从离散参考数据转向连续参考数据：基于定量结构模型(QSM)的水平树木生物量分布用于深度学习生物量估算
Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation
AI总结：
针对小地块生物量估算的边界效应误差问题，该研究采用QSM生成的连续水平生物量分布训练稀疏3D U-Net，相比传统方法显著提升小地块上的估算精度。
摘要：
传统基于激光雷达(LiDAR)的地上生物量(AGB)估算建模方法依赖离散的样地级清查聚合数据，该方法会引入边界效应不确定性，可能严重降低小地块内的模型性能。为解决这一局限，本文评估了从定量结构模型(QSM)连续映射得到的水平生物量分布(HBD)参考数据。我们在模拟的阔叶林结构上训练稀疏3D U-Net，使用三类AGB参考数据：标准森林清查(FI)样地级聚合数据、无边界效应的QSM样地级聚合数据，以及连续HBD映射数据。对从100到2500 $m^2$的不同训练样地尺寸进行评估后发现，基于QSM的模型在小尺寸样地上的表现系统性优于FI方法。具体而言，针对100 $m^2$的样地，HBD参考数据相对FI基线将相对均方根误差(RRMSE)降低了16.84 $\pm$ 4.37 %，并将$R^2$提升了0.22 $\pm$ 0.05。该方法通过将样地级聚合数据替换为HBD作为AGB参考，校正了边界效应，证明使用基于HBD的参考数据可提升小尺寸样地场景下的模型性能。
英文摘要：
Conventional modeling approaches for LiDAR-based above-ground biomass (AGB) estimation rely on discrete plot-level inventory aggregates. This methodology introduces boundary-effect uncertainties that may severely degrade model performance within small field plots. To solve this limitation, we evaluate a Horizontal Biomass Distribution (HBD) reference mapped continuously from Quantitative Structure Models (QSMs). We trained a sparse 3D U-Net on simulated broadleaved forest structures using three AGB reference types: a standard forest inventory (FI) plot-level aggregate, an edge-effect-free QSM plot-level aggregate, and a continuous HBD mapping. Evaluating training plot sizes scaling from 100 to 2500 $m^2$ , QSM-based models systematically outperformed FI approaches at small plot sizes. Specifically, for 100 $m^2$ plots, the HBD reference reduced the relative root mean square error (RRMSE) by 16.84 $\pm$ 4.37 % and increased $R^2$ by 0.22 $\pm$ 0.05 against the FI baseline. By replacing plot level aggregates with HBDs as AGB reference, this methodology corrects for edge-effects and shows that using an HBD-based reference enhances model performance for small plot sizes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
