用深度学习统一处理多种条件下的激光雷达数据，精准预测森林属性。
FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
- 基于八叉树结构融合多源数据，实现异构激光雷达点云的统一建模
- 单模型跨季节预测，主导高程误差仅12.3%，总蓄积量误差39%
- 适合需要大范围森林资源监测的政府机构与生态研究者
森林属性对全国尺度资源监测至关重要。机载激光雷达指标是国家森林清查（NFI）估算中与森林属性相关性最强的辅助变量之一。然而，当激光雷达数据采集条件不一致时，实现全域预测仍具挑战。随着欧洲各国激光雷达计划扩展，传感器、飞行参数、季节和扫描角度的差异限制了现有模型的鲁棒性，这些模型通常针对局部条件校准。本文提出FLORA（Forest LiDAR Octree Regression with Auxiliary Data），一种深度学习框架，可从异构激光雷达点云中预测六项森林属性：主导高程、总蓄积量、阔叶蓄积量、针叶蓄积量、断面积和株数密度。FLORA结合八叉树主干网络与生态及时空辅助变量，通过后期融合门控机制进行整合。模型在法国本土32,052个国家森林清查样地上的法国高精度激光雷达计划数据上训练与评估。一个在叶绿期与落叶期数据上联合训练的单一模型优于分季节模型，显著提升跨季节鲁棒性。辅助变量整体增益有限，但在物种特异性蓄积量预测中贡献更显著。FLORA对主导高程的rRMSE约为12.3%（R² = 0.88），总蓄积量为39%（R² = 0.74），为来自异构国家激光雷达计划的大规模森林属性估算提供了稳健基线。
原文摘要 · Abstract (English)
Forest attributes are essential for national-scale resource monitoring. Airborne LiDAR metrics are among the auxiliary variables most strongly correlated with forest attributes used in National Forest Inventory (NFI) estimates. However, producing wall-to-wall predictions remains challenging when LiDAR data are acquired under heterogeneous conditions. As national LiDAR programs expand across Europe, variability in sensors, flight parameters, seasons, and scan angles limits the robustness of existing models, which are often calibrated for local conditions. We present FLORA (Forest LiDAR Octree Regression with Auxiliary Data), a deep learning framework that predicts six forest attributes: dominant height, total volume, deciduous volume, coniferous volume, basal area, and stem density from heterogeneous LiDAR point clouds. FLORA combines an octree-based backbone with ecological and spatiotemporal auxiliary variables through a late-fusion gating mechanism. Models are trained and evaluated on 32,052 National Forest Inventory plots across mainland France using data from the French LiDAR HD program. A single model trained on both leaf-on and leaf-off acquisitions outperforms season-specific models and improves cross-season robustness. Auxiliary variables provide modest overall gains but contribute more strongly to species-specific volume prediction. FLORA achieves an rRMSE of about 12.3% (R2 = 0.88) for dominant height and 39% (R2 = 0.74) for total volume, providing a robust baseline for large-scale forest attribute estimation from heterogeneous national LiDAR programs.
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