Abstract Accurate digital elevation models are key data products used to inform forest management. Light detection and ranging (lidar) technologies have emerged as a useful tool for acquiring detailed terrain information, although the accuracy of this data is known to vary with topographic complexity and the density and characteristics of overlying vegetation. Single Photon Lidar (SPL) provides a high-density point cloud that can be acquired from a much higher altitude than discrete return, small-footprint lidar (hereafter, linear-mode lidar or LML), providing efficiencies and potential cost savings for operational mapping programs. Herein, we assess the absolute and relative accuracies of leaf-on and leaf-off SPL data acquired at different altitudes for characterizing terrain under varying vegetation types and densities and compare to results for LML data. Our assessment was forest-focused and primarily point based, using 299 Real-Time Kinematic survey checkpoints to quantify elevation errors (Δh); however, we also investigated and reported accuracy for linear transects, and conducted a wall-to-wall comparison of the SPL-derived 1-m digital elevation models (DEMs) against an LML-derived DEM. Point cloud characteristics for the leaf-on 2018 SPL data were markedly different, with 88% of returns as first returns, compared to 17% for the LML, and 59% and 46% for the leaf-off SPL data acquired at 3800 m and 2000 m, respectively. Of the datasets considered herein, the SPL data acquired under leaf-on conditions in 2018 had the lowest accuracy and precision for characterizing terrain underneath vegetation cover, with an RMSE of 10.97 cm and a 95th quantile of 24.03 cm; however these values are within commonly accepted error limits for elevation products. The leaf-off SPL data were most accurate overall; however, the differences between the leaf-off SPL data acquired at 3800 m versus 2000 m were often minor (

中文翻译：

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评估单光子激光雷达在一系列森林条件下进行地形表征的能力

摘要 准确的数字高程模型是用于告知森林管理的关键数据产品。光探测和测距（激光雷达）技术已成为获取详细地形信息的有用工具，尽管已知该数据的准确性会随地形复杂性以及上覆植被的密度和特征而变化。单光子激光雷达 (SPL) 提供了一个高密度点云，可以从比离散回波、小尺寸激光雷达（以下称为线性模式激光雷达或 LML）高得多的高度获取，从而为操作映射提供效率和潜在的成本节约程式。在此处，我们评估了在不同高度获取的叶上和叶下 SPL 数据的绝对和相对精度，以表征不同植被类型和密度下的地形，并与 LML 数据的结果进行比较。我们的评估以森林为重点，主要基于点，使用 299 个实时动态测量检查点来量化高程误差 (Δh)；然而，我们还调查并报告了线性断面的准确性，并对 SPL 派生的 1 米数字高程模型 (DEM) 与 LML 派生的 DEM 进行了墙到墙比较。2018 年叶上 SPL 数据的点云特征显着不同，88% 的回报为首次回报，而 LML 为 17%，而在 3800 米和 3800 米处采集的叶上 SPL 数据分别为 59% 和 46%。分别为 2000 m。在此处考虑的数据集中，2018 年在叶上条件下获得的 SPL 数据在表征植被覆盖下的地形方面的准确度和精度最低，RMSE 为 10.97 cm，第 95 分位数为 24.03 cm；然而，这些值在高程产品普遍接受的误差范围内。叶式 SPL 数据总体上最准确；然而，在 3800 m 和 2000 m 处获得的叶外 SPL 数据之间的差异通常很小（