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http://dx.doi.org/10.14578/jkfs.2011.100.3.10

Use of a Bootstrap Method for Estimating Basic Wood Density for Pinus densiflora in Korea  

Pyo, Jung Kee (Division of Forest management, Korea Forest Research Institute)
Son, Yeong Mo (Division of Forest management, Korea Forest Research Institute)
Kim, Yeong Hwan (Division of Forest management, Korea Forest Research Institute)
Kim, Rae Hyun (Division of Forest management, Korea Forest Research Institute)
Lee, Kyeong Hak (Division of Forest management, Korea Forest Research Institute)
Lee, Young Jin (Department of Forest Resources, Kongju National University)
Publication Information
Journal of Korean Society of Forest Science / v.100, no.3, 2011 , pp. 392-396 More about this Journal
Abstract
The purpose of this study was to develop the basic wood density (Abbreviated BWD) for Pinus densiflora and to evaluate the applicability of bootstrap simulation method. The data sets were divided into two groups based on eco-types in Korea, one from Gangwon type and the other from Jungbu type. The estimated BWDs derived from bootstrap simulation, which is one of the non-parametric statistics, were 0.418 ($g/cm^3$) in the Pinus densiflora in Gangwon while 0.464 ($g/cm^3$) in the Pinus densiflora in Jungbu. To evaluate the bootstrap simulation, the mean BWD, standard error and 95% confidence interval of probability density were estimated. The number of replication were 100, 500, 1,000, and 5,000 times that showed constant 95% confidence interval, while tended to decrease in terms of standard errors. The results of this study could be very useful to apply basic wood density values to calculate reliable carbon stocks for Pinus densiflora in Korea.
Keywords
Pinus densiflora; basic wood density; bootstrap simulation; uncertainty; resampling;
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Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 국립산림과학원. 2007. 산림 바이오매스 및 토양탄소 조사.분석 표준. 국립산림과학원. pp. 4.
2 국립산림과학원. 2010. 산림 온실가스 인벤토리를 위한 주요 수종별 탄소배출계수. 국립산림과학원. pp. 7.
3 김경섭. 2010. Bootstrap 기법을 이용한 BOD 평균 농도 및 신뢰구간 분석. 수질보전 한국물환경학회지 26(2): 297-302.
4 김우종, 강기훈. 2009. 붓스트랩을 이용한 다차원척도법의 효율성 연구. 한국데이터정보과학회지 20(2): 301-309.
5 김태규, 박상규, 하명호. 2010. 안정성 연구에서의 사용 기간에 관한 비모수적 추론. 품질경영학회지 39(1): 96-100.
6 박인협, 박관수, 이경학, 손영모, 서정호, 손요환, 이영진. 2005. 소나무의 생태형과 임령에 따른 물질 현존량 확장계수. 한국임학회 94: 441-445.
7 정석근, 최일수, 장대수. 2008. 부트스트랩과 베이지안 방법으로 추정한 수산자원관리에서의 생물학적 기준점의 신뢰구간. 한국수자원학회지 41(2): 107-112.
8 전명식, 정형철, 진서훈. 1997. 붓스트랩방법의 이해. 자유아카데미. pp. 3-9.
9 Chang, K.Y., Hong, K.O. and Park, S.I. 2007. Bootstrap simulation for quantification of uncertainty in risk assessment. Korean Journal of Veterinary Research 47(2): 259-263.
10 Chernick, M.R. 2007. Bootstrap Methods: A Guide for Practitioners and Researchers. John Wiley and Sons. 53-58.
11 Coulombe, S., Bernier, P.Y. and Raulier, F. 2010. Uncertainty in detecting climate change impact on the projected yield of black spruce (Picea mariana). Forest Ecology and Management 259: 730-738.   DOI   ScienceOn
12 Efron, B. 1979. Bootstrap methods: Another look at the jackknife. The annals of statistics 7: 1-26.   DOI   ScienceOn
13 Efron, B. 1981. Non parametric estimates of standard error: The jackknife, the bootstrap and other methods. Biometrika 68(3): 589-99.   DOI   ScienceOn
14 FAO. 2006. Global forest resources assessment 2005. Food and Agriculture Organization, Rome, Italy.
15 Fujiwara, T., Kana, Y. and Kuroda, K. 2007. Basic densities as a parameter for estimating the amount of carbon removal by forests and their variation. 森林綜合硏究所報告 6(4): 215-226.
16 Gehringer, K.R. 2006. Structure-based nonparametric target definition and assessment procedures with an application to riparian forest management. Forest Ecology and Management 223: 125-138.   DOI   ScienceOn
17 IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 1. General Guidance and Reporting. IPCC National Greenhouse Gas Inventory Programme. Institute for Global Environmental Strategies. pp. 3.6-3.78.
18 IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4. Agriculture, Forestry and Other Land Use. IPCC National Greenhouse Gas Inventory Programme. Institute for Global Environmental Strategies. pp. 4.73.
19 Jeong, H.C. and Kim, D.H. 2009. Constructing simultaneous confidence intervals for the difference of proportions from multivariate binomial distributions. The Korean Journal of Applied Statistics 22(1): 129-140.   DOI   ScienceOn
20 Kangas, A.S. and Kangas, J. 2004. Probability, possibility and evidence : approaches to consider risk and uncertainty in forestry decision analysis. Forest Policy and Economics 6: 169-188.   DOI   ScienceOn
21 Lehtonen, A., Mäkipää, R., Heikkinen, J., Sievänen, R. and Liski, J. 2004. Biomass Expansion factor (BEFs) for Scotes pine, Norway spruce and birch according to stand age for boreal forests. Forest Ecology and Management 188: 211-224.   DOI   ScienceOn
22 SAS Institute, Inc. 2006. SAS/STAT 9.1.3 User's Guide. SAS Institute, Inc. Cary. NC.
23 Macfarlane, D.W., Edwin, J.G. and Harry, T.V. 2000. Incorporating uncertainty into the parameters of a forest process model. Ecological Modelling 134: 27-40.   DOI   ScienceOn
24 Monte, L., Lars, H., Ulla, B., John, B. and Rudie, H. 1996. Uncertainty analysis and validation of environmental models : The empirically based uncertainty analysis. Ecological Modelling 91: 139-152.   DOI   ScienceOn
25 Refsgaard, J.C., Jeroen, P.V.D.S., Anker, L.H. and Peter, A.V. 2007. Uncertainty in the environmental modelling process-A framework and guidance. Environmental Modelling and Software 22: 1543-1556.   DOI   ScienceOn
26 Vaughn, N.R., Turnblom, E.C. and Ritchie, M.W. 2010. Bootstrap evaluation of a young douglas-fir height growth model for the pacific northwest. Forest Science 56(6): 592-602.
27 Wasserman, L. 2006. All of nonparametric statistics. Springer. pp. 27-41.