Example data for 'lacunr: Efficient 3-D lacunarity for voxelized LiDAR data from forested ecosystems'

This repository contains data and code for reproducing the results of the worked example in Smeds et al. 2025 "lacunr: Efficient 3-D lacunarity for voxelized LiDAR data from forested ecosystems", published in Methods in Ecology and Evolution (https://doi.org/10.1111/2041-210X.70126). The worked example analyzes terrestrial LiDAR scans of two forest stands at the Saddle Mountain Open Space Preserve in Sonoma County, California, which were burned in the 2020 Glass Fire. The first of these scans, Plot 1, in included in the 'lacunr' software package as an example dataset, while the second plot is archived here. The included R script allows users to reproduce the lacunarity curves displayed in Figure 2 and Figure S1 of Smeds et al. The deposited zip archive, lacunr_example.zip, can be decompressed into a corresponding folder with the following contents: data/ - a data folder which contains two height-normalized terrestrial LiDAR point clouds in .laz format: c6_tls_p6_prefire.laz - 24*24m forest stand before wildfire c10_tls_p6_postfire.laz - the same forest stand after wildfire lacunr_example_data.R - R script for replicating the lacunarity curves presented in the publication lacunr_example.Rproj - the R project file which allows users to open the R script in a self-contained environment. This file should be opened in RStudio prior to executing the R script output/ - the folder where external image files are exported by the R script README.txt - a text file containing instructions for opening the R project and running the associated code