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

Abstract

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

CLC Number:

S727.19
S711

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.

Figures/Tables 12

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References 0

	陈文义, 王智勇, 周梦岩, 等. 幼龄楸树生物量分配规律与异速生长模型. 植物生态学报, 2025, 49 (2): 356- 366.
	Chen W Y, Wang Z Y, Zhou M Y, et al. Biomass allocation and allometric growth model of young Catalpa bungei. Chinese Journal of Plant Ecology, 2025, 49 (2): 356- 366.
	丁　伟, 谷振军, 黄文晖, 等. 美国引种湿地松在赣南地区生长适应性评价. 南方林业科学, 2020, 48 (5): 37- 40.
	Ding W, Gu Z J, Huang W J, et al. Evaluation of growth and adaptability of American Pinus elliottii in south Jiangxi. South China Forestry Seience, 2020, 48 (5): 37- 40.
	冯宗炜, 陈楚莹, 张家武, 等. 湖南会同地区马尾松林生物量的测定. 林业科学, 1982, 28 (2): 127- 134.
	Feng Z W, Chen C Y, Zhang J W, et al. Biomass estimation of Pinus massoniana forests in huitong region, Hunan Province. Scientia Silvae Sinicae, 1982, 28 (2): 127- 134.
	国家林业局. 2014,. 立木生物量模型及碳计量参数—湿地松. 北京: 中国标准出版社.
	National Forestry Administration. 2014,. Tree biomass models and related parameters to carbon accounting for Pinus elliottii. Beijing: Standards Press of China. ［in Chinese］
	侯燕南, 吴惠俐, 项文化, 等. 亚热带4种森林生物量估算转换参数的研究. 中南林业科技大学学报, 2016, 36 (8): 57- 65.
	Hou Y N, Wu H L, Xiang W H, et al. Conversion parameters determination for stand biomass estimation off our subtropical forest types based on national forest inventory system. Journal of Central South University of Forestry & Technology, 2016, 36 (8): 57- 65.
	冷寒冰, 景　军, 奉树成. 上海两种常绿阔叶树种生物量分配及异速生长模型. 生态学杂志, 2018, 37 (12): 3590- 3596.
	Leng H B, Jing J, Feng S C, et al. Biomass distribution and allometric model of two evergreen broad-leaved tree species in Shanghai. Chinese Journal of Ecology, 2018, 37 (12): 3590- 3596.
	李康杰, 胡中岳, 刘　萍, 等. 广东省珠三角乔木林生物量碳储量评估. 林业资源管理, 2022, 51 (4): 54- 60.
	Li K J, Hu Z Y, Liu P, et al. Evaluation on forest biomass and carbon storage in Pearl River Delta in Guangdong Province. Forest and Grassland Resources Research, 2022, 51 (4): 54- 60.
	林　力. 2011,. 马尾松人工林生物量模型的研究. 福州: 福建农林大学.
	Lin L. 2011,. Studies on the biomass model of Pinus massoniana plantations. Fuzhou: Fujian Agriculture and Forestry University. ［in Chinese］
	刘　坤, 曹　林, 汪贵斌, 等. 银杏生物量分配格局及异速生长模型. 北京林业大学学报, 2017, 39 (4): 12- 20.
	Liu K, Cao L, Wang G B, et al. Biomass allocation patterns and allometric models of Ginkgo biloba. Jouranl of Beijing Forestry University, 2017, 39 (4): 12- 20.
	刘晓华, 杜阿朋, 许宇星, 等. 桉树、相思、湿地松生物量估算系数差异特征及其影响因素. 桉树科技, 2023, 40 (4): 9- 16.
	Liu X H, Du A P, Xu Y X, et al. Differences in biomass estimation coefficients of Eucalyptus spp. , Acacia spp., and Pinus elliottii and their key influencing factors. Eucalypt Science & Technology, 2023, 40 (4): 9- 16.
	刘迎春, 高显连, 付　超, 等. 基于森林资源清查数据估算中国森林生物量固碳潜力. 生态学报, 2019, 39 (11): 4002- 4010.
	Liu Y C, Gao X L, Fu C, et al. Estimation of carbon sequestration potential of forest biomass in China based on National Forest Resources Inventory. Acta Ecologica Sinica, 2019, 39 (11): 4002- 4010.
	马学威, 熊康宁, 张　俞, 等. 森林生态系统碳储量研究进展与展望. 西北林学院学报, 2019, 34 (5): 62- 72.
	Ma X W, Xiong K N, Zhang Y, et al. Research progresses and prospects of carbon storage in forest ecosystems. Journal of Northwest Forestry University, 2019, 34 (5): 62- 72.
	孟盛旺, 谷振军, 肖平江, 等. 江西省吉安地区火炬松地上生物量分配特征及模型研建. 北京林业大学学报, 2022, 44 (12): 41- 51.
	Meng S W, Gu Z J, Xiao P J, et al. Characteristics of aboveground biomass allocation and model construction for Pinus taeda in Ji ’an Region, Jiangxi Province of eastern China. Jouranl of Beijing Forestry University, 2022, 44 (12): 41- 51.
	欧光龙, 胥　辉. 森林生物量模型研究综述. 西南林业大学学报(自然科学), 2020, 40 (1): 1- 11.
	Ou G L, Xu H. A review on forest biomass models. Journal of Southwest Forestry University(Natural Sciences), 2020, 40 (1): 1- 11.
	孙操稳, 仲文雯, 洑香香, 等. 2022,. 青钱柳幼林地上部分生物量生长模型研究. 南京林业大学学报(自然科学版), 46(1): 138-144.
	Sun C W, Zhong W W, Fu X X, et al. 2022,. A study on growth and aboveground biomass production of juvenile Cyclocarya paliurus plantations. Journal of Nanjing Forestry University(Natural Sciences Edition), 46(1): 138-144. ［in Chinese］
	唐守正, 张会儒, 胥　辉. 相容性生物量模型的建立及其估计方法研究. 林业科学, 2000, 46 (S1): 19- 27.
	Tang S Z, Zhang H R, Xu H. Study on edtimate method of method oecompatible biomass model. Scientia Silvae Sinicae, 2000, 46 (S1): 19- 27.
	王辽阔, 何介南. 湘北湿地松人工林生物量分配格局与碳汇潜力研究. 湖南林业科技, 2025, 52 (2): 10- 18. doi: 10.3969/j.issn.1003-5710.2025.02.002
	Wang L K, He J N. Study on biomass allocation pattern and carbon sink potential of Pinus elliottii plantation in northern Hunan. Hunan Forestry Science & Technology, 2025, 52 (2): 10- 18. doi: 10.3969/j.issn.1003-5710.2025.02.002
	王轶夫, 孙玉军. 马尾松生物量模型的对比研究. 中南林业科技大学学报, 2012, 32 (10): 29- 33.
	Wang Y F, Sun Y J. Comparative study on biomass models for Pinus massoniana. Journal of Central South University of Forestry & Technology, 2012, 32 (10): 29- 33.
	韦理电, 唐生森, 韦钧翔, 等. 湿地松人工林生长规律. 广西林业科学, 2016, 45 (3): 276- 279,287.
	Wei L D, Tang S S, Wei J X, et al. The growth rules of slash pine plantation. Guangxi Forestry Science, 2016, 45 (3): 276- 279,287.
	向　玮, 雷相东, 刘　刚, 等. 近天然落叶松云冷杉林单木枯损模型研究. 北京林业大学学报, 2008, 30 (6): 90- 98.
	Xiang W, Lei X D, Liu G, et al. Individual tree mortality models for semi-natural larch-spruce-fir forests in Jilin Province, northeastern China. Journal of Beijing Forestry University, 2008, 30 (6): 90- 98.
	叶功富, 高　伟, 郑兆飞, 等. 闽北高海拔地区湿地松人工林的生物量和生长量研究. 林业资源管理, 2013, 42 (6): 76- 80.
	Ye G F, Gao W, Zheng Z F, et al. Biomass and increment of Pinus elliottii plantationin high altitude area of northern Fujian. Forest Resources Management, 2013, 42 (6): 76- 80.
	曾伟生. 森林蓄积量和生物量多元混合模型研建. 林业资源管理, 2021, 50 (6): 23- 28. doi: 10.13466/j.cnki.lyzygl.2021.06.005
	Zeng W S. Development of multivariate mixed models for forest volume and biomass. Forest Resources Management, 2021, 50 (6): 23- 28. doi: 10.13466/j.cnki.lyzygl.2021.06.005
	曾伟生, 唐守正. 一个新的通用性相对生长生物量模型. 林业科学, 2012, 48 (1): 48- 52.
	Zeng W S, Tang S Z. A new general biomass allometric model. Scientia Silvae Sinicae, 2012, 48 (1): 48- 52.
	张帅楠, 栾启福, 姜景民. 基于无损检测技术的湿地松生长及材性性状遗传变异分析. 林业科学, 2017, 53 (6): 30- 36.
	Zhang S N, Luan Q F, Jiang J M. Genetic variation analysis for growth and wood properties of slash pine based on the non-destructive testing technologies. Scientia Silvae Sinicae, 2017, 53 (6): 30- 36.
	张宇娇, 赵恒谦, 付含聪, 等. 基于无人机遥感多特征的单木地上生物量反演模型研究. 林业科学, 2025, 61 (8): 1- 16.
	Zhang Y J, Zhao H Q, Fu H C, et al. Research on the inversion model of aboveground biomass at individual tree scale based on the multiple features of UAV remote sensing. Scientia Silvae Sinicae, 2025, 61 (8): 1- 16.
	Aabeyir R, Adu-Bredu S, Agyare W A, et al. Allometric models for estimating aboveground biomass in the tropical woodlands of Ghana, West Africa. Forest Ecosystems, 2020, 7 (1): 41. doi: 10.1186/s40663-020-00250-3
	Basuki T M, van Laake P E, Skidmore A K, et al. Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. Forest Ecology and Management, 2009, 257 (8): 1684- 1694.
	Chave J, Andalo C, Brown S, et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 2005, 145 (1): 87- 99. doi: 10.1007/s00442-005-0100-x
	Chave J, Réjou‐Méchain M, Búrquez A, et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, 2014, 20 (10): 3177- 3190. doi: 10.1111/gcb.12629
	Ding X, Pelser P B, Xu C, et al. Leveraging close-range UAV phenotyping and GWAS for enhanced understanding of slash pine growth dynamics. Information Processing in Agriculture, 2025, 12 (4): 550- 564.
	Du L, Zhou T, Zou Z, et al. Mapping forest biomass using remote sensing and national forest inventory in China. Forests, 2014, 5 (6): 1267- 1283. doi: 10.3390/f5061267
	Feldpausch T R, Lloyd J, Lewis S L, et al. Tree height integrated into pantropical forest biomass estimates. Biogeosciences, 2012, 9 (8): 3381- 3403. doi: 10.5194/bg-9-3381-2012
	Fortier J, Truax B, Gagnon D, et al. Allometric equations for estimating compartment biomass and stem volume in mature hybrid poplars: general or site-specific?. Forests, 2017, 8 (9): 309. doi: 10.3390/f8090309
	Jaakkola A, Hyyppä J, Kukko A, et al. A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65 (6): 514- 522. doi: 10.1016/j.isprsjprs.2010.08.002
	Kittredge J. Estimation of the amount of foliage of trees and stands. Journal of Forestry, 1944, 42 (12): 905- 912. doi: 10.1093/jof/42.12.905
	Luo Y, Wang X, Ouyang Z, et al. A review of biomass equations for China’s tree species. Earth System Science Data, 2020, 12 (1): 21- 40. doi: 10.5194/essd-12-21-2020
	Ma Z, Hartmann H, Wang H, et al. Carbon dynamics and stability between native masson pine and exotic slash pine plantations in subtropical China. European Journal of Forest Research, 2014, 133 (2): 307- 321. doi: 10.1007/s10342-013-0763-5
	Pan Y, Birdsey R A, Phillips O L, et al. 2013,. The structure, distribution, and biomass of the world’s forests. Annual Review of Ecology, Evolution, and Systematics, 44(1): 593−622.
	Peichl M, Arain M A. Allometry and partitioning of above- and belowground tree biomass in an age-sequence of white pine forests. Forest Ecology and Management, 2007, 253 (1−3): 68- 80. doi: 10.1016/j.foreco.2007.07.003
	Poorter H, Niklas K J, Reich P B, et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytologist, 2012, 193 (1): 30- 50. doi: 10.1111/j.1469-8137.2011.03952.x
	Quegan S, Le Toan T, Chave J, et al. The European space agency biomass mission: measuring forest above-ground biomass from space. Remote Sensing of Environment, 2019, 227, 44- 60. doi: 10.1016/j.rse.2019.03.032
	Riofrío J, Herrero C, Grijalva J, et al., 2015,. Aboveground tree additive biomass models in Ecuadorian highland agroforestry systems. Biomass and Bioenergy, 80: 252-259.
	Ter-Mikaelian M T, Korzukhin M D. Biomass equations for sixty-five North American tree species. Forest Ecology and Management, 1997, 97 (1): 1- 24. doi: 10.1016/S0378-1127(97)00019-4
	Wang C. Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. Forest Ecology and Management, 2006, 222 (1): 9- 16.
	Xiang W, Liu S, Deng X, et al. General allometric equations and biomass allocation of Pinus massoniana trees on a regional scale in southern China. Ecological Research, 2011, 26 (4): 697- 711. doi: 10.1007/s11284-011-0829-0
	Xu Y, Zhang J, Franklin S B, et al. Improving allometry models to estimate the above- and belowground biomass of subtropical forest, China. Ecosphere, 2015, 6 (12): 1- 15.
	Zeng W, Duo H, Lei X, et al. Individual tree biomass equations and growth models sensitive to climate variables for Larix spp. in China. European Journal of Forest Research, 2017, 136 (2): 233- 249. doi: 10.1007/s10342-017-1024-9

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

Aboveground Biomass Models for Young Pinus elliottii Plantations Based on Various Growth Factors

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模型 Model	残差自由度 Residual degrees of freedom	残差平方和 Residual sum of squares	自由度 Degrees of freedom	平方和 Sum of squares	F	P
W=aDBH^b	168	218.70	2	10.462	4.1698	0.0171*
W=aDBH^bH^cρ^d	166	208.24	2	10.462	4.1698	0.0171*

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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_e^c	地上部分生物量Aboveground biomass	0.860	1.123	0.870	12.17	0.096	2.106	0.194
	主干生物量 Stem biomass	0.820	0.572	0.435	13.24	0.048	1.853	0.473
	分枝生物量Branch biomass	0.648	0.423	0.317	28.11	0.013	2.781	0.533
	针叶生物量 Leaf biomass	0.773	0.542	0.405	15.15	0.037	2.071	0.213

W=aDBH^b（H_eA_c）^c	地上部分生物量Aboveground biomass	0.862	1.112	0.846	11.78	0.121	1.999	0.103
	主干生物量 Stem biomass	0.804	0.596	0.452	13.77	0.006	1.959	0.062
	分枝生物量Branch biomass	0.642	0.426	0.308	27.05	0.011	2.232	0.161
	针叶生物量 Leaf biomass	0.778	0.537	0.393	14.62	0.048	1.929	0.127

W=aDBH^bH_e^cA_c^d	地上部分生物量Aboveground biomass	0.863	1.110	0.843	11.75	0.113	1.983	0.185	0.087
	主干生物量 Stem biomass	0.820	0.572	0.434	13.23	0.047	1.873	0.474	0.014
	分枝生物量Branch biomass	0.670	0.410	0.303	26.47	0.023	2.318	0.542	0.303
	针叶生物量 Leaf biomass	0.778	0.536	0.392	14.61	0.045	1.915	0.197	0.113