지 피복 분류를 수행하였다. 특히 다중 해상도 자료 융합의 적용성 평가를 위해 획득한 Landsat 영상만을 이용한 분류 결과와 Landsat 영상 및 융합 결과를 모두 이용한 분류 결과를 비교 평가하였다. 그 결과, Landsat 영상만을 이용한 분류 결과에서는 대상 지역의 주요 토지 피복인 옥수수와 콩 재배지에서 혼재 양상이 두드러지게 나타났다. 또한 건초 및 곡물 지역과 초지 지역 등 식생 피복 간의 혼재 양상도 큰 것으로 나타났다. 반면 Landsat 영상 및 융합 결과를 이용한 분류 결과에서는 옥수수와 콩 재배지의 혼재 양상과 식생 피복 간의 혼재 양상이 크게 완화되었다. 이러한 영향으로 Landsat 영상 및 융합 결과를 이용한 분류 결과에서 분류 정확도가 약 20%p 향상되었다. 이는 STGDFM을 통해 MODIS 영상이 갖는 시계열 분광 정보를 융합 결과에 반영하면서 Landsat 영상의 결측을 보완할 수 있었고, 이러한 시계열 분광 정보가 분류 과정에 결합되면서 오분류를 크게 줄일 수 있었던 것으로 판단된다. 본 연구 결과를 통해 토지 피복 분류에 다중 해상도 자료 융합이 효과적으로 적용될 수 있음을 확인하였다.

• 대한원격탐사학회지
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• 제27권3호
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• pp.259-276
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• 2011

Short-term land cover changes, such as forest fire scar and crop harvesting, can be detected by high temporal resolution satellite imagery like MODIS and AVHRR. Because these optical satellite images are often obscured by clouds, the static cloud-free composite methods (maximum NDVI, minblue, minVZA, etc.) has been used based on non-overlapping composite period (8-day, 16-day, or a month). Due to relatively long time lag between successive images, these methods are not suitable for observing short-term land cover changes in near-real time. In this study, we suggested a new dynamic cloud-free composite algorithm that uses cut-and-patch method of cloud-masked daily MODIS data using MOD35 products. Because this dynamic composite algorithm generates daily cloud-free MODIS images with the most recent information, it can be used to monitor short-term land cover changes in near-real time. The dynamic composite algorithm also provides information on the date of each pixel used in compositing, thereby makes accurately identify the date of short-term event.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.61-64
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• 2008

Seasonal distribution of the oceanic water intrusion was investigated using satellite SST (sea surface temperature) and chl-a (chlorophyll-a) images taken by the MODIS Aqua sensor. The warm water mass emanating periodically from the meandering Kuroshio Current brings the oceanic water intrusion, known as the 'Kyucho' phenomenon, into Kagoshima bay during the winter. Satellite SST images and buoy robot data show that this warm water intrusion has the characteristics of a semigeostrophic gravity current influenced by the Coriolis effect. However, it is difficult to find the oceanic water intrusion during the summer season considering that it is accompanied by thermal stratification, and SST shows almost the same temperature between the inner side of the bay and the ocean. In this research, the satellite chl-a images taken by MODIS Aqua were employed instead of SST images to reveal the oceanic water intrusion in each season. The enclosed bay has the tendency to undergo eutrophication caused by organic materials from land and differences in chl-a concentration of the bay water and the oceanic water. As a result, distribution of low concentration chl-a with oceanic water intrusion in summer season shows almost the same pattern in winter season. On the other hand, in spring season, both SST and chl-a images are available to differentiate the oceanic water intrusion. Therefore, applying the suitable satellite sensor images for each season is effective in the monitoring of oceanic water intrusion. Moreover, in this area, SST and chl-a distribution reveal not only the oceanic water intrusion into Kagoshima bay but also the intrusion at Fukiage seashore facing East China Sea.

• 대한원격탐사학회지
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• 제28권3호
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• pp.287-296
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• 2012

Although low spatial resolution satellite images like MODIS and GOCI can be important to observe land surface, it is often difficult to visually interpret the imagery because of the low contrast by prevailing cloud covers. We proposed a simple and adaptive stretching algorithm to enhance image contrast over land areas in cloudy images. The proposed method is basically a linear algorithm that stretches only non-cloud pixels. The adaptive linear stretch method uses two values: the low limit (L) from image statistics and upper limit (U) from low boundary value of cloud pixels. The cloud pixel value was automatically determined by pre-developed empirical function for each spectral band. We used MODIS and GOCI images having various types of cloud distributions and coverage. The adaptive contrast stretching method was evaluated by both visual interpretation and statistical distribution of displayed brightness values.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.47-51
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• 2005

Using satellite images, an optimal solution to water motion has been presented in this study. Since temperature patterns are suitable tracers in water motion, Sea Surface Temperature (SST) images of Caspian Sea taken by MODIS sensor on board Terra satellite have been used in this study. Two daily SST images with 24 hours time interval are used as input data. Computation of templates correspondence between pairs of images is crucial within motion algorithms using non-rigid body objects. Image matching methods have been applied to estimate water body motion within the two SST images. The least squares matching technique, as a flexible technique for most data matching problems, offers an optimal spatial solution for the motion estimation. The algorithm allows for simultaneous local radiometric correction and local geometrical image orientation estimation. Actually, the correspondence between the two image templates is modeled both geometrically and radiometrically. Geometric component of the model includes six geometric transformation parameters and radiometric component of the model includes two radiometric transformation parameters. Using the algorithm, the parameters are automatically corrected, optimized and assessed iteratively by the least squares algorithm. The method used in this study, has presented more efficient and robust solution compared to the traditional motion estimation schemes.