我国尺度兼容和树种分类的材积源森林碳储量模型

doi:10.11707/j.1001-7488.LYKX20230562

摘要/Abstract

摘要：

目的: 提出一种简单方便的森林碳储量估算方法，构建考虑林分特征的尺度兼容和树种分类的材积源森林碳储量模型，为估算多尺度和多树种森林碳储量提供方法和技术支持。方法: 基于第6~9次全国森林资源清查数据和异速生长方程，分别利用含哑变量的非线性最小二乘法的独立模型和非线性似然无关回归的联立方程组模型，构建考虑起源、龄组2个主要林分特征的尺度兼容和树种分类的森林碳储量模型，通过加权回归消除异方差，采用决定系数（R²）、估计值的标准差（SEE）、平均预估误差（MPE）、总相对误差（TRE）和差异百分比（VP）对模型进行评价；同时利用2021年林草综合监测数据，比较不同尺度模型估算全国森林碳储量的差异。结果: 1）共构建2 974类尺度兼容的森林碳储量模型，与独立模型相比，联立方程组模型的R²无明显差异。独立模型和联立方程组模型分别为1 383和1 591类，模型R²的平均值分别为0.966 1和0.965 2，MPE分别为0.75%和0.78%，联立方程组模型的R²仅下降0.000 9，MPE仅上升0.03%。2）共构建2 520类树种分类的森林碳储量模型，与尺度兼容模型结果一样，独立模型和联立方程组模型的R²无明显差异。独立模型和联立方程组模型均为1 260类，模型R²的平均值分别为0.944 3和0.942 4，MPE分别为0.48%和0.49%，联立方程组模型的R²仅下降0.001 9，MPE仅上升0.01%。3）构建4种不同建模方式（独立-尺度模型、独立-树种模型、联立-尺度模型、联立-树种模型）的森林碳储量模型。相比独立模型，联立方程组模型的参数变动幅度更小。4种不同建模方式共包含参数a和参数b分别为46 157和23 935个。独立模型和联立方程组模型参数a的平均值分别为0.596 5和0.620 0，极差分别为2.318 6和2.192 2，独立模型的参数极差偏高0.126 4；参数b的平均值分别为0.933 2和0.931 8，极差分别为0.672 3和0.506 5 ，独立模型的参数极差偏高0.166 7。4）不同尺度模型估算全国森林碳储量时，无论何种尺度，独立模型的估算差异均大于联立方程组模型，但总体上各种尺度模型的估算差异均在3%以内。结论: 1）本研究提出的从森林蓄积量直接到森林碳储量的材积源森林碳储量模型数据有效、方法可靠，可用于直接估算森林碳储量。2）基于含哑变量的非线性似然无关的联立方程组方法，可更好地建立尺度兼容和树种分类的森林碳储量模型。3）本研究构建的森林碳储量模型平均R²在0.95以上，MPE在1%以内，可用于林业实践中快速准确估算森林碳储量。4）根据模型的拟合精度以及参数的稳定性，建议使用以联立-尺度（以尺度为建模总体的联立树种分类模型）为建模方式的森林碳储量模型。5）在5%精度要求下，可使用国家尺度考虑林分起源、龄组的树种分类模型估算全国森林碳储量。

关键词: 森林碳储量模型, 非线性似然无关, 哑变量, 参数库, 尺度兼容, 树种分类

Abstract:

Objective: A simple and convenient method for estimating forest carbon storage was proposed, and a scale-compatible and tree species-classified forest carbon storage model of volume-derived was constructed, which provided a method and technology support for estimating forest carbon storage of multi-scale and multi-tree species. Method: Based on the data of the 6th?9th national forest resource inventory and allometric growth equation, the scale-compatible and tree species-classified stand carbon storage model considering stand origin and age group was constructed by using the independent model of non-linear least square method and the simultaneous equations model of non-linear seemingly unrelated regressions with dummy variables. Heteroscedasticity was eliminated by weighted regression, and the model was evaluated by determination coefficient (R²), standard error of estimate (SEE), mean prediction error (MPE), total relative error (TRE) and variance percentage (VP). Meanwhile, using the data of the National forest and grass ecological comprehensive monitoring in 2021, the differences of forest carbon storage estimation by different scale models were compared. Result: 1) A total of 2 974 scale-compatible forest carbon storage models were constructed. Compared with the independent model, there was no significant difference in R² of the simultaneous equations model. The independent model and the simultaneous equations model were 1 383 and 1 591 categories, respectively. The average values of the model R² were 0.966 1 and 0.965 2, MPE were 0.75% and 0.78%, respectively, the R² of the simultaneous equations model only decreased by 0.000 9, and the MPE only increased by 0.03%. 2) A total of 2 520 tree species-classified forest carbon storage models were constructed. As with the results of the scale-compatible model, there was no significant difference in R² between the independent model and the simultaneous equations model. The independent model and the simultaneous equations model were both 1 260 categories. The average values of the model R² were 0.944 3 and 0.942 4, MPE were 0.48% and 0.49%, respectively, the R² of the simultaneous equations model only decreased by 0.001 9, and the MPE only increased by 0.01%. 3) Four forest carbon storage models with different modeling methods (independent-scale model, independent-tree species model, simultaneous-scale model, simultaneous-tree species model) were established. Compared with the independent model, the parameters variation of the simultaneous equations model was smaller. Four forest carbon storage models with different modeling methods contained 46 157 and 23 935 parameters a and b respectively. The average values of parameter a in the independent model and the simultaneous equations model were 0.596 5 and 0.620 0, respectively, and the ranges were 2.318 6 and 2.192 2, respectively. The range of parameters in the independent model was 0.126 4 higher. The average values of parameter b were 0.933 2 and 0.931 8, respectively, and the ranges were 0.672 3 and 0.505 6, respectively. The range of the independent model was 0.166 7 higher. 4) When estimating national forest carbon storage by different scale models, regardless of the scale, the estimation difference of the independent model was higher than the simultaneous model. However, in general, the estimation differences at various scales were within 3%. Conclusion: 1) The volume-derived forest carbon storage model proposed in this paper from stand volume to stand carbon storage was effective and reliable, which can be used to directly estimate forest carbon storage. 2) Based on the simultaneous equations model of non-linear seemingly unrelated regressions with dummy variables, the scale-compatible and tree species-classified forest carbon storage model can be better established. 3) The average R² of the forest carbon storage model constructed in this paper was above 0.95, and MPE was less than 1%, which can be used to quickly and accurately estimate forest carbon storage in forestry practice. 4) According to the fitting accuracy of the model and the stability of the parameters, we recommend using the simultaneous-tree species model. 5) Under the accuracy requirement of 5%, the national scale model can be used to estimate the national forest carbon storage.

Key words: forest carbon storage model, nonlinear likelihood independent, dumb variable, parameter database, scale-compatible, tree species-classified

中图分类号:

S757

张聪,刘琪,李海奎,刘鹏举,詹思颖. 我国尺度兼容和树种分类的材积源森林碳储量模型[J]. 林业科学, 2025, 61(1): 57-69.

Cong Zhang,Qi Liu,Haikui Li,Pengju Liu,Siying Zhan. Scale-Compatible and Tree Species-Classified Forest Carbon Storage Model of Volume-Derived in China[J]. Scientia Silvae Sinicae, 2025, 61(1): 57-69.

图/表 13

表1

表2

表3

表4

我国气候区域尺度区划"

尺度Scale	具体尺度Specific scale
国家 Nation	全国 Nationwide
气温区 Temperature region	北温带、中温带、南温带、北亚热带、中亚热带、南亚热带、北热带、中热带、高原气候区 North temperate zone, middle temperate zone, south temperate zone, north subtropical zone, middle subtropical zone, south subtropical zone, north tropical zone, middle tropical zone, plateau climate zone
气候区 Climatic region	滇南河谷区、雷琼区、琼西区、元江区、根河区、江北区、秦巴区、波密_川西区、藏南区、柴达木区、昌都区、达旺_察隅区、祁连_青海湖区、青南区、河北区、晋陕甘区、辽东_胶东半岛区、鲁淮区、南疆区、渭河区、滇南区、闽南_珠江区、琼南_西沙区、北疆区、大兴安岭区、富蕴区、蒙东区、蒙甘区、蒙中区、三江_长白区、松辽区、塔城区、小兴安岭区、伊宁区、滇北区、贵州区、江南区、金沙江_楚雄_玉溪区、瓯江_闽江_南岭区、四川区 Yunnan river valley, Leiqiong district, Qiongxi district, Yuanjiang district, Genhe district, Jiangbei district, Qinba district, Bomi_western Sichuan district, southern Xizang district, Qaidam district, Changdu district, Dawang_Chayu district, Qilian_Qinghai Lake district, southern Qinghai district, Hebei district, Shanxi_Shaanxi_Gansu district, Liaodong_Jiaodong Peninsula district, Luhuai district, southern Xinjiang district, Weihe district, southern Yunnan district, southern Fujian_Pearl River district, southern Fujian_Xisha district, northern Xinjiang district, Daxing’anling district, Fuyun district, eastern Inner Mongolia district, Inner Mongolia_Gansu district, central Mongolia district, Sanjiang_Changbai district, Songliao district, Tacheng district, Xiaoxing’anling district, Yining district, northern Yunnan district, Guizhou district, Jiangnan district, Jinsha River_Chuxiong_Yuxi district, Oujiang River_Minjiang River_Nanling district, Sichuan district

表4

表5

图1

表6

表7

表8

国家尺度不考虑林分特征变量的森林亚类模型参数a（估计值±标准差）"

森林亚类 Forest subclass	样本量 Sample size	独立模型 Independent model	联立方程组模型 Simultaneous equations model
冷杉 Abies spp.	2 525	0.449 6±0.001 4	0.488 2±0.001 5
云杉 Picea spp.	5 673	0.517 7±0.001 5	0.559 2±0.001 5
铁杉 Tsuga spp.	78	0.460 5±0.004 7	0.500 0±0.007 0
油杉 Keteleeria spp.	215	0.600 5±0.007 3	0.636 2±0.010 7
落叶松 Larix spp.	9 215	0.476 3±0.001 3	0.510 3±0.001 3
红松 Pinus koraiensis	333	0.573 1±0.003 9	0.614 9±0.005 6
樟子松 Pinus sylvestris var. mongolica	583	0.471 2±0.003 0	0.504 1±0.004 3
赤松 Pinus densiflora	282	0.753 2±0.008 3	0.797 4±0.012 1
黑松 Pinus thunbergii	354	0.771 0±0.007 5	0.814 1±0.010 9
油松 Pinus tabuliformis	4 445	0.654 9±0.002 0	0.695 6±0.002 6
华山松 Pinus armandii	1 192	0.567 2±0.002 8	0.604 6±0.003 9
马尾松 Pinus massoniana	14 350	0.503 9±0.001 2	0.535 8±0.001 4
云南松 Pinus yunnanensis	3 508	0.469 4±0.001 6	0.500 4±0.002 1
思茅松 Pinus kesiya var. langbianensis	483	0.614 2±0.003 8	0.655 6±0.005 5
高山松 Pinus densata	787	0.556 4±0.002 5	0.598 4±0.003 4
国外松 Foreign pine	1 139	0.613 1±0.007 6	0.653 6±0.011 1
黄山松 Pinus taiwanensis	99	0.593 6±0.011 2	0.630 3±0.016 5
其他松类 Other pine trees	249	0.824 7±0.008 4	0.874 4±0.012 3
杉木 Cunninghamia spp.	13 516	0.454 3±0.001 2	0.484 2±0.001 3
柳杉 Cryptomeria spp.	386	0.482 5±0.003 6	0.517 1±0.005 3
水杉 Metasequoia spp.	220	0.427 1±0.005 3	0.454 8±0.007 8
柏木 Cupressus spp.	4 970	0.678 3±0.002 1	0.719 6±0.002 7
栎类 Quercus spp.	18 379	0.786 2±0.001 8	0.840 6±0.001 7
桦木 Betula spp.	4 079	0.609 4±0.001 8	0.649 6±0.002 2
白桦 Betula platyphylla	4 866	0.554 2±0.001 6	0.591 2±0.001 9
枫桦 Betula Costata	218	0.635 1±0.005 8	0.677 4±0.008 4
水胡黄 Fraxinus mandshurica, Juglans mandshurica, Phellodendron	622	0.838 0±0.008 1	0.889 5±0.011 9
樟木 Cinnamomum spp.	383	0.732 4±0.006 8	0.774 1±0.009 9
楠木 Phoebe spp.	89	0.667 5±0.009 5	0.714 1±0.014 1
榆树 Ulmus spp.	1 100	0.669 0±0.004 2	0.706 5±0.006 0
刺槐 Robinia spp.	852	0.638 2±0.005 2	0.671 1±0.007 5
木荷 Schima spp.	356	0.709 9±0.005 7	0.756 1±0.008 3
枫香 Liquidambar spp.	244	0.670 8±0.007 3	0.711 6±0.010 6
其他硬阔类 Other hard-and-broad trees	8 192	0.807 2±0.002 1	0.860 1±0.002 2
椴树 Tilia spp.	684	0.621 5±0.003 1	0.665 4±0.004 4
杨树 Populus spp.	13 878	0.541 1±0.001 3	0.575 5±0.001 4
柳树 Salix spp.	419	0.651 3±0.006 1	0.689 1±0.008 9
泡桐 Paulownia spp.	333	0.706 0±0.007 1	0.745 2±0.010 3
桉树 Eucalyptus spp.	2 556	0.633 3±0.002 6	0.669 2±0.003 6
相思 Abrus spp.	221	0.573 8±0.006 3	0.610 0±0.009 3
木麻黄 Casuarina equisetifolia	134	0.506 1±0.008 3	0.537 1±0.012 3
楝树 Melia spp.	30	0.613 6±0.025 6	0.645 6±0.037 3
其他软阔类 Other soft-and-broad trees	5 174	0.657 7±0.002 0	0.699 7±0.002 4
针叶混交林 Mixed coniferous forest	4 871	0.514 0±0.001 5	0.549 8±0.001 8
阔叶混交林 Mixed broad-leaved forest	32 545	0.723 5±0.001 6	0.773 0±0.001 4
针阔混交林 Mixed broad-leaf and coniferous forest	10 978	0.596 8±0.001 5	0.637 3±0.001 6

表8

表9

表10

表11

图2

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行政大区 Administrative region	森林蓄积量 Forest volume/(m³?hm^?2)	森林碳储量 Forest carbon storage/(t?hm^?2)
东北 Northeast	119.33±82.28	52.67±36.27
华北 North China	77.50±66.43	33.04±26.24
华东 East China	72.00±66.86	30.05±27.67
华南 South China	64.33±61.22	29.08±28.78
西北 Northwest	118.31±121.28	49.14±41.53
西南 Southwest	120.15±141.21	46.17±47.78

森林类 Forest class	森林蓄积量 Forest volume/ (m³?hm^?2)	森林碳储量 Forest carbon storage/ (t?hm^?2)
阔叶纯林Pure broad-leaved forest	79.63±75.71	38.21±36.06
阔叶混交林 Mixed broad-leaved forest	99.95±80.92	50.03±38.84
针阔混交林 Mixed broad-leaf and coniferous forest	93.44±85.59	38.66±32.90
针叶纯林 Pure coniferous forest	105.21±120.78	36.10±36.64
针叶混交林 Mixed coniferous forest	103.21±103.05	36.43±33.48

尺度Scale	具体尺度Specific scale
国家 Nation	全国 Nationwide
区域 Region	北方、南方Northern and southern
行政大区 Administrative region	东北、华北、西北、华南、华东、西南Northeast, north China, northwest, south China, east China, southwest
省份 Province	黑龙江、吉林、辽宁、北京、天津、河北、山西、内蒙古、陕西、宁夏、甘肃、青海、新疆、山东、江苏、上海、安徽、浙江、江西、福建、河南、湖北、湖南、广西、广东、海南、重庆、贵州、四川、云南、西藏Heilongjiang, Jilin, Liaoning, Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Shaanxi, Ningxia, Gansu, Qinghai, Xinjiang, Shandong, Jiangsu, Shanghai, Anhui, Zhejiang, Jiangxi, Fujian, Henan, Hubei, Hunan, Guangxi, Guangdong, Hainan, Chongqing, Guizhou, Sichuan, Yunnan, Xizang
副总体Subpopulation	甘肃、吉林、黑龙江、新疆和内蒙古划分为2~4个副总体 Gansu, Jilin, Heilongjiang, Xinjiang and Inner Mongolia were divided into 2?4 subpopulations

森林类型层级 Forest type level	具体树种（组）Specific tree species (group)
全部森林 Whole forest	全部树种 All tree species
森林类 Forest type	阔叶纯林、阔叶混交林、针阔混交林、针叶纯林、针叶混交林 Pure broad-leaved forest, mixed broad-leaved forest, mixed broad-leaf and coniferous forest, pure coniferous forest, mixed coniferous forest
森林亚类 Forest subclass	桉树、白桦、柏木、赤松、刺槐、椴树、枫桦、枫香、高山松、国外松、黑松、红松、华山松、桦木、黄山松、阔叶混交林、冷杉、栎类、楝树、柳杉、柳树、落叶松、马尾松、木荷、木麻黄、楠木、泡桐、其他软阔类、其他松类、其他硬阔类、杉木、水胡黄、水杉、思茅松、铁杉、相思、杨树、油杉、油松、榆树、云南松、云杉、樟木、樟子松、针阔混交林、针叶混交林 Eucalyptus spp., Betula platyphylla, Cupressus spp., Pinus densiflora, Robinia spp., Tilia spp., Betula costata, Liquidambar spp., Pinus densata, foreign pine, Pinus thunbergii, Pinus koraiensis, Pinus armandii, Betula spp., Pinus taiwanensis, mixed broad-leaved forest, Abies spp., Quercus spp., Melia spp., Cryptomeria spp., Salix spp., Larix spp., Pinus massoniana, Schima spp., Casuarina equisetifolia, Phoebe spp., Paulownia spp., other soft-and-broad trees, other pine trees, other hard-and-broad trees, Cunninghamia spp., Fraxinus mandshurica, Juglans mandshurica, Phellodendron, Metasequoia spp., Pinus kesiya var. langbianensis, Tsuga spp., Abrus spp., Populus spp., Keteleeria spp., Pinus tabuliformis, Ulmus spp., Pinus yunnanensis, Picea spp., Cinnamomum spp., Pinus sylvestris var. mongolica, mixed broad-leaf and coniferous forest, mixed coniferous forest

省份 Province	样本量 Sample size	独立模型 Independent model	联立方程组模型 Simultaneous equations model	省份 Province	样本量 Sample size	独立模型 Independent model	联立方程组模型 Simultaneous equations model
北京 Beijing	2 803	0.753 2±0.004 4	0.794 3±0.004 3	湖北 Hubei	6 064	0.681 7±0.003 1	0.718 7±0.002 8
天津 Tianjin	501	0.651 2±0.008 5	0.686 4±0.009 2	湖南 Hunan	8 175	0.597 5±0.002 6	0.629 6±0.002 3
河北 Hebei	4 999	0.694 3±0.003 4	0.731 3±0.003 1	广东 Guangdong	5 124	0.674 3±0.003 2	0.709 8±0.003 0
山西 Shanxi	4 503	0.803 3±0.003 8	0.847 6±0.003 5	广西 Guangxi	5 193	0.647 1±0.003 0	0.683 2±0.002 8
内蒙古 Inner Mongolia	9 442	0.585 4±0.002 4	0.620 9±0.001 8	海南 Hainan	2 539	0.844 7±0.004 3	0.895 8±0.003 8
辽宁 Liaoning	3 785	0.733 6±0.003 6	0.776 4±0.003 2	重庆 Chongqing	4 489	0.612 0±0.003 0	0.647 4±0.002 7
吉林 Jilin	13 176	0.734 2±0.002 8	0.781 4±0.001 8	四川 Sichuan	7 567	0.597 0±0.002 6	0.637 0±0.001 9
黑龙江 Heilongjiang	13 293	0.655 0±0.002 5	0.694 1±0.001 7	贵州 Guizhou	5 117	0.606 5±0.002 9	0.641 0±0.002 7
上海 Shanghai	647	0.681 6±0.007 8	0.718 1±0.008 7	云南 Yunnan	12 548	0.674 5±0.002 7	0.716 5±0.001 8
江苏 Jiangsu	2 775	0.602 4±0.003 7	0.635 2±0.003 7	西藏 Xizang	2 991	0.570 8±0.002 8	0.614 1±0.002 2
浙江 Zhejiang	6 213	0.644 4±0.003 0	0.678 9±0.002 7	陕西 Shaanxi	6 221	0.852 1±0.003 6	0.899 4±0.003 0
安徽 Anhui	8 682	0.644 9±0.002 7	0.680 2±0.002 3	甘肃 Gansu	7 210	0.686 0±0.002 9	0.731 9±0.002 2
福建 Fujian	9 579	0.637 2±0.002 6	0.675 7±0.001 9	青海 Qinghai	3 401	0.676 7±0.003 3	0.719 8±0.002 9
江西 Jiangxi	4 409	0.640 3±0.003 3	0.674 8±0.003 1	宁夏 Ningxia	1 105	0.675 5±0.005 8	0.712 9±0.006 2
山东 Shandong	2 977	0.654 7±0.004 0	0.688 9±0.004 1	新疆 Xinjiang	4 062	0.545 0±0.002 5	0.583 1±0.002 0
河南 Henan	6 281	0.744 9±0.003 3	0.785 0±0.002 9