Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR (Light Detection and Ranging), small-footprint full-waveform airborne LiDAR (FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates. A range of voxel sizes (from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxel-based LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest (RF) regression method. The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies (R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m (R2 = 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement (33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.

中文翻译：

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使用全波形机载LiDAR数据的体素大小对森林冠层高度估计的影响

林冠层高度是关键的森林结构参数。准确估算森林冠层高度对于改善森林管理和生态模型至关重要。与离散返回LiDAR（光检测和测距）相比，小尺寸全波形机载LiDAR（FWL）技术具有获取精确的森林结构信息的能力。这项研究主要集中在体素大小对森林冠层高度估计的影响上。测试了一系列体素大小（从10.0 m到40.0 m，间隔为2 m），以获取不同体素大小的森林冠层高度的估计精度。在这项研究中，将体素大小内的所有波形汇总为基于体素的LiDAR波形，并使用基于体素的LiDAR波形计算一系列波形指标。然后，我们通过基于随机体（RF）回归方法的基于体素的波形指标，建立了林冠高度估计模型。结果表明，基于体素的方法可以使用FWL数据可靠地估算森林冠层高度。另外，体素大小对森林冠层高度的估计精度（R2范围从0.625到0.832）有重要影响。但是，在这项研究中，R2值不会随体素大小的增加而单调增加或减少。当体素大小为18 m（R2 = 0.832，RMSE = 2.57 m，RMSE％= 20.6％）时，可以产生最佳估计精度。与最低的估计精度相比，使用最佳体素大小时，R2值有显着提高（33.1％）。最后，通过最佳体素尺寸，我们使用RF回归模型制作了该研究区的森林冠层高度分布图。我们的发现表明，需要确定最佳体素大小，以使用小尺寸FWL数据来提高森林参数的估计准确性。