林业科学 ›› 2020, Vol. 56 ›› Issue (11): 73-86.doi: 10.11707/j.1001-7488.20201108
王彬,田相林,曹田健*
收稿日期:
2020-01-16
出版日期:
2020-11-25
发布日期:
2020-12-30
通讯作者:
曹田健
基金资助:
Bin Wang,Xianglin Tian,Tianjian Cao*
Received:
2020-01-16
Online:
2020-11-25
Published:
2020-12-30
Contact:
Tianjian Cao
摘要:
目的: 以秦岭松栎林地带性树种油松幼树为研究对象,构建幼树树高生长模型,分析模型预测的不确定性来源,明确模型参数对模型预测不确定性的贡献程度,为提高幼树树高生长建模的可靠性提供理论依据。方法: 收集秦岭松栎林中132株油松幼树连年生长量信息,构建油松幼树树高5年生长量贝叶斯预测模型,采用马尔可夫链蒙特卡洛抽样方法估计模型参数的联合后验分布,量化模型预测时模型预测误差的不确定性、输入变量(自变量测量误差)的不确定性和模型参数的不确定性。结合贝叶斯统计框架与Sobol全局敏感性分析技术,从贝叶斯参数后验分布空间中抽样,量化每个参数或参数组合传递给模型输出的不确定性,通过变异系数和贝叶斯95%可信区间宽度评价其对模型输出不确定性的贡献和影响。结果: 1) 油松幼树树高5年生长量模拟中最大不确定性来源是模型预测误差的不确定性,占总体不确定性的51%;其次是模型参数的不确定性,占总体不确定性的43%;不确定性比例最小的是输入变量即自变量(树冠竞争因子、光截留)测量误差的不确定性,占总体不确定性的6%。模型总体预测的不确定性区间包含97%的观测点,可较准确覆盖模型中观测数据的随机误差。2)对油松幼树树高预测不确定性贡献最大的是控制树冠竞争因子的参数,占参数总体不确定性的64.87%;其次是控制立地因子(坡度)和光照因子(光截留)的参数,分别占参数总体不确定性的15.88%和10.02%;控制林木大小(树高)的参数,仅占参数总体不确定性的1.78%;其他参数贡献的不确定性低于1%。参数的相互作用除控制坡度和树冠竞争因子的参数外,其他参数的相互作用对模型输出不确定性的贡献均低于1%。3)贝叶斯MAP(最大后验概率)预测结果表明,油松幼树树高5年生长量与林分树冠竞争因子、光截留和坡度呈负相关,与当前树高呈正相关。结合参数的不确定性分析得出,参数不确定性越高,其控制的变量对模型预测结果的影响越不显著。结论: 油松幼树树高生长预测的不确定性来源复杂,贝叶斯统计框架与Sobol全局敏感性分析结合可量化和解释油松幼树树高生长预测中各种来源的不确定性,并精确到每个参数传递给模型预测不确定性的贡献。这种将不确定性量化分解的方法可为森林生态系统模拟中对数据和模型预测的变异进行量化、解释和模型改进提供新的参考依据。
中图分类号:
王彬,田相林,曹田健. 油松幼树树高生长预测的不确定性贝叶斯分析[J]. 林业科学, 2020, 56(11): 73-86.
Bin Wang,Xianglin Tian,Tianjian Cao. Uncertainty Analysis of Height Predictions for Young Pinus tabulaeformis Using a Bayesian Approach[J]. Scientia Silvae Sinicae, 2020, 56(11): 73-86.
表1
油松幼树调查样地基本信息"
项目Item | 林分平均高 Mean height (HD)/m | 林分平均胸径 Mean DBH (Dg)/cm | 林分密度 Stand density (SD)/hm-2 | 林分断面积 Basal area (BA)/(m2·hm-2) | 林分蓄积 Volume (V)/(m3·hm-2) | 幼树树高 Height of tree (HT)/m |
平均值Mean | 12.32 | 18.40 | 1 059 | 23.25 | 143.31 | 1.75 |
标准差SD | 2.79 | 4.07 | 487 | 7.81 | 61.98 | 0.69 |
最小值Min. | 7.20 | 10.41 | 353 | 11.50 | 55.66 | 0.37 |
最大值Max. | 17.30 | 27.70 | 2 400 | 48.96 | 286.12 | 3.30 |
变异系数Cofficient of variation(%) | 22.65 | 22.12 | 45.99 | 33.59 | 43.00 | 39.43 |
表2
油松幼树树高生长模型①"
序号 No | 模型类型 Model type | 模型结构Model structure | 贝叶斯信息 准则BIC | 均方根误差 RMSE |
1 | 线性 Linear | HTG=β0+β1×SL×cos(ASP)-β2×SL×sin(ASP)-β3×SL+β4× ln(HT)+β5×CCF+β6×LI+ε | 131.86 | 0.266 2 |
2 | HTG=β0+β1×SL×cos(ASP)-β2×SL×sin(ASP)-β3×SL+β4×HT+β5×HT2+β6 ln(CCF)+β7×LI+ε | 129.31 | 0.265 3 | |
3 | 对数线性 Log-linear | ln(HTG)=β0+β1×SL×cos(ASP)-β2×SL×sin(ASP)-β3×SL+β4×ln(HT)+β5×CCF+β6×LI+ε | 128.87 | 0.256 3 |
4 | ln(HTG)=β0+β1×SL×cos(ASP)-β2×SL×sin(ASP)-β3×SL+β4×ln(HT)+β5×ln(CCF)+β6×LI+ε | 126.75 | 0.223 4 | |
5 | 非线性 Nonlinear | HTG=exp[β0+β1×SL×cos(ASP)+β2×SL×sin(ASP)+β3×SL+β4×HT+β5×ln(HT)+β6×CCF+β7×LI]+ε | 127.13 | 0.241 1 |
6 | HTG=exp[β0+β1×SL×cos(ASP)-β2×SL×sin(ASP)-β3×SL+β4× HT+β5×HT2+β6×CCF+β7×LI]+ε | 128.78 | 0.249 4 |
表4
油松幼树树高生长模型的参数后验分布"
参数 Parameter | 变量 Variable | 最大后验概率 MAP | 均值 Mean | 标准差 SD | 95%可信区间95% CI | |
下限Lower | 上限Upper | |||||
β0 | 截距Intercept | 0.412 | 0.425 | 0.372 | -0.311 | 1.160 |
β1 | SL×cos(ASP) | 0.355 | 0.331 | 0.084 | 0.166 | 0.496 |
β2 | SL×sin(ASP) | 0.070 | 0.070 | 0.056 | -0.041 | 0.182 |
β3 | SL | 0.153 | 0.169 | 0.184 | -0.195 | 0.533 |
β4 | ln(HT) | 0.571 | 0.570 | 0.083 | 0.446 | 0.693 |
β5 | ln(CCF) | -0.107 | -0.108 | 0.075 | -0.255 | -0.040 |
β6 | LI | -0.717 | -0.713 | 0.134 | -0.980 | -0.448 |
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