Developing Stand Basal Area Growth Models for Pinus massoniana and Cunninghamia lanceolata in Hunan Province

doi:10.13466/j.cnki.lyzygl.2021.05.008

Abstract

Abstract:

Developing the stand basal area growth models for Pinus massoniana and Cunninghamia lanceolata in Hunan Province was a data support measure for forest sustainable management.Based on the data of 838 plots of Pinus massoniana and 1 484 plots of Cunninghamia lanceolata in Hunan province,an optimal basic model was selected from 9 candidate models with biological significance.Taking species,site class and stand origin as dummy variable,the stand basal area growth model was constructed.The results showed that the optimal fitting models of Pinus massoniana and Cunninghamia lanceolata were in the form of Richards,with corresponding coefficients of determination at 0.985 and 0.987,respectively.Compared with the basic model,the fitting accuracy of the stand basal area growth model was improved after introducing dummy variable such as species,site class and stand origin,with a R² at 0.992 and a RMSE dropping to 0.682.Conclusions:The growth pattern of stand basal for Pinus massoniana and Cunninghamia lanceolata in Hunan province can be reflected by the model with species and site class and stand origin as dummy variable,which reduced the workload of computation and provided a method for integrating different forest stands.The limit value of stand basal area for Cunninghamia lanceolata was higher than that for Pinus massoniana.Furthermore,in the same stand density,the growth rate of stand basal area for Pinus massoniana and Cunninghamia lanceolata both decreased with the decrease of site quality.

Key words: growth model of basal area, dummy variable, Pinus massoniana, Cunninghamia lanceolata, Richards equation

CLC Number:

HE Peng, CHEN Zhenxiong, LIU Xianzhao. Developing Stand Basal Area Growth Models for Pinus massoniana and Cunninghamia lanceolata in Hunan Province[J]. FOREST RESOURCES WANAGEMENT, 2021, (5): 56-61.

Figures/Tables 7

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References 19

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树种	起源	样地个数/块	林龄/a	林分密度指数/ (株/hm²)	林分断面积/ (m²/hm²)	林分平均高 /m
马尾松 Pinus massoniana	1	646	21(7)	406(224)	10.46(6.16)	9.3(3.1)
马尾松 Pinus massoniana	2	192	18(7)	373(205)	9.50(5.59)	8.4(3.1)
杉木 Cunninghamia lanceolata	1	325	19(7)	384(230)	9.76(6.15)	8.7(2.3)
杉木 Cunninghamia lanceolata	2	1159	18(8)	512(314)	13.19(8.83)	8.8(2.6)

模型	表达式
Richards (M1)	$BA=a{{(1-{{\text{e}}^{-b{{\left( \frac{S}{{{S}_{0}}} \right)}^{{{}^{c}}}}\cdot Age}})}^{d}}$
Schumacher (M2)	$BA=a{{\text{e}}^{-\frac{b}{Age}}}{{\left( \frac{S}{{{S}_{0}}} \right)}^{c}}$
Schumacher (M3)	$BA={{\text{e}}^{a+\frac{b}{Age}}}{{\left( \frac{S}{{{S}_{0}}} \right)}^{c+\frac{d}{Age}}}$
Hyperbola (M4)	$BA=\frac{a{{\left( Age\cdot S \right)}^{2}}}{{{\left( Age\cdot S+bAge+cS+d \right)}^{2}}}$
Linear(M5)	$BA={{e}^{a}}^{+bAge+cS+dAge\cdot S}$
Mitcherlich(M6)	$BA=a(1-{{\text{e}}^{-b{{\left( \frac{S}{{{S}_{0}}} \right)}^{c}}\cdot Age}})$
Korf(M7)	$BA=a{{\text{e}}^{-bAg{{e}^{-c{{\left( \frac{S}{{{S}_{0}}} \right)}^{d}}}}}}$
Gompertz(M8)	$BA=a{{\text{e}}^{-b{{\text{e}}^{-c{{\left( \frac{S}{{{S}_{0}}} \right)}^{d}}\cdot Age}}}}$
Logistic (M9)	$BA=\frac{a}{1+b{{\text{e}}^{-c{{\left( \frac{S}{{{S}_{0}}} \right)}^{d}}\cdot Age}}}$

树种	模型	参数				评价指标
树种	模型	a	b	c	d	E	RMSE	R²	TRE/%
马尾松 Pinus massoniana	M1	47.61	0.0785	5.756	0.175	0.005	0.728	0.985	0.377
	M3	4.214	-5.524	1.078	-1.484	-0.008	0.740	0.985	0.388
	M2	61.14	3.234	1.009		0.014	0.755	0.984	0.405
杉木 Cunninghamia lanceolata	M1	55.66	0.0449	7.925	0.133	0.019	0.958	0.987	0.408
	M3	4.171	-3.117	1.096	-0.834	0.002	0.985	0.986	0.431
	M2	61.23	2.050	1.049		0.024	0.997	0.986	0.441

哑变量	拟合精度指标
哑变量	AIC	BIC	RMSE	R²
a,b,c,d	8714	8768	0.882	0.987
a,b,c	8723	8770	0.886	0.987
a,b	8728	8769	0.888	0.987
a	8730	8763	0.889	0.987
b	8730	8763	0.889	0.987
c	8733	8766	0.891	0.987
d	8732	8765	0.890	0.987

哑变量			拟合精度指标
树种	立地等级	起源	AIC	BIC	RMSE	R²
a,b	a		8141	8236	0.687	0.992
a,b	b		8141	8235	0.687	0.992
a,b	a,b		8144	8293	0.686	0.992
a,b	b	a,b	8132	8307	0.681	0.992
a,b	b	a	8136	8244	0.685	0.992
a,b	b	b	8134	8264	0.682	0.992