Stand-level biomass estimation for Korean pine plantations based on four additive methods in Heilongjiang province, northeast China

ABSTRACT Background: Korean pine ( Pinus koraiensis Siebold & Zucc.) is one of the primary plantation conifer species of economic and ecological importance in northeast China. Forest biomass estimation in the broader landscape has been receiving attention from researchers and forest managers. The development of forest stand biomass models is regarded an effective method to estimate forest biomass at large scales. This study was carried out for developing stand-level biomass models for Korean pine plantations. Four additive methods were compared: Aggregation 1, Aggregation 2, Adjustment, and Disaggregation. All the stand biomass additive modeling systems (i.e., total, root, stem, branch, and leaf) included both stand volume and biomass conversion and expansion factors (BCEFs) as predictors. Results: The predictive performance of the four additive methods and Constant BCEFs were ranked as follows: Aggregation 1 > Disaggregation > Adjustment > Aggregation 2 > Constant BCEFs. The prediction accuracy of the four additive methods was not consistent across the stand volume intervals. Conclusion: The model based on the Aggregation 1 method was recommended for predicting stand biomass. However, different additive method should be selected according to the stand volume intervals of the Korean pine plantations.

摘要

背景：红松（Pinus koraiensis Siebold & Zucc.）是中国东北兼具经济与生态价值的主要人工针叶树种之一。大尺度森林生物量估算始终受到研究者与森林管理者的广泛关注。林分生物量模型的构建被视为大尺度森林生物量估算的有效途径。本研究旨在构建红松人工林的林分尺度生物量模型，共对比了4种可加性建模方法：聚合1法（Aggregation 1）、聚合2法（Aggregation 2）、调整法（Adjustment）与分解法（Disaggregation）。所有林分生物量可加性建模系统（涵盖总生物量、根系生物量、茎干生物量、枝条生物量与叶片生物量）均以林分材积和生物量转换与扩展因子（BCEFs）作为预测变量。

结果：4种可加性建模方法与恒定BCEFs法的预测性能排序为：聚合1法 > 分解法 > 调整法 > 聚合2法 > 恒定BCEFs法。且4种可加性建模方法的预测精度在不同林分材积区间内并不一致。

结论：本研究推荐采用聚合1法构建的模型开展林分生物量预测，但需根据红松人工林的林分材积区间选择适配的可加性建模方法。