• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.1-11
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• 2021

Detection of surface water features including river, wetland, reservoir from the satellite imagery can be utilized for sustainable management and survey of water resources. This research compared the water indices derived from the multispectral bands and the machine learning technique for detecting the surface water features from he Landsat-8 satellite image acquired in Daegu through the following steps. First, the NDWI(Normalized Difference Water Index) image and the MNDWI(Modified Normalized Difference Water Index) image were separately generated using the multispectral bands of the given Landsat-8 satellite image, and the two binary images were generated from these NDWI and MNDWI images, respectively. Then SVM(Support Vector Machine), the widely used machine learning techniques, were employed to generate the land cover image and the binary image was also generated from the generated land cover image. Finally the error matrices were used for measuring the accuracy of the three binary images for detecting the surface water features. The statistical results showed that the binary image generated from the MNDWI image(84%) had the relatively low accuracy than the binary image generated from the NDWI image(94%) and generated by SVM(96%). And some misclassification errors occurred in all three binary images where the land features were misclassified as the surface water features because of the shadow effects.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.135-148
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• 2015

This research is carried out for the land cover change detection in the Nakdong River basin before and after the 4 major rivers restoration project using the LiDAR DEM(Digital Elevation Model) and the multi-temporal Landsat imagery. Firstly the river basin polygon is generated by using the levee boundaries extracted from the LiDAR DEM, and the four river basin imagery are generated from the multi-temporal Landsat-5 TM(Thematic Mapper) and Landsat-8 OLI(Operational Land Imager) imagery by using the generated river basin polygon. Then the main land covers such as river, grass and bare soil are separately generated from the generated river basin imagery by using the image classification method, and the ratio of each land cover in the entire area is calculated. The calculated land cover changes show that the areas of grass and bare soil in the entire area have been significantly changed because of the seasonal change, while the area of the river has been significantly increased because of the increase of the water storage. This paper contributes to proposing an efficient methodology for the land cover change detection in the Nakdong River basin using the LiDAR DEM and the multi-temporal satellite imagery taken before and after the 4 major rivers restoration project.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.27-35
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• 2017

Human activity such as agriculture, industrial development and urban sprawl has been the major threat to wetlands ecosystem, which have caused the greatest losses of coastal wetlands. The Garorim bay provides one of the most important wetland habitate and Ministry of Oceans and Fisheries designated Garorim bay to marine ecosystem protected area in July 2016. The purpose of this research is to analysis the spatial pattern of Garorim bay using Landsat 5 (TM), Landsat 7 (ETM+), Landsat 8 (OLI & TIRS). The surface temperature and NDVI of Garorim bay were processed with spatial analysis method and time series analysis were applied to 25 years Landsat satellite 19 images. The results of time series distribution map compared with the several wetland habitate on remotely sensed images. Landsat images showed the change area of wetland vegetation distribution from 1988 to 2014. The southern part habitate of Garorim bay have been changed with vegetation patterns on coastal wetland which were covered with tidal flat.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.135-136
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• 2014

지구온난화에 따른 이상기후현상을 해결하기 위해 인공위성영상을 이용한 식생의 변화유무와 특성파악이 중요하다. 특히, 인공위성의 근적외선 영역과 가시광선 영역을 이용한 정규식생분포지수는 식생의 활력도를 파악하고 변화유무를 판단하는 지표로서 많이 사용되고 있다. 하지만, 최근 발사된 Landsat 8 OLI의 경우 정규식생분포지수에 영향을 주는 근적외선 밴드의 파장대역이 기존의 TM/ETM+ 위성의 근적외선 밴드의 파장대역보다 감소하였다. 또한 이러한 파장대역 변화에 의한 정규식생분포지수의 차이에 대해서 공식적으로 연구한 사례가 없다. 그러므로 본 연구는 Landsat 8 OLI 위성영상과 Landsat 7 ETM+ 위성영상을 식생이 활발한 여름철(9월)과 그렇지 않은 겨울철(1월)의 영상을 각각 취득하여, 식생, 도심지, 도로, 농경지, 나지의 5가지 항목으로 분류하여 각각의 정규식생분포지수를 비교해보고 상관관계분석을 시도하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.51-57
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• 2007

In this study, We analyzed change of sea surface temperature due to cooling water around nuclear power station. Study area is around of Uljin nuclear power station, which is relatively large power station. There are many problems in monitering environmental change around of nuclear power station, because area is relatively large. We used Landsat 5, 7 Imagery which are useful in temperature analysis and can be easily obtained. After we georeferenced Landsat Imagery, radiance and sea surface temperature were calculated. As a result, As we compared sea surface temperature of surrounding area of nuclear power station with same area located 3 km east, there are $2.049^{\circ}C$ temperature difference.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.765-779
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• 2022

This study evaluates the accuracy in identifying the burned area in South Korea using multi-temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.583-590
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• 2008

This study presents a correlation between timber Age, image bands and vegetation indices for timber age estimation. Basically, this study used Landsat TM images of three difference years (1994, 1994, 1998) and difference between Shuttle Radar Topography Mission (SRTM) and National Elevation Dataset (NED). Bands of 4, 5 and 7, Normalized Difference Vegetation Index (NDVI), Infrared Index (II), Vegetation Condition Index (VCI) and Soil Adjusted Vegetation Index (SA VI) were obtained from Landsat TM images. Tasseled cap - greenness and wetness images were also made by Tasseled cap transformation. Finally, analysis of correlation between timber age, difference between Shuttle Radar Topography Mission (SRTM) and National Elevation Dataset (NED), individual TM bands (4, 5, 7), Normalized Difference Vegetation Index (NDVI), Tasseled cap-Greenness, Wetness, Infrared Index (II), Vegetation Condition Index (VCI) and Soil Adjusted Vegetation Index (SAVI) using regression model. In this study about 1,992 datasets were analyzed. The Tasseled cap - Wetness, Infrared Index (II) and Vegetation Condition Index (VCI) showed close correlation for timber age estimation.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.2
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• pp.31-43
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• 2005

Four time points of the fractional area data during the 15 years of the highest group of land surface temperature and the lowest group of NDVl of the Kunsan city Chollabuk_do, Korea located beneath the Yellow sea coast, are observed and analyzed their correlations for the intention to detect the changes of urban land cover. As long as the effective contributions of satellite images in the continuous monitoring of the wide area for wide range of time period, Landsat-5 TM and Landsat-7 ETM+ artificial satellite images, acquisited over the Kunsan city area, are surveyed by the compared calibration after quantization and classification of the deviations between TM and ETM+ images substituted approved error correction thresholds such as gains and biases or offsets. This experiment and research applied Landsat-5 TM and Landsat-7 ETM+ artificial satellite images in change detection of urban land cover in urbanized Kunsan city, then detected strong and proportional correlation relationship between the highest group of land surface temperature and the lowest group of NDVI which exceeded R=(+)0.9478, so the proposed Correlation Analysis Model between the highest group of land surface temperature and the lowest group of NDVI will be able to give proof an effective suitability to the land city change detection monitoring.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.9-17
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• 2014

The Vegetation cover is a significant factor to comprehend characteristics of the ground surface for meterological and hydrological models, which measure energy in the atmosphere or predict the runoff of ground surface. Deardorff introduced vegetation cover fraction to quantitatively comprehend the vegetation cover in 1978. After Deardorff, most of previous researches were conducted on low-resolution or high-resolution images, but only few researches on Landsat that are in medium-resolution images. Therefore, this study aims to investigate a way of calculating the vegetation cover fraction by using NDVI of Landsat images, which were hardly handled previously. For accurate vegetation cover fraction, we compared the evaluated parameters from this study with past vegetation cover fraction parameters that have been calculated for using NDVI of Landsat OLI images. The result of research was shown that NDVI is quite correlated with the vegetation fraction cover in the previous researches. In fact, RMSE of vegetation cover fraction values that obtained through the suggested parameters on this study showed the highest accuracy of 7.3% among all the cases.