lidR-package: lidR: airborne LiDAR for forestry applications
In lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications
lidR-package R Documentation
lidR: airborne LiDAR for forestry applications
Description
lidR provides a set of tools to manipulate airborne LiDAR data in forestry contexts. The package
works with .las or .laz files. The toolbox includes algorithms for DSM, CHM, DTM, ABA,
normalisation, tree detection, tree segmentation, tree delineation, colourization, validation and
other tools, as well as a processing engine to process broad LiDAR coverage split into many files.
Details
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.
Package options
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.raster.defaultThe functions that return a raster are raster agnostic meaning
that they work either with rasters from packages 'raster', 'stars' or 'terra'. By default they return
rasters from 'stars'. Can be one of "raster", "stars" or "terra". Default: "terra"
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.
Author(s)
Maintainer: Jean-Romain Roussel jean-romain.roussel.1@ulaval.ca [copyright holder]
Authors:
David Auty (Reviews the documentation) [contributor]
Other contributors:
Florian De Boissieu (Fixed bugs and improved catalog features) [contributor]
Andrew Sánchez Meador (Implemented wing2015() for segment_snags()) [contributor]
Bourdon Jean-François (Contributed to Roussel2020() for track_sensor()) [contributor]
Gatziolis Demetrios (Implemented Gatziolis2019() for track_sensor()) [contributor]
Leon Steinmeier (Contributed to parallelization management) [contributor]
Stanislaw Adaszewski (Author of the C++ concaveman code) [copyright holder]
Benoît St-Onge (Author of the 'chm_prep' function) [copyright holder]
See Also
Useful links:
lidR documentation built on Sept. 11, 2024, 5:21 p.m.
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| lidR-package | R Documentation |
lidR: airborne LiDAR for forestry applications
Description
lidR provides a set of tools to manipulate airborne LiDAR data in forestry contexts. The package works with .las or .laz files. The toolbox includes algorithms for DSM, CHM, DTM, ABA, normalisation, tree detection, tree segmentation, tree delineation, colourization, validation and other tools, as well as a processing engine to process broad LiDAR coverage split into many files.
Details
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.
Package options
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.raster.defaultThe functions that return a raster are raster agnostic meaning that they work either with rasters from packages 'raster', 'stars' or 'terra'. By default they return rasters from 'stars'. Can be one of "raster", "stars" or "terra". Default: "terra"
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.
Author(s)
Maintainer: Jean-Romain Roussel jean-romain.roussel.1@ulaval.ca [copyright holder]
Authors:
David Auty (Reviews the documentation) [contributor]
Other contributors:
Florian De Boissieu (Fixed bugs and improved catalog features) [contributor]
Andrew Sánchez Meador (Implemented wing2015() for segment_snags()) [contributor]
Bourdon Jean-François (Contributed to Roussel2020() for track_sensor()) [contributor]
Gatziolis Demetrios (Implemented Gatziolis2019() for track_sensor()) [contributor]
Leon Steinmeier (Contributed to parallelization management) [contributor]
Stanislaw Adaszewski (Author of the C++ concaveman code) [copyright holder]
Benoît St-Onge (Author of the 'chm_prep' function) [copyright holder]
See Also
Useful links:
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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