北京市主要森林类型蓄积量生物量碳储量模型研建

doi:10.13466/j.cnki.lyzygl.2021.02.017

摘要/Abstract

摘要：

林分水平的蓄积量、生物量和碳储量模型,是开展森林资源规划设计调查的计量基础。基于北京市2016年森林资源连续清查的1 425个乔木林样地数据,分别利用非线性独立回归估计、误差变量联立方程组和含哑变量的误差变量联立方程组方法,建立了油松林、侧柏林、栎树林、桦木林、榆树林、刺槐林、杨树林、其他硬阔林、其他软阔林、乔木经济林等10种主要森林类型的林分蓄积量、生物量和碳储量模型。结果显示:10种主要森林类型的蓄积量、生物量和碳储量模型的确定系数(R²)都在0.93以上,总体相对误差(TRE)和平均系统误差(ASE)都在±3%以内且多数趋近于0,平均预估误差(MPE)都在5%以内,平均百分标准误差(MPSE)都在15%以内。结果表明:不同森林类型的蓄积量主要取决于林分断面积和平均高,生物量主要取决于蓄积量和林分平均高;含哑变量的非线性误差变量联立方程组方法,是建立林分水平三储量(森林蓄积量、生物量和碳储量)模型系统的可行方法;所建北京市10种主要森林类型的蓄积量、生物量和碳储量模型,其预估精度达到相关技术规定要求,可以在实践中推广试用;为进一步提高模型的准确度,可采用基于二元模型计算的蓄积量和生物量样地数据对所建模型进行修正。

关键词: 蓄积量, 生物量, 碳储量, 哑变量模型, 误差变量联立方程组

Abstract:

Stand-level volume,biomass and carbon stock models are quantitative tools for implementing forest resource management.Based on data of 1 425 permanent plots from forest inventory in 2016 in Beijing,and through approaches including independent nonlinear regression (INR),simultaneous error-in-variable equations (SEIVE),and SEIVE with dummy variable modeling,this study works out the stand-level volume,biomass and carbon stock models for ten major forest types,including Chinese pine (Pinus tabuliformis),cypress (Platycladus orientalis),oak (Quercus spp.),birch (Betula spp.),elm (Ulmus spp.),locust (Robinia pseudoacacia),poplar (Populus spp.),other hardwood broadleaved,other softwood broadleaved,and economic arboreal forest.The results show that the coefficients of determination (R²)of the stand-level volume,biomass and carbon stock models for 10 forest types are more than 0.93,the total relative errors (TREs)and average systematic errors (ASEs)are within ±3% and most of them are close to zero.The mean prediction errors (MPEs)are less than 5%,and the mean percent standard errors (MPSEs)are almost less than 15%.The following conclusions can be achieved that the volume stock per hectare of different forest types mainly depend upon basal area and average tree height of the forest stands,and the biomass stock mainly relate to volume stock and average tree height.The SEIVE with dummy variable modeling approach is a feasible method for developing stand-level stock models.The developed volume,biomass and carbon stock models for 10 forest types in Beijing in this study meet the need of precision requirements to the relevant regulation and can be applied on trial in practice.For improving the accuracy of the developed models,volume and biomass data of plot samples calculated by two-variable models need be used to further modify the models.

Key words: volume, biomass, carbon stock, dummy variable model, simultaneous error-in-variable equations

中图分类号:

S711

杨学云, 曾伟生, 陈新云. 北京市主要森林类型蓄积量生物量碳储量模型研建[J]. 林业资源管理, 2021,(2): 124-130.

YANG Xueyun, ZENG Weisheng, CHEN Xinyun. Research on Developing Stand Volume,Biomass and Carbon Stock Models for Major Forest Types in Beijing[J]. FOREST RESOURCES WANAGEMENT, 2021,(2): 124-130.

图/表 3

表1

表2

表3

参考文献 51

[1]	IUFRO. International guidelines for forest monitoring[R]. Vienna:IUFRO World Series:Volume 5, 1994.
[2]	IPCC. IPCC guidelines for national greenhouse gas inventory[R/OL]. [2021- 01- 05]. http://www.ipcc-nggip.iges.or.jp.
[3]	FAO. Global forest resources assessment 2020:Guidelines and specifications[R]. Rome:FRA working paper 189, 2018.
[4]	张雄清, 张建国, 段爱国. 基于单木水平和林分水平的杉木兼容性林分蓄积量模型[J]. 林业科学, 2014,50(1):82-87.
[5]	曾伟生, 杨学云, 陈新云. 单木和林分水平一元和二元材积模型的预估精度对比[J]. 中南林业调查规划, 2017,36(4):1-6.
[6]	Jagodziński A M, Dyderski M K, Gesikiewicz K, et al. Tree and stand level estimations of Abies alba Mill aboveground biomass[J]. Annals of Forest Science, 2019,76:56. doi: 10.1007/s13595-019-0842-y
[7]	中华人民共和国农林部. LY208-77立木材积表[S]. 北京: 中国标准出版社, 1977.
[8]	Luo Yunjian, Wang Xiaoke, Ouyang Zhiyun, et al. A review of biomass equations for China's tree species[J]. Earth System Science Data, 2020,12(1):21-40. doi: 10.5194/essd-12-21-2020
[9]	国家林业局. LY/T 2260-2014,立木生物量模型及碳计量参数—油松[S]. 北京: 中国标准出版社, 2015.
[10]	国家林业局. LY/T 2261-2014,立木生物量模型及碳计量参数—湿地松[S]. 北京: 中国标准出版社, 2015.
[11]	国家林业局. LY/T 2262-2014,立木生物量模型及碳计量参数—云南松[S]. 北京: 中国标准出版社, 2015.
[12]	国家林业局. LY/T 2263-2014,立木生物量模型及碳计量参数—马尾松[S]. 北京: 中国标准出版社, 2015.
[13]	国家林业局. LY/T 2264-2014,立木生物量模型及碳计量参数—杉木[S]. 北京: 中国标准出版社, 2015.
[14]	国家林业局. LY/T 2654-2016,立木生物量模型及碳计量参数—落叶松[S]. 北京: 中国标准出版社, 2017.
[15]	国家林业局. LY/T 2655-2016,立木生物量模型及碳计量参数—云杉[S]. 北京: 中国标准出版社, 2017.
[16]	国家林业局. LY/T 2656-2016,立木生物量模型及碳计量参数—冷杉[S]. 北京: 中国标准出版社, 2017.
[17]	国家林业局. LY/T 2657-2016,立木生物量模型及碳计量参数—柳杉[S]. 北京: 中国标准出版社, 2017.
[18]	国家林业局. LY/T 2658-2016,立木生物量模型及碳计量参数—栎树[S]. 北京: 中国标准出版社, 2017.
[19]	国家林业局. LY/T 2659-2016,立木生物量模型及碳计量参数—桦树[S]. 北京: 中国标准出版社, 2017.
[20]	国家林业局. LY/T 2660-2016,立木生物量模型及碳计量参数—木荷[S]. 北京: 中国标准出版社, 2017.
[21]	国家林业局. LY/T 2661-2016,立木生物量模型及碳计量参数—枫香[S]. 北京: 中国标准出版社, 2017.
[22]	Shiver B D, Brister G H. Tree and stand volume functions for Eucalyptus saligna[J]. Forest Ecology and Management, 1992,47:211-223. doi: 10.1016/0378-1127(92)90275-E
[23]	Næsset E. Stand volume functions for Picea abies in western Norway[J]. Scandinavian Journal of Forest Research, 1995,10:42-50. doi: 10.1080/02827589509382866
[24]	Næsset E, Tveite B. Stand volume functions for Picea abies in eastern,central and northern Norway[J]. Scandinavian Journal of Forest Research, 1999,14:164-174. doi: 10.1080/02827589950152890
[25]	Chamshama S A O, Mugasha A G, Zahabu E. Stand biomass and volume estimation for Miombo woodlands at Kitulangalo,Morogoro,Tanzania[J]. Southern African Forestry Journal, 2004,200:59-69. doi: 10.1080/20702620.2004.10431761
[26]	Castedo-Dorado F, Gómez-García E, Diéguez-Aranda U, et al. Aboveground stand-level biomass estimation:A comparison of two methods for major forest species in northwest spain[J]. Annals of Forest Science, 2012,69:735-746. doi: 10.1007/s13595-012-0191-6
[27]	Usoltsev V A, Shobairi S O R, Chasovskikh V P. Triple harmonization of transcontinental allometric of Picea spp.and Abies spp.forest stand biomass[J]. Ecology,Environment and Conservation, 2018,24(4):1966-1972.
[28]	Jagodziński A M, Dyderski M K, Gesikiewicz K, et al. How do tree stand parameters affect young Scots pine biomass? —Allometric equations and biomass conversion and expansion factors[J]. Forest Ecology and Management, 2018,409:74-83. doi: 10.1016/j.foreco.2017.11.001
[29]	Jagodziński A M, Dyderski M K, Gesikiewicz K, et al. Tree and stand-level biomass estimation in a Larix decidua Mill.chronosequence[J]. Forests, 2018,9:587. doi: 10.3390/f9100587
[30]	Jagodziński A M, Dyderski M K, Gęsikiewicz K, et al. Effects of stand features on aboveground biomass and biomass conversion and expansion factors based on a Pinus sylvestris L.chronosequence in western Poland[J]. European Journal of Forest Research, 2019,138:673-683. doi: 10.1007/s10342-019-01197-z
[31]	Burt A, Calders K, Cuni-Sanchez A, et al. Assessment of bias in pan-tropical biomass predictions[J]. Frontiers in Forests and Global Change, 2020,3:12. doi: 10.3389/ffgc.2020.00012
[32]	Zeng Weisheng. Developing one-variable individual tree biomass models based on wood density for 34 tree species in China[J]. Forest Research:Open Access, 2018,7:1.
[33]	方精云, 刘国华, 徐嵩龄. 我国森林植被的生物量和净生产量[J]. 生态学报, 1996,16(5):497-508.
[34]	Fang Jingyun, Chen Anping, Peng Changhui, et al. Changes in forest biomass carbon storage in China between 1949 and 1998[J]. Science, 2001,292:2320-2322. doi: 10.1126/science.1058629
[35]	余松柏, 叶金盛, 王登峰, 等. 编制林分形高表估计林分蓄积量方法的研究[J]. 中南林业调查规划, 2005,24(3):5-9.
[36]	侯振宏, 张小全, 徐德应, 等. 杉木人工林生物量和生产力研究[J]. 中国农学通报, 2009,25(5):97-103.
[37]	王斌, 刘某承, 张彪. 基于森林资源清查资料的森林植被净生产量及其动态变化研究[J]. 林业资源管理, 2009(1):35-42.
[38]	王艳婷, 李崇贵, 郝利军. 用岭估计估测以分类为前提的森林蓄积量[J]. 东北林业大学学报, 2014,42(9):39-42.
[39]	Hou Yannan, Wu Huili, Zeng Weixian, et al. Conversion parameters for stand biomass estimation of four subtropical forests in southern China[C]//2016 International Conference on Environment,Climate Change and Sustainable Development. DEStech Publications Incorporated, 2017.
[40]	Mei Guangyi, Sun Yujun, Saeed S. Models for predicting the biomass of Cunninghamia lanceolata trees and stands in southeastern China[J]. PLOS ONE, 2017,12(1):e0169747. doi: 10.1371/journal.pone.0169747
[41]	Zhao Miaomiao, Yang Jilin, Zhao Na, et al. Estimation of China's forest stand biomass carbon sequestration based on the continuous biomass expansion factor model and seven forest inventories from 1977 to 2013[J]. Forest Ecology and Management, 2019,448:528-534. doi: 10.1016/j.foreco.2019.06.036
[42]	Dong Lihu, Zhang Lianjun, Li Fengri. Evaluation of stand biomass estimation methods for major forest types in the eastern Da Xing'an Mountain,northeast China[J]. Forests, 2019,10:715. doi: 10.3390/f10090715
[43]	Soares P, Tome M. Biomass expansion factors for Eucalyptus globulus stands in Portugal[J]. Forest Systems, 2012,21(1):141-152. doi: 10.5424/fs/2112211-12086
[44]	李海奎, 雷渊才. 中国森林植被生物量和碳储量评估[M]. 北京: 中国林业出版社, 2010.
[45]	曾伟生, 唐守正. 非线性模型对数回归的偏差校正及与加权回归的对比分析[J]. 林业科学研究, 2011,24(2):137-143.
[46]	唐守正, 郎奎建, 李海奎. 统计和生物数学模型计算(ForStat教程)[M]. 北京: 科学出版社, 2008.
[47]	Zeng Weisheng, Zhang Huiru, Tang Shouzheng. Using the dummy variable model approach to construct compatible single-tree biomass equations at different scales—a case study for Masson pine(Pinus massoniana)in southern China[J]. Canadian Journal of Forest Research, 2011,41(7):1547-1554. doi: 10.1139/x11-068
[48]	Zeng Weisheng. Using nonlinear mixed model and dummy variable model approaches to construct origin-based single tree biomass equations[J]. Trees-Structure and Function, 2015,29(1):275-283. doi: 10.1007/s00468-014-1112-0
[49]	曾伟生, 唐守正. 立木生物量模型的优度评价和精度分析[J]. 林业科学, 2011,47(11):106-113.
[50]	国家质量监督检验检疫总局, 国家标准化管理委员会. GB/T 26424-2010,森林资源规划设计调查技术规程[S]. 北京: 中国标准出版社, 2011.
[51]	曾伟生, 孙乡楠, 王六如, 等. 基于激光雷达数据估计林分蓄积量及平均高和断面积[J]. 林业资源管理, 2020(2):79-86.

编辑推荐 0

Metrics

阅读次数

全文

454

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	24	0	0	430

来源	本网站	其他网站

次数	394	60
比例	87%	13%

摘要

762

最新录用	在线预览	正式出版

0	0	762

	来源	本网站

	次数	762
	比例	100%

森林类型	样地数/ 个	平均值/ (m³/hm²)	最小值/ (m³/hm²)	最大值/ (m³/hm²)	标准差/ (m³/hm²)	变动系数/ %
油松林	171	41.45	0.12	141.83	36.22	87.39
侧柏林	132	15.27	0.06	80.87	15.14	99.14
栎树林	248	47.68	1.95	187.63	36.33	76.20
桦木林	54	58.16	0.84	198.73	48.96	84.19
榆树林	77	25.07	0.57	114.57	23.15	92.33
刺槐林	93	27.45	0.15	138.02	30.03	109.40
杨树林	172	22.87	0.21	124.00	25.80	112.77
其他硬阔林	154	81.65	0.85	372.49	63.39	77.64
其他软阔林	152	39.64	0.12	176.37	39.82	110.47
乔木经济林	172	28.27	0.24	157.90	24.65	87.20

模型	目标变量	参数估计值						评价指标
模型	目标变量	a₀/b₀/c₀		a₁/b₁		a₂/b₂		R²		SEE		TRE/%	ASE/%	MPE/%	MPSE/%
独立模型	M	2.251	1.030		0.3064		0.941		9.97		-0.84		0.01	1.32	14.49
	B	2.030	0		-0.2033		0.943		11.95		-2.44		-2.65	1.24	14.02
	C	0.4886					0.945		5.64		-2.93		-2.74	1.20	13.70
联立模型	M	2.393	1.035		0.2692		0.938		10.22		-0.05		0.07	1.35	14.55
	B	2.059	0		-0.2260		0.940		12.20		1.62		0.19	1.26	14.19
	C	0.4885					0.945		5.64		1.13		0.10	1.20	13.77

森林类型	目标变量	参数估计值						评价指标
森林类型	目标变量	a₀/b₀/c₀		a₁/b₁		a₂/b₂		R²		SEE		TRE/%	ASE/%	MPE/%	MPSE/%
油松林	M	2.636	1.074		0.1317		0.970		6.31		-1.89		-0.18	2.31	11.63
	B	1.362	0		-0.0571		0.983		5.86		-1.56		0.51	1.77	10.87
	C	0.5081					0.984		2.93		-1.25		0.42	1.73	10.40
侧柏林	M	3.112	1.036		-0.0451		0.966		2.81		-1.75		0.06	3.17	13.31
	B	1.627	0		0.1072		0.990		3.17		-0.17		0.13	1.78	7.11
	C	0.4994					0.991		1.51		-0.02		0.10	1.70	6.81
栎树林	M	2.701	1.077		0.1043		0.979		5.26		-0.39		0.10	1.38	8.11
	B	1.573	0		0.0030		0.971		10.38		0.46		0.04	1.71	9.36
	C	0.4807					0.971		4.95		0.38		0.05	1.70	9.35
桦木林	M	2.910	1.080		0.0831		0.978		7.47		-0.44		0.22	3.50	9.19
	B	1.393	0		-0.0262		0.971		11.79		0.80		-0.05	4.15	8.92
	C	0.4856					0.969		5.78		0.66		0.00	4.20	9.09
榆树林	M	2.584	1.009		0.2726		0.949		5.27		0.73		0.03	4.77	11.80
	B	1.524	0		-0.1312		0.975		3.96		-1.02		0.04	3.11	8.14
	C	0.4845					0.973		1.96		-1.12		0.04	3.18	8.24
刺槐林	M	2.342	1.035		0.3050		0.986		3.54		2.19		-0.29	2.66	10.18
	B	2.064	0		-0.1208		0.979		6.46		0.29		0.01	3.12	7.37
	C	0.4844					0.980		3.02		0.31		0.01	3.01	7.20
杨树林	M	2.904	1.055		0.2330		0.959		12.87		0.09		0.26	2.52	11.75
	B	0.891	0		0.0272		0.944		14.69		0.11		0.47	2.98	13.30
	C	0.4745					0.944		6.89		0.07		0.59	2.95	13.44
其他硬阔林	M	2.600	1.024		0.2544		0.979		3.77		2.39		-0.16	2.49	11.01
	B	2.201	0		-0.2397		0.963		5.90		-0.76		0.03	2.94	8.75
	C	0.4851					0.962		2.87		-0.86		0.05	2.95	8.69
其他软阔林	M	2.444	1.041		0.2860		0.954		8.60		0.07		-0.07	3.48	11.48
	B	1.755	0		-0.2406		0.954		9.08		0.74		-0.02	3.40	9.74
	C	0.4867					0.944		4.76		-0.28		0.09	3.71	10.28
乔木经济林	M	2.477	1.077		0.2724		0.937		6.20		-0.92		0.24	3.31	13.10
	B	1.530	0		-0.1303		0.954		6.29		0.30		-0.01	2.80	9.91
	C	0.4942					0.953		3.12		0.28		-0.02	2.81	9.93