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Development and Validation of the Stand Density Management Diagram for Pinus densiflora Forests in Korea

소나무 임분밀도관리도 작성 및 실용성 검정

  • Park, Joon Hyung (Southern Forest Resources Research Center, National Institute of Forest Science) ;
  • Lee, Kwang Soo (Southern Forest Resources Research Center, National Institute of Forest Science) ;
  • Yoo, Byung Oh (Southern Forest Resources Research Center, National Institute of Forest Science) ;
  • Park, Yong Bae (Southern Forest Resources Research Center, National Institute of Forest Science) ;
  • Jung, Su Young (Southern Forest Resources Research Center, National Institute of Forest Science)
  • 박준형 (국립산림과학원 남부산림자원연구소) ;
  • 이광수 (국립산림과학원 남부산림자원연구소) ;
  • 유병오 (국립산림과학원 남부산림자원연구소) ;
  • 박용배 (국립산림과학원 남부산림자원연구소) ;
  • 정수영 (국립산림과학원 남부산림자원연구소)
  • Received : 2016.05.09
  • Accepted : 2016.08.16
  • Published : 2016.09.30

Abstract

This study aims to make the stand density management diagram which is very useful for establishing systematic management plan and obtaining management goal in Pinus densiflora forest. To estimate 5 models mainly composed of stand density management diagram, we used total of 1,886 sample plots having more than 75% of the total basal area of the pine trees in each stand. To test the goodness of fit, $X^2$ was computed with a significance level of 5%, and the acceptable error range as 20%. Also standard deviation of the model was $34.59m^3{\cdot}ha^{-1}$, minimum acceptable error range was 16.59% and coefficient of variation was 22.11%. If we use the stand density management diagram, it would be useful to establish the timber yield and thinning plan understanding the pathway of stand density management.

본 연구는 우리나라 소나무림의 체계적인 경영계획 수립 및 관리목표 달성을 위해 지침이 될 수 있는 임분밀도관리도를 개발하는데 그 목적이 있다. 임분밀도관리도를 구성하는 5개의 모형을 추정하기 위하여 분석에 이용된 총 조사 표본점 수는 1,886plot이었으며, 각 표준지는 소나무의 흉고단면적이 75%이상 점유하는 임분에서 추출되었다. 유의수준 5%에서 $X{^2}_{(0.05)}$ 적합도를 검정하였으며, 허용오차율은 20% 수준으로 나타났다. 모형의 표준편차는$34.59m^3{\cdot}ha^{-1}$이었으며, 허용오차율의 최소치는 16.59%, 변동계수는 22.11%로 나타났다. 본 연구에서 제작된 소나무 임분밀도관리도는 밀도관리의 경로별 수확량과 솎아베기 과정을 예측할 수가 있어 산림경영에서 목적하는 생산재의 양과 형질에 따른 경영계획 수립이 가능할 것으로 기대하고 있다.

Keywords

References

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