• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.159-172
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• 2006

In this study, the series of Landsat TM/ETM+ images was acquired to extract land surface temperature for wide-area and executed geometric correction and radiometric correction. And the land surface temperature was extracted using NASA Model, and achieved the first correction by performing land coverage category for study area and applied characteristic emission rate. Land surface temperature which was acquired by the first correction was analyzed in correlation with Meteorological Administration's temperature data by regression analysis, and established correction formula. And I wished to improve accuracy of land surface temperature extraction using satellite image by second correcting deviations between two data using establishing correction formula. As a result, land surface temperature acquired by 1st and 2st correction could be corrected in mean deviation of about ${\pm}3.0^{\circ}C$ with Meteorological Administration data. Also, I could acquire land surface temperature about study area by higher accuracy by applying to other Landsat images for re-verification of study results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.3
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• pp.163-170
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• 2001

The objective of this study was to evaluate land cover classification of PyeongTaeg area by Landsat Thematic Mapper Data June, 1997. This study was also to make more correct reference data using DGPS, aerophoto, and topographical chart etc.. The result of the area of paddy and upland were estimated 4,949 $\textrm{km}^2$ and 16,157 $\textrm{km}^2$, respectively. Correctness of estimation by using DGPS, aerophoto, topographical chart were shown over 90% correct in case of rice paddy field, water, and sea, while upland, vinyl house, forest, grassland, village were shown low correctness. Total average accuracy was shown to be 85.8%. Correctness of paddy field showed high value of 92%, showing that use of remote sensing data was proved to be effective methods to estimate spatial distribution and cultivation status of paddy field. Classification result of sea, water area, downtown had higher correctness, while upland, vinyl-house, grassland were proved to be relatively low correctness because of it's small area and mixed distribution.

• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.197-206
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• 2010

Recently, developed countries have continuously been trying to recognize many issues about heat island in urban area and to make up countermeasures for them. This research is designed to extract change of land cover in the area under condition of land development with satellite images and to analyze its effect on the heat change in there. Heat change upon change of land cover in daejeon was analyzed with the four Landsat satellite images taken in April 1985, August 1994, May 2001, and May 2009. In order to measure the temperature on the surface in the city, the land surface temperature was produced with Landsat TM Band 6. Heat change is to detected with it. As a result, The urban area has been increased up to 23.59 percent. On the other hand, the forest area has been decreased up to 27.91%. Due to the urbanization, the temperature on the surface in urban center was higher than surrounding area. In that case, the temperature of urban center area was higher 2.4 to $5.7^{\circ}C$ compared with the forest area.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.116-121
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• 2001

경기만 지역은 세계최대 규모의 갯벌이 조성되어 해양생태계에서 중요한 역할을 수행하는 자연의 보고이나, 강한 조류운동, 한강 유역으로부터의 토사이동, 그리고 계속되는 연안 개발등 지속적인 영향을 받고 있다. 본 연구에서는 이러한 경기만 지역의 지리적·환경적 요인에 기인한 갯벌지역의 지난 30년 동안 공간적 변화를 분석하고자 한다. 해안선·조간대 지형의 변화 특성은 1972년부터 1999년까지 약 5년 간격으로 촬영된 Landsat MSS 와 TM 영상들을 이용하여 분석하였다. MSS와 TM의 공통적인 파장대이며, 물과 조간대의 경계가 뚜렷한 근적외선 파장대를 이용하여 간조시 갯벌의 경계선을 추출하였다. 각 시기의 수면, 갯벌, 육지를 나타내는 수치지도가 제작된 후, 이들을 중첩함으로써 시기별 변화유형을 구분하였고, 변화유형을 다시 원인에 따라 인공적인 요인과 자연적인 요인으로 나누었다. 의미있는 변화 유형은 크게 8가지로 나타났으며, 변화유형과 변화요인을 연계하여 경기만 지역의 변화특성을 도출하였다.

• Korean Journal of Remote Sensing
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• v.2 no.2
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• pp.107-116
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• 1986

A unique approach to access Landsat satellite imagery has been implemented on IBM PC microcomputer in order to generate image maps to be used as a substitute and/or supplement for a conventional topographic map. This method enables user to automatically: o extract a nominal image map, o geoencode or calibrate as an image map, and o create a multitemporal image file using CCTs containing precision processed Landsat MSS and TM images. These map extraction process includes: o location of map area in the selected CCT, o conversion of map coordinates to image coordinates, o extraction of map area, and o rotation of image to the true North/South and East/Weat direction.

• Korean Journal of Remote Sensing
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• v.20 no.1
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• pp.13-21
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• 2004

The usefulness of the multi-temporal satellite image to monitoring the vegetation recovery process after forest fire was tested. Using multi-temporal Landsat TM and ETM＋data, NDVI and NBR changes over times were analyzed. Both NDVI and NBR values were rapidly decreased after the fire and gradually increased for all forest type and damage class. However, NBR curve showed much clearer tendency of vegetation recovery than NDVI. Both indices yielded the lowest values in severely damaged red pine forest. The results show the vegetation recovery process after forest fire can detect and monitor using multi-temporal Landsat image. NBR was proved to be useful to examine the recovering and development process of the vegetation after fire. In the not damaged forest, however the NDVI shows more potential capability to discriminate the forest types than NBR..

• Journal of Environmental Impact Assessment
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• v.10 no.1
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• pp.1-8
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• 2001

Detection of red tides by satellite remote sensing can be done either by detecting enhanced level of chlorophyll pigment or by detecting changes in the spectral composition of pixels. Using chlorophyll concentration, however, is not effective currently due to the facts: 1) Chlorophyll-a is a universal pigment of phytoplankton, and 2) no accurate algorithm for chlorophyll in case 2 water is available yet. Here, red band algorithm, classification and PCA (Principal Component Analysis) techniques were applied for detecting patches of Cochlodinium polykrikoides red tides which occurred in Korean waters in 1995. This dinoflagellate species appears dark red due to the characteristic pigments absorbing lights in the blue and green wavelength most effectively. In the satellite image, the brightness of red tide pixels in all the three visible bands were low making the detection difficult. Red band algorithm is not good for detecting the red tide because of reflectance of suspended sediments. For supervised classification, selecting training area was difficult, while unsupervised classification was not effective in delineating the patches from surrounding pixels. On the other hand, PCA gave a good qualitative discrimination on the distribution compared with actual observation.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.319-326
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• 2002

In this paper, we have designed and implemented cluttering classification systems- unsupervised classifiers-for the processing of satellite remote sensing images. Implemented systems adopt various design patterns which include a factory pattern and a strategy pattern to support various satellite images'formats and to design compatible systems. The clustering systems consist of sequential clustering, K-Means clustering, ISODATA clustering and Fuzzy C-Means clustering classifiers. The systems are tested by using a Landsat TM satellite image for the classification input. As results, these clustering systems are well designed to extract sample data for the classification of satellite images of which there is no previous knowledge. The systems can be provided with real-time base clustering tools, compatibilities and components' reusabilities as well.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.1-4
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• 2001

The classification performance of Artificial Neural Network (ANN) and RBF-NN was compared for Landsat TM image. The RBF-NN was validated for three unique landuse types (e.g. Mixed landuse area, Cultivated area, Urban area), different input band combinations and classification class. The bootstrap resampling technique was employed to estimate the confidence intervals and distribution for unit load, The pollutant generation was varied significantly according to the classification accuracy and percentile unit load applied. Especially in urban area, where mixed landuse is dominant, the difference of estimated pollutant load is largely varied.