R package for Airborne LiDAR Data Manipulation and Visualization for Forestry Applications
The lidR package provides functions to read and write .las
and .laz
files, plot point clouds, compute metrics using an area-based approach, compute digital canopy models, thin lidar data, manage a catalog of datasets, automatically extract ground inventories, process a set of tiles using multicore processing, individual tree segmentation, classify data from geographic data, and provides other tools to manipulate LiDAR data in a research and development context.
📖 Read the book and the wiki pages to get started with the lidR package.
To cite the package use citation()
from within R:
citation("lidR")
#> Roussel, J.R., Auty, D., Coops, N. C., Tompalski, P., Goodbody, T. R. H., Sánchez Meador, A., Bourdon, J.F., De Boissieu, F., Achim, A. (2020). lidR : An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sensing of Environment, 251 (August), 112061. <doi:10.1016/j.rse.2020.112061>.
#> Jean-Romain Roussel and David Auty (2021). Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. R package version 3.1.0. https://cran.r-project.org/package=lidR
In R-fashion style the function plot
, based on rgl
, enables the user to display, rotate and zoom a point cloud. Because rgl
has limited capabilities with respect to large datasets, we also made a package lidRviewer with greater display capabilities.
las <- readLAS("<file.las>")
plot(las)
lidR
has several algorithms from the literature to compute canopy height models either point-to-raster based or triangulation based. This allows testing and comparison of some methods that rely on a CHM, such as individual tree segmentation or the computation of a canopy roughness index.
las <- readLAS("<file.las>")
# Khosravipour et al. pitfree algorithm
thr <- c(0,2,5,10,15)
edg <- c(0, 1.5)
chm <- grid_canopy(las, 1, pitfree(thr, edg))
plot(chm)
lidR
enables the user to manage, use and process a catalog of las
files. The function catalog
builds a LAScatalog
object from a folder. The function plot
displays this catalog on an interactive map using the mapview
package (if installed).
ctg <- readLAScatalog("<folder/>")
plot(ctg, map = TRUE)
From a LAScatalog
object the user can (for example) extract some regions of interest (ROI) with clip_roi()
. Using a catalog for the extraction of the ROI guarantees fast and memory-efficient clipping. LAScatalog
objects allow many other manipulations that can be done with multicore processing, where possible.
The segment_trees()
function has several algorithms from the literature for individual tree segmentation, based either on the digital canopy model or on the point-cloud. Each algorithm has been coded from the source article to be as close as possible to what was written in the peer-reviewed papers. Our goal is to make published algorithms usable, testable and comparable.
las <- readLAS("<file.las>")
las <- segment_trees(las, li2012())
col <- random.colors(200)
plot(las, color = "treeID", colorPalette = col)
Most of the lidR functions can process seamlessly a set of tiles and return a continuous output. Users can create their own methods using the LAScatalog
processing engine via the catalog_apply()
function. Among other features the engine takes advantage of point indexation with lax files, takes care of processing tiles with a buffer and allows for processing big files that do not fit in memory.
# Load a LAScatalog instead of a LAS file
ctg <- readLAScatalog("<path/to/folder/>")
# Process it like a LAS file
chm <- grid_canopy(ctg, 2, p2r())
col <- random.colors(50)
plot(chm, col = col)
lidR
has many other tools and is a continuously improved package. If it does not exist in lidR
please ask us for a new feature, and depending on the feasibility we will be glad to implement your requested feature.
lidR is developed openly at Laval University.
- Development of the
lidR
package between 2015 and 2018 was made possible thanks to the financial support of the AWARE project (NSERC CRDPJ 462973-14); grantee Prof Nicholas Coops. - Development of the
lidR
package between 2018 and 2020 was made possible thanks to the financial support of the Ministère des Forêts, de la Faune et des Parcs of Québec.
# The latest released version from CRAN with
install.packages("lidR")
# The latest stable development version from github with
remotes::install_github("Jean-Romain/lidR")
To install the package from github make sure you have a working development environment.
- Windows: Install Rtools.exe.
- Mac: Install
Xcode
from the Mac App Store. - Linux: Install the following libraries:
# Ubuntu
sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install libgdal-dev libgeos++-dev libudunits2-dev libproj-dev libx11-dev libgl1-mesa-dev libglu1-mesa-dev libfreetype6-dev libnode-dev libxt-dev libfftw3-dev
# Fedora
sudo dnf install gdal-devel geos-devel udunits2-devel proj-devel mesa-libGL-devel mesa-libGLU-devel freetype-devel libjpeg-turbo-devel v8-devel