• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.479-482
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• 2003

Landsat-TM images were applied for evaluating the spatial variations of flow and water quality at the Saemankeum areas. For supervised classifications, the results from hydrodynamic modeling and water quality data were compared to the reflectance characteristics of the satellite images. Multiple regression analyses indicated that suspended sediment, transparency, salinity, total nitrogen, and total phosphorus showed a good relationship to the signature. Supervised classifications showed spatial variations of the water environments at the areas under construction. The results showed the satellite imagery may be applied for the project areas with a reasonable degree of accuracy.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.215-224
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• 2007

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. This Study concludes, each image was measured by the rate of class separability, values classified were showed highly about $1,600{\sim}2,000$.

• Journal of Korean Society on Water Environment
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• v.35 no.1
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• pp.1-8
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• 2019

The purpose of this study is to evaluate the nutritional status of Lake Jinyang using Landsat 8 satellite image band correlated with chlorophyll-a, which is also related to algae proliferation. We selected 20 Landsat 8 images dating from 2013 to 2017, taken close to water quality measurement date when the cloud cover was less than 20 %. Based on the results of the previous studies, analyzing the correlation between chlorophyll-a, and Landsat 8 satellite image band, we selected near infrared wavelength, band 5 which is closely related to the population of algae. The nutritional status was classified using the Aizaki trophic state index (TSIm). The results of the regression equation between band 5 and the observed chlorophyll-a data was used to calculate chlorophyll-a for the image data from 2013 to 2017. The concentration of chlorophyll-a ranged from 3 to $16.1mg/m^3$. To illustrate the spatial distribution of chlorophyll-a within the lake, the chlorophyll-a concentration was divided into five grades. The images on October 14, 2014 and April 10, 2016 showed relatively high value of chlorophyll-a, while January 18, 2015 and December 6, 2016 chlorophyll-a value were below 5. The images on October 14, 2014 and April 10, 2016 were rated as eutrophic status in most areas. The results of simulating water quality for the day when the water quality was not measured resulted to an approximate value for the Panmun station while the Naedong station needed some corrections.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.932-936
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• 2007

In this study, it analyzed the hydrosphere change up to recently since the construction of Daecheong dam using Landsat satellite images and qualitatively the hydrosphere change of the Daecheong dam basin. These study detected the hydrosphere change with applying supervised classification about Landsat satellite image corresponding to 4 periods of 1981, 1987, 1993, and 2002. For this, it designated the class of hydrosphere, vegetation, etc and achieved overlay analysis with extracting only the hydrosphere, and though this, These study monitored the change about hydrosphere of Daecheong dam basin efficiently.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.1
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• pp.30-40
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• 2020

Monitoring the urban environmental changes caused by the urban regeneration project is necessary for evaluating the effect of the various types of urban regeneration projects that have been carried out in Seoul, South Korea. However, there is few available data and professional expert for evaluating the effect of these urban regeneration projects. This research evaluated the effect of the construction of rainwater village in Jangwi-dong area, constructed through the Seoul urban regeneration project, by utilizing the land surface temperatures derived from the multi-temporal Landsat-8 satellite images through the following steps. In the first step, the land surface temperature images were generated using the multispectral bands of the Landsat-8 satellite images. In the final step, the effect of constructing the rainwater villages was assessed by calculating the seasonal LST statistics for Jangwi-dong area, its neighbor area and entire Seoul area. The experimental results led the following conclusion: the construction of rainwater villages did not have the significant effect on the land surface temperature changes in Jangwi-dong area.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.549-551
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• 2003

Satellite images are utilized for various purposes and many people are concerned about them. But it is necessary to process geometric correction for using of satellite images. However, common user regards geometric correction, which is basic preprocessing for satellite image, as laborious job. Therefore we should provide an automatic geometric correction method for Landsat image using GCP chip database. The GCP chip database is the collection of pieces of images with geoinformation and is provided by XML web service. More specifically, XML web service enables common users to easily use our GCP chip database for their own geometric correcting applications.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.597-609
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• 2012

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.3
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• pp.289-297
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• 2006

The Land Cover/Land Use Map have been constructed from 1998, which has hierarchical structure according to land cover/land use system. Level 1 classification Map have done using Landsat satellite image over whole Korean peninsula. Level II classification Map have been digitized using IRS-1C, 1D, KOMPSAT and SPOT5 satellite images resolution-merged with low resolution color images. Level II Land Cover/Land Use Map construction by digitizing method, however, is consuming enormous expense for satellite image acquisition, image process and Land Cover/Land Use Map construction. In this paper, the possibility of constructing Level II Land Cover/Land Use Map using hyperspectral satellite image of EO-1 Hyperion, which is studied a lot recently, is studied. The comparison of classifications using Hyperion satellite image offering more spectral information and Landsat-7 ETM+ image is performed to evaluate the availability of Hyperion satellite image. Also, the algorithm of the optimal band selection is presented for effective application of hyperspectral satellite image.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1133-1148
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• 2019

Tree growth and vitality in forest shows seasonal changes. So, in order to detect forest damage accurately, we have to use satellite images before and after damages taken at the same season. However, temporal resolution of high or medium resolution images is very low,so it is not easy to acquire satellite images of the same seasons. Therefore, in this study, we estimated spectral information of the same DOY using time-series Landsat images and used the estimates as reference values to assess forest damages. The study site is Hwasun, Jeollanam-do, where forest damage occurred due to hail and drought in 2017. Time-series vegetation index (NDVI, EVI, NDMI) maps were produced using all Landsat 8 images taken in the past 3 years. Daily normal vegetation index maps were produced through cloud removal and data interpolation processes. We analyzed the difference of daily normal vegetation index value before damage event and vegetation index value after event at the same DOY, and applied the criteria of forest damage. Finally, forest damage map based on daily normal vegetation index was produced. Forest damage map based on Landsat images could detect better subtle changes of vegetation vitality than the existing map based on UAV images. In the extreme damage areas, forest damage map based on NDMI using the SWIR band showed similar results to the existing forest damage map. The daily normal vegetation index map can used to detect forest damage more rapidly and accurately.

• 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.