This paper presents a non-parametric approach for segmenting trees from
airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the
approach collects crown information such as steepness and height on-the-fly to
delineate crown boundaries, and most importantly, does not require a priori
assumptions of crown shape and size. The approach segments trees iteratively
starting from the tallest within a given area to the smallest until all trees
have been segmented. To evaluate its performance, the approach was applied to
the University of Kentucky Robinson Forest, a deciduous closed-canopy forest
with complex terrain and vegetation conditions. The approach identified 94% of
dominant and co-dominant trees with a false detection rate of 13%. About 62% of
intermediate, overtopped, and dead trees were also detected with a false
detection rate of 15%. The overall segmentation accuracy was 77%. Correlations
of the segmentation scores of the proposed approach with local terrain and
stand metrics was not significant, which is likely an indication of the
robustness of the approach as results are not sensitive to the differences in
terrain and stand structures.

本文提出了一种基于无人机激光雷达数据的无参数分割决策树的方法，该方法可以准确地识别决策树的特征并不需要决策树先验信息。该方法适用于各种地形和植被条件，在测试中正确识别 94% 的优势树和共优势树，正确识别 62% 的中等、淹没和死树，总体分割准确度为 77%。