基于层次贝叶斯似乎不相关回归的人工长白落叶松生物量模型构建

doi:10.13287/j.1001-9332.202505.006

摘要/Abstract

摘要： 准确估算森林生物量对碳储量评估和森林资源管理具有重要意义,层次贝叶斯法作为一种可以有效提高参数稳定性的统计学方法,在森林生物量精准估算中展现出显著潜力。本研究基于黑龙江省孟家岗林场143株长白落叶松解析木数据,采用层次贝叶斯似乎不相关回归方法,构建了以胸径为自变量的一元似乎不相关混合效应模型(SURM1)和以胸径与树高为自变量的二元似乎不相关混合效应模型(SURM2),对比分析了限制最大似然估计(REML)与无先验信息(Br1)、基于数据自身先验信息(Br2)、基于历史先验信息(Br3)3种层次贝叶斯方法的拟合与预测效果。结果表明: SURM2模型在树干生物量和单木总生物量预测方面显著优于SURM1,平均绝对偏差百分比(MAPE)分别减少了7.8%和7.6%。基于数据自身先验信息的层次贝叶斯法(Br2)在参数估计稳定性方面(标准差为0.003～0.108)显著优于REML(标准差为0.052～0.540)、Br1(标准差为0.033～0.819)和Br3(标准差为0.038～0.771)。使用Br2进行预测时会产生更高的预测精度,SURM1模型在树干、树枝、树叶、树根和总生物量预测的MAPE分别为17.6%、45.1%、48.3%、25.2%、17.1%。与SURM1相比,SURM2模型在树干和总生物量的预测精度显著提升,MAPE分别减小7.3%和6.7%。在样本量较小(<60)时,有效的先验信息可以增加预测的稳定性。基于数据自身先验信息的贝叶斯方法在提高长白落叶松生物量模型预测精度与稳定性方面具有显著优势,为黑龙江地区长白落叶松生物量的精准估算提供了有效支持。

关键词: 生物量, 层次贝叶斯模型, 可加性, 长白落叶松

Abstract: Accurate estimation of forest biomass is of great significance for carbon stock assessment and forest resource management. Hierarchical Bayesian methods, as a statistical approach that can effectively enhance parameter stability, have large potentials in the precise estimation of forest biomass. Based on data from 143 sample trees of Larix olgensis in the Mengjiagang Forest Farm of Heilongjiang Province, we adopted hierarchical Bayesian see-mingly unrelated regression (SUR) to develop a univariate seemingly unrelated mixed-effects model (SURM1) with diameter at breast height (DBH) as the independent variable and a bivariate seemingly unrelated mixed-effects model (SURM2) with DBH and tree height as independent variables. We compared the fitting and predictive performance of restricted maximum likelihood estimation (REML) with three hierarchical Bayesian methods: no prior information (Br1), data-derived prior information (Br2), and historical prior information (Br3). The results showed that the SURM2 model significantly outperforms SURM1 in predicting stem biomass and total individual tree biomass, with mean absolute percentage errors (MAPE) reduced by 7.8% and 7.6%, respectively. The hierarchical Bayesian method utilizing data-derived prior information (Br2) demonstrated notably superior parameter estimation stability (with standard deviations ranging from 0.003 to 0.108) compared to REML (standard deviations from 0.052 to 0.540), Br1 (standard deviations from 0.033 to 0.819), and Br3 (standard deviations from 0.038 to 0.771). Predictions based on Br2 yield superior accuracy, with MAPE for SURM1 model predictions of stem, branch, leaf, root, and total biomass being 17.6%, 45.1%, 48.3%, 25.2%, and 17.1%, respectively. The SURM2 model improved the prediction accuracy for stem biomass and total biomass, reducing MAPE by 7.3% and 6.7%, respectively, compared to SURM1. Furthermore, when sample size was small (fewer than 60), incorporating effective prior information could enhance the stability of predictions. The use of data-derived prior information in the Bayesian method demonstrated significant advantages in improving both the accuracy and stability of biomass predictions for L. olgensis, providing valuable support for the precise estimation of biomass in the Heilongjiang Pro-vince.

Key words: biomass, hierarchical Bayesian model, additivity, Larix olgensis

王鹏飞, 董利虎, 谢龙飞, 苗铮. 基于层次贝叶斯似乎不相关回归的人工长白落叶松生物量模型构建[J]. 应用生态学报, 2025, 36(5): 1298-1308.

WANG Pengfei, DONG Lihu, XIE Longfei, MIAO Zheng. Construction of biomass models for Larix olgensis plantation using hierarchical Bayesian seemingly unrela-ted regression[J]. Chinese Journal of Applied Ecology, 2025, 36(5): 1298-1308.

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