LiDAR data acquired from airplanes and helicopters – known as airborne laser scanning (ALS) – are widely regarded as the gold standard for characterizing the 3D structure of forests at scale. But in the last decade, advances in unoccupied aerial vehicle (UAV) technologies have led to a rapid rise in the use of UAV laser scanning (ULS) for mapping forest structure. As both ALS and ULS data become increasingly available, they are being used to derive an ever‐growing number of metrics designed to measure different facets of canopy structure. However, which metrics can be robustly retrieved from both ALS and ULS platforms remains unclear. To address this question, we acquired coincident, high‐density ALS and ULS scans covering 115 plots (4‐ha in size) in an open‐canopy temperate ecosystem in Western Australia. Using this unique dataset, we quantified 32 canopy structural metrics related to canopy height, openness and heterogeneity, including metrics calculated directly from the point clouds and ones measured from derived canopy height models (CHM). Overall, we found that ALS and ULS‐derived metrics were strongly correlated (r2 = 0.90). However, this high degree of correlation masked considerable systematic differences between platforms. Specifically, point cloud metrics were less strongly (r2 = 0.87) correlated and had higher bias (10.7%) compared to CHM‐derived ones (r2 = 0.98; bias = 2.5%). Similarly, metrics of canopy openness and heterogeneity were less strongly correlated (r2 = 0.84 and 0.87) and exhibited greater bias (14.4 and 7.9%) than ones relating to canopy height (r2 = 0.96; bias = 3.8%). Our results indicate that only a small subset of the 32 metrics we tested were directly comparable between ALS and ULS platforms. Consequently, future efforts to combine laser scanning data across platforms and instruments should think carefully about which metrics are most appropriate, especially when working with point cloud data.

中文翻译：

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使用多平台机载激光雷达稳健检索森林冠层结构属性


从飞机和直升机获取的 LiDAR 数据（称为机载激光扫描 (ALS)）被广泛认为是大规模表征森林 3D 结构的黄金标准。但在过去十年中，无人飞行器 (UAV) 技术的进步导致使用无人机激光扫描 (ULS) 绘制森林结构图的情况迅速增加。随着 ALS 和 ULS 数据变得越来越可用，它们被用来导出越来越多的指标，这些指标旨在测量冠层结构的不同方面。然而，哪些指标可以从 ALS 和 ULS 平台中稳健地检索仍不清楚。为了解决这个问题，我们获得了覆盖西澳大利亚开放冠层温带生态系统中 115 个地块（面积为 4 公顷）的重合高密度 ALS 和 ULS 扫描。使用这个独特的数据集，我们量化了与冠层高度、开放性和异质性相关的 32 个冠层结构指标，包括直接从点云计算的指标和从导出的冠层高度模型 (CHM) 测量的指标。总体而言，我们发现 ALS 和 ULS 衍生指标密切相关 (r2 = 0.90)。然而，这种高度的相关性掩盖了平台之间相当大的系统差异。具体来说，与 CHM 衍生的指标（r2 = 0.98；偏差 = 2.5%）相比，点云指标的相关性较弱（r2 = 0.87），且偏差较高（10.7%）。同样，与冠层高度相关的指标（r2 = 0.96；偏差 = 3.8%）相比，冠层开放度和异质性指标的相关性较弱（r2 = 0.84 和 0.87），并且表现出更大的偏差（14.4 和 7.9%）。我们的结果表明，我们测试的 32 个指标中只有一小部分在 ALS 和 ULS 平台之间具有直接可比性。 因此，未来跨平台和仪器结合激光扫描数据的努力应该仔细考虑哪些指标最合适，特别是在处理点云数据时。