• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.260-263
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• 2005

This study was carried out to investigate the effects of spatial resolutions on digital image for detecting pine trees damaged by pine wilt disease. Color infrared images taken from PKNU-3 multispectral airborne photographing system with a spatial resolution of 50cm was used as a basic data. Further test images with spatial resolutions of 1m, 2m and 4m were made from the basic data to test the detecting capacity on each spatial resolution. The test was performed with visual interpretation both on mono and stereo modus and compared with field surveying data. It can be conclude that it needs less than 1m spational resolutions or 1m spatial resolutions with stereo pair in order to detect pine trees damaged by pine wilt disease.

• 한국가구학회지
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• 제17권2호
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• pp.9-23
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• 2006

Control particleboards were significantly higher in thickness swelling than wood particle-polypropylene fiber composites and their thickness swelling increased with the increase of target density. In the composites, thickness swelling did not vary significantly with the increase of target density but increased with the increase of wood particle content. And the composites with fine wood particles were significantly lower in thickness swelling than those with coarse wood particles irrespective of target density and formulation. In the scanning electron microscopy, significantly higher thickness swelling in the composites with coarse wood particles was thought to be the result of more interfacial separations by higher swelling stresses.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 춘계학술대회 논문집
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• pp.267-270
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• 2006

본 논문은 국산 고해상도 위성화상의 활용 일환으로 산불피해림의 정밀정성정보를 제공하기 위한 낙엽송 및 리기다 소나무의 산불피해등급 육안판독열쇠에 관한 연구이다. 본 육안 판독열쇠는 낙엽송과 리기다 소나무의 산불피해등급 판별을 위해 4가지 요소, 즉 색, 형태, 구조 그리고 질감에 기초로 개발하였다.

• 자원환경지질
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• 제33권4호
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• pp.321-332
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• 2000

The purpose of this study is to analyze landslide that occurred in Yongin area in 1991 using spatial database. For this, landslide locations are detected from aerial photographs interpretation and field survey. The locations of landslide, topography, soil, forest and geology were constructed to spatial database using Geographic Information System (GIS). To establish occurrence factors of landslide, slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective thickness of soil were extracted from the soil database, and type, age, diameter and density of wood were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the TM satellite image. Landslide was analyzed using spatial correlation between the landslide and the landslide occurrence factors by bivariate probability methods. GIS was used to analyze vast data efficiently and statistical programs were used to maintain specialty and accuracy. The result can be used to prevention of hazard, land use planning and construction planning as basic data.

• 한국산림과학회지
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• 제76권2호
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• pp.138-144
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• 1987

본(本) 연구(硏究)는 문헌(文獻)을 통(通)한 경관구조(景觀構造)의 개념(槪念)과 지표(指標), 지표간(指標間)의 상호관계(相互關係), 삼림경관(森林景觀)의 예측(豫測), 삼림(森林)의 풍치적(風致的) 취급(取扱)과 규모(規模) 및 시업방향(施業方向) 등(等)을 검토(檢討)하여 문제점(問題點)을 제시(提示)하고, 일본부사상근이두국립공원(日本富士箱根伊豆國立公園)에 있는 호호횡단교량계획(芦湖橫斷橋梁計劃)에 대(對)한 연구(硏究)를 통(通)하여 자연경관(自然景觀)의 해석기법(解析技法)의 일례(一例)를 제시(提示)한 결과(結果)로서, 특(特)히 전산기(電算機)에 의한 계획(計劃)은 전산기종(電算機種)과 사용자능력(使用者能力)에 따라, 또는 투시도상(透視圖上)에 그려진 표현자체(表現自體)에 한계(限界)가 있으며, 면밀(綿密)한 현장답사(現場踏査)가 없는 검토(檢討)만으로는 문제점(問題點)이 있다고 보나, 장차(將次) 우리나라의 자연경과지(自然景觀地)를 보호(保護)하고, 적정(適正)한 이용(利用)을 도모(圖謀)하여 국민(國民)의 보건(保健), 휴양(休養) 및 정서생활(情緖生活)의 함양(涵養)을 위한 삼림(森林) 및 공원풍경계획수립(公園風景計劃樹立)에 기여(寄與)되리라 사료(思料)됨.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.302-305
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• 2004

The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology, and land-use were constructed from a spatial database. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, diameter, and density of forest were extracted from the forest database. Lithology was extracted from the geological database and land-use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using landslide-occurrence factors by logistic multiple-regression methods. For validation and cross-validation, the result of the analysis was applied both to the study area, Boun, and another area, Youngin, Korea. The validation and cross-validation results showed satisfactory agreement between the susceptibility map and the existing data with respect to landslide locations. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.298-301
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• 2004

The aim of this study is to cross-validate of spatial probability model, artificial neural network at Boun, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the Boun, Janghung and Youngin areas from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, forest cover and land use were constructed to spatial data-sets. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database. Topographic type, texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter, age and density of forest were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using the landslide­occurrence factors by artificial neural network model. For the validation and cross-validation, the result of the analysis was applied to each study areas. The validation and cross-validate results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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• pp.64-64
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• 2003

The aim of this study is to apply and verify of logistic regression at Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite images, field surveys, and maps of the topography, soil type, forest cover, geology and land use were constructed to spatial database. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography were calculated from the topographic database.13${\times}$1ure, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter and density of forest were extracted from the forest database. Land use was classified from the Landsat TM image satellite image. As each factor's ratings, the logistic regression coefficient were overlaid for landslide susceptibility mapping. Then the landslide susceptibility map was verified and compared using the existing landslide location. The results can be used to reduce hazards associated with landslides management and to plan land use and construction.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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• pp.63-63
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• 2003

The aim of this study is to apply and verify of Bayesian probability model, the likelihood ratio and statistical model, at Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite images, field surveys, and maps of the topography, soil type, forest cover, geology and land use were constructed to spatial database. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter and density of forest were extracted from the forest database. Land use was classified from the Landsat TM image satellite image. As each factor's ratings, the likelihood ratio coefficient were overlaid for landslide susceptibility mapping, Then the landslide susceptibility map was verified and compared using the existing landslide location. The results can be used to reduce hazards associated with landslides management and to plan land use and construction.

• 한국대기환경학회지
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• 제22권6호
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• pp.940-953
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• 2006

The major source of carbon monoxide (CO) at the Earth's surface is the incomplete combustion of biomass and fossil fuels. Because the global lifetime of CO is about two months, it can be used as a tracer for pollution from anthropogenic activities and biomass hurtling. In this paper, we introduced the principle and algorithm of the Measurement of Pollution in the Troposphere (MOPITT) instrument for global CO monitoring. The MOPITT instrument, which was launched on the Satellite Terra in 1999, measures CO column and mixing ratio based on gas correlation radiometry. CO levels can be determined by a retrieval algorithm based on the maximum likelihood method minimizing the difference between observed and modeled radiances. MOPITT level 2 data (HDF format) can be downloaded through the Earth Observing System (EOS) data gateway of NASA. ASCII files of CO parameters can be extracted from HDF files, and then temporal and spatial distributions can be obtained. Finally, we showed an example of CO monitoring in April 2000. The locations of forest fires and distribution of MOPITT CO clearly indicated that not only anthropogenic emissions but also forest fires play an important role in CO levels and global CO distribution. Our introduction to MOPITT and the example of MOPITT data interpretation would be helpful for scientists who want to use the EOS data.