Construction of crown width model of <i>Larix</i> <i>gmelinii</i> plantation in Xiaoxing’an Mountains of northeastern China

Liu Suoming; Wang Junjie; Yan Yunfei; Jiang Lichun

doi:10.12171/j.1000-1522.20210551

Journal of Beijing Forestry University > 2023 > 45(5): 79-87. > DOI: 10.12171/j.1000-1522.20210551

Liu Suoming, Wang Junjie, Yan Yunfei, Jiang Lichun. Construction of crown width model of Larix gmelinii plantation in Xiaoxing’an Mountains of northeastern China[J]. Journal of Beijing Forestry University, 2023, 45(5): 79-87. DOI: 10.12171/j.1000-1522.20210551

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Construction of crown width model of Larix gmelinii plantation in Xiaoxing’an Mountains of northeastern China

School of Forestry, Key Laboratory of Sustainable Forest Ecosystem Management of Ministry of Education,Northeast Forestry University, Harbin 150040, Heilongjiang, China

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Received Date: December 25, 2021
Revised Date: April 16, 2022
Accepted Date: April 17, 2023
Available Online: April 19, 2023
Published Date: May 24, 2023

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Abstract

Abstract

Objective This paper uses nonlinear regression, mixed effect model, quantile regression and quantile regression combination to construct the crown width model of Larix gmelinii, which provides a reference for the accurate prediction of the crown width of Larix gmelinii in Xiaoxing’an Mountains of northeastern China.
Method In this study, the data were collected from sample plot of 60 Larix gmelinii plantations in Mayongshun Forest Farm, the generalized nonlinear model, quantile regression model and mixed effect model were constructed, respectively. 10-fold cross validation was used to compare the prediction. The number of 1 to 8 sample trees were randomly selected from each sample plot to calibrate the two kinds of quantile regression combination models, including QRc-1 (τ = 0.1, 0.5, 0.9) and QRc-2 (τ = 0.3, 0.5, 0.7), and mixed effect model to determine the best sampling scheme for quantile regression combination and mixed effect model, and comparison was carried out and analyzed for different methods.
Result (1) The model fitting results showed that the mixed effect model had the best fitting statistics. Median regression was the best quantile regression model. There was little difference between the fitting statistics of median regression and nonlinear regression model, but it was slightly better than nonlinear regression model. (2) The results of sampling calibration showed that, when the number of samples was greater than 2 trees, the order of the models was QRc-2 > the mixed effect model > QRc-1. (3) The significance test of cross validation showed that the best sampling scheme of the two kinds quantile regression combinations was 4 trees, and the best sampling scheme of the mixed effect model was 5 trees.
Conclusion In this study, both mixed effect model and quantile regression combination can improve the prediction accuracy of crown width model. Quantile regression combination (τ = 0.3, 0.5, 0.7) is slightly higher than the mixed effect model in the validation statistics when using the best sampling scheme. Due to the sample number of quantile regression combination is less, which saves more time and cost, therefore, the quantile regression combination (τ = 0.3, 0.5, 0.7) of 4 sample trees is selected as the best model to predict the crown width.
- crown width,
- quantile regression,
- mixed effect model,
- quantile regression combination

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References (34)

References

[1]	Hasenauer H, Monserud R A. Biased predictions for tree height increment models developed from smoothed ‘data’[J]. Ecological Modelling, 1997, 98(1): 13−22. doi: 10.1016/S0304-3800(96)01933-3
[2]	Matsumoto H, Ohtani M, Washitani I. Tree crown size estimated using image processing[J/OL]. Tropical Conservation Science, 2017, 10: 194008291772178[2021−12−14]. https://doi.org/10.1177/1940082917721787.
[3]	Hussain A, Shahzad M K, Jiang L. The effect of crown dimensions on stem profile for Dahurian larch, Korean spruce, and Manchurian fir in Northeast China[J/OL]. Forests, 2021, 12(4): 398[2021−12−29]. https://doi.org/10.3390/f12040398.
[4]	Gonzalez-Benecke C A, Gezan S A, Samuelson L J, et al. Estimating Pinus palustris tree diameter and stem volume from tree height, crown area and stand-level parameters[J]. Journal of Forestry Research, 2014, 25(1): 43−52. doi: 10.1007/s11676-014-0427-4
[5]	Goodman R C, Phillips O L, Baker T R. The importance of crown dimensions to improve tropical tree biomass estimates[J]. Ecological Applications, 2014, 24(4): 680−698. doi: 10.1890/13-0070.1
[6]	Gray A N, McIntosh A C S, Garman S L, et al. Predicting canopy cover of diverse forest types from individual tree measurements[J/OL]. Forest Ecology and Management, 2021, 501: 119682[2021−12−24]. https://doi.org/10.1016/j.foreco.2021.119682.
[7]	Shen H, Cai J, Li M, et al. On Chinese forest canopy biodiversity monitoring[J]. Biodiversity Science, 2017, 25(3): 229−236. doi: 10.17520/biods.2016294
[8]	Lowman M D, Moffett M. The ecology of tropical rain forest canopies[J]. Trends in Ecology & Evolution, 1993, 8(3): 104−107.
[9]	Fu L, Zhang H, Sharma R P, et al. A generalized nonlinear mixed-effects height to crown base model for Mongolian oak in northeast China[J]. Forest Ecology and Management, 2017, 384: 34−43. doi: 10.1016/j.foreco.2016.09.012
[10]	Raptis D, Kazana V, Kazaklis A, et al. A crown width-diameter model for natural even-aged black pine forest management[J/OL]. Forests, 2018, 9(10): 610[2021−12−19]. https://doi.org/10.3390/f9100610.
[11]	Sharma R P, Vacek Z, Vacek S. Individual tree crown width models for Norway spruce and European beech in Czech Republic[J]. Forest Ecology and Management, 2016, 366: 208−220. doi: 10.1016/j.foreco.2016.01.040
[12]	周泽宇, 符利勇, 张晓红, 等. 金沟岭林场天然云冷杉林冠幅模型和估计方法比较[J]. 北京林业大学学报, 2021, 43(8): 29−40. Zhou Z Y, Fu L Y, Zhang X H, et al. Comparison of crown width models and estimation methods of natural spruce fir forest in Jingouling Forest Farm of northeastern China[J]. Journal of Beijing Forestry University, 2021, 43(8): 29−40.
[13]	王君杰, 姜立春. 基于线性分位数组合的兴安落叶松冠幅预测[J]. 南京林业大学学报(自然科学版), 2021, 45(5): 1−13. Wang J J, Jiang L C. Predicting crown width for Larix gmelinii based on linear quantiles groups[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2021, 45(5): 1−13.
[14]	辛士冬, 姜立春. 利用分位数回归模拟人工樟子松树干干形[J]. 北京林业大学学报, 2020, 42(2): 1−8. Xin S D, Jiang L C. Modeling stem taper profile for Pinus sylvestris plantations using nonlinear quantile regression[J]. Journal of Beijing Forestry University, 2020, 42(2): 1−8.
[15]	Bohora S B, Cao Q V. Prediction of tree diameter growth using quantile regression and mixed-effects models[J]. Forest Ecology and Management, 2014, 319: 62−66. doi: 10.1016/j.foreco.2014.02.006
[16]	Cao Q V, Wang J. Evaluation of methods for calibrating a tree taper equation[J]. Forest Science, 2015, 61(2): 213−219. doi: 10.5849/forsci.14-008
[17]	Özçelik R, Cao Q 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, 419−420: 240−248. doi: 10.1016/j.foreco.2018.03.051
[18]	Fu L, Sun H, Sharma R P, et al. Nonlinear mixed-effects crown width models for individual trees of Chinese fir (Cunninghamia lanceolata) in south-central China[J]. Forest Ecology and Management, 2013, 302: 210−220. doi: 10.1016/j.foreco.2013.03.036
[19]	Sharma R P, Bílek L, Vacek Z, et al. Modelling crown width-diameter relationship for Scots pine in the central Europe[J]. Trees, 2017, 31(6): 1875−1889. doi: 10.1007/s00468-017-1593-8
[20]	Fu L, Sharma R P, Hao K, et al. A generalized interregional nonlinear mixed-effects crown width model for Prince Rupprecht larch in northern China[J]. Forest Ecology and Management, 2017, 389: 364−373. doi: 10.1016/j.foreco.2016.12.034
[21]	雷相东, 张则路, 陈晓光. 长白落叶松等几个树种冠幅预测模型的研究[J]. 北京林业大学学报, 2006, 28(6): 75−79. doi: 10.3321/j.issn:1000-1522.2006.06.013 Lei X D, Zhang Z L, Chen X G. Crown width prediction models for several tree species including Larix olgensis in northeastern China[J]. Journal of Beijing Forestry University, 2006, 28(6): 75−79. doi: 10.3321/j.issn:1000-1522.2006.06.013
[22]	Grégoire T G, Schabenberger O, Barrett J P. Linear modelling of irregularly spaced, unbalanced, longitudinal data from permanent-plot measurements[J]. Canadian Journal of Forest Research, 1995, 25(1): 137−156. doi: 10.1139/x95-017
[23]	Pinheiro J C, Bates D M. Mixed effects models in S and S-PLUS[M]. New York: Springer, 2000.
[24]	Davidian M, Giltinan D M. Nonlinear models for repeated measurement data[M]. New York: Chapman & Hall, 1995.
[25]	Meng S X, Huang S. Improved calibration of nonlinear mixed-effects models demonstrated on a height growth function[J]. Forest Science, 2009, 55(3): 238−248.
[26]	Zhang L, Bi H, Gove J H, 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. doi: 10.1139/x05-070
[27]	Crecente-Campo F, Tomé M, Soares P, et al. A generalized nonlinear mixed-effects height–diameter model for Eucalyptus globulus L. in northwestern Spain[J]. Forest Ecology and Management, 2010, 259(5): 943−952. doi: 10.1016/j.foreco.2009.11.036
[28]	Temesgen H, Monleon V J, Hann D W. Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests[J]. Canadian Journal of Forest Research, 2008, 38(3): 553−565. doi: 10.1139/X07-104
[29]	Bronisz K, Mehtätalo L. Mixed-effects generalized height-diameter model for young silver birch stands on post-agricultural lands[J/OL]. Forest Ecology and Management, 2020, 460: 117901[2021−12−18]. https://doi.org/10.1016/j.foreco.2020.117901.
[30]	Yang Y, Huang S. Suitability of five cross validation methods for performance evaluation of nonlinear mixed-effects forest models: a case study[J]. Forestry, 2014, 87(5): 654−662. doi: 10.1093/forestry/cpu025
[31]	Thorpe H C, Astrup R, Trowbridge A, et al. Competition and tree crowns: a neighborhood analysis of three boreal tree species[J]. Forest Ecology and Management, 2010, 259(8): 1586−1596. doi: 10.1016/j.foreco.2010.01.035
[32]	邓成, 吕勇, 雷渊才, 等. 以相对直径为竞争指标的单木直径生长模型研究[J]. 林业资源管理, 2011(1): 40−43. doi: 10.3969/j.issn.1002-6622.2011.01.009 Deng C, Lü Y, Lei Y C, et al. Study on individual tree diameter growth models with the relative diameter as competition indicator[J]. Forest Resources Wanagement, 2011(1): 40−43. doi: 10.3969/j.issn.1002-6622.2011.01.009
[33]	Zhang X, Wang H, Chhin S, et al. Effects of competition, age and climate on tree slenderness of Chinese fir plantations in southern China[J/OL]. Forest Ecology and Management, 2020, 458: 117815[2021−12−16]. https://doi.org/10.1016/j.foreco.2019.117815.
[34]	Zang H, Lei X D, Zeng W S. 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. doi: 10.1093/forestry/cpw022

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Construction of crown width model of Larix gmelinii plantation in Xiaoxing’an Mountains of northeastern China

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