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长白落叶松树冠光分布的动态模拟

孙一博 刘强 李凤日

孙一博, 刘强, 李凤日. 长白落叶松树冠光分布的动态模拟[J]. 北京林业大学学报, 2019, 41(12): 77-87. doi: 10.12171/j.1000-1522.20190324
引用本文: 孙一博, 刘强, 李凤日. 长白落叶松树冠光分布的动态模拟[J]. 北京林业大学学报, 2019, 41(12): 77-87. doi: 10.12171/j.1000-1522.20190324
Sun Yibo, Liu Qiang, Li Fengri. Dynamic simulation of light distribution in the live crown of Larix olgensis trees[J]. Journal of Beijing Forestry University, 2019, 41(12): 77-87. doi: 10.12171/j.1000-1522.20190324
Citation: Sun Yibo, Liu Qiang, Li Fengri. Dynamic simulation of light distribution in the live crown of Larix olgensis trees[J]. Journal of Beijing Forestry University, 2019, 41(12): 77-87. doi: 10.12171/j.1000-1522.20190324

长白落叶松树冠光分布的动态模拟

doi: 10.12171/j.1000-1522.20190324
基金项目: 国家重点研发计划课题(2017YFD0600402),长白落叶松高效培育技术省级资助项目(GX18B041)
详细信息
    作者简介:

    孙一博。主要研究方向:林分生长模型。Email:290172994@qq.com 地址:150040 黑龙江省哈尔滨市香坊区和兴路26号东北林业大学林学院

    责任作者:

    李凤日,教授,博士生导师。主要研究方向:林分生长模型。Email:fengrili@126.com 地址:同上

Dynamic simulation of light distribution in the live crown of Larix olgensis trees

  • 摘要: 目的消光系数(k)是模拟树冠光分布的重要指标,本研究通过对比常见的获得k值的方法,筛选最优方法,对长白落叶松人工林树冠内光合有效辐射(PAR)进行动态估计。方法(1)将实测的PAR数据按3∶1划分为拟合数据和检验数据,利用拟合数据构建k值预估模型(方法I)。(2)用拟合数据,采用人为设定不同梯度的k值估计树冠PAR,筛选最优的k值(方法II)。(3)基于叶倾角数据,采用2种不同的平均叶倾角公式(方法III-1、方法III-2),对k值进行计算。将检验数据作为独立样本对以上3种方法估计的PAR进行独立性检验。通过对比以上3种方法对树冠内PAR的估计效果,选择最优的k值计算方法,结合气象数据对PAR进行动态估计。结果根据实测PAR数据计算的树冠各轮层k值存在较大差异,总体在0.1 ~ 1.5之间,且与相对着枝深度(RDINC)呈明显的指数函数或幂函数关系。同时太阳高度角(Sa)、累积叶面积最大值(MCLA)、叶面积密度(NAD)和树冠表面积(CS)对k值的垂直变化也有明显影响。因此方法I将指数函数作为基础模型,以RDINC、Sa、CLA、NAD和CS为自变量建立了k值估计模型,模型的拟合效果较好(R2 = 0.736,RMSE = 0.124)。方法II中,当k取0.32时对PAR的估计效果最好。利用方法III计算的各轮层消光系数差异较小,总体在0.3 ~ 0.7之间。采用独立样本检验以上3种方法对PAR估计的效果,结果表明方法I对PAR的估计效果较好(平均误差ME = 2.88,平均误差绝对值MAE = 117.4,预估精度P = 91.53%),方法II对PAR的估计效果次之(ME = − 7.2,MAE = 217.5,P = 88.12%),方法III对PAR最差(方法III-1中 ME = 121.4,MAE = 210.1,P = 55.85%;方法III-2中 ME = 226.4,MAE = 259.0,P = 42.93%)。结论k值在不同林木、不同轮层及不同的太阳高度情况下并不是一个固定值。本研究建立的k估计模型充分考虑了以上3个重要变量,符合客观实际,且对估计长白落叶松树冠PAR有良好的效果,研究结果为人工长白落叶松树冠内不同位置净光合速率的模拟提供了基础。

     

  • 图  1  5株光合测定样木透光率平均值的垂直变化规律

    Figure  1.  Vertical variations of average light transmittance for five photosynthetic sample trees

    图  2  消光系数k在垂直方向上的变化

    Figure  2.  Changes in the vertical direction of extinction coefficient (k)

    图  3  不同k值计算结果对比

    Figure  3.  Comparison of calculation results of different k values

    图  4  4组k值计算的PAR预测值与实测值的比较

    Figure  4.  Comparison between the predicted and measured PAR values calculated by the four groups of k values

    图  5  长白落叶松树冠PAR的动态预测(以NO.1为例)

    Figure  5.  Predicted values of PAR for the whole year of 2016 for NO.1

    表  1  长白落叶松人工林样地及光合测定样木因子

    Table  1.   Attributes of sample plots and photosynthetic sample trees for Larix olgensis plantation

    树号
    Tree No.
    样木因子 Sample tree factor样地因子 Sample plot factor
    标准地
    Stand
    胸径
    DBH/cm
    树高
    Tree height/m
    胸径
    DBH/cm
    树高
    Tree height/m
    平均胸径
    Mean DBH/cm
    平均树高
    Mean tree
    height/m
    林分密度/(株·hm− 2
    Stand density/
    (tree·ha− 1)
    NO.1固定样地1
    Fixed plot 1
    10.7510.335.0~16.06.0~12.89.929.232 200
    NO.2固定样地2
    Fixed plot 2
    10.9010.815.2~14.17.1~10.59.739.623 200
    NO.3临时样地3
    Temporary plot 3
    11.1510.525.3~15.95.3~13.210.5910.192 400
    NO.4固定样地4
    Fixed plot 4
    12.711.85.1~21.58.9~12.710.9911.262 416
    NO.5固定样地5
    Fixed plot 5
    11.410.95.2~18.18.1~12.210.1710.53 366
    下载: 导出CSV

    表  2  测量变量的统计结果

    Table  2.   Summary of main measured variables

    统计量
    Statistics
    相对累积叶
    面积指数
    RCLAI
    叶倾角
    α
    树冠内光合
    有效辐射
    PAR
    冠顶入射光合辐射总量
    PAR0
    太阳高度角
    Sa
    样本量
    Sample size
    36 2 697 547 75 547
    平均值
    Mean
    2.98 52.3 347.61 1 446.87 46.77
    标准差
    Std.
    0.93 90 317.25 416.94 14.06
    最大值
    Max.
    4.59 1 1 677.63 1 936.27 68.42
    最小值
    Min.
    0.88 26.9 3.65 154.54 13.17
    下载: 导出CSV

    表  3  模型(14)参数拟合结果及拟合优度

    Table  3.   Results of parameter estimates and goodness of fit of model (14)

    树号
    Tree No.
    参数 Parameter拟合优度 Goodness of fit
    abR2RMSE
    NO.10.6120.9490.4610.152
    NO.21.1721.5610.8670.120
    NO.30.5180.7850.5690.100
    NO.41.7701.8330.9230.128
    NO.51.5092.4690.9370.122
    下载: 导出CSV

    表  4  模型(15)参数拟合结果及拟合优度

    Table  4.   Results of parameter estimates and goodness of fit of model (15)

    树号
    Tree No.
    参数 Parameter拟合优度 Goodness of fit
    abR2RMSE
    NO.10.1301.6360.5080.146
    NO.20.1122.4420.8780.116
    NO.30.1331.4730.6100.096
    NO.40.1112.8690.8600.167
    NO.50.0423.6720.9310.139
    下载: 导出CSV

    表  5  模型(16)参数拟合结果及拟合优度

    Table  5.   Results of parameter estimates and goodness of fit of the model (16)

    参数
    Parameter
    估计结果
    Estimate
    P
    P value
    标准误差
    Sd. error
    拟合优度
    Goodness of fit
    R2RMSE
    a00.008 <0.000 10.0010.7360.124
    a1− 0.197 <0.000 10.023
    a2− 0.668 <0.000 10.066
    a32.445 <0.000 10.251
    b0− 0.286 <0.000 10.060
    b10.007 <0.000 10.001
    b23.759 <0.000 10.477
    下载: 导出CSV

    表  6  5株光合测定样木k值计算结果

    Table  6.   Results of k value calculation for five photosynthetic sample trees

    树号
    Tree No.
    方法III-1
    Method III-1
    方法III-2
    Method III-2
    最大值
    Max.
    最小值
    Min.
    标准差
    SD
    平均值
    Mean
    最大值
    Max.
    最小值
    Min.
    标准差
    SD
    平均值
    Mean
    NO.1 0.634 3 0.597 4 0.0123 4 0.619 5 0.504 1 0.440 3 0.021 5 0.463 3
    NO.2 0.634 2 0.597 3 0.0123 7 0.619 5 0.431 9 0.382 6 0.016 5 0.409 8
    NO.3 0.634 3 0.597 4 0.0123 5 0.619 5 0.513 9 0.450 9 0.019 8 0.481 0
    NO.4 0.634 2 0.597 2 0.0123 7 0.619 5 0.483 4 0.427 9 0.019 3 0.457 5
    NO.5 0.634 2 0.597 2 0.0123 9 0.619 4 0.364 1 0.328 4 0.012 3 0. 346 8
    下载: 导出CSV

    表  7  不同方法确定的k值对PAR估计的检验结果

    Table  7.   Validation results of PAR calculated based on the k values obtained based on different methods

    方法
    Method
    平均误差
    ME/
    (μmol·m− 2·s− 1)
    平均绝对误差
    MAE/
    (μmol·m− 2·s− 1)
    估计精度
    Estimation
    accuracy (P)/%
    方法I
    Method I
    2.88 117.4 91.53
    方法II
    Method II
    − 7.2 217.5 88.12
    方法III-1
    Method III-1
    121.4 210.1 55.85
    方法III-2 Method III-2 226.4 259.0 42.93
    注:方法I为使用本研究建立的k值预估模型,方法II为使用固定值k = 0.32,方法III-1为使用式(7),方法III-2为使用式(8)。Notes: method I is the k-value prediction model established by this study. Method II uses fixed value of k = 0.32, method III-1 uses formula (7), and method III-2 uses formula (8).
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-08-30
  • 修回日期:  2019-09-18
  • 网络出版日期:  2019-12-17
  • 刊出日期:  2019-12-01

目录

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    返回文章
    返回