考虑随机效应的长白落叶松立木生物量模型构建及精度分析

doi:10.13287/j.1001-9332.202302.004

应用生态学报 ›› 2023, Vol. 34 ›› Issue (2): 333-341.doi: 10.13287/j.1001-9332.202302.004

考虑随机效应的长白落叶松立木生物量模型构建及精度分析

高羽¹, 谢龙飞², 郝元朔¹, 董利虎^1*

¹东北林业大学林学院, 森林生态系统可持续经营教育部重点实验室, 哈尔滨 150040;
²北华大学林学院, 吉林吉林 132013

收稿日期:2022-11-10 接受日期:2022-12-27 出版日期:2023-02-15 发布日期:2023-08-15
通讯作者: *E-mail: lihudong@nefu.edu.cn
作者简介:高羽, 女, 1996年生, 硕士研究生。主要从事生物量模型构建研究。E-mail: gaoyu12020@163.com
基金资助:
国家重点研发计划项目(2022YFD2201000)和国家自然科学基金项目(31971649)

Construction and precision analysis of individual tree biomass model of Larix olgensis considering random effects

GAO Yu¹, XIE Longfei², HAO Yuanshuo¹, DONG Lihu^1*

¹Ministry of Education Key Laboratory of Sustainable Forest Ecosystem Management, School of Forestry, Northeast Forestry University, Harbin 150040, China;
²School of Forestry, Beihua University, Jilin 132013, Jilin, China

Received:2022-11-10 Accepted:2022-12-27 Online:2023-02-15 Published:2023-08-15

摘要/Abstract

摘要： 准确评估我国森林生物量对研究全球陆地生态系统碳循环和碳储量控制机制具有重要意义。本研究基于黑龙江省376株长白落叶松生物量数据,采用似乎不相关回归(SUR)方法构建以胸径为自变量的一元生物量SUR模型,并在SUR模型的基础上考虑样地水平随机效应,进而构建似乎不相关混合效应(SURM)模型。根据SURM模型中随机效应的计算不需要全部因变量的实测值的特点,分析以下4类SURM模型在预测生物量时的偏差: 1) SURM1,随机效应根据干、枝、叶生物量实测值计算;2) SURM2,随机效应根据树高实测值计算;3) SURM3,随机效应根据冠长实测值计算;4) SURM4,随机效应根据树高和冠长实测值计算。结果表明: 考虑样地水平随机效应后,枝和叶生物量模型拟合效果改善较为明显,R²均提高20%以上,干和根生物量模型拟合效果改善较小,R²分别提高4.8%和1.7%。当使用随机抽取的5棵树计算样地水平随机效应时,SURM模型的预测表现要优于SUR模型和仅考虑固定效应的SURM模型,尤其是SURM1模型(干、枝、叶和根的平均绝对误差百分比分别为10.4%、29.7%、32.1%和19.5%)。除SURM1模型外,使用SURM4模型预测干、枝、叶和根生物量的偏差小于SURM2和SURM3模型。在实际预测中,SURM1模型的预测精度虽然最高,但需要实测若干株树木地上生物量,使用成本相对较高。因此,本研究推荐使用树高和冠长实测值的SURM4模型预测长白落叶松立木生物量。

关键词: 长白落叶松, 生物量, 似乎不相关混合效应模型, 随机效应

Abstract: Accurate estimation of forest biomass in China is crucial for the study of carbon cycle and mechanisms underlying carbon storage in global terrestrial ecosystems. Based on the biomass data of 376 individuals of Larix olgensis in Heilongjiang Province, we used seemingly unrelated regression (SUR) method to build a univariate biomass SUR model with diameter at breast height as the independent variable and considering the random effect at the sampling site level. Then, a seemingly unrelated mixed effect (SURM) model was constructed. As the calculation of random effects of SURM model did not require the empirically measured values of all dependent variables, we analyzed the deviations from the following four types in detail: 1) SURM1, the random effect was calculated according to the measured biomass of stem, branch and foliage; 2) SURM2, the random effect was calculated according to the measured value of tree height (H); 3) SURM3, the random effect was calculated according to the measured crown length (CL); 4) SURM4, the random effect was calculated according to the measured values of H and CL. The results showed that the fitting effect of branch and foliage biomass models was improved significantly after considering the horizontal random effect of the sampling plot, with R² being increased by more than 20%. The fitting effect of stem and root biomass models were improved slightly, with R² being increased by 4.8% and 1.7%, respectively. When using five randomly selected trees to calculate the horizontal random effect of the sampling plot, the prediction performance of SURM model was better than that of SUR model and SURM model considering only fixed effects, especially SURM1 model (MAPE% of stem, branch, foliage and root was 10.4%, 29.7%, 32.1% and 19.5%, respectively). Except for SURM1 model, the deviation of SURM4 in predicting stem, branch, foliage and root biomass was smaller than that of SURM2 and SURM3 models. In actual prediction, although the prediction accuracy of SURM1 model was the highest, it needed to measure aboveground biomass of several trees, and the use cost was relatively high. Therefore, the SURM4 modelled on measured H and CL was recommended to predict the standing tree biomass of L. olgensis.

Key words: Larix olgensis, biomass, seemingly unrelated mixed effect model, random effect

高羽, 谢龙飞, 郝元朔, 董利虎. 考虑随机效应的长白落叶松立木生物量模型构建及精度分析[J]. 应用生态学报, 2023, 34(2): 333-341.

GAO Yu, XIE Longfei, HAO Yuanshuo, DONG Lihu. Construction and precision analysis of individual tree biomass model of Larix olgensis considering random effects[J]. Chinese Journal of Applied Ecology, 2023, 34(2): 333-341.

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考虑随机效应的长白落叶松立木生物量模型构建及精度分析

Construction and precision analysis of individual tree biomass model of Larix olgensis considering random effects

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