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, (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

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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