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Development of Estimated Equation for Mortality Rates by Forest Type in Korea

우리나라 침엽수 및 활엽수림의 고사율 추정식 개발

  • Son, Yeong Mo (Division of Forest Industry Research, National Institute of Forest Science) ;
  • Jeon, Ju Hyeon (Division of Forest Industry Research, National Institute of Forest Science) ;
  • Lee, Sun Jeong (Division of Forest Industry Research, National Institute of Forest Science) ;
  • Yim, Jong Su (Division of Forest Industry Research, National Institute of Forest Science) ;
  • Kang, Jin Taek (Division of Forest Industry Research, National Institute of Forest Science)
  • 손영모 (국립산림과학원 산림산업연구과) ;
  • 전주현 (국립산림과학원 산림산업연구과) ;
  • 이선정 (국립산림과학원 산림산업연구과) ;
  • 임종수 (국립산림과학원 산림산업연구과) ;
  • 강진택 (국립산림과학원 산림산업연구과)
  • Received : 2017.08.09
  • Accepted : 2017.10.23
  • Published : 2017.12.31

Abstract

This study was conducted to develop estimated equation for mortality rates (volume of dead trees, %) on coniferous and broad-leaved forests, representative forest types of South Korea. There were 6 equation models applied for estimating mortality such as a exponential equation, a Hamilton equation and variables using were DBH, basal area, and site index. Raw data used for estimating mortality were $5^{th}$ and $6^{th}$ national forest inventory data, and mortality was calculated with the difference of stocks between lived trees and dead trees by each sample plots. The most applicable equation to describe mortality on coniferous forest and broad-leaved forest was indicated as $P=(1+e^{(a+b{\times}DBH+c{\times}BA+d{\times}no\_ha+e{\times}density)})^{-1}$ and their goodness of fit showed 34% and 51% respectively. Goodness of fit in both equations were not much high because there were various factors which affect the mortality such as topographic conditions, soil characteristic, climatic factors, site quality, and competition. Therefore, it is considered that explaining mortality in forest with only 2 or 3 variables like DBH, basal area used in this analysis could be very difficult facts. However, this study is certainly worth in that there is no useful information on mortality by each forest type throughout the country at the present, and we would make an effort to promote the fitness of estimated equation for mortality adding competition index, tree crown density etc.

본 연구는 우리나라의 침엽수와 활엽수림에서 발생하는 고사율(고사 입목의 재적량, %) 추정식을 개발하는 것이 목적이다. 고사율 추정을 위하여 적용한 모형은 지수식, Hamilton식 등 6개식이었으며, 이용한 변수는 흉고직경, 흉고단면적, 지위지수 등이었다. 고사율 추정에 이용한 원자료는 5차 및 6차 국가산림자원조사 자료였으며, 표본점별 고사목과 생존목의 재적량 비로서 고사율을 산정하였다. 적용한 식 중 침엽수와 활엽수의 고사율을 가장 잘 설명하는 식은 $P=(1+e^{(a+b{\times}DBH+c{\times}BA+d{\times}no\_ha+e{\times}density)})^{-1}$의 형태를 갖는 식이다. 침엽수는 약 34%, 활엽수는 약 51%의 적합도를 나타냈다. 두 식 모두 적합도가 높게 나타나지 않았는데, 이는 임목 고사에 영향을 미치는 인자가 지리적 환경, 토양, 기상, 지위, 경쟁 등 너무나 다양하기 때문이다. 따라서 본 분석에 이용한 흉고직경, 흉고단면적 등 2~3개의 변수로 산림 내 고사를 설명하기는 매우 어려운 일이라 판단된다. 그러나 전국적으로 활용될 수 있는 임상별 고사율 정보가 없는 현시점에서는 본 연구의 가치는 있다고 생각되며 추후 수관울폐도, 경쟁지수 등을 변수로 추가적으로 활용하여 고사율 추정식의 정도를 높여야 할 것이다.

Keywords

References

  1. Aikman, D.P. and Watkinson, A.R. 1980. A model for growth and self-thinning in even-aged monocultures of plants. Annals of Botany 45: 419-427. https://doi.org/10.1093/oxfordjournals.aob.a085840
  2. Clutter, J.L., Fortson, J.C., Pienaar, L.V., Brister, G.H. and Bailey, R.L. 1983. Timber management: A quantitative approach. John Wiley & Sons. pp. 333.
  3. Drew, T.J. and Flewelling, J.W. 1977. Some recent japanese theories of yeild-density relationships and their application to monterey pine plantations. For. Sci. 23: 517-534.
  4. Eid, T. and Tuhus, E. 2001. Models for individual tree mortality in Norway. Forest ecology and management 154: 69-84. https://doi.org/10.1016/S0378-1127(00)00634-4
  5. Hamilton, D.A. 1974. Event probabilities estimated by regression. USDA For. Serv., Res. pap. INT-152.
  6. Hamilton, D.A. and Edwards. B.M. 1976. Modelling the probability of individual tree mortality. USDA For. Serv., Res. Pap. INT-185.
  7. Hann, D.W. 1980. Development and evaluation of even- and uneven-aged ponderosa pine/ Arizona fescue stand simulator. USDA. For. Serv., Res. pap. INT-267.
  8. Kramer, H. 1977. Die qualitatsentwicklung junger kiefernbestande in abhangigkeit vom ausgangsverband. FoHo 32, 469-476.
  9. KFS (Korea Forest Service. 2016. Data base of national forest inventory. Inner information.
  10. Lonsdale, W.M. 1990. The self-thinning rule: dead or alive? Ecology 71:1373-1388. https://doi.org/10.2307/1938275
  11. Monserud, R.A. and Sterba, H. 1999. Modeling individual tree mortality for Austrian forest species. Forest Ecology and Management 113: 109-123. https://doi.org/10.1016/S0378-1127(98)00419-8
  12. NiFoS (National Institute of Forest Science). 2015. Development of dynamic growth model in major species. Inner information.
  13. Reineke, L.H/ 1933. Perfecting a stand density index for even-aged stands. J. Agric. Res. 46: 627-638.
  14. Sit, V. and Poulin-Costello, M. 1994. Catalogue of curves for curve fitting. Ministry of Forests Research Program, Province of British Columbia. pp. 110.
  15. Smith, N.J. and Hann, D.W. 1984. A new analytical model based on the -3/2 power rule of self-thinning. Can. J. For. Res. 14: 605-607. https://doi.org/10.1139/x84-110
  16. Vanclay, J.K. 1994. Modelling forest growth and yield: Applications to mixed tropical forests. CAB International. pp. 312.
  17. White, J. 1981. The allometric interpretation of the self-thinning rule. J. Theoretical biology 89: 475-500. https://doi.org/10.1016/0022-5193(81)90363-5
  18. Yim, K.B. et al. 1985. A principle of silviculture. Hyang Mun Co. pp. 491.
  19. Yoda, K., Kira, T., Ogawa, H. and Hozami, K. 1963. Self thinning in overcrowded pure stands under cultivated and natural conditions. J. Biol. 14: 107-129.