Abstract The ability to quantify understory vegetation structure in forested environments on a broad scale has the potential to greatly improve our understanding of wildlife habitats, nutrient cycling, wildland fire behavior, and wildland firefighter safety. Lidar data can be used to model understory vegetation density, but the accuracy of these models is impacted by factors such as the specific lidar metrics used as independent variables, overstory conditions such as density and height, and lidar pulse density. Few previous studies have examined how these factors affect estimation of understory density. In this study we compare two widely-used lidar-derived metrics, overall relative point density (ORD) and normalized relative point density (NRD) in an understory vertical stratum, for their respective abilities to accurately model understory vegetation density. We also use a bootstrapping analysis to examine how lidar pulse density, overstory vegetation density, and canopy height can affect the ability to characterize understory conditions. In doing so, we present a novel application of an automated field photo-based understory cover estimation technique as reference data for comparison to lidar. Our results highlight that NRD is a far superior metric for characterizing understory density than ORD (R2NRD = 0.44 vs. R2ORD = 0.14). In addition, we found that pulse density had the strongest positive effect on predictive power, suggesting that as pulse density increases, the ability to accurately characterize understory density using lidar increases. Overstory density and canopy height had nearly identical negative effects on predictive power, suggesting that shorter, sparser canopies improve lidar's ability to analyze the understory. Our study highlights important considerations and limitations for future studies attempting to use lidar to quantify understory vegetation structure.

中文翻译：

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使用小足迹机载激光雷达量化林下植被密度

摘要 大规模量化森林环境中林下植被结构的能力有可能大大提高我们对野生动物栖息地、养分循环、野地火灾行为和野地消防员安全的理解。激光雷达数据可用于对林下植被密度进行建模，但这些模型的准确性受到一些因素的影响，例如用作自变量的特定激光雷达指标、密度和高度等林上条件以及激光雷达脉冲密度。以前的研究很少研究这些因素如何影响林下密度的估计。在这项研究中，我们比较了林下垂直地层中两种广泛使用的激光雷达衍生指标，总体相对点密度 (ORD) 和归一化相对点密度 (NRD)，因为它们各自具有准确模拟林下植被密度的能力。我们还使用自举分析来检查激光雷达脉冲密度、上层植被密度和冠层高度如何影响表征下层条件的能力。在此过程中，我们提出了一种基于自动现场照片的林下覆盖估计技术的新应用，作为与激光雷达进行比较的参考数据。我们的结果突出显示，NRD 是表征林下密度远优于 ORD 的指标（R2NRD = 0.44 vs. R2ORD = 0.14）。此外，我们发现脉冲密度对预测能力的正面影响最强，这表明随着脉冲密度的增加，使用激光雷达准确表征林下密度的能力也会增加。覆盖层密度和树冠高度对预测能力的负面影响几乎相同，这表明更短、更稀疏的树冠可以提高激光雷达分析林下层的能力。我们的研究强调了未来尝试使用激光雷达量化林下植被结构的研究的重要考虑因素和局限性。