林业科学 ›› 2022, Vol. 58 ›› Issue (5): 31-39.doi: 10.11707/j.1001-7488.20220504
张兹鹏,王君杰,刘索名,姜立春*
收稿日期:
2021-04-16
出版日期:
2022-05-25
发布日期:
2022-08-19
通讯作者:
姜立春
基金资助:
Zipeng Zhang,Junjie Wang,Suoming Liu,Lichun Jiang*
Received:
2021-04-16
Online:
2022-05-25
Published:
2022-08-19
Contact:
Lichun Jiang
摘要:
目的: 以大兴安岭地区白桦为研究对象, 构建含有形率的材积模型, 并与部颁东北地区白桦二元材积模型和传统基础材积模型进行比较, 同时结合生物量转换因子, 研究形率对单木生物量的影响, 为单木材积和生物量的精准预测提供科学依据。方法: 将树干上15个形率引入传统基础材积模型分别构建二元和三元单木材积模型, 应用R软件GNLS模块拟合各模型, 引入方差函数消除各材积模型拟合过程中产生的异方差现象。以相对误差绝对值(MPB)、平均误差绝对值(MAB)、均方根误差(RMSE)和确定系数(R2)为评价指标对各模型进行对比分析, 采用交叉检验法对模型进行检验。结果: 1) 形率引入可显著提高材积模型拟合效果, 当取相对树高为40%时, 带有形率q0.4的二元和三元材积模型拟合效果均表现最好; 2) 交叉检验结果表明, 形率引入材积模型使得材积预测精度显著提高, 相较传统一元模型(1)和二元模型(2), 引入形率的二元模型(14)和三元模型(15)的均方根误差分别降低38.9%和45.8%; 3) 与东北地区当前使用的二元材积模型(5)相比, 引入形率的二元模型(14)的RMSE、MAB和MPB分别降低46.7%、40.2%和47.7%, 引入形率的三元模型(15)的RMSE、MAB和MPB分别降低59.1%、59.2%和64.1%; 4) 形率引入可显著提高单木生物量预测精度, 与不引入形率的基础模型相比, 以引入形率的三元材积模型(15)为基础, 结合生物量转换因子(BEF)构建的单木生物量模型的RMSE、MAB和MPB分别降低46.9%、46.5%和46.3%。结论: 在材积模型中引入形率, 不仅可提高单木材积预测精度, 还能为单木生物量精准预测提供帮助。引入形率的三元立木材积模型(15)更适合于预测该区域白桦单木材积和生物量。
中图分类号:
张兹鹏,王君杰,刘索名,姜立春. 形率对白桦单木材积和生物量预测精度的影响[J]. 林业科学, 2022, 58(5): 31-39.
Zipeng Zhang,Junjie Wang,Suoming Liu,Lichun Jiang. Effect of Form Quotient on Prediction Accuracy of Individual Tree Volume and Biomass of Betula platyphylla[J]. Scientia Silvae Sinicae, 2022, 58(5): 31-39.
表2
模型参数估计值及其拟合统计量①"
形率 Form quotient | a1 | a2 | a3 | a4 | RMSE | R2 | ||||||||||
(1) | (2) | (1) | (2) | (1) | (2) | (2) | (1) | (2) | (1) | (2) | ||||||
— | 0.000 7 | 0.000 08 | 1.974 3 | 1.747 3 | 1.000 2 | 0.054 1 | 0.046 7 | 0.943 4 | 0.957 8 | |||||||
q0 | 0.000 7 | 0.000 08 | 1.967 2 | 1.731 8 | -0.090 1 | 1.011 4 | -0.154 6 | 0.054 0 | 0.046 4 | 0.943 5 | 0.958 3 | |||||
q0.02 | 0.000 7 | 0.000 07 | 1.970 0 | 1.747 6 | -0.127 2 | 1.003 7 | 0.029 3 | 0.054 0 | 0.046 6 | 0.943 5 | 0.957 9 | |||||
q0.04 | 0.000 7 | 0.000 07 | 1.967 8 | 1.746 5 | -0.343 0 | 1.013 4 | 0.094 8 | 0.053 8 | 0.046 6 | 0.943 9 | 0.957 9 | |||||
q0.06 | 0.000 8 | 0.000 07 | 1.948 5 | 1.747 8 | -0.555 7 | 1.003 2 | 0.026 7 | 0.053 7 | 0.046 7 | 0.944 2 | 0.957 8 | |||||
q0.08 | 0.000 5 | 0.000 06 | 2.055 0 | 1.823 1 | 1.056 2 | 1.029 7 | 1.153 0 | 0.051 9 | 0.043 6 | 0.947 8 | 0.963 2 | |||||
q0.1 | 0.000 4 | 0.000 03 | 2.192 1 | 1.966 6 | 1.790 3 | 1.065 2 | 1.934 1 | 0.045 4 | 0.034 8 | 0.960 1 | 0.976 5 | |||||
q0.15 | 0.000 3 | 0.000 03 | 2.225 3 | 2.006 3 | 1.597 2 | 1.048 3 | 1.710 1 | 0.042 0 | 0.030 5 | 0.965 8 | 0.981 9 | |||||
q0.2 | 0.000 4 | 0.000 06 | 2.205 0 | 1.993 3 | 1.706 9 | 0.874 0 | 1.624 6 | 0.038 7 | 0.030 5 | 0.970 9 | 0.981 9 | |||||
q0.3 | 0.000 5 | 0.000 08 | 2.160 1 | 1.945 1 | 1.554 9 | 0.891 2 | 1.500 5 | 0.038 1 | 0.029 3 | 0.971 9 | 0.983 3 | |||||
q0.4 | 0.000 6 | 0.000 10 | 2.160 0 | 1.959 5 | 1.431 8 | 0.814 2 | 1.357 9 | 0.033 3 | 0.024 8 | 0.978 4 | 0.988 0 | |||||
q0.5 | 0.000 5 | 0.000 06 | 2.165 5 | 1.947 8 | 0.896 5 | 0.983 8 | 0.891 7 | 0.037 4 | 0.026 0 | 0.972 8 | 0.986 9 | |||||
q0.6 | 0.000 7 | 0.000 07 | 2.081 0 | 1.865 1 | 0.559 0 | 0.992 0 | 0.561 8 | 0.040 8 | 0.030 0 | 0.967 8 | 0.982 5 | |||||
q0.7 | 0.000 7 | 0.000 08 | 2.105 4 | 1.893 9 | 0.447 4 | 0.952 1 | 0.438 2 | 0.038 6 | 0.028 3 | 0.971 1 | 0.984 4 | |||||
q0.8 | 0.000 7 | 0.000 07 | 2.092 9 | 1.871 3 | 0.320 3 | 1.009 5 | 0.324 0 | 0.041 2 | 0.030 3 | 0.967 1 | 0.982 2 | |||||
q0.9 | 0.000 7 | 0.000 07 | 2.069 2 | 1.837 6 | 0.178 3 | 1.049 2 | 0.190 5 | 0.046 6 | 0.036 7 | 0.957 9 | 0.973 8 |
表3
材积模型误差方差函数结果比较"
误差函数 Error functions | 变量 Variable | AIC | BIC | |||||||
(1) | (2) | (14) | (15) | (1) | (2) | (14) | (15) | |||
指数函数 Exponential function | -1 660.4 | -1 848.0 | -1 823.3 | -2 263.4 | -1 644.6 | -1 828.2 | -1 803.5 | -2 239.7 | ||
V | -1 624.9 | -1 864.8 | -1 802.2 | -2 267.4 | -1 609.1 | -1 845.0 | -1 782.4 | -2 243.7 | ||
-1 705.1 | -1 303.4 | -870.7 | -1 833.2 | -1 689.3 | -1 283.6 | -850.9 | -1 809.5 | |||
D | -1 719.5 | -1 938.9 | -1 888.9 | -2 356.6 | -1 703.7 | -1 919.1 | -1 869.1 | -2 332.8 | ||
D2H | 5 313.3 | 5 905.8 | 5 333.2 | 5 929.6 | ||||||
幂函数 Power function | -1 737.8 | -1 933.6 | -1 945.4 | -2 393.3 | -1 720.9 | -1 913.8 | -1 925.5 | -2 369.6 | ||
V | -1 700.1 | -1 924.5 | -1 934.3 | -2 411.1 | -1 684.2 | -1 904.7 | -1 914.5 | -2 387.3 | ||
-774.7 | -1 273.3 | -931.3 | -1 892.7 | -758.9 | -1 253.5 | -911.5 | -1 869.0 | |||
D | -774.7 | -1 273.3 | -931.3 | -1 892.7 | -758.9 | -1 253.5 | -911.5 | -1 869.0 | ||
D2H | 5 313.3 | 5 905.8 | 5 333.2 | 5 929.6 | ||||||
常数加幂函数 Constant plus power function | -1 737.7 | -1 937.1 | -1 943.4 | -2 400.5 | -1 717.8 | -1 913.3 | -1 919.6 | -2 372.7 | ||
V | -1 716.7 | -1 945.3 | -1 933.2 | -2 415.3 | -1 696.9 | -1 921.5 | -1 909.5 | -2387.6 | ||
-1 271.3 | -762.6 | -929.2 | -1 890.7 | -752.9 | -1 247.5 | -905.5 | -1 863.0 | |||
D | -1 271.3 | -762.6 | -929.2 | -1 890.7 | -752.9 | -1 247.5 | -905.5 | -1 863.0 | ||
D2H | 5 313.3 | 5 905.8 | 5 333.2 | 5 929.6 |
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