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lidR

Airborne LiDAR data manipulation and visualisation for forestry application

https://github.com/r-lidar/lidr

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    Found .zenodo.json file
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    DOI references
    Found 1 DOI reference(s) in README
  • ○
    Academic publication links
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    Committers with academic emails
    2 of 18 committers (11.1%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (18.6%) to scientific vocabulary

Keywords

als forestry las laz lidar point-cloud r remote-sensing
Last synced: 6 months ago · JSON representation

Repository

Airborne LiDAR data manipulation and visualisation for forestry application

Basic Info
Statistics
  • Stars: 644
  • Watchers: 49
  • Forks: 137
  • Open Issues: 11
  • Releases: 40
Topics
als forestry las laz lidar point-cloud r remote-sensing
Created about 10 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Funding License

README.md

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 collection of LAS/LAZ files, automatically extract ground inventories, process a collection of tiles using multicore processing, segment individual trees, classify points from geographic data, and provides other tools to manipulate LiDAR data in a research and development context.

  • 📖 Read the book to get started with the lidR package.
  • 💻 Install lidR from R with: install.packages("lidR")
  • 💵 Sponsor lidR. It is free and open source, but requires time and effort to develop and maintain.

lidR has been cited by more than 1,000 scientific papers. To cite the package use citation() from within R:

```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. (2021). 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 (2023). Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. R package version 3.1.0. https://cran.r-project.org/package=lidR

```

You may also be interested by our new lasR package.

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.

r 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.

```r las <- readLAS("")

Khosravipour et al. pitfree algorithm

thr <- c(0,2,5,10,15) edg <- c(0, 1.5) chm <- rasterize_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 collection of las files. The function readLAScatalog builds a LAScatalog object from a folder. The function plot displays this collection on an interactive map using the mapview package (if installed).

r 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.


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.

```r las <- readLAS("")

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 seamlessly process 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.

```r

Load a LAScatalog instead of a LAS file

ctg <- readLAScatalog("")

Process it like a LAS file

chm <- rasterize_canopy(ctg, 2, p2r()) col <- random.colors(50) plot(chm, col = col) ```

Full waveform

lidR can read full waveform data from LAS files and provides interpreter functions to convert the raw data into something easier to manage and display in R. The support of FWF is still in the early stages of development.

```r fwf <- readLAS("")

Interpret the waveform into something easier to manage

las <- interpret_waveform(fwf)

Display discrete points and waveforms

x <- plot(fwf, colorPalette = "red", bg = "white") plot(las, color = "Amplitude", add = x) ```

About

lidR is developed openly by r-lidar.

The development of lidR was made possible through the financial support of Laval University, the AWARE project and Ministry of Natural Ressources and Forests of Québec. To continue the development of this free software, we now offer consulting, programming, and training services. For more information, please visit our website.

Install dependencies on GNU/Linux

```

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 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 ```

Owner

  • Name: R lidar
  • Login: r-lidar
  • Kind: organization

R + lidar

GitHub Events

Total
  • Issues event: 47
  • Watch event: 50
  • Delete event: 1
  • Issue comment event: 79
  • Push event: 19
  • Pull request event: 10
  • Fork event: 11
  • Create event: 1
Last Year
  • Issues event: 47
  • Watch event: 50
  • Delete event: 1
  • Issue comment event: 79
  • Push event: 19
  • Pull request event: 10
  • Fork event: 11
  • Create event: 1

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 2,577
  • Total Committers: 18
  • Avg Commits per committer: 143.167
  • Development Distribution Score (DDS): 0.043
Past Year
  • Commits: 60
  • Committers: 2
  • Avg Commits per committer: 30.0
  • Development Distribution Score (DDS): 0.05
Top Committers
Name Email Commits
Jean-Romain j****1@u****a 2,466
Dave Auty d****y@g****m 55
Florian de Boissieu f****s@g****m 22
Andrew Sánchez Meador a****r@n****u 12
Duncan Murdoch m****n@g****m 3
frank2165 m****0@g****m 3
Quinn Bowers q****n@r****m 3
bw4sz b****0@g****m 2
Jean-François Bourdon 3****n 2
David Auty d****y@l****n 1
Vijay v****a@i****u 1
Leon Steinmeier l****s@g****e 1
Markus Neteler n****r@g****m 1
Michael Koontz m****z@g****m 1
Piotr Tompalski p****i@g****m 1
Roger Bivand r****d@n****o 1
Walter Somerville w****m@g****m 1
cjber 4****r 1
Committer Domains (Top 20 + Academic)
nhh.no: 1 gmx.de: 1 iupui.edu: 1 localhost.localdomain: 1 robotics88.com: 1 nau.edu: 1 ulaval.ca: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 598
  • Total pull requests: 69
  • Average time to close issues: 17 days
  • Average time to close pull requests: 8 days
  • Total issue authors: 235
  • Total pull request authors: 22
  • Average comments per issue: 3.95
  • Average comments per pull request: 2.94
  • Merged pull requests: 47
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 33
  • Pull requests: 8
  • Average time to close issues: 18 days
  • Average time to close pull requests: 12 days
  • Issue authors: 24
  • Pull request authors: 3
  • Average comments per issue: 1.52
  • Average comments per pull request: 1.63
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Jean-Romain (99)
  • spono (34)
  • lucas-johnson (20)
  • wiesehahn (19)
  • jmmonnet (11)
  • Saadi4469 (11)
  • ptompalski (9)
  • ouroukhai (9)
  • bi0m3trics (8)
  • floriandeboissieu (8)
  • karnayogendra (7)
  • bw4sz (7)
  • komazsofi (7)
  • mzeybek583 (6)
  • rs806 (6)
Pull Request Authors
  • floriandeboissieu (21)
  • Jean-Romain (10)
  • MarcFletcher-HQP (4)
  • quinn-r88 (4)
  • ptompalski (3)
  • dmurdoch (3)
  • mikoontz (3)
  • jfbourdon (2)
  • bw4sz (2)
  • bi0m3trics (2)
  • Lenostatos (2)
  • jstrunk001 (2)
  • ppoyk (2)
  • rsbivand (1)
  • ergincankaya (1)
Top Labels
Issue Labels
Question (162) Bug (141) Enhancement (74) Feature request (65) Wontfix (41) Documentation (32) Not lidR (23) CRAN (14) No anwser (11) Duplicate (9) Rejected request (8) v4 beta (8) Segfault (7) Windows (4) rlas (4) Devel (1)
Pull Request Labels
Bug (3) Enhancement (2)

Packages

  • Total packages: 4
  • Total downloads:
    • cran 4,052 last-month
  • Total docker downloads: 990,939
  • Total dependent packages: 10
    (may contain duplicates)
  • Total dependent repositories: 23
    (may contain duplicates)
  • Total versions: 131
  • Total maintainers: 2
proxy.golang.org: github.com/r-lidar/lidr
  • Versions: 39
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.5%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 6 months ago
proxy.golang.org: github.com/r-lidar/lidR
  • Versions: 39
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.5%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 6 months ago
cran.r-project.org: lidR

Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

  • Versions: 52
  • Dependent Packages: 10
  • Dependent Repositories: 23
  • Downloads: 4,052 Last month
  • Docker Downloads: 990,939
Rankings
Forks count: 0.5%
Stargazers count: 0.7%
Dependent repos count: 5.8%
Dependent packages count: 5.9%
Average: 6.3%
Downloads: 6.8%
Docker downloads count: 18.1%
Maintainers (1)
jean-romain.roussel.1@ulaval.ca
Last synced: 6 months ago
spack.io: r-lidr

Airborne LiDAR data manipulation and visualisation for forestry application

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Average: 28.1%
Dependent packages count: 56.1%
Maintainers (1)
sidpbury
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • methods * depends
  • Rcpp >= 1.0.3 imports
  • classInt * imports
  • data.table >= 1.12.0 imports
  • glue * imports
  • grDevices * imports
  • lazyeval * imports
  • raster * imports
  • rgl * imports
  • rlas >= 1.5.0 imports
  • sf * imports
  • sp * imports
  • stars * imports
  • stats * imports
  • terra >= 1.5 imports
  • tools * imports
  • utils * imports
  • EBImage * suggests
  • RCSF * suggests
  • RMCC * suggests
  • future * suggests
  • geometry * suggests
  • gstat * suggests
  • knitr * suggests
  • mapedit * suggests
  • mapview * suggests
  • progress * suggests
  • rgdal * suggests
  • rmarkdown * suggests
  • testthat >= 2.1.0 suggests
.github/workflows/R-CMD-check.yaml actions
  • actions/cache v1 composite
  • actions/checkout v3 composite
  • actions/upload-artifact main composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite