【摘要】 【目的】评估将单木特异性状加入幼龄湿地松人工林异速生长方程对模型性能的影响，并构建适用于湿地松地上生物量估测的幂函数模型，以实现生物量的精准、快速与高效预测。【方法】对4年生湿地松人工林的170棵样本，采用全收获法测定地上部分各器官生物量并分析其分配特征，使用不同生长因子作为预测自变量，构建湿地松地上部分、主干、分枝和针叶的幂函数生物量模型，并验证其准确性。【结果】在基于不同距地高度的树干直径构建的湿地松各器官生物量模型中，拟合效果排序为胸径（DBH）>地径>距地1.0 m树干直径>距地1.5 m树干直径；基于最优生长因子实测树高（H）、DBH和木材密度（ρ）构建的三元生物量（W）模型（W=aDBH~bH~cρ~d,a、b、c、d为系数）对地上部分和主干生物量的预测效果较优（R~2分别为0.864和0.839;RMSE分别为1.107和0.541）；基于无人机估测的树高（H_e）、无人机提取的冠幅面积（A_c）和DBH这3种最优生长因子构建的三元模型W=aDBH~bH_e~cA_c~d，对分枝和针叶生物量的预测效果较优（R~2分别为0.670和0.778;RMSE分别为0.410和0.536）。【结论】引入单木特异性状的异速生长方程能够显著提高估测幼年湿地松人工林生物量的精度，基于最优生长因子构建的三元生物量模型可为浙江地区4年生湿地松人工幼林生物量的快速、准确估算提供可靠工具。更多还原

【Abstract】 【 Objective】 This study aims to assess the impact of incorporating individual tree-specific traits into allometric growth equations for young Pinus elliottii(slash pine) plantations on model performance, and to develop a power-type biomass model suitable for estimating aboveground biomass(AGB) of the young slash pine, achieving precise, rapid, and efficient biomass prediction.【Method】A total of 170 sample trees from a 4-year-old slash pine plantation were selected, and the AGB of various organs was determined through complete harvest method to analyze biomass allocation patterns. Different growth factors were used as predictive independent variables to construct power-function-based biomass models for total AGB, stems, branches, and needles. The performance of these models was then comprehensively evaluated.【Result】Among the biomass models based on stem diameters measured at different heights above ground, the fitting performance followed the order: diameter at breast height(DBH) > ground diameter > diameter at 1.0 m height > diameter at 1.5 m height. The ternary biomass model(W=aDBH~bH~cρ~d,where a, b, c, and d are coefficients), which incorporated the optimal growth factors of measured tree height(H), DBH, and wood density(ρ) as predictive variables, achieved the highest accuracy for estimating aboveground and stem biomass(R~2 = 0.864 and0.839, and RMSE = 1.107 and 0.541, respectively). In contrast, the model W=aDBH~bH_e~cA_c~d, incorporating UAV-estimated tree height(H_e), UAV-derived crown projection area(A_c), and DBH, provided the most accurate estimates for branch and needle biomass. The model achieved high accuracy, with R~2 values of 0.670 and 0.778, and RMSE values of 0.410 and 0.536 for branch and needle biomass, respectively.【 Conclusion】 Incorporating tree-specific traits into allometric equations can significantly enhance the accuracy of biomass estimation in young slash pine plantations. The ternary biomass model constructed based on optimal growth factors is a reliable tool for rapid and accurate assessment of biomass in 4-year-old slash pine plantations in Zhejiang Province.更多还原