• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.63-71
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• 2013

This study calculated vegetation indexes such as SR, NDVI, SAVI, and LAI to figure out correlations regarding vegetation by using high resolution KOMPSAT-2 images and LANDSAT images based on the forest biomass distribution map that utilized field survey data, satellite images and LiDAR data and then analyzed correlations between their values and forest biomass. The analysis results reveal that the vegetation indexes of high resolution KOMPSAT-2 images had higher correlations than those of LANDSAT images and that NDVI recorded high correlations among the vegetation indexes. In addition, the study analyzed the characteristics of hyperspectral images by using the COMIS of STSAT-3 and Hyperion images of a similar sensor, EO-1, and further the usability of biomass estimation in hyperspectral images by comparing vegetation index, which had relatively high correlations with biomass, with the vegetation indexes of LANDSAT with the same GSD conditions.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.5
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• pp.39-48
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• 2006

The objective of this research was to find an indirect method to estimate land surface temperature (LST) efficiently, using Landsat images. Agricultural fields including paddy fields have long been known to have multi-functions beneficial to the environment and ecology of the urban surrounding areas. Among these functions, the ambient temperature cooling (ATC) effect is widely acknowledged. However, quantitative and regional assessment of such effect has not been performed. Thermal remote sensing has been used over urban areas to assess the ATC effect, Thermal Island Effect(TIE), and as input for models of urban surface atmosphere exchange. Here, we review the use of thermal remote sensing in the study of paddy fields and urban climates, focusing primarily on the ATC effect. Landsat satellite images were used to determine the surface temperatures of different land cover types of a $44km^{2}$ study area in Cheongiu, Korea. The results show that the ATC is a function of paddy area percentage in Landsat pixels. Landsat pixels with higher paddy area percentage have much more cooling effect. The use of satellite data may contribute to a globally consistent method for analysis of ATC effect.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.129-139
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• 2019

Mapping urban areas using the earth observation satellites is useful for monitoring urban expansions and measuring urban developments. In this research, the different thresholds for detecting the urban areas separately from the remote-sensing index images (normalized-difference built-up index(NDBI) and urban index(UI) images) generated from the Landsat-8 image acquired in Daegu, South Korea were evaluated through the following steps: (1) the NDBI and UI images were separately generated from the given Landsat-8 image; (2) the different thresholds (-0.4, -0.2, and 0) for detecting the urban areas separately from the NDBI and UI images were evaluated; and (3) the accuracy of each detected urban area was assessed. The experiment results showed that the threshold -0.2 had the best performance for detecting the urban areas from the NDBI image, while the threshold -0.4 had the best performance for detecting the urban areas from the UI image. Some misclassification errors, however, occurred in the areas where the bare soil areas were classified into urban areas or where the high-rise apartments were classified into other areas. In the future research, a robust methodology for detecting urban areas, including the various types of urban features, with less misclassification errors will be proposed using the satellite images. In addition, research on analyzing the pattern of urban expansion will be carried out using the urban areas detected from the multi-temporal satellite images.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.542-542
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• 2002

Geometric correction is a critical step to remove geometric distortions in satellite images. For correct geometric correction, Ground Control Points (GCPs) have to be chosen carefully to guarantee the quality of corrected satellite images. In this paper, we present an automatic approach for geometric correction by constructing GCP Chip database (GCP DB) that is a collection of pieces of images with geometric information. The GCP DB is constructed by exploiting Landsat's nadir-viewing property and the constructed GCP DB is combined with a simple block matching algorithm for efficient GCP matching. This approach reduces time and energy for tedious manual geometric correction and promotes usage of Landsat images.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.34-43
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• 2005

We will catch the spatio-temporal changes that analyse the unknown topography of Go-Gunsan Islands using Landsat TM satellite images into an unsupervised ISODATA classification and a supervised nearest likelihood classification. Each sedimental topography has the different characteristics according to building the Saemangeum embarkment. We will deal with the distribution of sedimental shapes using ERDAS Imagine 8. 6. The result that classifies specifically topographic properties of our research area be considered to get use of establishing the reclaiming program and predicating the reclaimed sedimental topography. The research area can be classified into tidal flats and sea level using band 4 among 7 bands of Landsat TM. Also band 5 can be used to classify the special unknown shapes of tidal flats. We will clarify the efficiency that spatio-temporal sedimental changes can be extracted through processing satellite images. Therefore, the spatio-temporal unknown topography change monitoring using satellite images is expected to be very useful to clarify whether the tidal flat is generated or not in the Go-Gunsan Islands at the outer side of the embarkment after constructing completely the Saemangeum tidal embarkment.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.197-201
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• 2002

The high prices of satellite images prevent researchers from studying remote sensing and most non-professional people doesn't have the simple and easy solutions for the manipulation of satellite images. "Integrated Management of Satellite Imagery Information" project which will be promoted by ETRI (Electronics and Telecommunications Research Institute) will provide the solutions for the above mentioned problems. We will introduce the archiving center in this study. This includes the data construction, storage, management and distribution. We first review the background for this archiving center and introduce the interior and foreign institutes which archive and distribute satellite images. We review our H/W system and S/W system briefly. Finally, the further service of our project will be suggested. Since we will distribute the satellite images (Landsat, SPOT, JERS, Corona, Kompast-1) and will receive Landsat7 ETM+ in 2003 you, this will help the professional work dealing with the satellite image and attract the non-professional people for simple and easy manipulation solutions of satellite image.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.253-271
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• 2005

We have analyzed surface water temperature and seasonal stratification of lakes in the Han river system using time-series Landsat images and in situ measurement data. Using NASA equation, at-satellite temperature is derived from 29 Landsat-5 TM and Landsat-7 ETM+ images obtained from 1994 to 2004, and was compared with in situ surface temperature on river-type dam lakes such as Paro, Chuncheon, Euiam, Chongpyong, Paldang, and with 10m-depth temperature on lake-type dam lake Soyang. Although the in situ temperature at the time of satellite data acquisition was interpolated from monthly measurements, the number of images with standard deviation of temperature difference (at-satellite temperature - in situ interpolated temperature) less than $2^{\circ}C$ was 24 on which a novel statistical atmospheric correction could be applied. The correlation coefficient at Lake Soyang was 0.915 (0.950 after correction) and 0.951-0.980 (0.979-0.997 after correction) at other lakes. This high correlation implies that there exist a mixed layer in the shallow river-like dam lakes due to physical mixing from continuous influx and efflux, and the daily and hourly temperature change is not fluctuating. At Lake Soyang, an anomalous temperature difference was observed from April to July where at-satellite temperature is $3-5^{\circ}C$ higher than in situ interpolated temperature. Located in the uppermost part of the Han river system and its influx is governed only by natural precipitation, Lake Soyang develops stratification during this time with rising sun elevation and no physical mixture from influx in this relatively dry season of the year.

• 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.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.411-414
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• 2005

In this study, 8 Landsat(TM/ETM+) satellite images acquired from 1984 to 2002 were used to investigate the growth of Chuncheon city, Kangwon-do, Korea. The images were geocoded and classified using training set collected from field survey. Four land-use types were classified such as urban area, green zone, agricultural land and water body. It also showed rapid increase of urban area in the past two decades from 1166ha in 1984 to 3358ha in 2002. About 2182ha of agricultural land and green zone have been changed to urban area. Agricultural land was newly formed from the green zone.