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LiDAR를 활용한 국토환경성평가지도 산림부문 신규 평가항목의 도입 가능성 평가
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  • LiDAR를 활용한 국토환경성평가지도 산림부문 신규 평가항목의 도입 가능성 평가
저자명
전성우,홍현정,이종수,이우균,성현찬,Jeon. Seong-Woo,Hong. Hyun-Jung,Lee. Chong-Soo,Lee. Woo-Kyun,Sung. Hyun-Chan
간행물명
環境復元綠化
권/호정보
2007년|10권 5호|pp.20-30 (11 pages)
발행정보
한국환경복원기술학회
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

Environmental Conservation Value Assessment Map (ECVAM) is the class map to divide the national land into conservation areas and development areas based on legal and ecological assessment criteria. It contributes to enhancements of the efficiency and the scientificity when framing a policy in various fields including the environment. However, it is impossible to understand the multiphase vegetation structure as data on judging the national forest class in ECVAM are restricted to areal information of Ecological Nature Status, Degree of Green Naturality and Forest Map. This point drops the reliability of ECVAM. Therefore we constructed vegetation information using LiDAR (Light Detection And Raging) technology. We generated Biomass Class Maps as final results of this study, to introduce the new forest assessment criterion in ECVAM that alternates or makes up for existing forest assessment criteria. And then, we compared these with Forest Map and Landsat TM NDVI image. As a result, biomass classes are generally higher than stand age classes and DBH classes of Vegetation Map, and lower than NDVI of Landsat TM image because of the difference of time on data construction. However distributions between these classes are mostly similar. Therefore we estimates that it is possible to apply the biomass item to the new forest assessment criterion of ECVAM. The introduction of the biomass in ECVAM makes it useful to detect the vegetation succession, to adjust the class of the changed zone since the production of Vegetation Map and to rectify the class error of Vegetation Map because variations on tree heights, forest area, gaps between trees, vegetation vitality and so on are acquired as interim findings in process of computing biomass.