黑龙江省红松人工林林分乔木层可加性碳储量模型

doi:10.12302/j.issn.1000-2006.202007007

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南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (1): 115-121.doi: 10.12302/j.issn.1000-2006.202007007

黑龙江省红松人工林林分乔木层可加性碳储量模型

辛士冬¹(), 姜立春¹^,^*(), 穆林²

1.东北林业大学林学院,森林生态系统可持续经营教育部重点实验室,黑龙江哈尔滨 150040
2.东丰县国有总场大兴林场,吉林辽源 136300

收稿日期:2020-07-03 接受日期:2020-10-10 出版日期:2022-01-30 发布日期:2022-02-09
通讯作者: 姜立春
基金资助:
国家重点研发计划(2017YFB0502700);黑龙江省应用技术研究与开发计划项目(GA19C006)

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

摘要/Abstract

摘要：

【目的】大尺度森林碳储量的估算备受关注,而构建林分乔木层碳储量模型是一种评估森林碳储量快捷且准确的方式。【方法】以黑龙江省(东京城、林口、帽儿山、孟家岗)207块红松人工林样地数据为研究对象,选择聚合法、平差法、分解法作为构建林分碳储量模型的可加性方法,以加权回归来消除碳储量模型的异方差。采用留一交叉验证法(leave-one-out cross validation, LOOCV)对3种可加性方法的碳储量模型进行评价。【结果】基于3种可加性方法林分碳储量模型拟合结果之间存在略微的差异。聚合法的总体预测能力略优于平差法和分解法,具体预测精度排序为聚合法>平差法>分解法。当预测林分总碳储量时,3种可加性方法在不同林分断面积区间的预测能力表现并不一致。【结论】基于聚合法的林分碳储量模型更适合于黑龙江省红松人工林的碳储量预测,但当预测红松人工林的林分总碳储量时,应根据林分断面积区间选择合适的可加性方法。

关键词: 红松人工林, 可加性方法, 碳储量, 预测精度

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

中图分类号:

S758.5

辛士冬,姜立春,穆林. 黑龙江省红松人工林林分乔木层可加性碳储量模型[J]. 南京林业大学学报（自然科学版）, 2022, 46(1): 115-121.

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 (Natural Science Edition), 2022, 46(1): 115-121.DOI: 10.12302/j.issn.1000-2006.202007007.

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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}

黑龙江省红松人工林林分乔木层可加性碳储量模型

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

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