The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. We develop an algorithm based on cloth simulation for constructing a pit-free CHM. The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

中文翻译：

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基于机载LiDAR数据的基于布料模拟的无坑顶篷高度模型构建

LiDAR派生的树冠高度模型（CHM）中普遍分布的随机分布的黑洞（即树冠内出现数据坑）会对提取的森林资源参数的准确性产生负面影响。我们开发了一种基于布料模拟的算法来构建无坑的CHM。所提出的算法有效地填充了各种大小的数据坑，同时保留了树冠细节。我们从参考云在不同比例的数据坑中得到的无坑CHM明显优于使用其他算法构造的无坑CHM，这是由参考CHM与所构造的无坑CHM之间的最低均方根误差（0.4981 m）所证明的。 。此外，就最大树高估计（平均偏差= 0.9674 m）而言，我们的无坑式CHM表现出最佳的总体性能。