北京市主要森林类型蓄积量生物量碳储量模型研建

doi:10.13466/j.cnki.lyzygl.2021.02.017

摘要/Abstract

摘要：

林分水平的蓄积量、生物量和碳储量模型,是开展森林资源规划设计调查的计量基础。基于北京市2016年森林资源连续清查的1 425个乔木林样地数据,分别利用非线性独立回归估计、误差变量联立方程组和含哑变量的误差变量联立方程组方法,建立了油松林、侧柏林、栎树林、桦木林、榆树林、刺槐林、杨树林、其他硬阔林、其他软阔林、乔木经济林等10种主要森林类型的林分蓄积量、生物量和碳储量模型。结果显示:10种主要森林类型的蓄积量、生物量和碳储量模型的确定系数(R²)都在0.93以上,总体相对误差(TRE)和平均系统误差(ASE)都在±3%以内且多数趋近于0,平均预估误差(MPE)都在5%以内,平均百分标准误差(MPSE)都在15%以内。结果表明:不同森林类型的蓄积量主要取决于林分断面积和平均高,生物量主要取决于蓄积量和林分平均高;含哑变量的非线性误差变量联立方程组方法,是建立林分水平三储量(森林蓄积量、生物量和碳储量)模型系统的可行方法;所建北京市10种主要森林类型的蓄积量、生物量和碳储量模型,其预估精度达到相关技术规定要求,可以在实践中推广试用;为进一步提高模型的准确度,可采用基于二元模型计算的蓄积量和生物量样地数据对所建模型进行修正。

关键词: 蓄积量, 生物量, 碳储量, 哑变量模型, 误差变量联立方程组

Abstract:

Stand-level volume,biomass and carbon stock models are quantitative tools for implementing forest resource management.Based on data of 1 425 permanent plots from forest inventory in 2016 in Beijing,and through approaches including independent nonlinear regression (INR),simultaneous error-in-variable equations (SEIVE),and SEIVE with dummy variable modeling,this study works out the stand-level volume,biomass and carbon stock models for ten major forest types,including Chinese pine (Pinus tabuliformis),cypress (Platycladus orientalis),oak (Quercus spp.),birch (Betula spp.),elm (Ulmus spp.),locust (Robinia pseudoacacia),poplar (Populus spp.),other hardwood broadleaved,other softwood broadleaved,and economic arboreal forest.The results show that the coefficients of determination (R²)of the stand-level volume,biomass and carbon stock models for 10 forest types are more than 0.93,the total relative errors (TREs)and average systematic errors (ASEs)are within ±3% and most of them are close to zero.The mean prediction errors (MPEs)are less than 5%,and the mean percent standard errors (MPSEs)are almost less than 15%.The following conclusions can be achieved that the volume stock per hectare of different forest types mainly depend upon basal area and average tree height of the forest stands,and the biomass stock mainly relate to volume stock and average tree height.The SEIVE with dummy variable modeling approach is a feasible method for developing stand-level stock models.The developed volume,biomass and carbon stock models for 10 forest types in Beijing in this study meet the need of precision requirements to the relevant regulation and can be applied on trial in practice.For improving the accuracy of the developed models,volume and biomass data of plot samples calculated by two-variable models need be used to further modify the models.

Key words: volume, biomass, carbon stock, dummy variable model, simultaneous error-in-variable equations

中图分类号:

S711

杨学云, 曾伟生, 陈新云. 北京市主要森林类型蓄积量生物量碳储量模型研建[J]. 林业资源管理, 2021, 0(2): 124-130.

YANG Xueyun, ZENG Weisheng, CHEN Xinyun. Research on Developing Stand Volume,Biomass and Carbon Stock Models for Major Forest Types in Beijing[J]. FOREST RESOURCES WANAGEMENT, 2021, 0(2): 124-130.

图/表 3

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森林类型	样地数/ 个	平均值/ (m³/hm²)	最小值/ (m³/hm²)	最大值/ (m³/hm²)	标准差/ (m³/hm²)	变动系数/ %
油松林	171	41.45	0.12	141.83	36.22	87.39
侧柏林	132	15.27	0.06	80.87	15.14	99.14
栎树林	248	47.68	1.95	187.63	36.33	76.20
桦木林	54	58.16	0.84	198.73	48.96	84.19
榆树林	77	25.07	0.57	114.57	23.15	92.33
刺槐林	93	27.45	0.15	138.02	30.03	109.40
杨树林	172	22.87	0.21	124.00	25.80	112.77
其他硬阔林	154	81.65	0.85	372.49	63.39	77.64
其他软阔林	152	39.64	0.12	176.37	39.82	110.47
乔木经济林	172	28.27	0.24	157.90	24.65	87.20

模型	目标变量	参数估计值						评价指标
模型	目标变量	a₀/b₀/c₀		a₁/b₁		a₂/b₂		R²		SEE		TRE/%	ASE/%	MPE/%	MPSE/%
独立模型	M	2.251	1.030		0.3064		0.941		9.97		-0.84		0.01	1.32	14.49
	B	2.030	0		-0.2033		0.943		11.95		-2.44		-2.65	1.24	14.02
	C	0.4886					0.945		5.64		-2.93		-2.74	1.20	13.70
联立模型	M	2.393	1.035		0.2692		0.938		10.22		-0.05		0.07	1.35	14.55
	B	2.059	0		-0.2260		0.940		12.20		1.62		0.19	1.26	14.19
	C	0.4885					0.945		5.64		1.13		0.10	1.20	13.77

森林类型	目标变量	参数估计值						评价指标
森林类型	目标变量	a₀/b₀/c₀		a₁/b₁		a₂/b₂		R²		SEE		TRE/%	ASE/%	MPE/%	MPSE/%
油松林	M	2.636	1.074		0.1317		0.970		6.31		-1.89		-0.18	2.31	11.63
	B	1.362	0		-0.0571		0.983		5.86		-1.56		0.51	1.77	10.87
	C	0.5081					0.984		2.93		-1.25		0.42	1.73	10.40
侧柏林	M	3.112	1.036		-0.0451		0.966		2.81		-1.75		0.06	3.17	13.31
	B	1.627	0		0.1072		0.990		3.17		-0.17		0.13	1.78	7.11
	C	0.4994					0.991		1.51		-0.02		0.10	1.70	6.81
栎树林	M	2.701	1.077		0.1043		0.979		5.26		-0.39		0.10	1.38	8.11
	B	1.573	0		0.0030		0.971		10.38		0.46		0.04	1.71	9.36
	C	0.4807					0.971		4.95		0.38		0.05	1.70	9.35
桦木林	M	2.910	1.080		0.0831		0.978		7.47		-0.44		0.22	3.50	9.19
	B	1.393	0		-0.0262		0.971		11.79		0.80		-0.05	4.15	8.92
	C	0.4856					0.969		5.78		0.66		0.00	4.20	9.09
榆树林	M	2.584	1.009		0.2726		0.949		5.27		0.73		0.03	4.77	11.80
	B	1.524	0		-0.1312		0.975		3.96		-1.02		0.04	3.11	8.14
	C	0.4845					0.973		1.96		-1.12		0.04	3.18	8.24
刺槐林	M	2.342	1.035		0.3050		0.986		3.54		2.19		-0.29	2.66	10.18
	B	2.064	0		-0.1208		0.979		6.46		0.29		0.01	3.12	7.37
	C	0.4844					0.980		3.02		0.31		0.01	3.01	7.20
杨树林	M	2.904	1.055		0.2330		0.959		12.87		0.09		0.26	2.52	11.75
	B	0.891	0		0.0272		0.944		14.69		0.11		0.47	2.98	13.30
	C	0.4745					0.944		6.89		0.07		0.59	2.95	13.44
其他硬阔林	M	2.600	1.024		0.2544		0.979		3.77		2.39		-0.16	2.49	11.01
	B	2.201	0		-0.2397		0.963		5.90		-0.76		0.03	2.94	8.75
	C	0.4851					0.962		2.87		-0.86		0.05	2.95	8.69
其他软阔林	M	2.444	1.041		0.2860		0.954		8.60		0.07		-0.07	3.48	11.48
	B	1.755	0		-0.2406		0.954		9.08		0.74		-0.02	3.40	9.74
	C	0.4867					0.944		4.76		-0.28		0.09	3.71	10.28
乔木经济林	M	2.477	1.077		0.2724		0.937		6.20		-0.92		0.24	3.31	13.10
	B	1.530	0		-0.1303		0.954		6.29		0.30		-0.01	2.80	9.91
	C	0.4942					0.953		3.12		0.28		-0.02	2.81	9.93