• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.197-207
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• 2013

Difficulties of emissivity determination and atmospheric correction degrade the estimation accuracy of land surface temperature (LST). That is, since the emissivity determination of land surface material and the correction of atmospheric effect are not perfect, it is very difficult to estimate the precise LST from a thermal infrared image such as Landsat TM and ETM+, ASTER, etc. In this study, we propose an efficient method to estimate land surface temperature difference (LSTD) rather than LST from Landsat thermal band images. This method is based on the assumptions that 1) atmospheric effects are same over a image and 2) the emissivity of vegetation region is 0.99. To validate the performance of the proposed method, error sensitive analysis according to error variations of reference land surface temperature and the water vapor is performed. The results show that the estimated LSTD have respectively the errors of ${\pm}0.06K$, ${\pm}0.15K$ and ${\pm}0.30K$ when the water vapor error of ${\pm}0.302g/cm^2$ and the radiance differences of 0.2, 0.5 and $1.0Wm^{-2}sr^{-1}{\mu}m$ are considered. And also the errors of the LSTD estimation are respectively ${\pm}0.037K$, ${\pm}0.089K$, ${\pm}0.168K$ in the reference land surface temperature error of ${\pm}2.41K$. Therefore, the proposed method enables to estimate the LSTD with the accuracy of less than 0.5K.

• Journal of the Korean earth science society
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• v.25 no.8
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• pp.754-763
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• 2004

This study was aimed to examine the Landsat Enhanced Thematic Mapper Plus (ETM+) index, which matches well with the field survey data in the wildfire area of Gangneung, Gangweon Province, Korea. In the wildfire area NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and Tasseled Cap Transformation Index (Brightness, Wetness, Greenness) were compared with field survey data. NDVI and SAVI were very useful in detecting the difference between the wildfire and non-wildfire area, but not so in classify the soil types in the wildfire area. The soil plane based on the Tasseled Cap Transformation showed a better result in classifying the soil types in the wildfire areas than NDVI and SAVI, and corresponded well with field survey data. Using a linear function based on greenness and wetness in the Tasseled Cap Transformation is expected to provide a more efficient and quicker method to classify wildfire areas.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.3-11
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• 2011

The objective of this study was to select appropriate sites for reforestation CDM using satellite image and spatial data in North Korea. A suitable site for reforestation CDM can be defined as non-forest area since 1990. To detect land cover change between two different years and to delineate potential reforestation CDM site(unstocked forest, converted crop land, denuded bare land), Landsat TM satellite image which was scanned in 1988 and SPOT Pan-sharpened image which was scanned in 2007 were used. As a result of classification, 1,214 ha of forest area in 1989 had been converted to other land cover types in 2007. and 2,245 ha of the total study area was detected to be suitable for reforestation CDM. 79.2% of total potential CDM sites was converted crop land. Through topography and accessibility analysis for potential reforestation CDM sites detected by satellite image, suitability index was calculated and the potential reforestation area was reclassified into suitability grades.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.107-110
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• 2007

There it has been progressed study about the city of land use and change detection in different period. The aim of the study is to find the differences in spatial change for subway construction lines using Landsat TM and SPOT image. The result of study to use judge the data in subway role about the city growth. In the recently, it will be expected to use important basis data in development of the city.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.259-263
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• 2006

Development and maintenance of forests are very important at Korea which has mountainous topography of more than 60% of the national territory. Under the circumstances, variety period's data is being required for the continuous monitoring of forest area. In this study, change of forests type was analyzed using Landsat TM satellite image which have multi-spectral bands. Furthermore, change detection system for forests type was constructed on web for the periodical monitoring. By using this system, everyone can easily use for the monitoring of forest type's periodical change.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.110-115
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• 1998

Through the ISODATA method, the micro-landform of Julpo-Bay tidal flat was classified into mudflat, mixedflat, and sandflat using Landsat TM image. Each showed an apparent differences in its topographical characteristics and grain size composition. For example, mudflats are formed with flat faces and tidal channel of dissected gully. Its characteristics of grain size analysis that the grains have less than mean grain size 4 phi. Its sorting is bad (higher than 1 S.D.), and it showed strongly positive skewness. But sandflat is topographically flat without tidal channel. It has developed with ripple marks. According to the grain size analysis of deposits, the soil is coarse size with 90% of sand and its sorting is well(lower than 1 S.D.) Also, it showed strongly negative skewness. Mixed flat is in between mudflat and sandflat in its characteristics.

• Journal of Environmental Impact Assessment
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• v.10 no.3
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• pp.167-174
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• 2001

National Parks in Korea occupy about four percents of South Korean land. This paper aims to prove the potentiality of the application of remotely sensed data for the effective management of National Parks. Different satellite images such as Landsat TM, IRS-1C, Alternative image, and IKONOS image are analyzed for the detection of changes, the extraction of degraded areas, and the comparison of Normalized Difference Vegetation Index (NDVI) in Bukhansan National Park. The artificial structures such as buildings and paved areas are overvalued in relatively higher resolution data. The result showed that the choice of images should be determined according to specific purposes and the combination of different resolution data may be the solution for the effective management of National Park.

• Journal of the Korean Institute of Navigation
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• v.24 no.4
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• pp.227-234
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• 2000

In general, neural networks are widely used for the category classification of multispectral images. Since the input multispectral images into neural networks we, however, low contrast images, neural networks converge very slowly and are of bad performance. To overcome this problem, we propose a new image enhancement method which consists of smoothing process, finding the main valley and enhancement process. In addition the enhanced images by the proposed method are used as the input of neural networks for the category classification. When the new category classification method is applied to multispectral LANDSAT TM images, we verified that the neural networks converge very lastly and that the overall category classification performance is improved.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.221-227
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• 2007

We present an analysis of urban development and growth with reconnaissance satellite photographs of Columbus metropolitan area acquired by the Corona program in 1965. A two-dimensional polynomial linear transformation was used to rectify the photos against United State Geological Survey (USGS) Large-scale Digital Line Graph (DLG) data georeferenced to Universal Transverse Mercator (UTM) coordinates. The boundaries of the Columbus metropolitan area were extracted from the rectified Corona image mosaic using a Bayesian approach to image segmentation. The inferred 1965 urban boundaries were compared with 1976 USGS Land Use and Land Cover (LULC) data and boundaries derived from 1988 and 1994 Landsat TM images. The urban area in and around Columbus approximately doubled from 1965 to 1994 (${\sim}110%$) along with population growth from 1960 to 1998 (${\sim}50%$). Most of the urban expansion results from development of residential units.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.