Research on Developing Stand Volume,Biomass and Carbon Stock Models for Major Forest Types in Beijing

doi:10.13466/j.cnki.lyzygl.2021.02.017

Abstract

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

CLC Number:

S711

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.

Figures/Tables 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