ABSTRACT

Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R² = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions.

摘要

准确、快速地估计冠层盖度 (CC) 对许多生态和环境模型以及森林管理至关重要。无人机-光探测和测距 (UAV-LiDAR) 系统由于其高机动性、低成本和高点密度而成为一种很有前途的 CC 估计工具。然而，由于存在大量冠内间隙，来自 UAV-LiDAR 点云的 CC 值可能被低估。为了减轻冠内间隙的负面影响，我们提出了一种基于无坑CHM的估计CC的方法，其中使用布料模拟方法来填充冠内间隙。为了评估 CC 值和冠内间隙比例对所提出方法的影响，所提出方法的性能在具有不同 CC 值 (40-70%) 的 18 个样品和具有不同冠内间隙比例 (10-60%) 的 6 个样品上进行了测试。结果表明，所提方法的CC精度高于未填充冠内间隙的方法（R ²  = 0.99 对 0.98；RMSE = 1.49% 对 2.2%）。所提出的方法对冠内间隙比例不敏感，尽管CC精度随着冠内间隙比例的增加而略有下降。