TLSL e AF: automatic leaf angle estimates from single‐scan terrestrial laser scanning
TLSL e AF: automatic leaf angle estimates from single‐scan terrestrial laser scanning
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TLSL e AF：通过单扫描地面激光扫描自动估计叶角
DOI：
10.1111/nph.17548
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发表时间：
2021
期刊：
影响因子：
9.4
通讯作者：
Yang, Xi
中科院分区：
文献类型：
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作者：
Stovall, Atticus E. L.;Masters, Benjamin;Fatoyinbo, Lola;Yang, Xi
森林冠层的叶角分布（LAD）影响叶面积、光截获和全球尺度光合作用的估计，但通常被简化为单一理论值。在这里，我们提出了 TLSLeAF（地面激光扫描叶角函数），这是一种从地面激光扫描导出 LAD 的自动化开源方法。TLSLeAF 依靠网格激光扫描数据生成冠层尺度的叶角和 LAD。该方法提高了处理速度，改进了角度估计，并且需要最少的用户输入。主要功能包括自动化、叶木分类、β 参数输出以及在 R 中的实现，以提高生态社区的可访问性。TLSLeAF 精确估计叶角，对角度估计的距离影响最小，同时在消费级机器上快速生成 LAD。我们挑战了流行的球形 LAD 假设，显示了植物面积指数和转化为 toc 的叶子剖面估计中生态系统类型的敏感性。 25% 等。 冠层净光合作用 (c. 25%) 和太阳诱导的叶绿素荧光 (c. 11%) 增加了 11%。TLSLeAF 现在可以应用于全球生态系统中已有的大量激光扫描数据。易用性将使该方法能够在遥感专家之外得到广泛采用，从而更容易地解决生态假设和大规模生态系统建模工作。
Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global‐scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open‐source method of deriving LADs from terrestrial laser scanning.TLSLeAF produces canopy‐scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf–wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community.TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer‐grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate toc. 25% andc. 11% increases in canopy net photosynthesis (c. 25%) and solar‐induced chlorophyll fluorescence (c. 11%).TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote‐sensing experts, allowing greater accessibility for addressing ecological hypotheses and large‐scale ecosystem modeling efforts.