法国NFI用LiDAR HD数据训练的FLORA模型，单模型跨季节预测森林属性，精度超过季节专用模型。
法国国家森林调查局（NFI）利用32,052个样地数据与LiDAR HD项目，训练FLORA深度学习框架。FLORA可从异构LiDAR点云预测六种森林属性：优势木高度、总材积、落叶木材积、针叶木材积、胸高断面积和树干密度。模型采用八叉树主干结合生态和时空辅助变量，通过晚融合门控机制整合。单模型在落叶和常绿两种季节数据上均优于季节专用模型，优势木高度rRMSE为12.3%（R2=0.88），总材积rRMSE为39%（R2=0.74）。
FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
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.