Study on the Volume Growth Rate Model of Pinus yunnanensis of Individual Tree

doi:10.13466/j.cnki.lyzygl.2020.06.007

Abstract

Abstract:

Based on the data of the sixth and seventh Continuous Forest Inventories (CFI),this paper used the nonlinear regression model to establish the volume growth rate model of Pinus yunnanensis of individual tree with different altitudes,origin,age classes and stand density.Compared to the optimal model in the case of each index by determination coefficient and root mean square error,the model precision was inspected by sum relative error,mean relative error,absolute mean relative error and predict precision.The result showed that the volume growth rate decreased with the increase of diameter.The fitting precision of the volume growth rate model of Pinus yunnanensis of individual tree was high with different altitudes,origin,age classes and stand density.The model parameters were stable and the determination coefficient of optimal models was more than 0.8.The overall model prediction precision was more than 80 percent except for the over-mature forest with seventy percent due to the less data.The fitting precision of optimal model was high and the applicability was strong.The optimal volume growth rate models can be used to estimate the volume growth,compile the volume growth rate table and forecast the dynamic changes of Pinus yunnanensis forest resources,and provide reference value for the forest resources inventory.

Key words: Pinus yunnanensis, individual tree, volume growth rate model, nonlinear regression model, Central Yunnan Province

CLC Number:

WEI Anchao, ZHANG Dawei. Study on the Volume Growth Rate Model of Pinus yunnanensis of Individual Tree[J]. FOREST RESOURCES WANAGEMENT, 2020, 0(6): 40-46.

Figures/Tables 5

Tab.1

The volume growth rate model

模型编号	模型类型	参数
1	y=a× ln $D$ +b^[19]	a,b
2	y=a×b^D+c^[19]	a,b,c
3	y=a×D^b+c^[20]	a,b,c
4	y=a×exp $b × D$ +c× D^[1]	a,b,c

Tab.1

Tab.2

Tab.3

Tab.4

Tab.5

References 22

[1]	陈利, 王福生, 管远保, 等. 湖南省杉木材积生长率模型研究[J]. 中南林业科技大学学报, 2015,30(11):49-54.
[2]	Brown R M, Gevorkiantz S R. Volume,Yield,and Stand Tables for Tree Species in the Lake States[M]. Minnesota:Minnesota Agricultural Experiment Station, 1934.
[3]	Vries F W T P D. Modeling of Growth and Production[M]. Berlin Heidelberg:Springer, 1983: 117-150.
[4]	Gadow K V, Hui G. Modelling Forest Development[M]. Springer,Dordrecht:Springer Netherlands, 1999.
[5]	Vanclay J K. Modelling forest growth and yield:applications to mixed tropical forests[M]. Southern Cross University:CAB International, 1994.
[6]	黄中立. 我国杉木材积生长率等问题的探讨[J]. 林业科学, 1957(1):1-41.
[7]	曾伟生, 骆期邦. 二元材积生长率标准动态模型的研究[J]. 中南林业调查规划, 1992(3):1-6.
[8]	曾伟生. 西藏天然云杉林兼容性材积生长率模型系统研究[J]. 北京林业大学学报, 2008,30(5):87-90.
[9]	沈家智, 杨智勇. 利用复位样木建立湿地松二元材积生长率动态模型的研究[J]. 江西林业科技, 1996(6):10-15.
[10]	程光明. 杉木人工林材积生长率表编制的研究[J]. 福建林业科技, 2006,33(3):56-59.
[11]	廖志云, 曾伟生. 西藏自治区主要树种生长率模型的研建[J]. 林业资源管理, 2006(3):36-38.
[12]	甘世书. 基于海南省连清资料建立松树和橡胶树材积生长率模型[J]. 中南林业调查规划, 2015,34(3):35-38.
[13]	姜磊, 陆元昌, 廖声熙, 等. 滇中高原云南松林分直径结构研究[J]. 林业科学研究, 2008,21(1):126-130.
[14]	彭建松, 柴勇, 孟广涛, 等. 云南金沙江流域云南松天然林林隙更新研究[J]. 西北林学院学报, 2005,20(2):114-117.
[15]	袁凤军, 崔凯, 廖声熙, 等. 滇中云南松天然林空间结构特征[J]. 西南农业学报, 2013,26(3):1223-1226.
[16]	廖声熙, 李昆, 陆元昌, 等. 滇中高原云南松林目标树优势群体的生长过程分析[J]. 林业科学研究, 2009,22(1):80-84.
[17]	白超. 空间结构参数及其在锐齿栎天然林结构动态分析中的应用[D]. 北京:中国林业科学研究院, 2016.
[18]	孟宪宇. 测树学[M]. 北京: 中国林业出版社, 2006.
[19]	刘雪惠, 王海龙, 温小荣, 等. 广西桂北马尾松不同起源单木和林分材积生长率模型研究[J]. 林业资源管理, 2016(3):54-60.
[20]	陈丽聪. 湖南省杉木人工林生长率(量)与生产潜力评价[D]. 北京:北京林业大学, 2014.
[21]	胥辉. 一种生物量模型构建的新方法[J]. 西北农林科技大学学报:自然科学版, 2001,29(3):35-40.
[22]	骆期邦, 曾伟生, 贺东北. 林业数表模型-理论、方法与实践[M]. 长沙: 湖南科技出版社, 2001.

海拔	模型形式	模型参数			拟合评价		最优模型	精度检验
海拔	模型形式	a	b	c	R²	RMSE	最优模型	RS	EE	RMA	P
海拔 <1500m	1	-6.918	24.565	—	0.919	1.382	3	4.689	16.525	55.892	81.984
	2	24.616	0.904	0.231	0.939	1.182
	3	55.498	-0.517	-7.087	0.940	1.180
	4	24.517	-0.092	0.003	0.938	1.185
1500m≤海拔<2000m	1	-5.919	23.267	—	0.917	1.299	3	-3.948	-2.864	15.656	93.561
	2	22.457	0.874	2.318	0.928	1.182
	3	50.194	-0.647	-2.914	0.936	0.981
	4	20.157	-0.086	0.045	0.927	1.187
2000m≤海拔<2500m	1	-5.541	22.112	—	0.921	1.748	3	-6.921	-7.618	22.984	90.816
	2	17.846	0.921	1.149	0.927	1.784
	3	41.299	-0.291	-12.149	0.932	1.689
	4	17.498	-0.065	0.016	0.916	1.791
海拔≥2500m	1	-2.946	13.316	—	0.767	2.243	3	-8.911	-4.651	18.916	90.292
	2	9.485	0.902	2.831	0.808	1.898
	3	21.165	-0.310	-4.004	0.810	1.894
	4	10.216	0.030	-0.701	0.799	1.954

起源	模型类型	模型参数			拟合评价		最优模型	精度检验
起源	模型类型	a	b	c	R²	RMSE	最优模型	RS	EE	RMA	P
天然林	1	-6.674	18.684	—	0.918	2.526	2	1.984	5.984	23.986	91.983
	2	16.984	0.938	0.847	0.947	1.949
	3	-514.685	0.012	567.984	0.938	1.840
	4	20.845	-0.071	0.016	0.927	2.298
人工林	1	-5.986	17.942	—	0.818	2.685	3	5.653	7.652	17.986	86.641
	2	22.984	0.817	3.541	0.859	2.286
	3	60.983	-1.112	1.713	0.899	2.031
	4	24.583	-0.153	0.147	0.897	2.098

林分密度	模型形式	模型参数			拟合评价		最优模型	精度检验
林分密度	模型形式	a	b	c	R²	RMSE	最优模型	RS	EE	RMA	P
Ⅰ密度级	1	-8.651	30.683	—	0.948	0.910	3	6.651	9.985	25.685	90.961
	2	27.651	0.917	0.401	0.959	0.896
	3	60.651	-0.350	-16.91	0.979	0.782
	4	27.653	-0.092	0.012	0.975	0.799
Ⅱ密度级	1	-5.385	23.651	—	0.916	0.965	3	4.856	8.651	22.653	90.615
	2	19.651	0.918	0.666	0.929	0.917
	3	47.653	-0.269	-16.978	0.981	0.817
	4	17.984	-0.068	0.011	0.971	0.903
Ⅲ密度级	1	-5.986	16.653	—	0.949	0.685	2	9.987	12.385	28.684	89.686
	2	14.653	0.960	-0.657	0.952	0.679
	3	426.653	-0.012	-390.533	0.950	0.682
	4	12.685	-0.048	-0.011	0.951	0.681
Ⅳ密度级	1	-2.983	12.651	—	0.794	1.685	2	4.653	8.984	21.685	89.685
	2	70.685	0.997	-62.986	0.858	1.264
	3	567.968	-0.005	-550.893	0.846	1.565
	4	6.988	0.006	-0.196	0.857	1.259

林分密度	模型形式	模型参数			拟合评价		最优模型	精度检验
林分密度	模型形式	a	b	c	R²	RMSE	最优模型	RS	EE	RMA	P
幼龄林	1	-7.651	23.654	—	0.953	2.009	3	1.983	3.653	15.652	90.653
	2	21.653	0.924	0.933	0.958	1.873
	3	51.965	-0.241	-17.163	0.968	1.532
	4	21.623	-0.076	0.019	0.957	1.913
中龄林	1	-4.321	20.354	—	0.912	1.127	3	-2.312	-7.651	22.532	91.353
	2	15.685	0.937	1.489	0.918	1.117
	3	48.685	-0.155	-23.653	0.930	0.986
	4	16.653	-0.063	0.019	0.898	1.523
近熟林	1	-4.685	18.651	—	0.893	1.283	3	2.353	4.651	18.353	91.533
	2	19.353	0.870	2.914	0.899	1.183
	3	59.983	-9.853	0.924	0.934	0.886
	4	17.652	-0.081	0.050	0.897	1.193
成熟林	1	-7.653	26.685	—	0.834	2.236	3	5.685	10.653	35.653	80.685
	2	43.332	0.839	1.748	0.923	1.876
	3	123.653	-1.127	-0.649	0.928	1.458
	4	37.653	-0.146	0.048	0.901	2.183
过树林	1	-15.652	46.653	—	0.893	2.158	3	-14.653	-15.352	39.689	70.619
	2	67.651	0.849	0.753	0.951	1.987
	3	467.353	-1.521	-1.239	0.959	1.697
	4	67.653	-0.153	0.023	0.948	2.086