Quantifying vegetation and canopy structural complexity from terrestrial LiDAR data using the forestr r package. Issue 10 (14th August 2018)

• Record Type: Journal Article
• Authors: Atkins, Jeff W.; Bohrer, Gil; Fahey, Robert T.; Hardiman, Brady S.; Morin, Timothy H.; Stovall, Atticus E. L.; Zimmerman, Naupaka; Gough, Christopher M.
• Editors: Goslee, Sarah
• Is Part Of: Methods in ecology and evolution. Volume 9:Issue 10(2018)
• Page Start: 2057
• Page End: 2066
• Publication Date: 2018-08-14
• Subjects: canopy complexity -- ecosystem -- forestry -- LiDAR -- r package -- structure -- structure‐function -- terrestrial ecology; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13061
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: Terrestrial LiDAR (light detection and ranging) technologies have created new means of quantifying forest canopy structure, allowing not only the estimation of biomass, but also descriptions of the position and variability in canopy elements in space. Such measures provide novel structural information broadly useful to ecologists. There is a growing need for both a detailed taxonomy of forest canopy structural complexity (CSC) and open, transparent, and flexible tools to quantify complexity in ways that will advance foundational ecological knowledge of structure‐function relationships. The CSC taxonomy we present groups structural descriptors into five categories: leaf area and density, canopy height, canopy arrangement, canopy openness, and canopy variability. This paper also introduces the r package forestr, the first open‐source r package for the calculation of CSC metrics from terrestrial LiDAR data. The r package forestr is an analysis toolbox that works with portable canopy LiDAR (PCL) data and other pixelated/voxelized point clouds derived from terrestrial LiDAR scanning (TLS) data to calculate CSC metrics of interest to ecologists, modellers, forest managers, and remote sensing scientists.