包含哑变量的黑龙江省落叶松人工林碳储量预测模型系统

doi:10.13287/j.1001-9332.201903.013

摘要/Abstract

摘要： 利用2005—2010年两期黑龙江省落叶松人工林固定监测样地数据,分析黑龙江省落叶松人工林各林分变量因子之间的关系,建立地位级指数曲线模型和林分密度指数模型,采用两步最小二乘的方法建立预测包含林分平均断面积和林分碳储量的联立方程组,将以上所构建的模型统称为黑龙江省落叶松人工林碳储量预测模型系统.同时将龄组和区域作为哑变量加入到预测模型中.结果表明: 模型系统中除地位级指数曲线模型之外,剩余模型的确定系数(R²)均大于0.98,均方根误差均小于4,而加入哑变量的模型R²有所增加,均方根误差均小于3,说明模型稳定性较好,预估参数较为精确.各模型的平均相对误差均小于2%,大部分模型平均相对误差绝对值小于15%,模型精度均在95%以上,研究结果可以对黑龙江省不同区域和龄组的落叶松人工林林分平均树高进行精确拟合.根据地位级指数曲线模型和联立方程组的拟合参数进行分析,当调查样地在同一区域时,林分年龄越大,林分平均树高、林分平均断面积和林分碳储量越大,符合实际生长规律;而在林分年龄相同、区域不同时,不同区域林分平均树高由大到小的排列顺序为: 平原地区、小兴安岭南坡地区、张广才岭东坡地区、完达山地区、张广才岭西坡地区、小兴安岭北坡地区.不同区域林分断面积和林分碳储量由多到少的排列顺序为: 张广才岭东坡地区、小兴安岭北坡地区、张广才岭西坡地区、小兴安岭南坡地区、完达山地区、平原地区.

关键词: 落叶松人工林, 哑变量, 林分碳储量, 联立方程组

Abstract: Based on monitoring data from fixed plots of larch plantation in Heilongjiang Province obtained in 2005 and 2010, we analyzed the relationship among stand variables of larch plantation in Heilongjiang Province and established site class index curve model and stand density index model. A two-stage least square method was used to establish a simultaneous equations system for predicting average basal area and carbon storage of stands. Together, they were called prediction model system for carbon storage of larch plantation in Heilongjiang Province. The system included two dummy variables of age group and region. Results showed that the determination coefficients of those models were all greater than 0.98, and the root mean square errors were less than 4, except for the site class index curve model. The model with dummy variables increased the determination coefficient, and the root mean square error was less than 3, indicating that the model had good stability with accutrate estimated parameters. The average relative error of all models was less than 2%, the absolute value of the average relative error of most models was less than 15%, and the accuracy of all models was above 95%, indicating that the models could be used to accurately predict the carbon storage of larch plantations in different regions and age groups in Heilongjiang Pro-vince. According to the analysis of the site class index curve model and the estimated parameters of the simultaneous equations, the greater stand age, the larger stand average height, average basal area and carbon storage when the survey plots were located in the same area, which fitted natural growth rules. Under the same stand but different regions, the stand average height decreased in the order of plain area, southern slope area of Xiao-xing’anling, eastern slope area of Zhangguangcai-ling, Wandashan area, western slope area of Zhangguangcailing, and northern slope area of Xiao-xing’anling, while the order of stand basal area and carbon storage was eastern slope area of Zhangguangcailing, northern slope area of Xiaoxing’anling, western slope area of Zhangguangcailing, southern slope area of Xiaoxing’anling, Wandashan area, and plain area.

Key words: dummy variables, stand carbon storage, simultaneous equations, larch plantation

贾炜玮, 孙赫明, 李凤日. 包含哑变量的黑龙江省落叶松人工林碳储量预测模型系统[J]. 应用生态学报, 2019, 30(3): 814-822.

JIA Wei-wei, SUN He-ming, LI Feng-ri. Prediction model system with dummy variables for carbon storage of larch plantation in Heilongjiang Province, China[J]. Chinese Journal of Applied Ecology, 2019, 30(3): 814-822.

参考文献

[1] Liu G-H (刘国华), Fu B-J (傅伯杰). Effects of global climate change on forest ecosystems. Journal of Natural Resources (自然资源学报), 2001, 16(1): 71-78 (in Chinese)
[2] Chen P-Q (陈泮勤), Huang Y (黄耀), Yu G-R (于贵瑞). Carbon Cycle of Earth System. Beijing: Science Press, 2004 (in Chinese)
[3] Qin D-H (秦大河), Luo Y (罗勇). The causes of global climate change and future trends. Impact of Science on Society (科学对社会的影响), 2008(2): 16-21 (in Chinese)
[4] Sohngen B, Tian X. Global climate change impacts on forests and markets. Forest Policy & Economics, 2016, 72: 18-26
[5] Zhang S-L (张士亮), Zhang Y-C (张艳春). Research progress and methods of carbon storage in forest ecosystems. Protection Forest Science and Technology (防护林科技), 2017(10): 97-100 (in Chinese)
[6] Gough CM, Vogel CS, Kazanski C, et al. Coarse woody debris and the carbon balance of a north temperate forest. Forest Ecology and Management, 2007, 244: 60-67
[7] Rodríguez-Veiga P, Wheeler J, Louis V, et al. Quantify-ing forest biomass carbon stocks from space. Current Forestry Reports, 2017, 3: 1-18
[8] Xu S-S (续珊珊). A review of forest carbon storage estimation methods. Forest Inventory and Planning (林业调查规划), 2014, 39(6): 28-33 (in Chinese)
[9] Gil MV, Blanco D, Carballo MT, et al. Carbon stock estimates for forests in the Castillay Len region, Spain. A GIS based method for evaluating spatial distribution of residual biomass for bio-energy. Biomass & Bioenergy, 2011, 35: 243-252
[10] Návar J. Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. Forest Ecology and Management, 2009, 257: 427-434
[11] Fang J-Y (方精云), Chen A-P (陈安平). Dynamic forest biomass carbon pools in China and their significance. Acta Botanica Sinica (植物学报), 2001, 43(9): 967-973 (in Chinese)
[12] Guang Z-Y(光增云). Study on changes in forest biomass carbon storage in Henan Province. Forest Resources Management (林业资源管理), 2006(4): 56-60 (in Chinese)
[13] Li S-M (李士美),Yang C-Q (杨传强), Wang H-N (王宏年), et al. Carbon storage of forest stands in Shandong Province estimated by forestry inventory data. Chinese Journal of Applied Ecology (应用生态学报), 2014, 25(8): 2215-2220 (in Chinese)
[14] Gao HL, Dong LH, Li FR, et al. Evaluation of four methods for predicting carbon stocks of Korean Pine plantations in Heilongjiang Province. PLoS One, 2015, 10(12): e0145017
[15] Zeng WS, Zhang HR, Tang SZ. 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. Canadian Journal of Forest Research, 2011, 41: 1547-1554
[16] Zeng WS. Using nonlinear mixed model and dummy varia-ble model approaches to develop origin-based individual tree biomass equations. Trees, 2015, 29: 275-283
[17] Fu L-Y (符利勇), Tang S-Z (唐守正), Zhang H-R (张会儒), et al. Generalized above-ground biomass equations for two main species in northeast China. Acta Ecologica Sinica (生态学报), 2015, 35(1): 150-157 (in Chinese)
[18] Jia W-W (贾炜玮), Li F-R (李凤日), Dong L-H (董利虎), et al. Carbon density and storage for Pinus sylvestris var. mongolica plantation based on compatible biomass models. Journal of Beijing Forestry University (北京林业大学学报), 2012, 34(1): 6-13 (in Chinese)
[19] Yang G-T (杨国亭), Li F-R (李凤日), Yin T (殷彤), et al. Distribution and Dynamics of Forest Carbon Storage in Heilongjiang Province. Harbin: Northeast Forestry University Press, 2016(in Chinese)
[20] Chen D-S (陈东升). The Optimal Management Mode of Large and Medium Diameter Timber in Larch Plantation. Harbin: Northeast Forestry University Press, 2010 (in Chinese)
[21] Tang S-Z (唐守正), Lang K-J (朗奎建), Li H-K (李海奎). Statistics and Computation of Biomathematical Models. Beijing: Science Press, 2009 (in Chinese)
[22] Hui S-R (惠淑荣), Liu Q (刘强), Zhang G-W (张国伟). The application of Reineke density index in research of Japanese larch natural sparse model. Journal of Shenyang Agricultural University (沈阳农业大学学报), 1999, 30(5): 520-522 (in Chinese)
[23] Tang S-Z (唐守正), Li Y (李勇). Statistical Foundation for Biomathematical Models. Beijing: Science Press, 2002 (in Chinese)
[24] Xu H (胥辉). Studies on Standing Tree Biomass Models and the Corresponding Parameter Estimation. Beijing: Beijing Forestry University Press, 1998 (in Chinese)
[25] Zianis D, Mencuccini M. On simplifying allometric analyses of forest biomass. Forest Ecology and Management, 2004, 187: 311-332