Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories. We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees. Unlike object reconstruction methods, our approach is based on simple metrics computed on vertical slices that summarize information on point distances, angles, and geometric attributes of the space between and around the points. Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans. We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios. Our approach provides a simple, clear, and scalable solution that can be adapted to different situations both for research and more operational mapping.

中文翻译：

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从密集的机载 LiDAR 点云估算木材质量属性

从高密度激光雷达点云中绘制单棵树木的质量参数是改善森林清单的重要一步。我们提出了一种基于机器学习的新颖工作流程，该工作流程使用无人机激光扫描的单个树木点云来预测直立树木的木材质量指标。与对象重建方法不同，我们的方法基于在垂直切片上计算的简单度量，这些度量总结了有关点距离、角度以及点之间和周围空间的几何属性的信息。我们的模型使用这些切片指标作为预测因子，并通过测量级无人机激光扫描来预测每个原木最大分支的直径 (DLB) 和不同高度的茎直径 (DS)，从而实现高精度。我们表明，当对通过减少点数或模拟次优单树分割场景生成的数据的次优版本进行测试时，我们的模型也是稳健和准确的。我们的方法提供了一个简单、清晰且可扩展的解决方案，可以适应不同的情况，用于研究和更具操作性的绘图。