Large-scale Individual Tree Growth Models for Pinus massoniana in China

doi:10.13466/j.cnki.lyzygl.2017.02.006

Abstract

Abstract:

Individual tree growth models are quantitative basis for predicting forest development and evaluating productivity potential in the future.Based on the mensuration data of sample trees of Masson pine (Pinus massoniana) in southern China,individual tree diameter and height growth models on national level were developed,and effects of region and origin on growth models were analyzed.The results showed that diameter growth model of Masson pine was only related to region,whereas height growth model was affected by both region and origin;coefficients of determination (R ²) of the developed population average (PA) diameter and height growth models were 0.79 and 0.71 respectively,and mean prediction precisions were more than 96%;R ² of the diameter and height growth models with region and origin in variables were 0.83 and 0.74 respectively,and mean prediction precisions were all improved,indicating that the models were significantly better than the PA models.The growth models developed in this study could provide quantitative basis for forecasting stock volume,biomass and carbon storage of Masson pine forest on large scale.

Key words: Pinus massoniana, growth model, Logistic function, dummy variable, region, origin

CLC Number:

S791.248

LIU Sihai, ZENG Weisheng. Large-scale Individual Tree Growth Models for Pinus massoniana in China[J]. FOREST RESOURCES WANAGEMENT, 2017, (2): 28-33.

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总体	样本量/株	变量	平均值	最小值	最大值	标准差	变动系数/%
总体1	147	胸径/cm	16.7	1.5	47.2	11.9	73.5
		树高/m	12.0	2.0	27.6	7.2	60.9
		年龄/a	308.1	3.0	52.0	12.3	61.0
总体2	151	胸径/cm	16.4	1.2	40.1	11.8	71.8
		树高/m	12.3	1.6	30.3	7.2	58.8
		年龄/a	26.4	5.0	65.0	15.9	60.2

目标变量	模型	参数估计值			确定系数 R²
目标变量	模型	a	b	c	确定系数 R²
胸径	(1)	35.11	16.27	0.1161	0.7949
	(2)	42.12	0.04520	2.034	0.7857
	(3)	38.60	3.766	0.06644	0.7909
	(4)	72.16	43.07	7.044	0.7830
	(5)	61.52	1.068	0.01723	0.7777
	(6)	-4.223	1.103	-0.006936	0.7803
树高	(1)	21.50	8.890	0.1156	0.7145
	(2)	24.19	0.04979	1.534	0.7075
	(3)	22.71	2.901	0.07391	0.7120
	(4)	35.62	28.44	5.864	0.7044
	(5)	26.84	1.077	0.03305	0.7040
	(6)	-1.415	0.8065	-0.007095	0.7097

目标变量	参数估计值						统计指标
目标变量	a	a₂	b	c	c₁	c₂	R²	SEE	TRE/%	ASE/%	MPE/%	MPSE/%
胸径	36.74	—	16.92	0.09889	0.02897	—	0.8303	4.88	0.08	-0.01	3.41	29.32
树高	24.65	-4.01	9.116	0.08737	0.02663	0.02144	0.7401	3.70	-0.03	-0.34	3.49	27.85

目标变量	地域起源	参数估计值			拐点年龄/a	统计指标
目标变量	地域起源	a	b	c	拐点年龄/a	R²	SEE	TRE/%	ASE/%	MPE/%	MPSE/%
胸径	总体1	36.74	16.92	0.1279	22	0.8248	5.00	0.50	0.46	5.06	31.21
胸径	总体2	36.74	16.92	0.09889	29	0.8357	4.80	-0.33	-0.47	4.72	27.47
树高	总体1天然	20.64	9.116	0.1354	16	0.7592	3.17	-3.34	-4.20	6.09	22.03
	总体1人工	24.65	9.116	0.1140	19	0.7641	3.94	1.86	0.42	7.92	31.66
	总体2天然	20.64	9.116	0.1088	20	0.7630	3.57	2.47	2.11	6.25	29.73
	总体2人工	24.65	9.116	0.08737	25	0.6585	4.31	-1.38	-0.09	8.69	27.67