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.

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.

Content

1. Key features
2. Some examples
3. Install lidR
4. Changelog

Key features

• Read and write .las and .laz files
• Plot 3D LiDAR data
• Area-based approach using any set of metrics
• Individual tree segmentation
• Classify and clip data from geographic shapefiles
• Manage a catalog of tiles
• Automatically extract a set of ground plot inventories
• Analyse a full set of tiles in parallel computing
• Compute a digital canopy model (DCM)
• Compute a digital terrain model (DTM)
• Normalize a point cloud by substracting a DTM

Some examples

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 capabilites.

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 (grid_canopy) or triangulation based (grid_tincanopy). 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
th = c(0,2,5,10,15)
edge = c(0, 1.5)
chm = grid_tincanopy(las, thresholds = th, max_edge = edge)

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.

ctg = catalog("<folder/>")
ctg@crs = sp::CRS("+proj=utm +zone=17")

# CRS set: will be displayed on an interactive map
plot(ctg)

From a LAScatalog object the user can (for example) extract some regions of interest (ROI) with lasclip or catalog_queries. Using a catalog for the extraction of the ROI guarantees fast and memory-efficient clipping. LAScatalog objects allow many other manipulations that are usually done with multicore processing, where possible.

Individual tree segmentation

The lastrees 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-reviwed papers. Our goal is to make published algorithms usable, testable and comparable.

las = readLAS("<file.las>")

lastrees(las, algorithm = "li2012")

col = random.colors(200)
plot(las, color = "treeID", colorPalette = col)

Other tools

lidR has many other tools and is a continuouly improved package. If it does not exist in lidR please ask us for a new feature, and depending on the feasability we will be glad to implement your requested feature.

Install lidR

• The latest released version from CRAN with

install.packages("lidR")

• The latest stable development version from github with

devtools::install_github("Jean-Romain/rlas", dependencies=TRUE)
devtools::install_github("Jean-Romain/lidR", dependencies=TRUE)

• The latest unstable development version from github with

devtools::install_github("Jean-Romain/rlas", dependencies=TRUE, ref="devel")
devtools::install_github("Jean-Romain/lidR", dependencies=TRUE, ref="devel")

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 R development package, usually called r-devel or r-base-dev

Changelog

See changelogs on NEW.md