东北地区落叶松人工林生物量转换与扩展因子空间自回归模型

doi:10.11707/j.1001-7488.20211005

Abstract

Abstract:

Objective: Based on the national forest inventory sample plot data of larch plantation in northeast China, the best model form of biomass conversion and expansion factor(BCEF) were discussed, and the spatial autoregressive BCEF model was established for larch plantation in northeast China. The model is useful for accurate stand biomass estimations. Method: Selecting a variety of model forms to establish BCEF general regression model, from which the best fitting model is selected. The two spatial autoregressive methods, spatial error model(SEM) and spatial lag model(SLM), were used to renew the BCEF model. The determination coefficient(R²), root mean square error (RMSE) and relative root mean square error(rRMSE) were used to evaluate the model. Moran index(MI) was applied to test the spatial autocorrelation of all variables and BCEF model residuals. Result: 1) There is obvious spatial autocorrelation in BCEF data. When the spatial distance is small, the BCEF attributes of stands within a province are similar. The differences of BCEF attributes among provinces are gradually appeared with the increase of spatial distance, and tend to random distribution finally. 2) The fitting results of allometric model, logarithmic model and hyperbolic model are better than those of other regression models, and the optimal models varied with independent variables. Stand quadratic mean diameter (D_g) is the best variable for interpreting BCEF. The R² of the effective model with D_g as an independent variable is between 0.945 and 0.958. Followed by stand mean height(H) and volume(V), the R² of the effective model is more than 0.60. The explanatory ability of stand average age is slightly lower than that of D_g, H and V, and the R² of its effective model is only about 0.50. Stand basal area(BA) and density(N) are poorly to explain the variance of BCEF with R² less than 0.50. The residuals of the general regression model showed spatial autocorrelation. 3) The spatial autoregressive model of hyperbolic function with D_g as an independent variable is the best one with SEM better than SLM. Compared with the corresponding ordinary regression model, the R² of SEM is increased by 3%, and the RMSE and rRMSE are reduced by 33% and 35%, respectively. The MI of the model residual is less than 0.02, which eliminates the spatial autocorrelation. Conclusion: The hyperbolic model is the most stable model for BCEF, and D_g is the best independent variable. It is recommended to adopt the hyperbolic function with the inclusion of spatial error model and with D_g as the predictor to estimate BCEF.

Key words: biomass conversion and expansion factors(BCEF), spatial autoregressive model, stand quadratic mean diameter, larch plantations

CLC Number:

S757

Xiao He,Xiangdong Lei. Spatial Autoregressive Biomass Conversion and Expansion Factor Models for Larch Plantations in Northeast China[J]. Scientia Silvae Sinicae, 2021, 57(10): 49-58.

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References 0

	陈传国, 朱俊凤. 东北主要林木生物量手册. 北京: 中国林业出版社, 1989.
	Chen C G , Zhu J F . Biomass tables for main tree species in northeast China. Beijing: China Forestry Publishing House, 1989.
	董文福, 管东生. 森林生态系统在碳循环中的作用. 重庆环境科学, 2002, 24 (3): 25- 27, 31. doi: 10.3969/j.issn.1674-2842.2002.03.010
	Dong W F , Guan D S . Effect of forest ecosystems on carbon cycling. Chongqing Environmental Science, 2002, 24 (3): 25- 27, 31. doi: 10.3969/j.issn.1674-2842.2002.03.010
	方精云, 刘国华, 徐嵩龄. 我国森林植被的生物量和净生产量. 生态学报, 1996, 16 (5): 497- 508.
	Fang J Y , Liu G H , Xu S L . Biomass and net production of forest vegetation in China. Acta Ecologica Sinica, 1996, 16 (5): 497- 508.
	国家林业局. 立木生物量模型及碳计量参数——落叶松. 北京: 中国林业出版社, 2016.
	State Forestry Administration . Tree biomass models and related parameters to carbon accounting for Larix. Beijing: China Forestry Publishing House, 2016.
	李海奎, 赵鹏祥, 雷渊才, 等. 基于森林清查资料的乔木林生物量估算方法的比较. 林业科学, 2012, 48 (5): 44- 52.
	Li H K , Zhao P X , Lei Y C , et al. Comparison on estimation of wood biomass using forest inventory data. Scientia Silvae Sinicae, 2012, 48 (5): 44- 52.
	李海奎, 欧强新, 赵嘉诚, 等. 模型和林分因子对区域尺度碳计量参数的影响——以杉木为例. 林业科学, 2017, 53 (9): 55- 62.
	Li H K , Ou Q X , Zhao J C , et al. Effects of model and stand factors on the parameters to carbon accounting at the regional scale—a case study for Cunninghamia lanceolata. Scientia Silvae Sinicae, 2017, 53 (9): 55- 62.
	娄明华, 张会儒, 雷相东, 等. 天然云冷杉针阔混交林单木胸径树高空间自回归模型研究. 北京林业大学学报, 2016, 38 (8): 1- 9.
	Lou M H , Zhang H R , Lei X D , et al. An individual height-diameter model constructed using spatial autoregressive models within natural spruce-fir and broadleaf mixed stands. Journal of Beijing Forestry University, 2016, 38 (8): 1- 9.
	罗云建, 王效科, 张小全, 等. 华北落叶松人工林的生物量估算参数. 林业科学, 2010, 46 (2): 6- 11.
	Luo Y J , Wang X K , Zhang X Q , et al. Biomass estimation factors of Larix principis-rupprechtii plantations in northern China. Scientia Silvae Sinicae, 2010, 46 (2): 6- 11.
	罗云建, 张小全, 侯振宏, 等. 我国落叶松林生物量碳计量参数的初步研究. 植物生态学报, 2007, 31 (6): 1111- 1118.
	Luo Y J , Zhang X Q , Hou Z H , et al. Biomass carbon accounting factors of Larix forests in China based on literature data. Journal of Plant Ecology, 2007, 31 (6): 1111- 1118.
	罗云建, 张小全, 王效科, 等. 森林生物量的估算方法及其研究进展. 林业科学, 2009, 12 (8): 129- 134. doi: 10.3321/j.issn:1001-7488.2009.08.023
	Luo Y J , Zhang X Q , Wang X K , et al. Forest biomass estimation methods and their prospects. Scientia Silvae Sinicae, 2009, 12 (8): 129- 134. doi: 10.3321/j.issn:1001-7488.2009.08.023
	张茂震, 王广兴, 刘安兴. 基于森林资源连续清查资料估算的浙江省森林生物量及生产力. 林业科学, 2009, 45 (9): 13- 17. doi: 10.3321/j.issn:1001-7488.2009.09.003
	Zhang M Z , Wang G X , Liu A X . Estimation of forest biomass and net primary production for Zhejiang Province based on continuous forest resources inventory. Scientia Silvae Sinicae, 2009, 45 (9): 13- 17. doi: 10.3321/j.issn:1001-7488.2009.09.003
	竹万宽, 许宇星, 王志超, 等. 中国桉树人工林生物量估算系数及影响要素. 林业科学, 2020, 56 (5): 1- 11.
	Zhu W K , Xu Y X , Wang Z C , et al. Biomass estimation coefficient and its impacting factors for Eucalyptus plantation in China. Scientia Silvae Sinicae, 2020, 56 (5): 1- 11.
	左舒翟, 任引, 王效科, 等. 中国杉木林生物量估算参数及其影响因素. 林业科学, 2014, 50 (11): 1- 12.
	Zuo S D , Ren Y , Wang X K , et al. Biomass estimation factors and their determinants of Cunninghamia lanceolata forests in China. Scientia Silvae Sinicae, 2014, 50 (11): 1- 12.
	Anselin L . Spatial econometrics: methods and models. Dordrecht: Kluwer Academic Publishers, 1988.
	Brown S . Measuring carbon in forests: current status and future challenges. Environment Pollution, 2002, 116, 363- 372. doi: 10.1016/S0269-7491(01)00212-3
	Canadell J G , Raupach M R . Managing forests for climate change mitigation. Science, 2008, 320 (5882): 1456- 1457. doi: 10.1126/science.1155458
	Fang J Y , Chen A P , Peng C H , et al. Changes in forest biomass carbon storage in China between 1949 and 1998. Science, 2001, 292 (5525): 2320- 2322. doi: 10.1126/science.1058629
	Gregorie T G . Generalized error structure for forestry yield models. Forest Science, 1987, 33 (2): 423- 444.
	IPCC. 2003. Good practice guidance for land use, Land-Use change and forestry. IPCC/IGES, ISBN, Hayama, Japan, 4-88788-032-0.
	IPCC. 2006. IPCC guidelines for national greenhouse gas inventory. IPCC/IGES, ISBN, Hayama, Japan, 4-88788-032-4.
	Jagodziński A M , Dyderski M K , Gęsikiewicz K , et al. Tree and stand level estimations of Abies alba Mill. aboveground biomass. Annals of Forest Science, 2019, 76 (2): 1- 14. doi: 10.1007/s13595-019-0842-y?shared-article-renderer
	Jalkanen A , Mäkipää R , Ståhl G , et al. Estimation of the biomass stock of trees in Sweden: comparison of biomass equations and age-dependent biomass expansion factors. Annals of Forest Science, 2005, 62 (8): 845- 851. doi: 10.1051/forest:2005075
	Joosten R , Schumacher J , Wirth C , et al. Evaluating tree carbon predictions for beech (Fagus sylvatica L.) in western Germany. Forest Ecology and Management, 2004, 189 (1/3): 87- 96.
	Kissling W D , Carl G . Spatial autocorrelation and the selection of simultaneous autoregressive models. Global Ecology and Biogeography, 2008, 17, 59- 71.
	Lehtonen A , Cienciala E , Tatarinov F , et al. Uncertainty estimation of biomass expansion factors for Norway spruce in the Czech Republic. Annals of Forest Science, 2007, 64 (2): 133- 140. doi: 10.1051/forest:2006097
	Lehtonen A , Mäkipää R , Heikkinen J , et al. Biomass expansion factors (BEFs) for Scots pine, Norway spruce and birch according to stand age for boreal forests. Forest Ecology and Management, 2004, 188 (1/3): 211- 224.
	Levy P E , Hale S E , Nicoll B C . Biomass expansion factors and root: shoot ratios for coniferous tree species in Great Britain. Forestry: An International Journal of Fonest Research, 2004, 77 (5): 421- 430. doi: 10.1093/forestry/77.5.421
	Liski J , Lehtonen A , Palosuo T , et al. Carbon accumulation in Finland's forests 1922—2004-an estimate obtained by combination of forest inventory data with modelling of biomass, litter and soil. Annals of Forest Science, 2006, 63 (7): 687- 697. doi: 10.1051/forest:2006049
	Lu J F , Zhang L J . Modeling and prediction of tree height-diameter relationships using spatial autoregressive models. Forest Science, 2011, 57 (3): 252- 264. doi: 10.1093/forestscience/57.3.252
	Mateu J , Uso J , Montes F . The spatial pattern of a forest ecosystem. Ecological Modelling, 1998, 108 (1): 163- 174.
	Meng Q , Cieszewski C J , Strub M R , et al. Spatial regression modeling of tree height-diameter relationships. Canadian Journal of Forest Research, 2009, 39 (12): 2283- 2293. doi: 10.1139/X09-136
	Pan Y , Birdsey R A , Fang J , et al. A large and persistent carbon sink in the world's forests. Science, 2011, 333 (6045): 988- 993. doi: 10.1126/science.1201609
	Paradis E. 2009. Moran's autocorrelation coefficient in comparative methods. R Foundation for Statistical Computing, Vienna.
	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.
	Shvidenko A , Nilsson S . Dynamics of Russian forests and the carbon budget in 1961—1998: an assessment based on long-term forest inventory data. Climatic Change, 2002, 55 (1/2): 5- 37. doi: 10.1023/A:1020243304744
	Soares P , Tomé M . Biomass expansion factors for Eucalyptus globulus stands in Portugal. Forest Systems, 2012, 21 (1): 141- 152. doi: 10.5424/fs/2112211-12086
	Wang C . Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. Forest Ecology and Management, 2006, 222 (1/3): 9- 16.
	West P W , Ratkowsky D A , Davis A W . Problems of hypothesis testing of regressions with multiple measurements from individual sampling units. Forest Ecology and Management, 1984, 7 (3): 207- 224. doi: 10.1016/0378-1127(84)90068-9

变量Variables	平均值Mean	最大值Max.	最小值Min.	变异系数CV(%)
生物量转换与扩展因子Biomass conversion and expansion factors(BCEF)/(mg·m^-³)	0.77	0.81	0.74	1.70
林分蓄积Volume(V)/(m³·hm^-2)	85.22	299.32	4.23	63.75
林分平方平均直径Quadratic mean diameter(D_g)/cm	12.40	26.40	5.70	33.66
林分断面积Basal area(BA)/(m²·hm^-2)	11.65	34.60	0.81	55.48
林分平均高Mean height(H)/m	11.90	24.00	3.50	35.16
林分密度Density(N)/(tree·hm^-2)	1 020.00	3 600.00	250.00	56.00
林分平均年龄Mean age(Age)/a	25.00	52.00	8.00	38.26

模型名称Model name	模型表达式Model expression
异速生长模型Allometric model	lnBCEF= a+b×ln X+ε
指数模型Exponential model	lnBCEF= a+b×X+ε
对数模型Logarithmic model	BCEF= a+b×ln X+ε
双曲线模型Hyperbolic model	BCEF= a+b/X+ε
S形曲线模型S-shaped model	1/BCEF= a+b×exp(- X) +ε

模型形式 Model function	序号 Number	自变量 Independent variable	参数Parameters		R²	RMSE/(mg·m^-³)	rRMSE(%)
模型形式 Model function	序号 Number	自变量 Independent variable	a	b	R²	RMSE/(mg·m^-³)	rRMSE(%)
异速生长模型 Allometric model： lnBCEF= a+b×ln X+ε	1	V	-0.188^***	-0.016^***	0.603	0.008	1.07
	2	D_g	-0.133^***	-0.050^***	0.958	0.003	0.35
	3	BA	-0.219^***	-0.017^***	0.473	0.010	1.23
	4	H	-0.165^***	-0.038^***	0.710	0.007	0.91
	5	N	-0.326^***	0.010^***	0.103	0.012	1.61
	6	Age	-0.158^***	-0.032^***	0.543	0.009	1.15
指数模型 Exponential model： lnBCEF= a+b×X+ε	7	V	-0.237^***	-2.324E-4^***	0.555	0.009	1.13
	8	D_g	-0.209^***	-3.872E-3^***	0.907	0.004	0.52
	9	BA	-0.237^***	-1.686E-3^***	0.415	0.010	1.30
	10	H	-0.216^***	-3.456E-3^***	0.719	0.007	0.90
	11	N	-0.267^***	9.749E-6^***	0.106	0.012	1.61
	12	Age	-0.224^***	-1.303E-3^***	0.524	0.009	1.17
对数模型 Logarithmic model： BCEF= a+b×ln X+ε	13	V	0.827^***	-0.013^***	0.604	0.008	1.07
	14	D_g	0.869^***	-0.039^***	0.957	0.003	0.35
	15	BA	0.803^***	-0.013^***	0.474	0.010	1.23
	16	H	0.844^***	-0.029^***	0.712	0.007	0.91
	17	N	0.721^***	0.008^***	0.100	0.012	1.61
	18	Age	0.850^***	-0.024^***	0.543	0.009	1.15
双曲线模型 Hyperbolic model： BCEF= a+b/X+ε	19	V	0.767^***	0.298^***	0.397	0.010	1.32
	20	D_g	0.735^***	0.431^***	0.945	0.003	0.40
	21	BA	0.766^***	0.056^***	0.364	0.010	1.36
	22	H	0.749^***	0.258^***	0.632	0.008	1.03
	23	N	0.781^***	-5.360^***	0.079	0.013	1.63
	24	Age	0.751^***	0.474^***	0.511	0.009	1.19
S形曲线模型 S-shaped model: 1/BCEF= a+b×exp(- X) +ε	25	V	1.293^***	-5.972^***	0.031	0.013	1.67
	26	D_g	1.298^***	-31.389^***	0.414	0.010	1.30
	27	BA	1.296^***	-0.200^***	0.204	0.012	1.52
	28	H	1.295^***	-3.274^***	0.152	0.012	1.56
	29	N	1.293^***	-1.517E+7	0.004	0.013	1.69
	30	Age	1.294^***	-204.884^*	0.069	0.013	1.64

模型形式 Model function	序号 Number	SAR模型 SAR model	参数Parameters		滞后参数 Lag parameter	R²	RMSE/(mg·m^-3)	rRMSE(%)
模型形式 Model function	序号 Number	SAR模型 SAR model	a	b	滞后参数 Lag parameter	R²	RMSE/(mg·m^-3)	rRMSE(%)
异速生长模型 Allometric model	31	ESM	-0.133^***	-0.050^***	λ= -142.780^***	0.980	0.002	0.24
异速生长模型 Allometric model	32	LSM	-0.204	-0.050^***	ρ= -0.277	0.958	0.003	0.35
对数模型 Logarithmic model	33	ESM	0.870^***	-0.039^***	λ= -142.070^***	0.980	0.002	0.24
对数模型 Logarithmic model	34	LSM	1.097^***	-0.039^***	ρ= -0.293	0.957	0.003	0.35
双曲线模型 Hyperbolic model	35	ESM	0.737^***	0.413^***	λ= -150.940^***	0.976	0.002	0.26
双曲线模型 Hyperbolic model	36	LSM	1.199^***	0.433^***	ρ= -0.599^***	0.945	0.003	0.40

模型 Model	序号 Number	SAR模型 SAR model	参数Parameters				滞后参数 Lag parameter	R²	RMSE/(mg·m^-3)	rRMSE(%)
模型 Model	序号 Number	SAR模型 SAR model	a	b	c	d	滞后参数 Lag parameter	R²	RMSE/(mg·m^-3)	rRMSE(%)
BCEF= a+b/D_g+ c/H	37	SEM	0.736^***	-0.436^***	-0.019^***	—	λ= -151.280^***	0.978	0.002	0.26
BCEF= a+b/D_g+ c/H	38	SLM	1.220^***	0.456^***	-0.020^***	—	ρ= -0.628^**	0.947	0.003	0.39
BCEF= a+b/D_g + c/Age	39	SEM	0.737^***	-0.419^***	-0.012	—	λ= -150.620^***	0.976	0.002	0.26
BCEF= a+b/D_g + c/Age	40	SLM	1.194^***	0.438^***	-0.001	—	ρ= -0.593	0.946	0.003	0.40
BCEF= a+b/H+c/Age	41	SEM	0.748^***	0.112^***	0.308^***	—	λ= -182.050^***	0.907	0.004	0.52
BCEF= a+b/H+c/Age	42	SLM	0.342	0.187^***	0.205^***	—	ρ= -0.523	0.682	0.007	0.96
BCEF= a+b/D_g +c/ H+d/Age	43	SEM	0.736^***	0.437^***	-0.019^***	-0.004	λ= -150.230^***	0.977	0.002	0.26
BCEF= a+b/D_g +c/ H+d/Age	44	SLM	1.219^***	0.457^***	-0.020^***	-0.002	ρ= -0.626^**	0.947	0.003	0.39

Spatial Autoregressive Biomass Conversion and Expansion Factor Models for Larch Plantations in Northeast China

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