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Forest and Grassland Resources Research >

2020 , Vol. 0 >Issue 1: 151 - 157

DOI: https://doi.org/10.13466/j.cnki.lyzygl.2020.01.019

Technical Application

Stem Shape Curves Simulation for Larixprincipis-rupprechtii Using Quantile Regressions

  • Lihua FU ,
  • Jinchao HOU ,
  • He SUN ,
  • Hezhi WANG ,
  • Qiang LIU ,
  • Shun CHENG
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  • 1. Saihanba Mechanized Forestry Farm of Hebei,Chengde,Hebei 068466
    2. World Bank Loan Project Management Center,National Forestry and Grassland Administration,Beijing 100714
    3. Academy of Forest and Grassland Inventory and Planning,National Forestry and Grassland Administration,Beijing 100714
    4. College of Forestry,Agricultural University of Hebei,Baoding,Hebei 071000

Received date: 2019-11-25

  Revised date: 2019-12-16

  Online published: 2020-05-18

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Abstract

Stem shape is an important index to reflect the external shape of stem and it is also an important basis for evaluating the value of trunk.The taper equation is an important quantitative index to describe the quality of tree stem shape.The accuracy of its simulation of stem shape directly determines the estimation of trunk volume and forest volume.In this study,four simple taper equations were used to estimate the stem shape of planted Larixprincipis-rupprechtii and the model fit and validation were assessed byadjusted determination coefficient(Ra2),root mean square error(RMSE),mean error(ME)and absolute mean error(MAE).The optimal model was selected and the taper shape of Larixprincipis-rupprechtii was modelled by using quantile regression technology.Result showed that Kozak equation has the best fitting result(Ra2=0.934, RMSE=1.985cm)and the validation result(ME=0.125cm, MAE=1.212cm)on the stem shape among the four simple taper equations.Thus,the taper shape of Larixprincipis-rupprechtii was established by using quantile regression technology based on Kozak equation.Compared with the basic model,the goodness-of-fit of quantile regression model was improved.When the quantile is set to 0.1,the model has a better fitting effect on the stem shape near the lower and upper part of trunk,when the quantile is set to 0.5,the model has a better fitting effect on the trunk at the middle part of trunk,and when the quantile is set to 0.9,the model has a better fitting effect on the base of trunk.It can be seen that quantile regression technology makes the model more flexible.

Key words： quantile regression; taper curve; Saihanba machinery forest farm; Larixprincipis-rupprechtii

Cite this article

Lihua FU , Jinchao HOU , He SUN , Hezhi WANG , Qiang LIU , Shun CHENG . Stem Shape Curves Simulation for Larixprincipis-rupprechtii Using Quantile Regressions[J]. Forest and Grassland Resources Research, 2020 , 0(1) : 151 -157 . DOI: 10.13466/j.cnki.lyzygl.2020.01.019

References

[1] Jiang Lichun, Brooks J R. Taper, Volume, and Weight Equations for Red Pine in West Virginia[J]. Northern Journal of Applied Forestry, 2008,25(3):151-153.
[2] Yang Yuqing, Huang Shongming, Trincado G , et al. Nonlinear mixed-effects modeling of variable-exponent taper equations for lodgepole pine in Alberta,Canada[J]. European Journal of Forest Research, 2009,128(4):415-429.
[3] Kublin E, Breidenbach J, K?ndler G . A flexible stem taper and volume prediction method based on mixed-effects B-spline regression[J]. European Journal of Forest Research, 2013,132(5-6):983-997.
[4] Kozak B, Munro D O, Smith J H G. Taper functions and their applicbtion in forest inventory[J]. The Forestry Chronicle, 1969,45(4):278-283.
[5] 孟宪宇 . 削度方程和出材表的研究[J]. 南京林产工业学院学报, 1982,5(1):122-133.
[6] Max T B, Burkhart H E . Segmented polynomial regression applied to taper equations[J]. Forest Science, 1976,22:283-289.
[7] CaoQuang V., Harold E. Burkhart,Timothy A.Max.Evaluation of Two Methods for Cubic-Volume Prediction of Loblolly Pine to Any Merchantable Limit[J]. Forest Science, 1980,26(1):71-80.
[8] Newnham RM . Variable-form taper functions for four Alberta tree species[J]. Canadian Journal of Forest Research, 1992,22(2):210-223.
[9] Kozak A . A variable-exponent taper equation[J]. Canadian Journal of Forest Research, 1988,18(11):1363-1368.
[10] 梅光义, 孙玉军 . 国内外削度方程研究进展[J]. 世界林业研究, 2015,28(4):44-49.
[11] 庞丽峰, 贾宏炎, 陆元昌 等. 热带地区几个珍贵树种干形曲线研究[J]. 南京林业大学学报:自然科学版, 2016,40(5):93-98.
[12] 王明亮 . 理论造材: 削度方程和出材率表的编制[J]. 林业科学研究, 1998,11(3):271-276.
[13] 胡春祥, 杨胜利, 贾炜玮 . 落叶松人工林树干形状模型和可变参数[J]. 应用生态学报, 2011,22(7):1685-1701.
[14] 李元 . 红松人工林树干削度方程的研究[J]. 绿色科技, 2017,17:87-91.
[15] 欧建德, 吴志庄 . 峦大杉与杉木人工林的生长形质、林分分化和空间利用比较[J]. 东北林业大学学报, 2018,46(7):7-11.
[16] 姜立春, 马英莉, 李耀翔 . 大兴安岭不同区域兴安落叶松可变指数削度方程[J]. 2016,52(2):17-25.
[17] 陈振雄, 贺东北, 肖前辉 . 海南省桉树、木麻黄、马占相思削度方程研建[J]. 中南林业调查规划, 2012,3:11-14.
[18] 佟金权, 盛炜彤 . 杉木人工林广义干形模型的研究[J]. 林业科学研究, 2000,13(3):239-248.
[19] Demaerschalk J P . Converting volume equations to compatible taper equations[J]. Forest Science. 1972,18:241-245.
[20] Kozak A.Munro D D, Smith JHG . Taper functions and their application in forest inventory[J]. The Forest Chronicle, 1969,45:278-283
[21] Forslund R R . The power function as a simple stem profile examination tool[J]. Canadian Journal of Forest Research, 1991,21(2):193-198.
[22] Metcalf C, Jessica E, Clark J S, Clark D A . Tree growth inference and prediction when the point of measurement changes:modelling around buttresses in tropical forests[J]. Journal of tropical ecology, 2008,25:1-12.
[23] 李凤日 . 测树学[M]. 北京: 中国林业出版社, 2019.
[24] 胥辉, 孟宪宇 . 天山云杉削度方程与材种出材率表的研究[J]. 北京林业大学学报, 1995,18(3):21-30.
[25] Weiskittel A R, Hann D W, Kershaw J A, et al. Forest growth and yield modeling[J/OL].(2006 -09)[2019-10-10].https://www.researchgate.net/publication/228010008_Forest_Growth_and_Yield_Modeling .
[26] Zang Hao, Lei Xiangdong, Zeng Weisheng . Height-diameter equations for larch plantations in northern and northeastern China:a comparison of the mixed-effects,quantile regression and generalized additive models[J]. Forestry, 2016,89(4):434-445.
[27] Oz?elik R, CaoQuang V., Trincado G, et al. Predicting tree height from tree diameter and dominant height using mixed-effects and quantile regression models for two species in Turkey[J]. Forest Ecology and Management, 2018,419:240-248.
[28] Zhang Lianjun, Bi Huiquan, Gove JH , et al. A comparison of alternative methods for estimating the self-thinning boundary line[J]. Canadian Journal of Forest Research, 2005,35(6):1507-1514.
[29] Meht?talo L, Gregoire TG, Burkhart HE . Comparing strategies for modeling tree diameter percentiles from remeasured plots[J]. Environmetrics, 2008,19(5):529-548.
[30] Bohora S B, CaoQuang V. Prediction of tree diameter growth using quantile regression and mixed-effects models[J]. Forest Ecology and Management, 2014,319:62-66.
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