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Quantifying vegetation and canopy structural complexity from terrestrial Li DAR data using the forestr r package
Author(s) -
Atkins Jeff W.,
Bohrer Gil,
Fahey Robert T.,
Hardiman Brady S.,
Morin Timothy H.,
Stovall Atticus E. L.,
Zimmerman Naupaka,
Gough Christopher M.
Publication year - 2018
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13061
Subject(s) - canopy , structural complexity , remote sensing , tree canopy , environmental science , computer science , ecology , geography , biology , artificial intelligence
Terrestrial Li DAR (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 Li DAR data. The r package forestr is an analysis toolbox that works with portable canopy Li DAR ( PCL ) data and other pixelated/voxelized point clouds derived from terrestrial Li DAR scanning ( TLS ) data to calculate CSC metrics of interest to ecologists, modellers, forest managers, and remote sensing scientists.