Developing stand volume, biomass and carbon stock models for ten major forest types in forest region of northeastern China
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摘要:
目的 林分水平的蓄积量、生物量和碳储量模型或数表，是开展森林资源规划设计调查的必备计量工具。研建东北林区10种主要森林类型的蓄积量、生物量和碳储量模型，既是方法学探索，也为生产实践提供参考成果。 方法 基于东北林区云冷杉林、落叶松林、红松林、樟子松林、栎树林、桦树林、杨树林、榆树林、椴树林和水胡黄林10种主要森林类型的2 000个样地的实测数据，分别利用非线性独立回归估计、非线性误差变量联立方程组和含哑变量的非线性误差变量联立方程组方法，建立了林分水平的蓄积量、生物量和碳储量模型。 结果 基于全部样地通过误差变量联立方程组方法建立的蓄积量、生物量和碳储量总体平均模型，其确定系数分别为0.945、0.805和0.839，而包含森林类型参数的蓄积量、生物量和碳储量哑变量模型，其确定系数分别达到0.959、0.949和0.951。10种主要森林类型的蓄积量、生物量和碳储量模型，确定系数（R2）都在0.86以上，平均预估误差（MPE）都在3%以内，平均百分标准误差（MPSE）大多数在10%以内。蓄积量模型的R2在0.876 ~ 0.980之间，MPE在0.90% ~ 1.95%之间，MPSE在5.14% ~ 11.89%之间；生物量模型的R2在0.864 ~ 0.988之间，MPE在0.66% ~ 2.07%之间，MPSE在3.61% ~ 11.60%之间；碳储量模型的R2在0.866 ~ 0.988之间，MPE在0.67% ~ 1.96%之间，MPSE在3.65% ~ 11.57%之间。 结论 不同森林类型的蓄积量主要取决于林分断面积和平均高，生物量主要取决于蓄积量和林分平均高。含哑变量的非线性误差变量联立方程组方法，是建立林分水平储量模型系统的可行方法。本研究所建立的东北地区10种主要森林类型的蓄积量、生物量和碳储量模型，其预估精度达到森林资源规划设计调查技术规定要求，可以在实践中推广应用。 Abstract:Objective Stand-level volume, biomass and carbon stock models or tables are necessary quantitative tools for implementing forest management inventory. Developing stand volume, biomass and carbon stock models for ten major forest types in forest region of northeastern China is not only an exploration of methodology, but also provides reference results for practice. Method Based on the field measurement data of 2 000 sample plots distributed in 10 forest types in northeastern China, i.e. spruce & fir (Picea spp. & Abies spp.), larch (Larix spp.), Mongolian scotch pine (Pinus sylvestris var. mongolica), Korean pine (Pinus koraiensis), oak (Quercus spp.), birch (Betula spp.), poplar (Populus spp.), elm (Ulmus spp.), linden (Tilia spp.), and other three precious broadleaved species (Fraxinus mandshurica, Juglans mandshurica & Phellodendron amurense), the stand-level volume, biomass and carbon stock models were developed through independent nonlinear regression (INR), simultaneous error-in-variable equations (SEIVE), and SEIVE with dummy variable modeling approach. Result The coefficients of determination (R2) of the population-averaged stand-level volume, biomass and carbon stock models based on all sample plots were 0.945, 0.805 and 0.839, respectively; and those of tthe models with type-specific parameters were 0.959, 0.949 and 0.951, respectively. The R2 values of stand-level volume, biomass and carbon stock models for 10 forest types were all more than 0.86, the mean prediction errors (MPE) were all less than 3%, and the mean percent standard errors (MPSE) were almost less than 10%. For the volume stock models, the R2 values were between 0.876−0.980, MPE were between 0.90%−1.95%, and MPSE were between 5.14%−11.89%; for the biomass stock models, the R2 values were between 0.864−0.988, MPE were between 0.66%−2.07%, and MPSE were between 3.61%−11.60%; and for carbon stock models, the R2 values were between 0.866−0.988, MPE were between 0.67%−1.96%, and MPSE were between 3.65%−11.57%. Conclusion The volume stock per hectare of different forest types mainly depends upon basal area and mean tree height of forest stands, and the biomass stock mainly relates to volume stock and mean tree height. The SEIVE with dummy variable modeling approach is a feasible method for developing stand-level stock models. The developed volume, biomass and carbon stock models for 10 major forest types in northeastern China in this study meet the need of precision requirements to the regulation on forest management inventory, indicating that the models can be applied in practice. -
Key words:
- volume /
- biomass /
- carbon stock /
- dummy variable model /
- simultaneous error-in-variable equations
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图 1 云冷杉林蓄积量、生物量和碳储量的相对残差分布
Figure 1. Distribution of relative residuals for stand volume, biomass and carbon stock models for Picea spp. & Abies spp. forest
表 1 建模样地主要林分特征参数变化范围
Table 1. Ranges of main forest stand parameters for modeling sample plots
森林类型
Forest type样地数
Sample plot
number蓄积量/(m3·hm−2)
Volume/(m3·ha−1)生物量/(t·hm−2)
Biomass/(t·ha−1)断面积/(m2·hm−2)
Basal area/(m2·ha−1)平均高
Mean height/m最小值
Min.最大值
Max.最小值
Min.最大值
Max.最小值
Min.最大值
Max.最小值
Min.最大值
Max.云冷杉林 Picea spp. & Abies spp. forest 198 5.29 491.82 6.72 326.25 1.93 45.22 4.53 21.40 落叶松林 Larix spp. forest 202 5.94 334.97 6.17 268.10 1.33 34.66 6.37 20.08 樟子松林 Pinus sylvestris var. mongolica forest 200 11.89 476.58 12.02 328.06 3.19 46.87 5.82 20.68 红松林 Pinus koraiensis forest 200 9.94 669.29 12.30 478.14 3.28 65.43 4.21 19.75 栎树林 Quercus spp. forest 196 6.47 246.64 6.94 301.55 1.59 35.07 3.56 15.07 桦树林 Betula spp. forest 201 4.78 212.50 5.45 184.31 1.37 29.37 6.37 15.88 杨树林 Populus spp. forest 210 2.45 376.62 1.96 284.11 0.58 45.87 5.79 19.85 榆树林 Ulmus spp. forest 199 25.10 306.16 38.03 359.19 5.05 35.26 6.42 16.64 椴树林 Tilia spp. forest 196 65.03 385.31 62.07 529.66 10.68 50.17 6.46 17.49 水胡黄林 Fraxinus mandshurica, Juglans
mandshurica & Phellodendron amurense forest198 35.88 296.93 40.54 251.62 7.84 33.27 8.44 19.54 
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表 2 独立和联立储量模型的参数估计值和模型评价指标
Table 2. Parameter estimates and model evaluation indices of independent and simultaneous stock models
模型
Model目标变量
Target
variable参数估计值 Parameter estimate 评价指标 Evaluation index a0/b0/c0 a1/b1 a2/b2 R2 SEE TRE/% ASE/% MPE/% MPSE/% 独立
IndependentV 0.831 56 (0.024 34) 1.100 79 (0.007 00) 0.746 32 (0.013 71) 0.945 22.65 0.21 0.00 0.61 9.75 B 3.599 24 (0.170 57) 0.043 00 (0.011 40) −0.617 75 (0.023 75) 0.812 32.19 0.63 1.23 0.99 14.30 C 0.477 52 (0.000 31) 0.845 14.01 1.22 1.66 0.89 13.01 联立
SimultaneousV 0.840 83 (0.026 22) 1.103 15 (0.007 50) 0.738 86 (0.014 52) 0.945 22.61 0.22 0.00 0.61 9.76 B 3.147 67 (0.129 90) 0.029 72 (0.009 10) −0.541 02 (0.018 69) 0.805 32.81 −0.90 −0.08 1.01 14.29 C 0.479 45 (0.003 59) 0.839 14.28 −0.72 −0.05 0.91 12.91 注：括号内数据为标准差。V. 单位面积蓄积量；B. 单位面积生物量；C. 单位面积碳储量；SEE. 估计值的标准差；TRE. 总体相对误差；ASE. 平均系统误差；MPE. 平均预估误差；MPSE. 平均百分标准误差。下同。Notes: data in brackets is SD. V, volume per unit area; B, biomass per unit area; C, carbon stock per unit area; SEE, standard error of estimate; TRE, total relative error; ASE, average system error; MPE, mean prediction error; MPSE, mean percentage standard error. The same below.
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表 3 东北林区10种森林类型储量模型的参数估计值和模型评价指标
Table 3. Parameter estimates and evaluation indices of stock models for 10 forest types in forest region of northeastern China
森林类型
Forest type目标变量
Target
variable参数估计值 Parameter estimate 评价指标 Evaluation index a0/b0/c0 a1/b1 a2/b2 R2 SEE TRE/% ASE/% MPE/% MPSE/% 云冷杉
Picea spp. &
Abies spp.V 1.110 92 1.108 20 0.654 12 0.968 18.29 −0.32 −0.02 1.40 8.28 B 2.048 77 0.000 00 −0.399 77 0.936 17.13 −0.04 0.00 1.74 7.72 C 0.489 02 0.944 7.78 −0.07 −0.01 1.62 7.34 落叶松
Larix spp.V 1.093 95 1.032 65 0.710 65 0.968 11.66 −0.08 −0.01 1.35 6.70 B 1.818 69 0.000 00 −0.301 28 0.977 7.85 0.38 −0.02 1.08 4.81 C 0.488 54 0.977 3.82 0.35 −0.01 1.07 4.79 樟子松
Pinus sylvestris
var. mongolicaV 1.309 96 1.121 72 0.573 13 0.954 20.40 0.04 0.00 1.37 7.54 B 1.993 64 0.000 00 −0.427 15 0.958 14.22 0.16 0.00 1.23 6.68 C 0.497 88 0.958 6.83 0.13 0.00 1.23 6.67 红松
Pinus koraensisV 1.414 19 1.063 71 0.596 03 0.911 39.76 −0.23 0.00 1.95 11.89 B 1.391 50 0.000 00 −0.227 46 0.877 27.67 −0.82 −0.08 2.07 11.52 C 0.483 33 0.892 12.90 −0.68 −0.07 1.94 10.85 栎树
Quercus spp.V 0.601 32 1.085 50 0.860 68 0.968 8.12 −0.92 0.28 1.37 6.96 B 2.813 30 0.000 00 −0.394 95 0.969 8.82 0.37 0.00 1.23 5.77 C 0.481 27 0.971 4.07 0.35 0.00 1.18 5.64 桦树
Betula spp.V 0.894 57 1.023 98 0.777 91 0.980 5.74 −0.13 −0.01 0.90 5.14 B 1.904 00 0.000 00 −0.302 37 0.988 3.79 0.03 0.00 0.66 3.61 C 0.487 10 0.988 1.89 0.02 0.00 0.67 3.65 杨树
Populus spp.V 1.378 40 1.086 41 0.573 36 0.952 17.31 −0.12 0.09 1.48 7.13 B 2.832 52 0.000 00 −0.466 15 0.941 13.48 0.69 −0.05 1.55 7.75 C 0.478 60 0.939 6.49 0.59 −0.05 1.57 8.03 榆树
Ulmus spp.V 0.938 61 1.033 57 0.768 68 0.924 14.18 0.13 0.01 1.53 8.55 B 3.473 86 0.000 00 −0.403 54 0.953 12.93 0.00 −0.02 1.06 6.03 C 0.452 60 0.960 5.34 −0.04 −0.03 0.96 5.48 椴树
Tilia spp.V 0.966 60 1.223 60 0.513 24 0.876 24.03 −0.85 0.61 1.72 10.01 B 3.503 66 0.000 00 −0.453 16 0.864 31.94 0.04 0.02 1.98 11.60 C 0.476 25 0.866 15.07 0.07 0.03 1.96 11.57 水胡黄
F. mandshurica,
J. mandshurica &
P. amurenseV 0.865 43 1.095 14 0.717 54 0.887 16.67 0.03 −0.03 1.33 7.94 B 2.225 80 0.000 00 −0.425 17 0.927 9.97 −0.04 0.05 0.94 5.64 C 0.475 83 0.933 4.53 −0.06 0.05 0.89 5.34
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