中国桉树人工林生物量估算系数及影响要素

doi:10.11707/j.1001-7488.20200501

摘要/Abstract

摘要：

目的: 分析中国桉树人工林生物量3个估算系数与林分结构特征、气候因子及地形因子的关系，确定生物量估算系数的主要影响因素，以期为区域尺度准确估算桉树人工林生物量提供科学依据。方法: 收集、整理、筛选中国桉树人工林的相关文献数据，分析桉树人工林生物量3个常见估算系数（生物量转换与扩展系数BCEF、生物量扩展系数BEF和根茎比R）与林分结构特征（林龄、平均胸径、平均树高、林分密度、林分蓄积量）、气候因子（年均气温、年均降水量）和地形因子（海拔）间的关系。结果: 桉树人工林BCEF、BEF和R的平均值分别为0.658 Mg·m^-3、1.251和0.190，其对应范围值分别为0.46~0.76 Mg·m^-3、1.05~1.35和0.04~0.36；BCEF随平均胸径（r²=0.306）增大而先减后增，随平均树高（r²=0.366）和林分密度（r²=0.430）及林分蓄积量（r²=0.405）增大均呈逐渐减小并渐趋稳定的趋势；BEF随林龄（r²=0.765）和平均树高（r²=0.734）及林分蓄积量（r²=0.578）增大均呈逐渐减小并渐趋稳定的趋势，随平均胸径（r²=0.644）增大呈先减后增的趋势；R随林龄（r²=0.665）、平均树高（r²=0.338）、林分密度（r²=0.275）和林分蓄积量（r²=0.403）增大均呈逐渐减小并渐趋稳定的趋势；BCEF随年均气温（r²=0.193）和降水量（r²=0.200）增大均呈先减后增的趋势，遵循二次多项式关系；BEF和R随年均气温和降水量的变化未表现出明显的函数关系；3个生物量估算系数均随海拔升高表现出增大趋势，关系式的拟合精度分别为0.455、0.501和0.314。结论: 林分密度与BCEF的拟合优度较高，林龄与BEF和R的拟合优度较高，林分密度和林龄是生物量估算系数的主要影响因素。地形因子对3个生物量估算系数的影响仅次于林分密度和林龄。气候因子仅对BCEF产生一定影响。在估算区域桉树人工林生物量时应考虑林分结构特征及地形因子引起的生物量估算系数差异。

关键词: 桉树人工林, 生物量转换与扩展系数, 生物量扩展系数, 根茎比, 林分结构特征, 海拔

Abstract:

Objective: The relationship between the three biomass estimation coefficients and the stand structure, climatic factors, and topographic factors was analyzed, and the main influencing factors of biomass estimation coefficients were determined to provide a scientific basis for accurately estimating the biomass of Eucalyptus plantations at regional scale. Method: By collecting and collating historical data from published literature on biomass measurements of Eucalyptus plantations, we analyzed and explored the relationship between three common biomass estimation coefficients (biomass conversion and expansion factor BCEF, biomass expansion factor BEF, and root/shoot ratio R) and stand structure (stand age, mean DBH, mean tree height, stand density, and standing volume), climatic factors (annual average temperature and annual average precipitation), and topographic factor (elevation). Result: Means of BCEF, BEF, and R were 0.658 Mg·m^-3 (n=119, SD=0.150), 1.251 (n=176, SD=0.167), and 0.190 (n=144, SD=0.102), respectively. The corresponding range values were 0.46-0.76 Mg·m^-3, 1.05-1.35, and 0.04-0.36, respectively. BCEF value displayed a decrease followed by an increase with the increase of mean DBH. BCEF value gradually decreased with the increase of mean tree height (r²=0.366), stand density (r²=0.430), and standing volume (r²=0.405). BEF value decreased and became stable with the increase of stand age(r²=0.765), mean tree height(r²=0.734), and standing volume(r²=0.578), decreased first and then increased with the increase of mean DBH(r²=0.644). R value decreased gradually and then became stable with the increase of stand age(r²=0.665), mean tree height(r²=0.338), stand density(r²=0.275), and standing volume(r²=0.403).BCEF value showed a decrease first followed by an increase with the increases of temperature (r²=0.193) and precipitation (r²=0.200). Values of BEF and R did not show a significant functional relationship with changes in temperature and precipitation. Three biomass estimation coefficients showed a trend of increase with elevation, and the equation fitting accuracies were 0.455, 0.501, and 0.314. Conclusion: The goodness of fit between stand density and BCEF was relatively high, and so did the stand age and the BEF and R. Stand density and age were the main factors affecting the estimation coefficient of biomass. The influence of topographic factors on the three biomass estimation coefficients was next only to the stand density and the age. The climatic factor only had some effects on the BCEF. Therefore, the difference in biomass estimation coefficients caused by stand structure and topographic factors should be considered when estimating the biomass of Eucalyptus plantations at regional scale.

Key words: Eucalyptus plantations, biomass conversion and expansion factor, biomass expansion factor, root/shoot ratio, stand structure, elevation

中图分类号:

S718.55

竹万宽,许宇星,王志超,杜阿朋. 中国桉树人工林生物量估算系数及影响要素[J]. 林业科学, 2020, 56(5): 1-11.

Wankuan Zhu,Yuxing Xu,Zhichao Wang,Apeng Du. Biomass Estimation Coefficient and Its Impacting Factors for Eucalyptus Plantation in China[J]. Scientia Silvae Sinicae, 2020, 56(5): 1-11.

图/表 6

表1

数据来源及研究地概况"

省(区) Province (autonomous region)	研究地 Site	经度 Longitude/(°E)	纬度 Latitude/(°N)	年均气温 Mean annual temperature/℃	年均降水量 Mean annual precipitation/ mm	海拔 Elevation/m	样株数 Sample trees number	数据源 Sources
福建 Fujian	永安市 Yong’an City	116.93	25.55	19.1	1 688.0	20~310	5	张琼，2005
福建 Fujian	龙浔林业站 Longxun Forestry Station	118.25	25.50	18.0	1 724.0	500	3	郭明丽，2006
广东 Guangdong	桉树生态站 Eucalyptus Ecological Station	111.63	21.50	23.1	1 500.0	88~144	19	韩斐扬等，2012; 王志超，2014; 张利丽等，2017
	石岭林场 Shiling Forest Farm	109.75	21.42	22.9	1 750.0	43	14	韩斐扬等，2010; 周建辉等，2013
	雷州林区 Leizhou Forest	110.06	21.58	23.5	1 453.0	23~30	11	潘永言，1990; 肖文光等，1999;谭绍满等，1984
	北坡林场 Beipo Forest Farm	109.98	21.25	23.5	1 567.0	22	13	许宇星等，2013; 周群英等，2013
	河头林场 Hetou Forest Farm	109.84	21.08	23.3	1 677.0	29	4	余雪标等，1999
	西江流域 Xijiang River Drainage Area	111.98	23.40	21.5	1 502.4	200	2	郭乐东，2009
	小良生态站 Xiaoliang Ecological Station	110.91	21.46	23.0	1 550.0	29	1	邓瑞文等，1985
	天鹅山林场 Tian’e shan Forest Farm	113.45	25.95	16.8	1 400.0	400	3	李志辉等，2007
	东安镇 Dong’an Town	112.30	22.18	22.1	1 686.0	25	1	梁坤南等，2002
	南华林场 Nanhua Forest Farm	110.21	20.41	22.7	1 578.0	70	7	郑海水等，1997
	广西 Guangxi	兴宾区 Xingbin District	108.30	23.73	19.1	1 688.0	125	1	吴立素，2016
高峰林场 Gaofeng Forest Farm		108.30	22.97	21.6	1 304.2	99~200	3	骆栋卿，2016; 叶绍明等，2010
派阳山林场 Paiyangshan Forest Farm		106.63	21.42	22.1	1 200.0	135	2	李春宁等，2017
东门林场 Dongmen Forest Farm		107.26	22.29	21.8	1 200.0	86~150	25	陈婷等，2005; 邓龙江，2015; 梁宏温等，2008; 卢婵江，2017; 温远光等，2000; 叶绍明等，2007; 朱宇林等，2006; 左花，2015
广西东南部 Southeastern of Guangxi		107.80— 111.55	22.63— 24.40	21.8	1 550.0	30~145	5	付威波等，2014
七坡林场 Qipo Forest Farm		108.24	22.66	21.8	1 350.0	250	7	李况，2013; 吴华静等，2014
城中镇 Chengzhong Town		106.63	21.85	22.1	1 200.0	135	4	杨卫星等，2016
钦州市茅坡村 Maopo Village of Qinzhou City		108.65	21.80	21.5	1 600.0	55	14	刘正富，2012
沙塘林场 Shatang Forest Farm		108.28	24.45	20.1	1 429.7	91	3	陶玉华等，2011
凭祥市青山实验场 Qingshan Experimental Field of Pingxiang City		106.68	21.95	21.9	1 222.0	254	2	卢文科，2017
石康镇 Shikang Town		109.34	21.75	22.9	1 670.0	38	1	韦宇静等，2014
平果县 Pingguo County		107.35	23.20	21.5	1 350.0	117	1	赵瑞等，2015
山口林场 Shankou Forest Farm		109.68	21.58	23.0	1 589.0	35	2	温远光等，2000
田林县 Tianlin County		105.85	24.38	21.6	1 137.0	650	9	谢伟东等，2007
贵州 Guizhou	大河乡怀所村 Huaisuo Village of Dahe Township	107.82	25.92	18.0	1 400.0	800	1	甘桂春等，2016
海南 Hainan	琼中 Qiongzhong County	109.84	19.04	24.1	1 667.0	120~308	6
	临高 Lingao County	109.70	19.92	24.4	1 689.0	7~14	6
	儋州 Danzhou County	109.59	19.53	24.3	1 774.0	15~148	6	向仰州，2012
	万琼 Wanqiong County	110.48	19.01	24.9	1 698.0	7~22	6
	临城县 Lincheng County	109.47	19.80	23.5	1 418.0	20	4	李东海等，2007
湖南 Hunan	资兴市 Zixing City	113.37	25.87	17.9	1 506.0	300	3	李跃林等，2001
湖南 Hunan	汝城县 Rucheng County	113.63	25.60	15.8	1 627.0	355	6	李志辉等，2008; 朱宾良等，2007
四川 Sichuan	来复森林经营所 Laifu Forest Station	104.59	28.47	18.1	1 022.0	350	5	谢贤健等，2005
云南 Yunnan	元谋干热河谷区 Yuanmou Arid-Hot Valleys	101.83	25.77	21.9	613.8	1 165~1 210	11	杨忠等，2001
	普洱市 Puer City	99.15	22.03	19.0	1 597.8	1 302	31	张胜伟，2008
	元谋荒漠生态站 Yuanmou Desert Ecological Station	101.86	25.67	21.9	1 120.0	1 120	1	李彬等，2013

表1

图1

图2

表2

图3

表3

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因变量 Dependent variable (y)	自变量 Independent variable(x)	模型 Model	a	b	c	n	r²	P
BCEF	平均胸径Mean DBH	y=c+bx+ax²	0.003±6.493×10^-4	-0.054±0.016	1.010±0.089	55	0.306	< 0.001
	平均树高Mean tree height	y=ax^b	3.154±0.561	-0.607±0.070	—	112	0.366	< 0.001
	林分密度Stand density	y=ax^b	8.893±2.602	-0.353±0.040	—	95	0.430	< 0.001
	林分蓄积量Standing volume	y=b+a/x	11.264±1.655	0.550±0.028	—	70	0.405	< 0.001
BEF	林龄Stand age	y=b+a/x	0.650±0.027	1.045±0.010	—	175	0.765	< 0.001
	平均胸径Mean DBH	y=c+bx+ax²	0.007±6.599×10^-4	-0.177±0.015	2.287±0.081	117	0.644	< 0.001
	平均树高Mean tree height	y=ax^b	2.625±0.091	-0.298±0.014	—	140	0.734	< 0.001
	林分蓄积量Standing volume	y=b+a/x	7.244±0.786	1.070±0.014	—	64	0.578	< 0.001
R	林龄Stand age	y=ax^b	0.326±0.017	-0.463±0.037	—	75	0.665	< 0.001
	平均树高Mean tree height	y=ax^b	2.080±0.719	-0.975±0.138	—	82	0.338	< 0.001
	林分密度Stand density	y=ae^-bx	0.341±0.037	3.745×10^-4±6.651 ×10^-5	—	105	0.275	< 0.001
	林分蓄积量Standing volume	y=b+a/x	3.328±0.519	0.109±0.009	—	63	0.403	< 0.001

因变量 Dependent variable(y)	自变量 Independent variable(x)	模型 Model	a	b	c	n	r²	P
BCEF	年均气温 Mean annual temperature	y=c+bx+ax²	0.015±0.003	-0.642±0.128	7.277±1.297	125	0.193	< 0.001
	年均降水量 Mean annual precipitation	y=c+bx+ax²	6.639×10^-7±2.321×10^-7	-0.002 ±6.368×10^-4	2.431±0.435	122	0.200	< 0.001
	海拔Elevation	y=b+ax	7.584×10^-4±1.029 ×10^-4	0.561±0.019	—	67	0.455	< 0.001
BEF	海拔Elevation	y=c+bx+ax²	1.453×10^-6±2.300×10^-7	-5.006×10^-4±1.490×10^-4	1.216±0.015	130	0.501	< 0.001
R	海拔Elevation	y=ax^b	0.047±0.009	0.283±0.036	—	129	0.314	< 0.001

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