lidR v3.1.1

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Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.

Readme

lidR

license R build status Codecov test coverage

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

Functions in lidR

Name Description
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
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Vignettes of lidR

Name
lidR-LAS-class.Rmd
lidR-LAScatalog-class.Rmd
lidR-LAScatalog-engine.Rmd
lidR-catalog-apply-examples.Rmd
lidR-computation-speed-LAScatalog.Rmd
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Details

Type Package
Date 2021-01-22
URL https://github.com/Jean-Romain/lidR
BugReports https://github.com/Jean-Romain/lidR/issues
License GPL-3
LazyData true
RoxygenNote 7.1.1
LinkingTo BH (>= 1.72.0),Rcpp,RcppArmadillo
Encoding UTF-8
ByteCompile true
VignetteBuilder knitr
biocViews
Collate 'Class-LASheader.R' 'Class-LAS.R' 'Class-LAScatalog.R' 'Class-LAScluster.R' 'RcppExports.R' 'add_attribute.R' 'algorithm-dec.R' 'algorithm-dsm.R' 'algorithm-gnd.R' 'algorithm-itd.R' 'algorithm-its.R' 'algorithm-noi.R' 'algorithm-out.R' 'algorithm-shp.R' 'algorithm-snag.R' 'algorithm-spi.R' 'algorithm-trk.R' 'catalog_apply.R' 'catalog_fakerun.R' 'catalog_index.R' 'catalog_intersect.R' 'catalog_laxindex.R' 'catalog_makecluster.R' 'catalog_merge_results.R' 'catalog_overlaps.R' 'catalog_retile.R' 'catalog_select.R' 'classify_ground.R' 'classify_noise.R' 'clip_roi.R' 'cloud_metrics.R' 'clusters_apply.R' 'decimate_points.R' 'delineate_crowns.R' 'deprecated.R' 'doc-drivers.R' 'doc-lidR.R' 'doc-parallelism.R' 'filter_duplicates.R' 'filter_poi.R' 'filter_roi.R' 'filter_surfacepoints.R' 'find_localmaxima.R' 'find_trees.R' 'generate_las.R' 'grid_canopy.R' 'grid_density.R' 'grid_metrics.R' 'grid_terrain.R' 'hexbin_metrics.R' 'io_readLAS.R' 'io_readLAScatalog.R' 'io_readXLAS.R' 'io_writeANY.R' 'io_writeLAS.R' 'las_check.R' 'las_tools.R' 'merge_las.R' 'merge_spatial.R' 'methods-LAS.R' 'methods-LAScatalog.R' 'methods-LAScluster.R' 'methods-LASheader.R' 'normalize_height.R' 'normalize_intensity.R' 'plot.R' 'plot.s3.R' 'point_metrics.R' 'print.R' 'projection.R' 'retrieve_info.R' 'segment_shapes.R' 'segment_snags.R' 'segment_trees.R' 'sensor_tracking.R' 'smooth_height.R' 'tree_metrics.R' 'utils_assertive.R' 'utils_catalog_options.R' 'utils_colors.R' 'utils_define_constant.R' 'utils_delaunay.R' 'utils_geometry.R' 'utils_is.R' 'utils_metrics.R' 'utils_misc.R' 'utils_raster.R' 'utils_spatial_index.R' 'utils_threads.R' 'utils_typecast.R' 'voxel_metrics.R' 'voxelize_points.R' 'zzz.R'
NeedsCompilation yes
Packaged 2021-01-25 10:45:12 UTC; jr
Repository CRAN
Date/Publication 2021-01-27 11:10:02 UTC

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