• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1231-1244
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• 2022

The frequency of exposure of field crops to stress situations is increasing due to abnormal weather conditions. In South Korea, large-scale diseases in representative paddy rice cultivation area were happened. There are limits to field investigation on the crop damage due to large-scale. Satellite-based remote sensing techniques are useful for monitoring crops in cities and counties, but the sensitivity of vegetation index measured from satellite under abnormal growth of crop should be evaluated. The goal is to evaluate satellite-based normalized difference vegetation index (NDVI) retrieved from different spatial scales using drone imagery. In this study, Sentinel-2 and Landsat-8 satellites were used and they have spatial resolution of 10 and 30 m. Drone-based NDVI, which was resampled to the scale of satellite data, had correlation of 0.867-0.940 with Sentinel-2 NDVI and of 0.813-0.934 with Landsat-8 NDVI. When the effects of bias were minimized, Sentinel-2 NDVI had a normalized root mean square error of 0.2 to 2.8% less than that of the drone NDVI compared to Landsat-8 NDVI. In addition, Sentinel-2 NDVI had the constant error values regardless of diseases damage. On the other hand, Landsat-8 NDVI had different error values depending on degree of diseases. Considering the large error at the boundary of agricultural field, high spatial resolution data is more effective in monitoring crops.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.132-137
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• 2005

재해분야에 인공위성의 활용도가 높아짐에 따라 본 연구에서는 Landsat 영상과 RADARSAT 영상을 이용하여 안성천유역을 대상으로 침수지역을 추출하고자 하였다. Landsat 영상은 침수 전과 후의 영상을 각각 선정하였으며 RADARSAT 영상은 침수 중과 침수 후 의 영상을 선정하였다. 각 영상에 대하여 전처리와 기하보정을 걸친 후 침수지역을 파악하기 위한 방법으로 토지피복분류 방법을 사용하였고, 그 중 Landsat 영상은 분광반사계를 이용하여 감독분류를 실시하였고, RADARSAT 영상은 무감독 분류를 실시하여 침수 지역을 확인할 수 있었다.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.15-23
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• 2004

In order to utilize remote sensed images effectively, it is necessary to correct geometric distortion. Geometric correction is a critical step to remove geometric distortions in satellite images. For geometric correction, Ground Control Points (GCPs) have to be chosen carefully to guarantee the quality of geocoded satellite images, digital maps, GPS surveying or other data. Traditional approach to geometric correction used GCPs requires substantial human operations. Also that is necessary much time and manpower. In this paper, we presented an on-line automatic geometric correction by constructing GCP Chip database. The Proposed on-line automatic geometric correction system is consists of four part. Input image, control the GCP Chip, revision of selected GCP, and output setting part. In conclusion, developed system reduced the processing time and energy for tedious manual geometric correction and promoted usage of Landsat imagery.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1085-1095
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• 2023

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.

• Journal of the Korean GEO-environmental Society
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• v.6 no.4
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• pp.73-80
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• 2005

The domestic Remote Sensing field uses mainly Landsat TM image that is used to the monitoring of the wide area. In this study, it is analyzed the land cover change of rural and urban area by time series using satellite images and is proposed the vision for a urban balanced development. It execute an analysis for urban change which is a fundamental data of city planning through the integration of the spatial analysis technique of GIS and Remote Sensing using satellite data.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.723-728
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• 2016

This study is analyzed the optical property of red tide pixel by using Landsat-7 ETM+, Landsat-8 OLI and COMS/GOCI image. In order to sample red tide pixel, Landsat-7, 8 true color image were used and obtained coordinate of red tide pixel in the true color image. Normalized water leaving radiance(nLw) and absorption coefficient were obtained from GOCI image in the same coordinate of the true color image. When red tide was not occurred the main absorption range was 412nm and 660nm but when red tide occurred it was 660nm and absorption coefficient in 412nm are drastically reduced. It made no difference of nLw spectrum between red tide pixel and non red tide pixel in nLw, but the absolute value of nLw was low than non red tide pixel, especially 660nm and 680nm wavelength sharply decrease.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.2 s.36
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• pp.63-68
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• 2006

Although it is more recommended to use satellite images for an accurate understanding of the natural environment over a large area, what should proceed obtaining such satellite images is to make basic model equations based firmly upon the on-land experiments and field experiments. It may be more accurate and objective to investigate correlations between satellite images and actual water quality factors obtained for the same area. Thus, this study was conducted in order to determined which band of Landsat ETM+ images is appropriate to estimate the density of chlorophyll-a in a closed laboratory without atmospheric interference, using pure water and sea water. As a result of this study, it was found that the best band that exhibited the highest degree of correlations among the compounded bands rated (B3-B4)/B2 in pure water and (B2+B4)/B3 in sea water. The correlation coefficient here is 0.9747 and 0.9892 respectively. Thus, compounding this band ran be quite useful for estimation density of Chlorophyll-a using Landsat ETM+ image data.

• Korean Journal of Environment and Ecology
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• v.18 no.2
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• pp.231-235
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• 2004

Landsat satellite images were analyzed to study vegetation change patterns of warm-temperate forests from 1991 to 2002 in Wando. For this purpose, Landsat TM satellite image of 1991 and Landsat ETM image of 2002 were used for vegetation classification using ENVI image processing software. Four different forest types were set as a classification criteria; evergreen broadleaf, evergreen conifer, deciduous broadleaf, and others. Unsupervised classification method was applied to classily forest types. Although it was impossible to draw exact forest types in rocky areas because of differences in data detection time and rough resolution of image, 2002 data revealed that total 2,027ha of evergreen broadleaf forests were growing in Wando. Evergreen broadleaves and evergreen conifers increased in total areas compared to 11 years ago, but there was sharp decrease in deciduous broadleaves. GIS-based management system for warm-temperate forest was done using Arc/Info. Geographic and attribute database of Wando such as vegetation, soils, topography, land owners were built with Arc/Info and ArcView. Graphic user interface which manages and queries necessary data was developed using Avenue.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.53-61
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• 2014

Landsat OLI satellite imagery can be applied to various remote sensing applications, such as generation of land cover map, urban area analysis, extraction of vegetation index and change detection, because it includes various multispectral bands. In addition, land cover map is an important information to monitor and analyze land cover using GIS. In this paper, land cover map is generated by using Landsat OLI and existing land cover map. First, training dataset is obtained using correlation between existing land cover map and unsupervised classification result by K-means, automatically. And then, spectral signatures corresponding to each class are determined based on training data. Finally, abundance map and land cover map are generated by using iterative spectral mixture model. The experiment is accomplished by Landsat OLI of Cheongju area. It shows that result by our method can produce land cover map without manual training dataset, compared to existing land cover map and result by supervised classification result by SVM, quantitatively and visually.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.4
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• pp.73-86
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• 2000

This study focused on extracting an efficient method in the fruit cropping distribution mapping with various classification methods using multi-temporal satellite images and Geographic Information Systems(GIS). For this study, multi-temporal Landsat TM images, in observation data and existing fruit cropping area statistics were used to compare and analyze the properties of fruit cropping and seasonal distribution per classification method. As a result, this study concludes that Maximum Likelihood Method with earlier autumn satellite image was most efficient for the fruit cropping mapping using Landsat TM image. In addition, it was clarified that cropping area per administrative boundary was prepared and distribution pattern was identified efficiently using GIS spatial analysis.