基于林层划分的湖南栎类天然次生林断面积生长模型

doi:10.11707/j.1001-7488.20200920

Abstract

Abstract:

Objective: This paper was implemented to study the effects of forest storey of multi-storied stand on Oaks natural forest basal area growth model in Hunan Province. It would reflect the basal area growth rules of different storeys by building basal area growth models from the perspective of forest storey in order to provide references for the growth harvesting and management of the Oaks natural secondary forest in Hunan Province. Method: After the storey identification by using whole tree height clustering,IUFRO standard and TRSRAT,the model of Schumacher was used to fit the basal area of whole stand,upper layer and lower layer based on the 51 plots of Oaks natural secondary forest in Hunan Province,and the optimal method of storey identification was selected. Considering the compatibility of the total basal area and each forest layer basal area,the compatible storey basal area growth model was built by adjustment in proportion. Result: The results of storey identification by whole tree height clustering,IUFRO standard and TRSRAT were all satisfied with the requirements of technical regulations for forest resources planning and design investigation,and the F testing of different forest layer statistics were significant. The model of Schumacher could better simulate the growth of whole stand and different forest layers,the coefficients of determination (R²) were all above 0.92. Compared with the whole stand basal area growth model without storey identification,the coefficient of determination (R²) of different forest layers with storey identification was increased from 0.925 9 to 0.945 5-0.984 6.The IUFRO standard was selected as the optimal method of storey identification,and the accuracy of the basal area growth model was the highest (R²=0.970 4,MAE=1.458 8,RMSE=2.178 6).Compared with the whole stand basal area growth model without storey identification,the determination coefficient (R²) was increased by 5.0%,the mean absolute error (MAE) and the root mean square error (RMSE) was respectively decreased by 39.8% and 26.4%. The proportional adjustment method based on total basal area could better solve the problem of the compatibility between total basal area and each forest layer basal area. Compared with the independent prediction model of each forest layer,the determination coefficient (R²) of upper layer was increased from 0.974 6 to 0.986 8,and the determination coefficient (R²) of lower layer was increased from 0.980 2 to 0.988 8. Conclusion: The effects of forest storey of multi-storied stand on Oaks natural forest basal area growth model in Hunan Province was significant. It was important to study the characteristics of different forest storeys and improve the modeling accuracy by considering storey effection. The research results were expected to reveal the growth process of forest basal area in subtropical Oaks forest,which was of great significance to growth harvesting and management of the Oaks natural secondary forest.

Key words: storey identification, basal area growth model, adjustment in proportion, compatible, Oaks

CLC Number:

S757

Song Hu,Guangyu Zhu,Zhenxiong Chen,Kan Lu,Lang Huang,Zhuo Liu. Basal Area Growth Model for Oaks Natural Secondary Forest in Hunan Province Based on Storey Identification[J]. Scientia Silvae Sinicae, 2020, 56(9): 184-192.

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变量 Variable	建模数据Modelling data				检验数据Validation data
变量 Variable	最小值 Min.	最大值 Max.	平均值 Mean	标准差 SD	最小值 Min.	最大值 Max.	平均值 Mean	标准差 SD
G/(m²·hm^-2)	14.4	59.4	30.1	10.7	14.6	47.7	30.3	10.4
N/(tree·hm^-2)	722.0	4 118.0	1 518.2	810.7	961.5	3 913.8	1 556.3	841.3
D_g/cm	9.1	26.6	16.7	3.9	9.6	21.4	16.4	3.7
H/cm	9.2	23.2	13.7	2.7	10.5	17.3	12.8	1.8
V/m³	69.9	368.0	179.7	52.4	80.1	362.8	182.3	58.4
P	0.63	0.90	0.79	0.06	0.7	0.88	0.78	0.06
HT/m	11.1	27.5	16.4	3.2	13.1	20.5	15.2	2.1
T/a	18	72	38	14.0	21	56	35.7	11.8

划分方法 Division method	变量 Variable	主林层Main storey				次林层Second storey
划分方法 Division method	变量 Variable	最小值 Min.	最大值 Max.	平均值 Mean	标准差 SD	最小值 Min.	最大值 Max.	平均值 Mean	标准差 SD
全树高聚类法 Whole tree height clustering	D_g/cm	10.2	69.1	22.8	9.3	5.7	19.7	11.6	2.9
	H/cm	10.5	27.5	15.3	3.1	6.5	12.9	9.3	1.4
	V/m³	51.5	271.6	150.1	57.3	30.5	107.1	48.9	19.4
	P	0.21	0.76	0.47	0.14	0.20	0.74	0.32	0.14
国际林联标准 IUFRO standard	D_g/cm	9.9	58.6	23.2	8.9	5.5	20.3	11.8	2.9
	H/cm	10.7	33.3	16.7	4.7	6.9	19.7	10.2	2.3
	V/m³	47.4	280.4	140.3	56.1	32.0	139.3	58.3	26.5
	P	0.29	0.75	0.45	0.15	0.20	0.78	0.34	0.15
光竞争高度法 TRSRAT	D_g/cm	10.6	69.1	22.9	9.5	5.8	20.3	11.9	3.2
	H/cm	11.1	29.7	16.3	3.9	6.9	14.7	9.8	1.8
	V/m³	38.9	279.0	140.3	57.2	35.2	132.5	56.2	26.8
	P	0.23	0.75	0.46	0.16	0.21	0.70	0.33	0.15

划分方法 Division method	蓄积 Volume/(m³·hm^-2)			平均胸径 Mean DBH/cm			平均高 Average height/m				郁闭度 Crown density
划分方法 Division method	上层林 Upper layer	下层林 Lower layer	F	上层林 Upper layer	下层林 Lower layer	F	上层林 Upper layer	下层林 Lower layer	F	高差/% Relative elevation	上层林 Upper layer	下层林 Lower layer	F
全树高聚类法 Whole tree height clustering	161.3±83.5	43.0±12.9	**	22.8±9.3	11.6±2.9	**	15.3±3.1	9.3±1.4	**	40.0±7.5	0.41±0.17	0.38±0.17	**
国际林联标准 IUFRO standard	146.6±81.6	57.2±24.9	**	24.1±8.9	12.2±3.2	**	15.8±3.2	9.8±1.5	**	37.0±8.0	0.42±0.16	0.37±0.16	**
光竞争高度法 TRSRAT	146.1±88.2	57.3±25.4	**	23.6±9.3	12.1±3.2	**	15.6±3.0	9.7±1.7	**	37.0±8.2	0.47±0.14	0.32±0.14	**

划分方法 Division method	模型类型 Model type	参数估计 Parameter estimates						建模精度 Modeling accuracy			检验结果 Validation result
划分方法 Division method	模型类型 Model type	b₀	b₁	b₂	b₃	b₄	b₅	R²	MAE	RMSE	MAE	RMSE
不分层 No stratification	全林分 Whole stand	2.814 4	-47.004 2	1.203 0	-6.553 2	-0.238 6	17.976 3	0.925 9	2.337 5	2.953 3	2.769 4	2.996 4
全树高聚类法 Whole tree height clustering	上层林 Upper layer	-0.234 5	106.498 9	1.057 3	4.979 3	0.954 3	-42.116 7	0.964 5	1.309 3	1.706 3	0.434 5	0.536 1
全树高聚类法 Whole tree height clustering	下层林 Lower layer	3.571 4	-68.343 1	1.426 3	-7.654 3	-0.511 1	24.996 1	0.945 5	1.724 5	2.175 0	1.501 0	2.414 1
国际林联标准 IUFRO standard	上层林 Upper layer	1.991 4	-11.884 2	1.274 5	-11.917 3	0.142 8	4.538 5	0.974 6	1.021 7	1.308 9	1.211 5	1.403 1
国际林联标准 IUFRO standard	下层林 Lower layer	3.300 7	-42.158 9	1.087 7	-0.717 6	-0.376 5	14.003 5	0.980 2	0.599 7	0.862 3	0.376 1	0.499 3
光竞争高度法 TRSRAT	上层林 Upper layer	1.124 8	31.098 1	1.017 4	-3.502 3	0.524 4	-13.767 4	0.967 8	1.265 9	1.630 8	1.026 9	1.196 2
光竞争高度法 TRSRAT	下层林 Lower layer	3.928 0	-57.403 8	1.058 4	-0.780 6	-0.601 6	19.575 6	0.984 6	0.593 3	0.761 4	0.648 4	0.908 4

划分方法Division method	样本量Sample capacity	R²	MAE	RMSE
不分层No stratification	51	0.924 2	2.422 2	2.961 8
全树高聚类法Whole tree height clustering	51	0.943 0	1.887 3	2.568 8
国际林联标准IUFRO standard	51	0.959 0	1.458 8	2.178 6
光竞争高度法TRSRAT	51	0.947 5	1.694 2	2.464 6

Basal Area Growth Model for Oaks Natural Secondary Forest in Hunan Province Based on Storey Identification

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模型类型 Model type	建模精度Modeling accuracy			检验结果Validation result
模型类型 Model type	R²	MAE	RMSE	MAE	RMSE
全林分Whole stand	0.925 9	2.337 5	2.953 3	2.769 4	2.996 4
上层林Upper layer	0.986 8	0.575 9	0.944 5	0.366 2	0.406 6
下层林Lower layer	0.988 8	0.481 1	0.648 3	0.386 2	0.424 6

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