lidR provides a set of tools to manipulate airborne LiDAR data in forestry contexts. The package works essentially with .las or .laz files. The toolbox includes algorithms for DSM, CHM, DTM, ABA, normalisation, tree detection, tree segmentation and other tools, as well as an engine to process wide LiDAR coverages split into many files.
lidR.progressSeveral functions have a progress bar for long operations (but not all). Should lengthy operations show a progress bar? Default: TRUE
lidR.progress.delayThe progress bar appears only for long operations. After how many seconds of computation does the progress bar appear? Default: 2
lidR.verboseMake the package verbose. Default: FALSE
lidR.buildVRTThe functions grid_* can write the rasters sequentially on the
disk and load back a virtual raster mosaic (VRT) instead of the list of written files. Should
a VRT be built? Default: TRUE
lidR.check.nested.parallelismThe catalog processing engine (catalog_apply) checks the parallel strategy chosen by the user and verify if C++ parallelization with OpenMP should be disabled to avoid nested parallel loops. Default: TRUE. If FALSE the catalog processing engine will not check for nested parallelism and will respect the settings of set_lidr_threads.
To learn more about lidR, start with the vignettes: browseVignettes(package = "lidR"). Users can also
find unofficial supplementary documentation in the lidR book.
To ask "how to" questions please ask on gis.stackexchange.com
with the tag lidr.
Useful links: