用连续分布替代离散数据，提升小地块生物量估算精度
Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation
- 用定量结构模型生成连续水平生物量分布作为参考
- 100平方米地块上误差降低16.84%，决定系数提升0.22
- 适合小尺度森林生物量建模，尤其改善边界效应问题
基于激光雷达的地上生物量（AGB）估算传统方法依赖离散的样地级调查数据，此类方法在小样地内易受边界效应影响，严重降低模型性能。为解决此问题，本文评估了从定量结构模型（QSM）连续生成的水平生物量分布（HBD）作为参考数据的可行性。在模拟阔叶林结构上，使用三种生物量参考类型训练稀疏3D U-Net：标准森林调查（FI）样地聚合值、无边界效应的QSM样地聚合值、以及连续的HBD映射。在100至2500平方米的样地尺度下评估，基于QSM的方法在小样地表现更优。具体而言，在100平方米样地，采用HBD参考使相对均方根误差（RRMSE）降低16.84 ± 4.37%，决定系数（R²）提升0.22 ± 0.05，显著优于传统FI基线。该方法通过以连续HBD替代样地聚合值，有效纠正边界效应，证明其对小样地生物量估算具有明显优势。
原文摘要 · Abstract (English)
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.
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