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|Table of Contents|

落叶松人工林树皮厚度预测模型(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2019年06期
Page:
97-104
Column:
研究论文
publishdate:
2019-11-25

Article Info:/Info

Title:
Bark thickness prediction models for larch plantation
Article ID:
1000-2006(2019)06-0097-08
Author(s):
JIA Weiwei LIANG Yuzhao LI Fengri*
(College of Forestry, Northeast Forestry University, Harbin 150040, China)
Keywords:
larch plantation bark factor bark thickness linear mixed model the prediction model disk of larch tree
Classification number :
S758
DOI:
10.3969/j.issn.1000-2006.201810024
Document Code:
-
Abstract:
【Objective】The prediction model of bark factor and bark thickness at any height of larch plantation was established in order to predict bark thickness more accurately and provide more accurate prediction model and guidance for actual wood production and forest management.【Method】Based on 1 186 disk data of 49 artificial larch trees in Mengjiagang Forest Farm of Jiamusi City, Heilongjiang Province in 2015, the bark thickness(bark factor, bark thickness at any height)of larch plantation linear mixed effects prediction model was constructed using the MIXED module in SAS 9.4 software. The model evaluation indicators were Akaike Information Criterion(AIC), Bayesian Information Criterion(BIC), -2 log likelihood(-2LL)and likelihood ratio test(LRT). 【Result】For the bark factor model, the bark factor model with b1,b2, b4 and random parameter combination is the optimal mixed model based on the tree effect, and the model with b1, b2 random parameter combination is the optimal model based on the plot effect. For the bark thickness model at any height, the combination of b1, b2 is the optimal mixed model based on the tree effect, and the combination of b0,b2,b3 is the optimal model based on the plot effect. All the optimal models have the best fitting effect when they have unstructured(UN)variance-covariance matrix. 【Conclusion】The tree effect has the greatest influence on the model whether it is the bark factor or the bark thickness model. The prediction accuracy of the mixed-effect model is significantly improved compared with the traditional regression model.

References

[1] HEATH L S, HANSEN M H, SMITH J E, et al. Investigation into calculating tree biomass and carbon in the FIADB using a biomass expansion factor approach[C]//USDA Forest Service Rocky Mountain Research Station, 2009.
[2] 孟宪宇. 测树学[M]. 北京:中国林业出版社, 2006. MENG X Y. Tree survey[M]. Beijing: China Forestry Publishing House,2006.
[3] GROSENBAUGH L R. STX-Fortran-4 program for estimates of tree populations from 3P sample-tree-measurements[R]. USDA Forest Serv Res Pap Psw, 1967.
[4] LAASASENAHO J, MELKAS T, ALDéN S. Modelling bark thickness of Picea abies with taper curves[J]. Forest Ecology and Management, 2005, 206(1/2/3): 35-47. DOI:10.1016/j.foreco.2004.10.058
[5] MAX T, BURKHART H. Segmented polynomial regression applied to taper equations[J]. Forest Science, 1976, 22(3): 283-289.
[6] MALONE T, LIANG J J. A bark thickness model for white spruce in Alaska northern forests[J]. International Journal of Forestry Research, 2009: 1-5. DOI:10.1155/2009/876965.
[7] KOZAK A, YANG R C. Equations for estimating bark volume and thickness of commercial trees in British Columbia[J]. The Forestry Chronicle, 1981, 57(3): 112-115. DOI:10.5558/tfc57112-3
[8] COURBET F, HOULLIER F. Modelling the profile and internal structureof tree stem. application to Cedrus atlantica(Manetti)[J]. Annals of Forest Science, 2002, 59(1): 63-80. DOI:10.1051/forest:2001006.
[9] LI R X, WEISKITTEL A R. Estimating and predicting bark thickness for seven conifer species in the Acadian Region of North America using a mixed-effects modeling approach: comparison of model forms and subsampling strategies[J]. European Journal of Forest Research, 2011, 130(2): 219-233. DOI:10.1007/s10342-010-0423-y.
[10] 王晓林, 蔡可旺, 姜立春. 落叶松树皮厚度变化规律的研究[J]. 森林工程, 2011, 27(2): 8-11. DOI:10.16270/j.cnki.slgc.2011.02.022. WANG X L, CAI K W, JIANG L C. Study on bark thickness of dahurian larch[J]. Forest Engineering, 2011, 27(2): 8-11.
[11] 王君山, 闫菁. 树皮厚度和树皮系数变化规律的研究[J]. 河北林果研究, 2011, 26(3): 235-237. WANG J S, YAN J. Rules of bark thickness and bark coefficient[J]. Hebei Journal of Forestry and Orchard Research, 2011, 26(3): 235-237.
[12] 郭孝玉, 孙玉军, 马炜, 等. 适于FVS的长白落叶松树皮因子[J]. 东北林业大学学报, 2011, 39(10): 28-31. DOI:10.13759/j.cnki.dlxb.2011.10.037. GUO X Y, SUN Y J, MA W, et al. Bark factor of Larix olgensis suitable for forest vegetation simulator[J]. Journal of Northeast Forestry University, 2011, 39(10): 28-31.
[13] FANG Z,BALEY R L.Nonlinear mixed effects modeling for slash pine dominant height growth following intensive sil-vicultural treatments[J].Forest Science,2001,47:287-300.
[14] JIANG L C, DU S L, LI F R. Simulation of Larix gmelinii tree volume growth based on random effect[J]. The Journal of Applied Ecology, 2011, 22(11): 2963-2969.
[15] CALEGARIO N, DANIELS R F, MAESTRI R, et al. Modeling dominant height growth based on nonlinear mixed-effects model: a clonal Eucalyptus plantation case study[J]. Forest Ecology and Management, 2005, 204(1): 11-21. DOI:10.1016/j.foreco.2004.07.051
[16] 姜立春, 刘瑞龙. 基于非线性混合模型的落叶松树干削度模型[J]. 林业科学, 2011, 47(4): 101-106. JIANG L C, LIU R L. A stem taper model with nonlinear mixed effects for dahurian larch[J]. Scientia Silvae Sinicae, 2011, 47(4): 101-106.
[17] GROOM J D, HANN D W, TEMESGEN H. Evaluation of mixed-effects models for predicting Douglas-fir mortality[J]. Forest Ecology and Management, 2012, 276: 139-145. DOI:10.1016/j.foreco.2012.03.029.
[18] JIANG L C, ZHANG R, LI F R. Modeling branch length and branch angle with linear mixed effects for dahurian larch[J]. Forestry Science and Technology, 2012, 11(3): 57.
[19] HEIN S, MÄKINEN H, YUE C F, et al. Modelling branch characteristics of Norway spruce from wide spacings in Germany[J]. Forest Ecology and Management, 2007, 242(2/3): 155-164. DOI:10.1016/j.foreco.2007.01.014.

Last Update: 2019-11-30