Journal of Korean Society of Forest Science (한국산림과학회지)

Korean Society of Forest Science (한국산림과학회)
권호 표지
DOI QR코드

DOI QR Code

Effect of Climate Changes on the Distribution of Productive Areas for Quercus mongolica in Korea

기후변화가 신갈나무의 적지분포에 미치는 영향

  • Lee, Young Geun (Division of Forest Ecology, Korea Forest Research Institute) ;
  • Sung, Joo Han (Division of Forest Ecology, Korea Forest Research Institute) ;
  • Chun, Jung Hwa (Division of Forest Ecology, Korea Forest Research Institute) ;
  • Shin, Man Yong (Department of Forest, Environment, and System, Kookmin University)
  • 이영근 (국립산림과학원 산림생태연구과) ;
  • 성주한 (국립산림과학원 산림생태연구과) ;
  • 천정화 (국립산림과학원 산림생태연구과) ;
  • 신만용 (국민대학교 산림환경시스템학과)
  • Received : 2014.07.02
  • Accepted : 2014.07.09
  • Published : 2014.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study was conducted to predict the changes of yearly productive area distribution for Quercus mongolica under climate change scenarios. For this, site index equations by ecoprovinces were first developed using environmental factors. Using the large data set from both a digital forest site map and a climatic map, a total of 48 environmental factors including 19 climatic variables were regressed on site index to develop site index equations. Two climate change scenarios, RCP 4.5 and RCP 8.5, were then applied to the developed site index equations and the distribution of productive areas for Quercus mongolica were predicted from 2020 to 2100 years in 10-year intervals. The results from this study show that the distribution of productive areas for Quercus mongolica generally decreases as time passes. It was also found that the productive area distribution of Quercus mongolica is different over time under two climate change scenarios. The RCP 8.5 which is more extreme climate change scenario showed much more decreased distribution of productive areas than the RCP 4.5. It is expected that the study results on the amount and distribution of productive areas over time for Quercus mongolica under climate change scenarios could provide valuable information necessary for the policies of suitable species on a site.

본 논문은 환경인자를 이용하여 우리나라에 생태권역별로 분포하는 신갈나무의 지위지수 추정식을 개발하고, 기후변화 시나리오를 적용하여 적지면적 및 적지분포의 연도별 변화를 추정하기 위해 수행하였다. 이를 위해 산림입지도와 전자기후도 및 기후변화 시나리오 RCP 4.5와 RCP 8.5를 사용하여 산림생산력에 영향을 미칠 것으로 판단되는 19개의 기후변수를 포함한 총 48개 환경인자를 도출한 후, 최적 조합에 의해 신갈나무의 생태권역별 지위지수 추정식을 개발하였다. 최종 생태권역별 신갈나무의 지위지수 추정식에는 각각 4~6개의 환경인자가 독립변수로 사용되었고, 지위지수 추정식의 설명력을 나타내는 결정계수는 0.36~0.49의 범위에 있는 것으로 분석되었다. 이 추정식은 모형의 평균편의, 정도, 표준오차의 3가지 평가통계량에 근거하여 검증을 실시한 결과 비교적 지위 추정능력이 높은 것으로 판명되었다. 또한 본 연구에서는 생태권역별 신갈나무의 지위지수 추정식과 기후변화 시나리오 RCP 4.5와 RCP 8.5를 연계하여 시간 경과에 따른 신갈나무의 연도별 적지면적 및 적지분포의 변화를 2020년부터 2100년까지 10년 단위로 추정하였다. 그 결과 시간이 경과함에 따라 신갈나무의 적지면적은 감소하는 것으로 나타났으며, 극한 기후변화 시나리오인 RCP 8.5를 적용할 경우 RCP 4.5에 비해 적지의 감소 폭이 훨씬 더 큰 것으로 예측되었다. 본 연구에서 얻어진 결과는 적지적수와 관련된 산림정책 수립에 유용한 정보로 활용될 수 있을 것으로 기대된다.

Keywords

References

  1. Albert, M. and Schmidt, M. 2010. Climate-sensitive modelling of site-productivity relationships for Norway spruce (Picea abies (L.) Karst.) and common beech (Fagus sylvatica L.). Forest Ecology and Management 259: 739-749. https://doi.org/10.1016/j.foreco.2009.04.039
  2. Arbatzis, A.A. and Burkhart, H.E. 1992. An evaluation of sampling methods and model forms for estimating heightdiameter relationships in loblolly pine plantation. Forest Science 38(1): 192-198.
  3. Belsley, D.A., Kuh, E., and Welsch, R.E. 1980. Regression diagnostics. John Wiley & Sons. New York. pp. 292.
  4. Corona, P., Scotti, R., and Kutner, M.H. 1998. Relationship between environmental factors and site index in Douglas-Fir plantation in central Italy. Forest Ecology and Management 110: 195-207. https://doi.org/10.1016/S0378-1127(98)00281-3
  5. Curt, T., Bouchaud, M., and Agrech, G. 2001. Predicting site index of Douglas-Fir plantations from ecological variables in the Massif Central area of France. Forest Ecology and Management 149: 61-74. https://doi.org/10.1016/S0378-1127(00)00545-4
  6. Judge, G.G., Hill, R.C., Griffiths, W.E., Lutkepohl, H., and Lee, T.C. 1988. Introduction to the theory and practice of econometrics. John Wiley & sons. New York. pp. 1024.
  7. Kabrick, J.M., Shifley, S.R., Jensen, R.G., Fan, Z., and Larsen, D.R. 2004. Factors associated with oak mortality in Missouri Ozak forest. USDA Forest Service General Technical Reports NE-316. pp. 27-35.
  8. Kang, Y.H., Jeong, J.H., Kim, Y.G., and Lee, W.G. 1996. Mapping of the righteous tree selection for a given site by using of digital terrain analysis on a northern temperate forest. Journal of Forest Science 54: 94-103.
  9. Kang, Y.H., Jeong, J.H., Kim, Y.G., and Park, J.W. 1997. Mapping of the righteous tree selection for a given site by using of digital terrain analysis on a central temperate forest. Journal of Korean Forest Society 86(2): 241-250.
  10. Kim, D.H., Kim, E.G., Park, S.B., Kim, H.G., and Kim, H.H. 2012. Analysis of the effect of climate change on the site index of Larix leptolepis. Journal of Korean Forest Society 101(1): 53-61.
  11. Koo, K.S., Kim, I.H., Jeong, J.H., Won, H.K., and Shin, M.Y. 2003. Estimation of site index by species in Gyungi and Chungcheong Provinces using a digital forest site map. Korean Journal of Agricultural and Forest Meteorology 5(4): 247-254.
  12. Korea Forest Research Institute. 1992. Illustrated Woody Plants of Korea. pp. 562.
  13. Korea Forest Research Institute. 2011. Development of site index equations and estimation of productive areas for main species based on environmental and climatic factors. pp. 71.
  14. Korea Forest Research Institute. 2012. Development of site index equations for main tree species by ecoprovince classification based on environmental and climatic factors. pp. 101.
  15. Korea Forest Research Institute. 2013. Development of site index equations for main tree species by ecoprovince classification based on environmental and climatic factors(3). pp. 135.
  16. Lee, S.W., Won, H.K., Shin, M.Y., Son, Y.M., and Lee, Y.Y. 2007. Estimation of forest productive area of Quercus acutissima and Quercus mongolica using site environmental variables. Korean Journal of Soil Science and Fertilizer 40(5): 429-434.
  17. Myers, R.H. 1986. Classical and modern regression with applications. Duxbury Press. pp. 395.
  18. Nakawatase, J.M. and Peterson, D.L. 2006. Spatial variability in forest growth-climate relationships in the Olympic Mountains, Washington. Canadian Journal of Forest Research 36(1): 77-91. https://doi.org/10.1139/x05-224
  19. Shin, J.W. and Kim, C.M. 1996. The ecosystem classification in Korea(I): Ecoprovince classification. Journal of Forest Science 54: 188-189.
  20. Shin, M.Y. 1990. The use of ridge regression for yield prediction models with multicolinearity. Journal of Korean Forest Society 79(3): 260-268.
  21. Shin, M.Y., Jung, I.B., Koo, K.S., and Won, H.G. 2006. Development of a site index equation for Pinus koraiensis based on environmental factors and estimation of productive areas for reforestation. Korean Journal of Agricultural and Forest Meteorology 8(2): 97-106.
  22. Shin, M.Y., Yun, J.W., and Cha, D.S. 1996. Local correction of tree volume equation for Larix Ieptolepis by ratio-of-means estimator. Journal of Korean Forest Society 85(1): 56-65.
  23. Snee, R.D. 1977. Validation of regression models : Methods and example. Technometrics 19:415-428. https://doi.org/10.1080/00401706.1977.10489581
  24. Son, Y.M., Lee, K.H., Kwon, S.D., and Lee, W.K. 2003. Evaluation and prediction system of tree resources. Report of Forest Research 04-01 pp. 49-52.
  25. Tyler, A.L., Macmillan, D.C., and Dutch, J. 1996. Models to predict the general yield class of Douglas-fir, Japanese larch and Scots pine on better quality land in Scotland. Forestry 1: 13-24.
  26. Won, H.K., Jeong, J.H., Koo, K.S., Song, M.H., and Shin, M.Y. 2005. Estimation of forest site productivity by regional environment and forest soil factors. Korean Journal of Agricultural and Forest Meteorology 7(2): 132-140.

Cited by

  1. Predicting the Potential Habitat and Risk Assessment of Amaranthus patulus using MaxEnt vol.36, pp.4, 2018, https://doi.org/10.11626/KJEB.2018.36.4.672
  2. MaxEnt를 활용한 청비름(Amaranthus viridis)의 기후변화 시나리오에 의한 서식지 분포 변화 예측 vol.34, pp.4, 2014, https://doi.org/10.11626/kjeb.2016.34.4.240
  3. Prediction of Land Use/Land Cover Change in Forest Area Using a Probability Density Function vol.33, pp.4, 2017, https://doi.org/10.7747/jfes.2017.33.4.305
  4. Effect of elevation on the insect herbivory of Mongolian oaks in the high mountains of southern South Korea vol.22, pp.3, 2014, https://doi.org/10.1016/j.aspen.2019.08.004
  5. Mapping Species-Specific Optimal Plantation Sites Using Random Forest in Gyeongsangnam-do Province, South Korea vol.53, pp.6, 2019, https://doi.org/10.14397/jals.2019.53.6.65
  6. Landscaping Trees under the Impacts of Climate Changes: Construction Professionals’ Perceptions in the Field of Landscape Architecture in South Korea vol.8, pp.1, 2020, https://doi.org/10.14246/irspsd.8.1_94
  7. 낙동정맥 마루금 일대의 신갈나무우점군락 특성 -백병산, 칠보산, 백암산, 운주산, 고헌산, 구덕산을 중심으로- vol.34, pp.4, 2014, https://doi.org/10.13047/kjee.2020.34.4.318
  8. Tree growth response to recent warming of two endemic species in Northeast Asia vol.162, pp.3, 2014, https://doi.org/10.1007/s10584-020-02718-1