Predictive model of stand tree layer additive carbon storage of Korean pine plantation in Heilongjiang Province, China

doi:10.12302/j.issn.1000-2006.202007007

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (1): 115-121.doi: 10.12302/j.issn.1000-2006.202007007

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Predictive model of stand tree layer additive carbon storage of Korean pine plantation in Heilongjiang Province, China

XIN Shidong¹(), JIANG Lichun¹^,^*(), MU Lin²

1. Key Laboratory of Sustainable Forest Ecosystem Management Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China
2. Daxing Forest Farm, State Owned General Farm of Dongfeng County, Liaoyuan 136300,China

Received:2020-07-03 Accepted:2020-10-10 Online:2022-01-30 Published:2022-02-09
Contact: JIANG Lichun E-mail:774933353@qq.com;jlichun@nefu.edu.cn

Abstract

Abstract:

【Objective】 The estimation of large-scale forest carbon storage has attracted much attention, and the establishment of forest tree layer carbon storage model is an effective method to evaluate forest carbon storage. 【Method】 A total of 207 plots in a Korean pine plantation in Heilongjiang Province (Dongjingcheng, Linkou, Maoershan, Mengjiagang) were studied for modeling stand carbon storage, and choose the aggregation method, adjustment method, disaggregation method as the additivity method of establishing the stand carbon storage model, the research used the weighted regression to eliminate the heteroscedasticity of the carbon storage model, and adopted the leave-one-out cross validation method to evaluate the carbon stock model based on three additivity methods. 【Result】 There were slight differences between the fitting results of the forest carbon storage model based on the three additivity methods. The overall prediction ability of the aggregation method was slightly better than the adjustment method and the disaggregation method, and the specific prediction precision was ranked as the aggregation method > adjustment method > disaggregation method. When predicting stand total carbon storage, the predictability of the three additivity methods in different stand basal area intervals was not consistent. 【Conclusion】 The stand carbon storage model based on aggregation method was more suitable for the prediction of carbon storage of Korean pine plantation in Heilongjiang Province. However, when predicting the total carbon storage of Korean pine plantations, the appropriate additive method should be selected according to the stand basal area interval.

Key words: Pinus koraiensis (Korean pine) plantation, additivity methods, carbon storage, prediction precision

CLC Number:

S758.5

XIN Shidong, JIANG Lichun, MU Lin. Predictive model of stand tree layer additive carbon storage of Korean pine plantation in Heilongjiang Province, China[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(1): 115-121.

Figures/Tables 7

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References 31

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统计量 statistic	海拔/ m altitude	坡度/ (°) slope	林分平均直径/cm quadratic mean diameter	平均树高/m mean tree height	林分密度/ (株·hm^-2) stand density	林分断面积/ (m²·hm^-2) stand basal area
最小值 min.	195	5	4.6	3.8	300.0	1.3
最大值 max.	673	25	30.9	17.5	2 900.0	69.1
平均值 mean	363	8	15.4	10.3	1 365.4	24.5
标准差 SD	117	5	4.6	2.5	478.1	11.6

统计量 statistic	碳储量 carbon storage
统计量 statistic	总计 total	树根 root	树干 stem	树枝 branch	树叶 leaf
最小值 min.	1.93	0.30	0.98	0.40	0.25
最大值 max.	137.48	26.48	73.71	29.56	7.73
平均值 mean	46.56	8.69	25.84	9.45	2.58
标准差 SD	24.26	4.77	13.01	5.34	1.29

方法 method	分量 component	α_i		β_i		γ_i
方法 method	分量 component	估计值 estimate	标准误 SE	估计值 estimate	标准误 SE	估计值 estimate	标准误 SE
聚合法 aggregation	总量 total	—	—	—	—	—	—
	树根 root	0.138 6	0.005 5	1.050 2	0.010 4	0.323 5	0.022 8
	树干 stem	0.728 4	0.027 1	0.995 8	0.007 1	0.161 4	0.018 8
	树枝 branch	0.121 4	0.007 1	1.043 9	0.011 6	0.421 2	0.027 1
	树叶 leaf	0.080 9	0.007 4	1.077 8	0.016 6	-0.001 3	0.043 8
平差法 adjustment	总量 total	0.990 3	0.061 3	1.013 3	0.016 4	0.253 0	0.033 5
	树根 root	0.143 0	0.010 5	1.049 2	0.021 4	0.310 5	0.043 4
	树干 stem	0.817 2	0.064 7	1.011 4	0.019 9	0.091 3	0.042 3
	树枝 branch	0.115 7	0.009 9	1.030 9	0.018 4	0.458 2	0.039 4
	树叶 leaf	0.071 0	0.007 4	1.074 5	0.019 5	0.056 1	0.050 1

项目item	α_t	β_t	γ_t	m₁	k₁	f₁	m₂	k₂	f₂	m₃	k₃	f₃
估计值 estimate	1.147 4	1.036 4	0.160 7	7.391 6	-0.042 7	-0.352 5	0.632 8	0.022 9	-0.385 1	1.290 8	0.007 2	-0.149 6
标准误 SE	0.061 0	0.011 4	0.030 0	0.582 6	0.017 0	0.039 5	0.047 7	0.014 1	0.036 8	0.074 8	0.014 2	0.028 1

方法 method	分量 component	R²	均方根误差/ (t·hm^-2) RMSE	权函数 weight functions
聚合法 aggregation	总量 total	0.973 8	3.923 7	G^{2.352 5}H^{-2.582 9}
	树根 root	0.971 4	0.806 5	G^{1.599 2}
	树干 stem	0.963 0	2.502 7	G^{-0.062 1}
	树枝 branch	0.969 3	0.936 3	G^{-1.103 0}H^{-2.036 2}
	树叶 leaf	0.931 9	0.335 9	G^{-1.112 2}
平差法 adjustment	总量 total	0.973 8	3.930 2	G^{2.311 6}H^{-2.309 7}
	树根 root	0.971 4	0.806 1	G^{1.599 9}
	树干 stem	0.963 2	2.493 5	G^{0.248 8}
	树枝 branch	0.969 3	0.937 0	G^{-1.225 1}H^{-2.309 7}
	树叶 leaf	0.931 1	0.338 0	G^{-1.083 1}
分解法 disaggregation	总量 total	0.973 7	3.934 6	G^{2.329 6}H^{-2.334 2}
	树根 root	0.970 9	0.812 8	G^{1.717 7}
	树干 stem	0.963 4	2.489 8	G^{-0.062 3}
	树枝 branch	0.969 2	0.937 5	G^{-0.522 3}
	树叶 leaf	0.931 6	0.336 7	G^{-1.109 5}

Predictive model of stand tree layer additive carbon storage of Korean pine plantation in Heilongjiang Province, China

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方法 method	分量 component	平均误差绝对值 MAE	相对误差绝对值 MPE	平均相对误差/% MRE
聚合法 aggregation	总量 total	2.420 2	5.197 5	-1.375 5
	树根 root	0.535 9	6.166 7	-1.663 1
	树干 stem	1.687 9	6.532 1	-2.766 6
	树枝 branch	0.666 3	7.054 7	-0.975 7
	树叶 leaf	0.264 4	10.215 8	1.847 6
平差法 adjustment	总量 total	2.437 5	5.234 8	-1.011 3
	树根 root	0.538 2	6.192 7	-1.173 7
	树干 stem	1.673 7	6.477 0	-2.595 4
	树枝 branch	0.672 9	7.124 3	-0.348 6
	树叶 leaf	0.267 7	10.342 0	2.924 8
分解法 disaggregation	总量 total	2.464 4	5.292 5	-1.579 9
	树根 root	0.542 5	6.243 1	-1.677 8
	树干 stem	1.697 5	6.569 1	-3.403 5
	树枝 branch	0.679 0	7.189 1	-0.366 2
	树叶 leaf	0.266 1	10.278 6	2.374 4