TLSLeAF: automatic leaf angle estimates from single‐scan terrestrial laser scanning. Issue 4 (8th July 2021)

• Record Type: Journal Article
• Authors: Stovall, Atticus E. L.; Masters, Benjamin; Fatoyinbo, Lola; Yang, Xi
• Is Part Of: New phytologist. Volume 232:Issue 4(2021)
• Page Start: 1876
• Page End: 1892
• Publication Date: 2021-07-08
• Subjects: 3D -- forest -- leaf angle distribution (LAD) -- light detection and ranging (LiDAR) -- radiative transfer model -- terrestrial laser scanning (TLS) -- trees; Botany -- Periodicals; 580
• DOI: 10.1111/nph.17548
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: 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 TLSLe AF (Terrestrial Laser Scanning Leaf Angle Function), an automated open‐source method of deriving LADs from terrestrial laser scanning. TLSLe AF 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. TLSLe AF 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 to c . 25% and c . 11% increases in canopy net photosynthesis ( c . 25%) and solar‐induced chlorophyll fluorescence ( c . 11%). TLSLe AF 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.