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lidR

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.

:book: 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

Key features

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 lidRviewer 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)

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.

Individual tree segmentation

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)

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)

Other tools

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.

About

lidR is developed openly at Laval University.

Install lidR

# 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

Changelog

See changelogs on NEW.md

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Version

Install

install.packages('lidR')

Monthly Downloads

3,838

Version

3.1.1

License

GPL-3

Issues

Pull Requests

Stars

Forks

Last Published

January 27th, 2021

Functions in lidR (3.1.1)

asprs

ASPRS LAS Classification
classify_noise

Classify points as 'noise'
LAD

Leaf area density
csf

Ground Segmentation Algorithm
grid_canopy

Digital Surface Model
clip

Clip points in regions of interest
las_check

Inspect a LAS object
las_utilities

LAS utilities
pitfree

Digital Surface Model Algorithm
grid_metrics

Area-Based Approach
grid_terrain

Digital Terrain Model
plot

Plot a LAS* object
$<-,LAS-method

Inherited but modified methods from sp
is

A set of boolean tests on objects
ivf

Noise Segmentation Algorithm
grid_density

Map the pulse or point density
rumple_index

Rumple index of roughness
lidR-parallelism

Parallel computation in lidR
segment_snags

Snag classification
segment_shapes

Estimation of the shape of the points neighborhood
lidR-package

lidR: airborne LiDAR for forestry applications
segment_trees

Individual tree segmentation
set.colors

Automatic colorization
LAS-class

An S4 class to represent a .las or .laz file
LAScatalog-class

An S4 class to represent a catalog of .las or .laz files
as.list.LASheader

Transform to a list
as.spatial

Transform a LAS* object into an sp object
add_attribute

Add attributes into a LAS object
voxel_metrics

Voxelize the space and compute metrics for each voxel
delineate_crowns

Compute the hull of each tree.
filters

Predefined point of interest filters
deprecated

Deprecated functions in lidR
voxelize_points

Voxelize a point cloud
LASheader-class

An S4 class to represent the header of .las or .laz files
LASheader

Create a LASheader object
area

Surface covered by a LAS* object
catalog_makechunks

Subdivide a LAScatalog into chunks
catalog_intersect

Subset a LAScatalog with a Spatial* object
classify_ground

Classify points as 'ground'
find_localmaxima

Local Maximum Filter
catalog_select

Select LAS files manually from a LAScatalog
li2012

Individual Tree Segmentation Algorithm
lidR-LAScatalog-drivers

LAScatalog drivers
kriging

Spatial Interpolation Algorithm
filter_duplicates

Filter duplicated points
lidrpalettes

Palettes
knnidw

Spatial Interpolation Algorithm
extent,LAS-method

Extent
lidR-spatial-index

Spatial index
filter_poi

Filter points of interest with matching conditions
dalponte2016

Individual Tree Segmentation Algorithm
decimate_points

Decimate a LAS object
filter_surfacepoints

Filter the surface points
merge_spatial

Merge a point cloud with a source of spatial data
plot.lasmetrics3d

Plot voxelized LiDAR data
retrieve_pulses

Retrieve individual pulses, flightlines or scanlines
manual

Individual Tree Detection Algorithm
lmf

Individual Tree Detection Algorithm
homogenize

Point Cloud Decimation Algorithm
normalize_height

Remove the topography from a point cloud
readLAS

Read .las or .laz files
sor

Noise Segmentation Algorithm
rbind.LAS

Merge LAS objects
Roussel2020

Sensor tracking algorithm
normalize_intensity

Normalize intensity
stdmetrics

Predefined standard metrics functions
plot_3d

Add a spatial object to a point cloud scene
writeLAS

Write a .las or .laz file
p2r

Digital Surface Model Algorithm
readLASheader

Read a .las or .laz file header
readLAScatalog

Create an object of class LAScatalog
print

Summary and Print for LAS* objects
silva2016

Individual Tree Segmentation Algorithm
projection

Get or set the projection of a LAS* object
catalog_retile

Retile a LAScatalog
VCI

Vertical Complexity Index
catalog_options_tools

Get or set LAScatalog processing engine options
dsmtin

Digital Surface Model Algorithm
entropy

Normalized Shannon diversity index
gap_fraction_profile

Gap fraction profile
find_trees

Individual tree detection
smooth_height

Smooth a point cloud
tree_metrics

Compute metrics for each tree
hexbin_metrics

Area-Based Approach in hexagonal cells.
watershed

Individual Tree Segmentation Algorithm
pmf

Ground Segmentation Algorithm
point_metrics

Point-based metrics
highest

Point Cloud Decimation Algorithm
wing2015

Snags Segmentation Algorithm
util_makeZhangParam

Parameters for progressive morphological filter
range_correction

Intensity normalization algorithm
shape_detection

Algorithms for shape detection of the local point neighborhood
set_lidr_threads

Set or get number of threads that lidR should use
random

Point Cloud Decimation Algorithm
tin

Spatial Interpolation Algorithm
track_sensor

Reconstruct the trajectory of the LiDAR sensor using multiple returns
Gatziolis2019

Sensor tracking algorithm
catalog_apply

LAScatalog processing engine
cloud_metrics

Compute metrics for a cloud of points