Developing One-variable Individual Tree Biomass Models Based on Wood Density for 34 Tree Species in China

doi:10.13466/j.cnki.lyzygl.2017.06.008

Abstract

Abstract:

Forest is the largest carbon bank on land,and assessment of forest carbon sequestration must be based on biomass models.Based on the general biomass model M=0.3pD ^7/3 presented in 2012,one variable individual tree aboveground biomass models for all 34 tree species or groups in China were established using the data of wood basic density of all tree species published;and based on the mensuration data of the National Biomass Modeling Program in Continuous Forest Inventory(NBMP-CFI),the aboveground biomass models of 14 tree species(groups)were validated.Additionally,compatible below ground biomass models and root-to-shoot ratio models for two species groups,coniferous and broadleaved,were developed and evaluated.The results showed that average absolute relative errors of above and below-ground biomass estimates from one-variable biomass models developed in this study were less than the allowances 10% and 15%,respectively.The developed biomass models here could be applied to estimate forest biomass on macro levels,and would be an important supplement to the ministerial standards on biomass models which were promulgated and implemented in the past years.

Key words: aboveground biomass, belowground biomass, wood density, root-to-shoot ratio

CLC Number:

S718.55

ZENG Weisheng. Developing One-variable Individual Tree Biomass Models Based on Wood Density for 34 Tree Species in China[J]. FOREST RESOURCES WANAGEMENT, 2017, (6): 41-46.

Figures/Tables 4

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References 41

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树种(组)	生物量模型参数a	木材基本密度p	样木株数n	数据来源
栎树	0.1729	0.5762	670	[33]
冷杉	0.1039	0.3464	751	[30]
云杉	0.1119	0.3730	901	[31]
桦树	0.1454	0.4848	690	[33]
落叶松	0.1218	0.4059	602	[29]
杉木	0.0929	0.3098	301	[28]
马尾松	0.1343	0.4476	301	[27]
杨树	0.1253	0.4177	901	①
云南松	0.1050	0.3499	150	[26]
树种(组)	生物量模型参数a	木材基本密度p	样木株数n	数据来源
高山松	0.1416	0.472	106	[39]
椴树	0.0960	0.320	u	[39-40]
柏木	0.1792	0.597	189	[39]
水胡黄	0.1392	0.464	19	[39]
木荷	0.1669	0.5563	150	[35]
油松	0.1273	0.4243	149	[24]
红松	0.0940	0.313	u	[40]
榆树	0.1374	0.458	27	[39]
思茅松	0.1362	0.454	17	[39]
华山松	0.1179	0.393	29	[39]
铁杉	0.1326	0.442	34	[39]
樟子松	0.1125	0.375	22	[39]
枫香	0.1511	0.5035	152	[36]
湿地松	0.1235	0.4118	154	[25]
柳树	0.1323	0.441	29	[39]
桉树	0.1746	0.582	110	[39]
柳杉	0.1048	0.3493	150	[32]
黄山松	0.1354	0.451	34	[39]
乔松	0.1014	0.338	3	[39]
刺槐	0.2022	0.674	25	[39]
泡桐	0.0711	0.237	56	[39]
樟檫楠	0.1380	0.460	90	[39]
其它松	0.1351	0.450	88	[39]
其它杉	0.1182	0.394	54	[39]
其它硬阔	0.1875	0.625	410	[39]
其它软阔	0.1329	0.443	474	[39]

树种(组)	相对误差RE/%	树种(组)	相对误差RE/%
栎树	-0.98	杨树	-3.89
冷杉	-9.86	云南松	1.26
云杉	-7.41	木荷	19.72
桦树	4.06	油松	-2.32
落叶松	1.25	枫香	-12.78
杉木	-0.40	湿地松	4.29
马尾松	-1.13	柳杉	19.89

树种组	a₁	a₂	b₁	b₂	c₁	c₂
针叶树	0.138	2.28	0.249	-0.001	0.0343	2.28
阔叶树	0.128	2.36	0.468	-0.200	0.0599	2.16

树种(组)	相对误差RE/%	树种(组)	相对误差RE/%
栎树	2.09	杨树	18.69
冷杉	11.60	云南松	54.41
云杉	3.68	木荷	-17.53
桦树	-9.00	油松	-0.33
落叶松	-4.89	枫香	-19.93
杉木	7.38	湿地松	-3.98
马尾松	18.60	柳杉	-28.77