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基于地面激光扫描的新树高异速生长关系揭示了热带雨林中与森林清查方法存在显著差异。

New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests.

机构信息

Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium.

ISOFYS - Isotope Bioscience Laboratory, Department of Green Chemistry and Technology, Ghent University, Ghent, Belgium.

出版信息

Glob Chang Biol. 2024 Aug;30(8):e17473. doi: 10.1111/gcb.17473.

Abstract

Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) and tree height (H). However, these databases often combine H measurements obtained through various measurement methods, each with distinct error patterns, affecting the resulting H:D allometries. In recent decades, terrestrial laser scanning (TLS) has emerged as a widely accepted method for accurate, non-destructive tree structural measurements. This study used TLS data to evaluate the prediction accuracy of forest inventory-based H:D allometries and to develop more accurate pantropical allometries. We considered 19 tropical rainforest plots across four continents. Eleven plots had forest inventory and RIEGL VZ-400(i) TLS-based D and H data, allowing accuracy assessment of local forest inventory-based H:D allometries. Additionally, TLS-based data from 1951 trees from all 19 plots were used to create new pantropical H:D allometries for tropical rainforests. Our findings reveal that in most plots, forest inventory-based H:D allometries underestimated H compared with TLS-based allometries. For 30-metre-tall trees, these underestimations varied from -1.6 m (-5.3%) to -7.5 m (-25.4%). In the Malaysian plot with trees reaching up to 77 m in height, the underestimation was as much as -31.7 m (-41.3%). We propose a TLS-based pantropical H:D allometry, incorporating maximum climatological water deficit for site effects, with a mean uncertainty of 19.1% and a mean bias of -4.8%. While the mean uncertainty is roughly 2.3% greater than that of the Chave2014 model, this model demonstrates more consistent uncertainties across tree size and delivers less biased estimates of H (with a reduction of 8.23%). In summary, recognizing the errors in H measurements from forest inventory methods is vital, as they can propagate into the allometries they inform. This study underscores the potential of TLS for accurate H and D measurements in tropical rainforests, essential for refining tree allometries.

摘要

树木的生长方程模型对于监测和预测陆地碳储量至关重要,传统上是基于包含树木胸径(D)和树高(H)测量数据的全球数据库来建立的。然而,这些数据库通常将通过各种测量方法获得的 H 测量值进行组合,每种方法都有不同的误差模式,从而影响最终的 H:D 生长方程。近几十年来,地面激光扫描(TLS)作为一种准确、非破坏性的树木结构测量方法已经得到广泛认可。本研究利用 TLS 数据来评估基于森林清查的 H:D 生长方程的预测精度,并建立更准确的泛热带生长方程。我们考虑了四大洲的 19 个热带雨林样地。其中 11 个样地具有森林清查和 RIEGL VZ-400(i)基于 TLS 的 D 和 H 数据,这使得可以对基于本地森林清查的 H:D 生长方程的精度进行评估。此外,还利用来自所有 19 个样地的 1951 棵树的基于 TLS 的数据,为热带雨林创建了新的泛热带 H:D 生长方程。研究结果表明,在大多数样地中,基于森林清查的 H:D 生长方程低估了与基于 TLS 的生长方程相比的 H 值。对于 30 米高的树木,这些低估值的范围从-1.6 米(-5.3%)到-7.5 米(-25.4%)。在马来西亚的样地中,树木的高度高达 77 米,低估值高达-31.7 米(-41.3%)。我们提出了一种基于 TLS 的泛热带 H:D 生长方程,该方程将站点的最大气候水分亏缺纳入其中,平均不确定性为 19.1%,平均偏差为-4.8%。虽然平均不确定性比 Chave2014 模型高 2.3%,但该模型在树木大小范围内的不确定性更为一致,并且对 H 的估计偏差更小(减少了 8.23%)。总之,认识到森林清查方法中 H 测量值的误差非常重要,因为这些误差会传播到它们所提供的生长方程中。本研究强调了 TLS 在热带雨林中准确测量 H 和 D 的潜力,这对于改进树木生长方程至关重要。

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