R package for Airborne LiDAR Data Manipulation and Visualization for Forestry Applications
The lidR package provides functions to read and write
.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.
To cite the package use
citation() from within R:
Read and display a las file
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 PointCloudViewer with greater display capabilities.
las <- readLAS("<file.las>") plot(las)
Compute a canopy height model
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)
Read and display a catalog of las files
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)
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.
Individual tree segmentation
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)
Wall-to-wall dataset processing
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
lidRpackage 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
lidRpackage 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
Xcodefrom 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