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, 0(5): 56-61.

Figures/Tables 7

Tab.1

Tab.2

Tab.3

Tab.4

Tab.5

Fig.1

Tab.6

References 19

[1]	杜纪山. 林木生长和收获预估模型的研究动态[J]. 世界林业研究, 1999, 12(4):19-22.
[2]	Fu L, Sharma R P, Zhu G, et al. A basal area increment-based approach of site productivity evaluation for multi-aged and mixed forests[J]. Forests, 2017, 8(4):1-18. doi: 10.3390/f8010001
[3]	王冬至, 张冬燕, 张志东, 等. 塞罕坝华北落叶松人工林断面积预测模型[J]. 北京林业大学学报, 2017, 14(7):10-17.
[4]	Pienaar L V, Shiver B D. An analysis and models of basal area growth in 45-year-old unthinned and thinned slash pine plantation plots[J]. Forest Science, 1984, 30(4):933-942.
[5]	颜伟, 段光爽, 王一涵, 等. 河南省栎类和杨树林分断面积和蓄积生长模型构建[J]. 北京林业大学学报, 2019, 41(6),55-61.
[6]	姚丹丹, 雷相东, 张则路. 基于贝叶斯法的长白落叶松林分优势高生长模型研究[J]. 北京林业大学学报, 2015, 12(3):94-100.
[7]	李春明. 利用非线性混合模型进行杉木林分断面积生长模拟研究[J]. 北京林业大学学报, 2009, 6(1):44-49.
[8]	李春明, 唐守正. 基于非线性混合模型的落叶松云冷杉林分断面积模型[J]. 林业科学, 2010, 46(7):106-113.
[9]	朱光玉, 胡松, 符利勇. 基于哑变量的湖南栎类天然林林分断面积生长模型[J]. 南京林业大学学报:自然科学版, 2018, 42(2):155-162.
[10]	杜纪山, 唐守正. 林分断面积生长模型研究评述[J]. 林业科学研究, 1997, 10(6):599-606.
[11]	郭艳荣, 吴保国, 刘洋, 等. 立地质量评价研究进展[J]. 世界林业研究, 2012, 25(5):47-52.
[12]	Skovsgaard J P, Vanclay J K. Forest site productivity:a review of spatial and temporal variability in natural site conditions[J]. Forestry, 2013, 86(3):305-315. doi: 10.1093/forestry/cpt010
[13]	国家林业和草原局. 中国森林资源报告[M].中国林业出版社, 2019.
[14]	刘洵, 曾思齐, 龙时胜, 等. 湖南省栎类天然次生林胸径地位指数表研制[J]. 森林与环境学报, 2019, 39(3):265-272.
[15]	李海奎, 法蕾. 基于分级的全国主要树种树高-胸径曲线模型[J]. 林业科学, 2011, 47(10):83-90.
[16]	雷相东, 符利勇, 李海奎, 等. 基于林分潜在生长量的立地质量评价方法与应用[J]. 林业科学, 2018, 54(12):116-126.
[17]	孟宪宇. 测树学[M]. 北京: 中国林业出版社, 2006.
[18]	段光爽, 李学东, 冯岩, 等. 华北落叶松天然次生林树高曲线的混合效应模型[J]. 南京林业大学学报:自然科学版, 2018, 42(2):163-169.
[19]	唐守正. 统计和生物数学模型计算[M]. 北京: 科学出版社, 2009.

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