立木含碳量估算方法比较

doi:10.11707/j.1001-7488.20200405

Abstract

Abstract:

Objective: Forest biomass and carbon, the foundation of researching many forestry and ecology problems, is a basic quantity character of the forest ecological system. Thus, accurate measurement of biomass and carbon is very important. Biomass and carbon model development is an efficacious way to biomass and carbon estimation. Based on the data of biomass and carbon for Populus×xiaohei, we compared the partition and variation of biomass and carbon concentration for four tree components(i.e. stem, root, branch, and foliage), and studied how to establish the additive system of individual tree biomass and carbon equation. Furthermore, five approaches(i.e. carbon allometric equation, the respective mean carbon concentration, the weighted mean carbon concentration, the generic carbon concentration proportion Ⅰ and the generic carbon concentration proportion Ⅱ)for calculating carbon stock of individual trees were evaluated and compared. These were expected to provide technical and theoretical support for accounting and monitoring the Chinese forest biomass and carbon stock. Methods: The aggregation system was used to establish the individual tree biomass and carbon additive models, while nonlinear seemly unrelated regression was used to estimate the parameters in the additive system of biomass and carbon equations. The individual tree biomass and carbon model validation was accomplished by Jackknifing technique in this study. ANOVA based on the SAS POC GLM was applied to test the differences between the five approaches to estimate carbon stock(treatment), using the sampling trees as blocks, followed by the contrasts between the five approaches. Results: The model fitting results showed that all biomass and carbon equations fitted the data well, of which the adjusted coefficient of determination(R_a²)of biomass and carbon additive systems for were above 0.80, the mean relative error(MRE)was between -2%-2%, the mean absolute relative error(MARE)was less than 30%, and all models had a good prediction precision(85% or more). Furthermore, the results of five approaches for calculating carbon stock of individual trees showed that the carbon allometric equations and the estimated biomass multiplied by weighted mean carbon concentration were more advantageous, whereas the approach using the generic carbon concentration constants(i.e. 0.45 or 0.50)might produce significant biases in estimating the carbon stock of individual trees. Conclusion: In order to estimate model parameters more effectively, the additive property of estimating tree total, sub-totals, and component biomass or carbon should be taken into account. Overall, the biomass and carbon models would be suitable for predicting individual tree biomass and carbon of Populus×xiaohei in west plain of Heilongjiang Province.

Key words: biomass and carbon partitioning, additive equations, quantifying carbon stock

CLC Number:

S718.55

Lihu Dong,Yongshuai Liu,Bo Song,Yifei Zhou,Fengri Li. Comparison of Individual Tree Carbon Estimation Approaches[J]. Scientia Silvae Sinicae, 2020, 56(4): 46-54.

Figures/Tables 6

Table 1

Table 2

Table 3

Fig.1

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Fig.2

References 0

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径级 Diameter class/cm	株数 Numbers	胸径 Diameter/cm	树高 Tree height/m	总生物量 Total biomass/kg	总含碳量 Total carbon/kg
5≤D < 10	12	4.4~9.3	6.2~8.2	4.57~18.59	2.14~8.88
10≤D < 15	14	10.1~14.8	9.6~11.9	24.40~62.39	11.59~27.90
15≤D < 20	10	15.2~20.0	11.6~13.5	61.17~113.15	28.13~51.78

各分项Each component	最大值Max.	最小值Min.	均值Mean	标准差Std
树干Stem	0.506 5	0.418 8	0.477 4 a	0.018 4
树枝Branch	0.516 1	0.432 9	0.483 2 a	0.019 5
树叶Foliage	0.521 8	0.455 6	0.484 6 a	0.019 0
树根Root	0.500 0	0.423 1	0.459 8 b	0.023 1
加权平均含碳率Weighted mean carbon concentration(C_weight)	0.496 4	0.443 8	0.474 3	0.015 1

模型类型 Model type	各分项 Each component	β_i0		β_i1		R_a²	RMSE	权函数 Weight function
模型类型 Model type	各分项 Each component	参数估计值 Estimate	参数标准误 SE	参数估计值 Estimate	参数标准误 SE	R_a²	RMSE	权函数 Weight function
生物量模型 Biomass model	树根Root	-3.186 9	0.137 1	2.1663	0.051 4	0.978	1.08	D^{2.661 0}
	树干Stem	-2.306 7	0.072 8	2.1877	0.027 9	0.978	2.69	D^{2.535 7}
	树枝Branch	-4.047 9	0.122 5	2.2776	0.055 4	0.818	2.18	D^{6.360 1}
	树叶Foliage	-4.287 2	0.174 3	1.9348	0.071 0	0.880	0.43	D^{4.440 1}
	树冠Crown	—	—	—	—	0.848	2.45	D^{5.724 7}
	地上Aboveground	—	—	—	—	0.981	3.34	D^{2.640 5}
	总量Total	—	—	—	—	0.987	3.61	D^{3.925 3}
含碳量模型 Carbon model	树根Root	-3.986 7	0.140 6	2.179 0	0.055 0	0.965	0.63	D^{3.824 2}
	树干Stem	-2.953 1	0.087 4	2.147 7	0.032 3	0.972	1.41	D^{1.439 6}
	树枝Branch	-4.868 4	0.159 3	2.311 1	0.066 3	0.805	1.08	D^{6.088 2}
	树叶Foliage	-5.106 8	0.185 1	1.967 5	0.076 7	0.853	0.24	D^{4.524 5}
	树冠Crown	—	—	—	—	0.831	1.24	D^{6.257 5}
	地上Aboveground	—	—	—	—	0.971	1.91	D^{2.905 8}
	总量Total	—	—	—	—	0.979	2.09	D^{3.432 8}

来源 Source	总P值 Total P-value	地上P值 Aboveground P-value	树根P值 Root P-value	树干P值 Stem P-value	树枝P值 Branch P-value	树叶P值 Foliage P-value	树冠P值 Crown P-value
区组(树木)Block(tree)	< 0.000 1	< 0.0001	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1
处理(5种方法) Treatment(five approaches)	< 0.000 1	< 0.0001	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1
方法1 vs方法2 A1 vs A2	0.277 7	0.056 0	0.100 7	0.029 5	0.565 8	0.131 3	0.436 1
方法1 vs方法3 A1 vs A3	0.289 6	0.707 4	0.001 9	0.250 0	0.022 8	0.025 1	0.023 3
方法1 vs方法4 A1 vs A4	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1
方法1 vs方法5 A1 vs A5	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1
方法2 vs方法3 A2 vs A3	0.978 7	0.123 0	< 0.000 1	0.298 2	0.004 6	0.000 2	0.002 5
方法2 vs方法4 A2 vs A4	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1
方法2 vs方法5 A2 vs A5	< 0.000 1	< 0.000 1	0.001 4	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1
方法3 vs方法4 A3 vs A4	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1
方法3 vs方法5 A3 vs A5	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1
方法4 vs方法5 A4 vs A5	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1	< 0.000 1

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Comparison of Individual Tree Carbon Estimation Approaches

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