基于生长因子的湿地松人工幼林地上生物量模型

doi:10.11707/j.1001-7488.LYKX20250455

摘要/Abstract

摘要：

目的: 评估将单木特异性状加入幼龄湿地松人工林异速生长方程对模型性能的影响，并构建适用于湿地松地上生物量估测的幂函数模型，以实现生物量的精准、快速与高效预测。方法: 对4年生湿地松人工林的170棵样本，采用全收获法测定地上部分各器官生物量并分析其分配特征，使用不同生长因子作为预测自变量，构建湿地松地上部分、主干、分枝和针叶的幂函数生物量模型，并验证其准确性。结果: 在基于不同距地高度的树干直径构建的湿地松各器官生物量模型中，拟合效果排序为胸径（DBH）> 地径> 距地1.0 m树干直径>距地1.5 m树干直径；基于最优生长因子实测树高（H）、DBH和木材密度（ρ）构建的三元生物量（W）模型（W=aDBH^bH^cρ^d，a、b、c、d为系数）对地上部分和主干生物量的预测效果较优（R²分别为0.864和0.839；RMSE分别为1.107和0.541）；基于无人机估测的树高（H_e）、无人机提取的冠幅面积（A_c）和DBH这3种最优生长因子构建的三元模型W=aDBH^bH_e^cA_c^d，对分枝和针叶生物量的预测效果较优（R²分别为0.670和0.778；RMSE分别为0.410和0.536）。结论: 引入单木特异性状的异速生长方程能够显著提高估测幼年湿地松人工林生物量的精度，基于最优生长因子构建的三元生物量模型可为浙江地区4年生湿地松人工幼林生物量的快速、准确估算提供可靠工具。

关键词: 生物量, 湿地松, 异速生长方程, 单木特异性状, 预测模型

Abstract:

Objective: This study aims to assess the impact of incorporating individual tree-specific traits into allometric growth equations for young Pinus elliottii (slash pine) plantations on model performance, and to develop a power-type biomass model suitable for estimating aboveground biomass (AGB) of the young slash pine, achieving precise, rapid, and efficient biomass prediction. Method: A total of 170 sample trees from a 4-year-old slash pine plantation were selected, and the AGB of various organs was determined through complete harvest method to analyze biomass allocation patterns. Different growth factors were used as predictive independent variables to construct power-function-based biomass models for total AGB, stems, branches, and needles. The performance of these models was then comprehensively evaluated. Result: Among the biomass models based on stem diameters measured at different heights above ground, the fitting performance followed the order: diameter at breast height (DBH) > ground diameter > diameter at 1.0 m height > diameter at 1.5 m height. The ternary biomass model (W=aDBH^bH^cρ^d, where a, b, c, and d are coefficients), which incorporated the optimal growth factors of measured tree height (H), DBH, and wood density (ρ) as predictive variables, achieved the highest accuracy for estimating aboveground and stem biomass (R² = 0.864 and 0.839, and RMSE = 1.107 and 0.541, respectively). In contrast, the model W=aDBH^bH_e^cA_c^d, incorporating UAV-estimated tree height (H_e), UAV-derived crown projection area (A_c), and DBH, provided the most accurate estimates for branch and needle biomass. The model achieved high accuracy, with R² values of 0.670 and 0.778, and RMSE values of 0.410 and 0.536 for branch and needle biomass, respectively. Conclusion: Incorporating tree-specific traits into allometric equations can significantly enhance the accuracy of biomass estimation in young slash pine plantations. The ternary biomass model constructed based on optimal growth factors is a reliable tool for rapid and accurate assessment of biomass in 4-year-old slash pine plantations in Zhejiang Province.

Key words: biomass, Pinus elliottii, allometric equations, tree-specific traits, prediction model

中图分类号:

S727.19
S711

华夏辉,丁显印,吴绍泽,黄琴韵,刁姝,吴亚荻,栾启福. 基于生长因子的湿地松人工幼林地上生物量模型[J]. 林业科学, 2026, 62(3): 211-222.

Xiahui Hua,Xianyin Ding,Shaoze Wu,Qinyun Huang,Shu Diao,Yadi Wu,Qifu Luan. Aboveground Biomass Models for Young Pinus elliottii Plantations Based on Various Growth Factors[J]. Scientia Silvae Sinicae, 2026, 62(3): 211-222.

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生长因子 Growth parameter	最小值 Minimum	最大值 Maximum	平均值 Average	标准差 Standard deviation	变异系数 Coefficient of variation（%）
树高Tree height（H）/m	2.820	5.740	4.130	0.548	13.27
胸径 Diameter at breast height（DBH）/cm	4.043	9.708	6.910	1.180	17.08
地上部分生物量Aboveground biomass（AGB）/kg	2.012	15.373	7.750	3.000	38.79
主干生物量 Stem biomass（SB）/kg	0.933	6.920	3.530	1.351	38.29
分枝生物量Branch biomass （BB）/kg	0.213	3.800	1.370	0.715	52.22
针叶生物量 Leaf biomass（LB）/kg	0.741	6.460	2.850	1.141	40.06
木材密度Density of wood（ρ）/（g·cm^?3）	0.286	0.605	0.400	0.047	11.76
地径Ground diameter（SB）/cm	4.775	14.579	10.05	1.774	17.65
距地1.0 m树干直径Diameter at 1.0 m height（D_{1.0 m}）/cm	4.200	10.190	7.220	1.198	16.60
距地1.5 m树干直径Diameter at 1.5 m height（D_{1.5 m}）/cm	3.597	9.167	6.380	1.218	19.10
地上部分碳储量Aboveground carbon storage（ACG）/kg	1.035	7.420	3.838	1.496	38.97

指标Index	树木编号Tree No.						平均值 Average	标准差 Standard deviation
指标Index	1	2	3	...	169	170	平均值 Average	标准差 Standard deviation
无人机估测树高UAV-estimated tree height （H_e）/m	5.668	5.506	5.411	...	3.164	3.121	4.221	0.550
冠幅面积Canopy area （A_c）/m²	6.500	6.064	5.954	...	1.065	0.988	2.975	1.034

模型 Model	器官生物量 Organ biomass	R²	RMSE	MAE	MAPE（%）	参数 Parameter
模型 Model	器官生物量 Organ biomass	R²	RMSE	MAE	MAPE（%）	a	b
W=aDBH^b	地上部分生物量Aboveground biomass	0.857	1.134	0.893	12.46	0.107	2.198
	主干生物量 Stem biomass	0.802	0.599	0.451	13.77	0.061	2.080
	分枝生物量Branch biomass	0.635	0.431	0.317	27.97	0.009	2.547
	针叶生物量 Leaf biomass	0.770	0.546	0.411	15.33	0.041	2.175

W=a（DBHH）^b	地上部分生物量Aboveground biomass	0.751	1.496	1.171	16.78	0.116	1.247
	主干生物量 Stem biomass	0.764	0.654	0.493	49.33	0.054	1.241
	分枝生物量Branch biomass	0.460	0.524	0.381	36.46	0.017	1.301
	针叶生物量 Leaf biomass	0.675	0.649	0.488	18.51	0.045	1.227

W=a（DBH²H）^b	地上部分生物量Aboveground biomass	0.825	1.255	0.960	13.50	0.088	0.840
	主干生物量 Stem biomass	0.814	0.581	0.432	13.36	0.044	0.822
	分枝生物量Branch biomass	0.544	0.482	0.348	31.82	0.016	0.912
	针叶生物量 Leaf biomass	0.741	0.580	0.434	16.25	0.035	0.827

模型 Model	器官生物量 Organ biomass	R²	RMSE	MAE	MAPE（%）	参数 Parameter
模型 Model	器官生物量 Organ biomass	R²	RMSE	MAE	MAPE（%）	a	b
W=aD^b	地上部分生物量Aboveground biomass	0.783	1.398	1.118	15.85	0.066	2.05
	主干生物量 Stem biomass	0.703	0.734	0.589	18.01	0.044	1.887
	分枝生物量Branch biomass	0.628	0.435	0.321	28.18	0.004	2.477
	针叶生物量 Leaf biomass	0.706	0.617	0.47	18.12	0.025	2.044

W=aD_{1.0 m}^b	地上部分生物量Aboveground biomass	0.838	1.206	0.931	12.90	0.093	2.217
	主干生物量 Stem biomass	0.764	0.655	0.495	14.75	0.058	2.063
	分枝生物量Branch biomass	0.665	0.413	0.302	26.51	0.006	2.666
	针叶生物量 Leaf biomass	0.749	0.571	0.432	16.28	0.036	2.19

W=aD_{1.5 m}^b	地上部分生物量Aboveground biomass	0.752	1.492	1.111	16.45	0.268	1.801
	主干生物量 Stem biomass	0.737	0.692	0.505	16.09	0.134	1.752
	分枝生物量Branch biomass	0.514	0.497	0.371	34.68	0.032	1.999
	针叶生物量 Leaf biomass	0.67	0.654	0.485	19.10	0.106	1.765

模型 Model	器官生物量 Organ biomass	R²	RMSE	MAE	MAPE（%）	参数 Parameter
模型 Model	器官生物量 Organ biomass	R²	RMSE	MAE	MAPE（%）	a	b	c	d
W=aDBH^bH^c	地上部分生物量Aboveground biomass	0.861	1.118	0.866	12.13	0.094	2.107	0.216
	主干生物量 Stem biomass	0.824	0.565	0.421	12.89	0.046	1.868	0.496
	分枝生物量Branch biomass	0.647	0.424	0.318	28.20	0.013	2.730	-0.478
	针叶生物量Leaf biomass	0.773	0.542	0.407	15.17	0.036	2.085	0.204

W=aDBH^bH^cρ^d	地上部分生物量Aboveground biomass	0.864	1.107	0.849	11.85	0.104	2.167	0.190	0.202
	主干生物量 Stem biomass	0.839	0.541	0.387	11.73	0.058	1.997	0.442	0.449
	分枝生物量Branch biomass	0.650	0.422	0.316	27.84	0.0152	2.834	-0.527	0.309
	针叶生物量Leaf biomass	0.775	0.540	0.409	15.26	0.0337	2.040	0.224	-0.153