高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

塞罕坝华北落叶松人工林断面积预测模型

王冬至 张冬燕 张志东 黄选瑞

王冬至, 张冬燕, 张志东, 黄选瑞. 塞罕坝华北落叶松人工林断面积预测模型[J]. 北京林业大学学报, 2017, 39(7): 10-17. doi: 10.13332/j.1000-1522.20170072
引用本文: 王冬至, 张冬燕, 张志东, 黄选瑞. 塞罕坝华北落叶松人工林断面积预测模型[J]. 北京林业大学学报, 2017, 39(7): 10-17. doi: 10.13332/j.1000-1522.20170072
WANG Dong-zhi, ZHANG Dong-yan, ZHANG Zhi-dong, HUANG Xuan-rui. Prediction model for basal area of Larix principis-rupprechtii plantation in Saihanba of Hebei Province, northern China[J]. Journal of Beijing Forestry University, 2017, 39(7): 10-17. doi: 10.13332/j.1000-1522.20170072
Citation: WANG Dong-zhi, ZHANG Dong-yan, ZHANG Zhi-dong, HUANG Xuan-rui. Prediction model for basal area of Larix principis-rupprechtii plantation in Saihanba of Hebei Province, northern China[J]. Journal of Beijing Forestry University, 2017, 39(7): 10-17. doi: 10.13332/j.1000-1522.20170072

塞罕坝华北落叶松人工林断面积预测模型

doi: 10.13332/j.1000-1522.20170072
基金项目: 

“十二五”国家科技支撑计划项目 2015BAD09B01

林业公益性行业科研专项 20150430304

国家自然科学基金项目 31370636

详细信息
    作者简介:

    王冬至,博士,讲师。主要研究方向:森林可持续经营。Email: wangdz@126.com  地址: 071000  河北省保定市南市区乐凯南大街2596号河北农业大学西校区林学院

    责任作者:

    黄选瑞,教授,博士生导师。主要研究方向:森林可持续经营。Email: hxr1962@163.com  地址:同上

  • 中图分类号: S758.5+7

Prediction model for basal area of Larix principis-rupprechtii plantation in Saihanba of Hebei Province, northern China

  • 摘要: 如何实现林分水平和单木水平预测林分断面积的兼容性,并提高不同水平断面积模型的预估精度,在森林经营过程中是一个亟待解决的科学问题。本文以华北暖温带华北落叶松人工林为研究对象,运用105块连续观测的固定样地数据,首先采用Gauss-Newton算法建立林分水平和单木水平断面积生长模型;其次分别采用不同形式的逻辑斯蒂方程对单木生存概率方程进行拟合;最后将不同水平最优预测模型进行组合并建立组合方程,采用最小二乘法估计组合方程参数,以提高对不同水平断面积的预测精度。结果表明:在约束参数法中,林分密度模型、林分断面积预测模型和单木断面积模型均具有较好的预测效果,并均能解释90%以上的变异;在分解法中采用逻辑斯蒂方程来预测单木生存概率和林分密度,经检验获得的ROC曲线下面积为0.906,表明该方程可以较好地预测林木生存概率;在组合预测法中,采用不同水平的最优模型进行组合后的预测效果最佳。在预测林分密度和断面积时,组合预测方程预测精度最高,林分水平模型预测精度次之,单木水平模型预测精度最低。组合预测法能够预测不同水平下的林分密度、立木生存概率、林分断面积及单木断面积,提高了模型预测精度,为预测林分生长动态、空间结构变化及经营效果评价等提供参考依据。

     

  • 图  1  不同水平最优模型残差分布

    M4、M7、M9分别代表林分密度、林分断面积和单木断面积预测模型,即对应文中的公式(4)、(7)和(9)。

    Figure  1.  Residual distribution of different level optimal models

    M4, M7, M9 represent prediction models of stand density, basal area for stand, basal area models for individual trees, which corresponding to formula (4), (7) and (9) of the paper, respectively.

    图  2  单木生存概率ROC曲线

    Figure  2.  ROC curve for the individual tree mortality model fitted

    表  1  建模数据与检验数据统计

    Table  1.   Summary statistics for modeling and validation data sets

    数据
    Data
    变量
    Variable
    2011年调查Inventory in 20112016年调查Inventory in 2016
    最小值
    Min.
    最大值
    Max.
    均值
    Mean
    标准差
    SD
    最小值
    Min.
    最大值
    Max.
    均值
    Mean
    标准差
    SD
    建模数据Modelling data(n=70)林分年龄Stand age134231.510.22184737.09.21
    胸径Diameter at breast height(DBH)/cm6.2728.0217.175.619.0631.5219.456.13
    平均高Mean height/m4.7116.3411.042.336.5419.0212.842.97
    林分密度/(株·hm-2)Stand density/(tree·ha-1)900.03 353.01 927.7958.4500.02 282.01 295.6611.2
    林分断面积/(m2·hm-2)Stand basal area/(m2·ha-1)8.8267.4336.4512.0711.5190.3142.7117.38
    立地质量Site index/m4.6616.8112.362.917.0618.5614.143.43
    生存概率Survival probability/%0.351.000.770.17
    检验数据Validation data(n=35)林分年龄Stand age134033.510.43184738.09.46
    胸径Diameter at breast height(DBH)/cm6.8729.1117.585.079.1232.4220.245.97
    平均高Mean height/m5.1117.2311.802.047.0518.9213.273.09
    林分密度/(株·hm-2)Stand density/(tree·ha-1)900.03 178.01 802.7907.8500.02 077.01 226.7578.4
    林分断面积/(m2·hm-2)Stand basal area/(m2·ha-1)9.0270.5837.0912.5410.9785.6645.0715.06
    立地质量Site index/m5.2416.3313.113.136.9819.2415.373.55
    生存概率Survival probability/%0.411.000.740.19
    注:n为样本数。Note:n is sample number.
    下载: 导出CSV

    表  2  不同水平模型参数估计与统计检验

    Table  2.   Parameter estimating and goodness-of-fit statistics in different levels

    模型Model参数Parameter估计值EstimateSER2BiasRMSE
    M4a10.005 10.003 50.900 21.952 52.186 3
    a20.406 50.399 5
    a31.271 30.526 7
    M5a1-0.405 80.095 40.804 32.005 62.334 2
    a2-0.738 70.415 8
    a3-0.045 20.009 0
    M6b1-0.000 80.000 30.812 31.280 93.155 0
    b27.143 82.056 7
    b33.222 40.684 5
    M7b124.978 61.529 10.914 40.899 71.833 4
    b21.215 30.191 7
    b30.799 20.040 5
    M8b10.999 30.000 80.867 11.008 42.304 1
    b20.000 50.000 3
    b3-2.853 60.677 6
    M9c00.011 20.002 30.913 20.097 50.918 7
    c1-4.284 30.163 3
    c24.396 30.151 7
    c3-24.096 12.385 7
    M10c00.000 20.000 10.908 80.105 81.007 6
    c10.756 70.111 3
    c2-0.528 10.262 4
    c31.093 00.081 3
    c4-0.042 20.012 1
    注:M4~M10对应文中公式(4)~(10)。Note: M4-M10 correspond to formula (4)-(10) of the paper, respectively.
    下载: 导出CSV

    表  3  不同水平生存概率模型参数估计与统计检验

    Table  3.   Parameter estimating and goodness-of-fit statistics in different levels of survival probability

    模型Model参数Parameter估计值EstimateSER2BiasRMSE
    M11f0-3.294 00.780 40.910 41.022 42.077 1
    f10.000 50.000 3
    f20.009 20.002 9
    f324.111 617.138 5
    M12f0-3.663 00.616 00.904 61.075 82.332 4
    f111.015 14.372 4
    f20.036 00.007 8
    f30.007 90.002 3
    注:M11、M12对应文中公式(11)、(12)。Note: M11,M12 correspond to formula (11),(12) of the paper, respectively.
    下载: 导出CSV

    表  4  林分、单株生长模型与组合方程预测林分变量评价

    Table  4.   Evaluation statistics for predicted stand variables from the stand and individual-tree growth models and forecast combination

    变量
    Variable
    每公顷株数预测Predicted tree number per ha林分断面积预测Predicted stand basal area
    RMSERRMSEMADRMSERRMSEMAD
    林分生长Stand growth305.739 47.151.933 82.054 78.440.880 1
    单木生长Individual tree growth364.008 713.152.406 12.931 615.660.973 4
    组合预测Combination forecast284.446 35.421.603 21.664 2-5.610.791 6
    下载: 导出CSV
  • [1] ZHANG S, AMATEIS R L, BURKHART H E. Constraining individual tree diameter increment and survival models for loblolly pine plantations[J]. Forest Science, 1997, 43(6): 414-423. http://europepmc.org/abstract/AGR/IND21235335
    [2] CIESZEWSKI C J. Comparing fixed-and variable-base-age site equations having single versus multiple asymptotes[J]. Forest Science, 2002, 48(1): 7-23. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ027400739/
    [3] TEWARI V P, ÁLVAREZ-GONZALEZ J G, GARCÍA O. Developing a dynamic growth model for teak plantations in India[J]. Forest Ecosystems, 2014, 1(1): 9-17. doi: 10.1186/2197-5620-1-9
    [4] RITCHIE M W, HANN D W. Implications of disaggregation in forest growth and yield modeling[J]. Forest Science, 1997, 43 (2): 223-233. http://europepmc.org/abstract/AGR/IND21235281
    [5] QIN J, CAO Q V. Using disaggregation to link individual-tree and whole-stand growth models[J]. Canadian Journal of Forest Research, 2006, 36: 953-960. doi: 10.1139/x05-284
    [6] GONZALEZ J G, ZINGG A, GADOW K V. Estimating growth in beech forests: a study based on longterm experiments in Switzerland[J]. Annals of Forest Science, 2009, 307(9): 1-13. doi: 10.1051%2Fforest%2F2009113
    [7] RITCHIE M W, HANN D W. Implications of disaggregation in forest growth and yield modeling[J]. Forest Science, 1997, 43 (2): 223-233. http://europepmc.org/abstract/AGR/IND21235281
    [8] BURKHART H E, TOME M. Modeling forest trees and stands[M]. Berlin: Springer, 2012.
    [9] 陈清, 张令峰, 傅松玲.树木年龄和断面积对加拿大北方林树木死亡率的影响[J].应用生态学报, 2011, 22(9): 2477-2481. http://d.old.wanfangdata.com.cn/Periodical/yystxb201109038

    CHEN Q, ZHANG L F, FU S L. Effects of tree age and basal area on boreal forest tree mortality in Canada[J]. Chinese Journal of Applied Ecology, 2011, 22(9): 2477-2481. http://d.old.wanfangdata.com.cn/Periodical/yystxb201109038
    [10] 雷相东, 李永慈, 向玮.基于混合模型的单木断面积生长模型[J].林业科学, 2009, 45(1): 74-80. doi: 10.3321/j.issn:1001-7488.2009.01.014

    LEI X D, LI Y C, XIANG W. Individual basal area growth model using multi-level linear mixed model with repeated measures[J]. Scientia Silvae Sinicae, 2009, 45(1): 74-80. doi: 10.3321/j.issn:1001-7488.2009.01.014
    [11] 符利勇, 唐守正, 张会儒, 等.基于多水平非线性混合效应蒙古栎林单木断面积模型[J].林业科学研究, 2015, 28(1): 23-31. http://d.old.wanfangdata.com.cn/Periodical/lykxyj201501004

    FU L Y, TANG S Z, ZHANG H R, et al. Multilevel nonlinear mixed-effects basal area models for individual trees of Quercus mongolica[J]. Forest Research, 2015, 28(1): 23-31. http://d.old.wanfangdata.com.cn/Periodical/lykxyj201501004
    [12] 倪成才, 王庆丰.火炬松人工林胸高断面积差分模型的拟合与筛选[J].北京林业大学学报, 2011, 33(3): 1-7. doi: 10.3969/j.issn.1671-6116.2011.03.001

    NI C C, WANG Q F. Model selection and fit of algebraic difference models for basal area of loblolly pine plantations[J]. Journal of Beijing Forestry University, 2011, 33(3): 1-7. doi: 10.3969/j.issn.1671-6116.2011.03.001
    [13] 李春明, 唐守正.基于非线性混合模型的落叶松云冷杉林分断面积模型[J].林业科学, 2010, 46(7): 106-113. http://d.old.wanfangdata.com.cn/Periodical/lykx201007016

    LI C M, TANG S Z. The basal area model of mixed stands of Larix olgensis, Abies nephrolepis and Picea jezoensis based on nonlinear mixed model[J]. Scientia Silvae Sinicae, 2010, 46(7): 106-113. http://d.old.wanfangdata.com.cn/Periodical/lykx201007016
    [14] 张雄清, 张建国, 段爱国.杉木人工林林分断面积生长模型的贝叶斯法估计[J].林业科学研究, 2015, 28(4): 538-542. doi: 10.3969/j.issn.1001-1498.2015.04.013

    ZHANG X Q, ZHANG J G, DUAN A G. Application of bayesian method in stand basal area prediction of Chinese fir plantation[J]. Forest Research, 2015, 28(4): 538-542. doi: 10.3969/j.issn.1001-1498.2015.04.013
    [15] 张雄清, 雷渊才, 陈新美.林分断面积组合预测模型权重确定的比较[J].林业科学, 2011, 47(7): 36-41. http://d.old.wanfangdata.com.cn/Periodical/lykx201107006

    ZHANG X Q, LEI Y C, CHEN X M. Comparison of weight computation in stand basal area combined model[J]. Scientia Silvae Sinicae, 2011, 47(7): 36-41. http://d.old.wanfangdata.com.cn/Periodical/lykx201107006
    [16] VALBUENA P, DELPESO C, BRAVO F. Stand density management diagrams for two mediterranean pine species in eastern spain[J]. Investigación Agraria: Sistemas Recursos Forestales, 2008, 17(2): 97-104. doi: 10.5424/srf/2008172-01026
    [17] ZHANG X, LEI Y, CAO Q V, et al. Improving tree survival prediction with forecast combination and disaggregation[J]. Canadian Journal of Forest Research, 2011, 41: 1928-1935. doi: 10.1139/x11-109
    [18] ANDREA H, CAO Q V, ALVAREZ J G, et al. Compatibility of whole-stand and individual-tree models using composite estimators and disaggregation[J]. Forest Ecology and Management, 2015, 348(11): 46-56 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=38160e55b539dc3a70a82cf617f9ea25
    [19] ARANDA D U, GRANDAS J A, ALVAREZ J G, et al. Site quality curves for birch stands in north-western Spain[J]. Silva Fennica, 2006, 40 (4): 631-644. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=e6b7779e4c77cdd32c556b781c213438
    [20] 王冬至, 张冬燕, 王方, 等.塞罕坝主要立地类型针阔混交林树高曲线构建[J].北京林业大学学报, 2016, 38(10): 7-14. doi: 10.13332/j.1000-1522.20150359

    WANG D Z, ZHANG D Y, WANG F, et al. Height curve construction of needle and broadleaved mixed forest under main site types in Saihanba, Hebei of northern China[J]. Journal of Beijing Forestry University, 2016, 38(10): 7-14. doi: 10.13332/j.1000-1522.20150359
    [21] GARCIA O. A parsimonious dynamic stand model for interior spruce in British Columbia[J]. Forest Science, 2011, 57 (4): 265-280. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=47de0ae6bf440ef1a8dd3cec62145e98
    [22] GARCIA O. Building a dynamic growth model for trembling aspen in western Canada without age data[J]. Canadian Journal of Forest Research, 2013, 43 (3): 256-265. doi: 10.1139/cjfr-2012-0366
    [23] GARCIA O, BURKHART H E, AMATEIS R L. A biologically-consistent stand growth model for loblolly pine in the Piedmont physiographic region, USA[J]. Forest Ecology and Management, 2011, 262 (11): 2035-2041. doi: 10.1016/j.foreco.2011.08.047
    [24] CAO Q V. Linking individual-tree and whole-stand models for forest growth and yield prediction[J]. Forest Ecosystems, 2014, 1: 1-8. doi: 10.1186/2197-5620-1-1
    [25] JUMA R, PUKKALA T, DEMIGUEL S, et al. Evaluation of different approaches to individual tree growth and survival modelling using data collected at irregular intervals-a case study for Pinus patula in Kenya[J]. Forest Ecosystems, 2014, 8(1): 1-14. doi: 10.1186/s40663-014-0014-3
    [26] BATES J M, GRANGER C W J. The combination of forecasts[J]. A Quarterly Journal of Operations Research, 1969, 20 (4): 451-468. doi: 10.1057/jors.1969.103
    [27] VANCLAY J K. Modelling forest growth and yield: application to mixed tropical forests[M]. Wallingford: CAB International, 1994.
    [28] ZHANG X, LEI Y. A linkage among whole-stand model, individual-tree model and diameter-distribution model[J]. Journal of Forest Science, 2010, 56: 600-608. doi: 10.17221/102/2009-JFS
    [29] CRECENTE-CAMPO F, SOARES P, TOME M, et al. Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observations[J]. Forest Ecology and Management, 2010, 260: 1965-1974. doi: 10.1016/j.foreco.2010.08.044
    [30] CAO Q V. Prediction of annual diameter growth and survival for individual trees from periodic measurements[J]. Forest Science, 2000, 46: 127-131. http://europepmc.org/abstract/AGR/IND22301989
    [31] NORD-LARSEN T. Modeling individual-tree growth from data with highly irregular measurement intervals[J]. Forest Science, 2006, 52: 198-208.
    [32] CAO Q V, STRUB M. Evaluation of four methods to estimate parameters of an annual tree survival and diameter growth model[J]. Forest Science, 2008, 54 (6): 617-624. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=456a6d7c29b0fedca89786f7af26405a
    [33] WYKOFF W R. A basal area increment model for individual conifers in the northern Rocky Mountains[J]. Forest Science, 1990, 36 (4): 1077-1104. http://europepmc.org/abstract/AGR/IND91008565
    [34] MONSERUD R A, STERBA H. A basal area increment model for individual trees growing in even-and uneven-aged forest stands in Austria[J]. Forest Ecology and Management, 1996, 80 (3): 57-80. https://www.sciencedirect.com/science/article/pii/0378112795036385
  • 加载中
图(2) / 表(4)
计量
  • 文章访问数:  1266
  • HTML全文浏览量:  233
  • PDF下载量:  42
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-03-10
  • 修回日期:  2017-03-27
  • 刊出日期:  2017-07-01

目录

    /

    返回文章
    返回