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.


license

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.
  • Development of the lidR package between 2018 and 2019 was made possible thanks to the financial support of the Ministère des Forêts, de la Faune et des Parcs of Québec.

📖 Read the Wiki pages to get started with the lidR package.

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 <- catalog("<folder/>")
plot(ctg, map = TRUE)

From a LAScatalog object the user can (for example) extract some regions of interest (ROI) with lasclip. 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 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-reviewed papers. Our goal is to make published algorithms usable, testable and comparable.

las <- readLAS("<file.las>")
 
las <- lastrees(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 <- catalog("<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.

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")
devtools::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:
sudo apt-get install libgdal-dev libgeos++-dev libudunits2-dev libproj-dev libx11-dev libgl-dev libglu-dev libfreetype6-dev libv8-3.14-dev libcairo2-dev 

Changelog

See changelogs on NEW.md

News

lidR v2.0.3 (Release date: 2019-05-02)

  • Fix: in li2012() the doc states that If R = 0 all the points are automatically considered as local maxima and the search step is skipped (much faster). This is now true.
  • Fix: in lasmergespatial used with a SpatialPolygonDataFrame when the bounding boxes do not match the full search was performed uselessly. Now the function exits early without searching anything.
  • Fix: #242 on Windows when using multicore options to process a LAScatalog the parameter of the algorithms were not exported to each session.
  • Enhance: internally the function tsearch that searches in a triangulation is 25% faster giving a small speed-up to pitfree() and tin() algorithms.
  • Enhance: in lasmergespatial used with a SpatialPolygonDataFrame the function checks the bounding box of the polygon to speed-up the computation with complex polygons.
  • Doc: add a ?lidR page to the manual.

lidR v2.0.2 (Release date: 2019-03-02)

  • Fix: #222 grid_*() functions return consistently a RasterLayer if there is a single layer. virtual raster mosaic were returned as RasterStack no matter the number of layers.
  • Fix: #223 lasmergespatial() wrongly copied shapefile attributes to each point when the paramter attribute was the name of an attribute of the shapefile.
  • Fix: #225 laspulse(), lasflightline(), lasscanline() were broken since v2.0.0.
  • Fix: #227 When processing a LAScatalog the chunks are better computed. In former version it was possible to have chunks that lie on tile only because of the buffer. These chunks are not build anymore.
  • Fix: #227 When processing a LAScatalog some chunks may belong in a file/tile but when actually reading the points in the file the chunks could be empty with points only in the buffer region. In these case an empty point cloud is returned and the computation is be skipped.
  • Fix: #228 lasmergespatial() and lasclip() loose precision when extracting polygons due to missing digits in the WKT string used to rebuild the polygons at C++ level.

lidR v2.0.1 (Release date: 2019-02-02)

  • Change: the function catalog has been slightly modified in prevision of the release of the package rlas 1.3.0 to preserve future compatibility. This is invisible for the users.
  • New: lasnormalize gained a parameter na.rm = TRUE
  • Fix: an error occurend when plotting a LAScatalog with the option chunk_pattern = TRUE: objet 'ctg' introuvable.
  • Fix: examples in documentation of tin() and knnidw() were inverted.
  • Fix: #213 bug when using option keep_lowest in grid_terrain.
  • Fix: #212 bug when merging big rasters that exeed the memory allowed by the raster package
  • Fix: bug when merging rasters when some of then only have one cell
  • Fix: bug when printing a 0 point LAS object

lidR v2.0.0 (Release date: 2019-01-02)

Why versions > 2.0 are incompatible with versions 1.x.y?

The lidR package versions 1 were mainly built upon "personal R scripts" I wrote 3 years ago. These scripts were written for my own use at a time when the lidR package was much smaller (both in term of code and users). The lidR package became a relatively large framework built on top of an unstructured base so it became impossible to develop it further. Many features and functions were missing because the way lidR was built did not allow them to be written. The new release (lidR version 2) breaks the former code to build a more robust, more consistent and more scalable framework that is intended and expected to continue for years without the need to break anything more in the future.

Old binaries can still be found here for 6 months:

Overview of the main visible changes

lidR as a GIS tool

lidR versions 1 was not a GIS tool. For example, rasterization functions such as grid_metrics() or grid_canopy() returned a data.frame. Tree tops extraction with tree_detection() also returned a data.frame. Tree segmentation with lastrees() accepted RasterLayer or data.frame as input in a very inconsistent way. Moreover, the CRS of the point cloud was useless and never propagated to the outputs because outputs were not spatial objects.

lidR version 2 consistently uses Raster* and Spatial* objects everywhere. Rasterization functions such as grid_metrics() or grid_canopy() return Raster* objects. Tree tops extraction returns SpatialPointDataFrame objects. Tree segmentation methods accept SpatialPointDataFrame objects only in a consistent way across functions. The CRS of the point cloud is always propagated to the outputs. LAS objects are Spatial objects. LAScatalog objects are SpatialPolygonDataFrame objects. In short, lidR version 2 is now a GIS tool that is fully compatible with the R ecosystem.

No longer any update by reference

Several lidR functions used to update objects by reference. In lidR versions 1 the user wrote: lasnormalize(las) instead of las2 <- lasnormalize(las1). This used to make sense in R < 3.1 but now the gain is no longer as relevant because R makes shallow copies instead of deep copies.

To simplfy, let's assume that we have a 1 GB data.frame that stores the point cloud. In R < 3.1 las2 was a copy of las1 i.e. las1 + las2 = 2GB . This is why we made functions that worked by reference that implied no copy at all. This was memory optimized but not common or traditional in R. The question of memory optimization is now less relevant since R >= 3.1. In the previous example las2 is no longer a deep copy of las1, but a shallow copy. Thus lidR now consistently uses the traditional syntax y <- f(x).

Algorithm dispatch

The frame of lidR versions 1 was designed at a time when there were fewer algorithms. The increasing number of algorithms led to inconsistent ways to dispatch algorithms. For example:

  • grid_canopy() implemented one algorithm and a second function grid_tincanopy() was created to implement another algorithm. With two functions the switch was possible by using two different names (algorithms dispatched by names).
  • grid_tincanopy() actually implemented two algorithms in one function. The switch was possible by changing the input parameters in the function (algorithm dispatched by input).
  • lastrees() had several variants that provided access to several algorithms: lastrees_li(), lastrees_dalpontes(), lastrees_watershed(), and so on. With several functions the switch was possible by using several different names (algorithms dispatched by names).
  • tree_detection did not have several variants, thus it was impossible to introduce a new algorithm (no dispatch at all).

lidR version 2 comes with a flexible and scalable dispatch method that unifies all the former functions. For example, grid_canopy() is the only function to make a CHM. There is no longer the need for a second function grid_tincanopy(). grid_canopy() unifies the two functions by accepting as input an algorithm for a digital surface model:

chm = grid_canopy(las, res = 1, algo = pitfree())
chm = grid_canopy(las, res = 1, algo = p2r(0.2))

The same idea drives several other functions including lastrees, lassnags, tree_detection, grid_terrain, lasnormalize, and so on. Examples:

ttops = tree_detection(las, algo = lmf(5))
ttops = tree_detection(las, algo = lidRplugins::multichm(1,2))
lastrees(las, algo = li2012(1.5, 2))
lastrees(las, algo = watershed(chm))
lasnormalize(las, algo = tin())
lasnormalize(las, algo = knnidw(k = 10))

This allows lidR to be extended with new algorithms without any restriction either in lidR or even from third-party tools. Also, how lidR functions are used is now more consistent across the package.

LAScatalog processing engine

lidR versions 1 was designed to run algorithms on medium-sized point clouds loaded in memory but not to run algorithms over a set of files covering wide areas. In addition, lidR 1 had a poorly and inconsistently designed engine to process catalogs of las files. For example:

  • It was possible to extract a polygon of points from a LAScatalog but not multipart-polygons or polygons with holes. This was only possible with LAS objects i.e loaded in memory (inconsistent behaviors within a function).
  • It was possible to run grid_metrics() on a LAScatalog i.e. over a wide area not loaded in memory, but not lasnormalize, lasground or tree_detection (inconsistent behavior across the functions).

lidR version 2 comes with a powerful and scalable catalog processing engine. Almost all the lidR functions can be used seamlessly with either LAS or LAScatalog objects. The following chunks of code are now possible:

ctg = catalog("folfer/to/las/file")
opt_output_file(ctg) <- "folder/to/normalized/las/files/{ORIGINALFILENAME}_normalized"
new_ctg = lasnormalize(ctg, algo = tin())

Complete description of visible changes

LAS class

  • Change: the LAS class is now a Spatial object or, more technically, it inherits a Spatial object.
  • Change: being a Spatial object, a LAS object no longer has a @crs slot. It has now a slot @proj4string that is accessible with the functions raster::projection or sp::proj4string
  • New: being a Spatial object, a LAS object inherits multiple functions from raster and sp, such $ and [[ accessors or raster::extent, sp::bbox, raster::projection, and so on. However, the replacement method $<-, [[<- have restricted capabilities to ensure a LAS object cannot be modified in a way that implies loosening the properties of the LAS specifications.
  • New: empty LAS objects with 0 points are now allowed. This has repercussions for several functions including lasfilter, lasclip, and readLAS that do not return NULL for empty data but a LAS object with 0 points. This new behavior has been introduced to fix the old inconsistent behavior of functions that return either LAS or NULL objects. LAS objects are always returned.

LAScatalog class

  • Change: the LAScatalog class is now a SpatialPolygonsDataFrame or, more technically, it inherits a SpatialPolygonsDataFrame.
  • Change: being a SpatialPolygonsDataFrame object, a LAScatalog no longer has a @crs slot. It has now a slot @proj4string that is accessible with the functions raster::projection or sp::proj4string.
  • Change: being a SpatialPolygonsDataFrame a LAScatalog can be plotted with sp::spplot().
  • Change: there are no longer any slots @cores, @by_file, @buffer, and so on. They are replaced by more generic and scalable slots @processing_options, @output_options, @clustering_options and @input_options that are list of options classified by their main roles.
  • Change: documentation has been entirely rewritten to explain the whole potential of the class.
  • Change: functions by_file, progress, tiling_size, buffer were replaced by opt_chunk_size, opt_chunk_buffer, opt_progress, and so on. These allow for a consistent set of functions that do not overlap with functions from raster or sp.
  • Change: standard column names were renamed to make syntactically-valid names and for compatibility with sp functions.

readLAS

  • Change: readLAS no longer supports option PFC. Users must use the functions laspulse, lasflightlines manually.

lasclip

  • New: lasclip now works both with a LAS object and a LAScatalog object in a seamless and consistent way. There are no longer any differences between the capabilities of the LAS version or the LAScatalog one.
  • New: lasclip support many geometries including multipart polygons and polygons with holes, both with a LAS object and a LAScatalog object.
  • Change: The option inside has been removed for consistency because it cannot be safely supported both on LAS and LAScatalog.
  • Change: The option ofile has been removed for consistency and this option in now managed by the LAScatalog processing engine. For example, one can extract ground inventories and write them in laz files automatically named after their center coordinates like this:
ctg = catalog(folder)
output_files(ctg) <- "path/to/a/file_{XCENTER}_{YCENTER}"
laz_compression(ctg) <- TRUE
new_ctg = lasclipCircle(ctg, xc,yc, r)
  • Change: documentation has been reviewed and extended
  • Change: lasclip does not return NULL anymore for empty queries but an empty LAS object.
  • Fix: lasclipRectangle returns the same output both with a LAS and a LAScatalog. With a LAS the rectangle is now closed on the bottom and the left and open on the right and the top.

catalog_queries

  • Change: catalog_queries has been removed because it is superseded by lasclip.

lasnormalize

  • Change: lasnormalize() no longer updates the original object by reference.
  • Change: remove the old option copy = TRUE that is now meaningless.
  • Change: lasnormalize() now relies on lidR algorithms dispatch (see also the main new features above).
  • New: lasnormalize() can be applied on a LAScatalog to write a new normalized catalog using the catalog processing engine (see also the main new features above).

lasclassify

  • Change: lasclassify() is now named lasmergespatial() to free the name lasclassify that should be reserved for other usage.
  • Change: lasmergespatial() no longer updates the original object by reference.
  • Fix: the classification, when made with a RasterLayer, preserves the data type of the RasterLayer. This also fixes the fact that lastrees() used to classify the tree with double instead of int.

tree_detection

  • Change: tree_detection() now relies on the new dispatch method (see also the main new features above).
  • New: algorithm lmf has user-defined variable-sized search windows and two possible search window shapes (square or disc).
  • New: introduction of the manual algorithm for manual correction of tree detection.
  • New: tree_detection algorithms are seamlessly useable with a LAScatalog object by using the catalog processing engine (see also the main new features above). Thus, the following just works:
ctg  <- catalog(folder)
ttop <- tree_detection(ctg, lmf(5))
  • Change: the lmf algorithm, when used with a RasterLayer as input, expects parameters given in the units of the map and no longer in pixels.
  • Change: tree_detection() function consistently returns a SpatialPointsDataFrame whatever the algorithm.
  • Change: tree_detection() function based on a CHM no longer support a lasmetric object as input. Anyway, this class no longer exists.

tree_metrics

  • Change: tree_metrics() returns a SpatialPointsDataFrame.
  • Change: tree_metrics() is seamlessly useable with a LAScatalog using the catalog processing engine (see also the main new features above). Thus, this just works if the las file has extra bytes attributes that store the tree ids:
ctg <- catalog(folder)
metrics <- tree_metrics(ctg, list(`Mean I` = mean(Intensity)))

lastrees

  • Change: lastrees() now relies on the new algorithms dispatch method (see also the main new features above).
  • New: introduction of the mcwatershed algorithm that implements a marker-controlled watershed.

grid_metrics

  • Change: grid_metrics() as well as other grid_* functions consistently return a RasterLayer or a RasterBrick instead of a data.table.
  • Change: option splitlines has been removed. grid_metrics() used to return a data.table because of the splitlines option and lidR was built on top of that feature from the very beginning. Now lidR consistently usessp and raster and this option is no longer supported.

grid_terrain

  • Change: grid_terrain() now relies on the new algorithms dispatch method (see also the main new features above).
  • Change: grid_terrain() consistently returns a RasterLayer instead of a data.table, whatever the algorithm used.

grid_canopy

  • Change: grid_canopy() now relies on the new algorithms dispatch method (see also the main new features above). It unifies the former functions grid_canopy() and grid_tincanopy().
  • Change: grid_canopy() consistently returns a RasterLayer instead of a data.table, whatever the algorithm used.
  • Fix: the pitfree algorithm fails if a layer contains only 1 or 2 points.
  • Fix: the p2r algorithm is five times faster with the subcircle tweak.

grid_tincanopy

  • Change: grid_tincanopy() has been removed. Digital Surface Models are consistently driven by the function grid_canopy() and the lidR algorithm dispatch engine. The algorithms that replaced grid_tincanopy() are dsmtin and pitfree.

grid_hexametrics

  • Change: as for grid_metrics, the parameter splitlines has been removed.
  • Change: the function returns a hexbin object or a list of hexbin objects and no longer data.table objects.

grid_catalog

  • Change: grid_catalog() has been removed. The new LAScatalog processing engine means that this function is no longer useful.

class lasmetrics

  • data.table with a class lasmetrics no longer exists. It has been consistently replaced by RasterLayer and RasterBrick everywhere.
  • as.raster no longer exists because it used to convert lasmetrics into RasterLayer and RasterStack.
  • as.spatial no longer converts lasmetrics to SpatialPixelsDataFrame but still converts LAS to SpatialPointsDataFrame.
  • plot.lasmetrics has been removed obviously.

lasroi

  • Change: lasoi() has been removed. It was not useful and 'buggy'. It might be reintroduced later in lasclipManual.

lascolor

  • Change: lascolor() has been removed. It was one of the first functions of the package and is no longer useful because plot() has enhanced capabilities.

lasfilterdecimate

  • Change: now relies on the new algorithms dispatch method (see also the main new features above).
  • New: introduction of the algorithm highest available in lasfilterdecimate(). This supersedes the function lasfiltersurfacepoints().

lassnags

  • Change: lassnags() now relies on the new algorithms dispatch method (see also the main new features above).
  • New: lasnsnags() can be applied on a LAScatalog to write a new catalog using the catalog processing engine (see also the main new features above).

lidr_options

  • Change: lidr_option() has been removed. The options are now managed by regular R base options with function options(). Available lidR options are named with the prefix lidR.

Example files

  • New: the three example files are now georeferenced with an EPSG code that is read and converted to a proj4string.
  • New: the example file MixedConifers.laz contains the segmented trees in extra bytes 0.

plot

  • New: plot() for LAS objects supports RGB as a color attribute.
  • New: option color supports lazy evaluation. This syntax is correct: plot(las, color = Classification).
  • New: option clear_artifact = TRUE shifts the point cloud to (0,0) and reduces the display artifact due to the use of floating point in rgl.
  • New: new functions add_treetops3d, add_dtm3d and plot_dtm3d add elements in the point cloud.
  • Change: trim does not trim on a percentile of values but on the values themselves.

Coordinate reference system

  • New: coordinate reference system is supported everywhere and can be written in las files. See function epsg().
  • New: function lastranform that returns transformed coordinates of a LAS object using the CRS argument.

New functions

  • New: function lasfilterduplicates
  • New: function lascheck
  • New: function lasvoxelize

Other changes that are not directly visible

  • Change: the code that drives the point_in_polygon algorithm relies on boost and drastically simplifies the former code of lasmergespatial()
  • Change: many memory optimizations

lidR v1.6.1 (2018-08-21)

BUG FIXES

  • [#161] Fix tree ID matching.
  • Fix undefined variable in cluster_apply on mac and linux if multicore processing is used.
  • Fix rare case of unit test failure due to the random nature of the test dataset using seeds.
  • [#165] Unexported function in catalog_apply on Windows.

lidR v1.6.0 (2018-07-20)

NEW FEATURE

  • New function tree_hulls that computes a convex or concave hull for each segmented tree.
  • New option stop_early that enables processing of an entire catalog or stops if an error occurs.
  • New function catalog_retile supersedes the function catalog_reshape and performs the same task while adding much more functionality.

ENHANCEMENTS

  • When processing a LAScatalog, error handling has been seriously improved. A process can now run until the end even with errors. In this case clusters with errors are skipped.
  • When processing a LAScatalog, the graphical progress now uses 3 colors. green: ok, red: error, gray: null.
  • as.spatial() for LAS object preserves the CRS.
  • All the functions now have strong assertions to check user inputs.
  • plot.LAScatalog always displays the catalog with mapview by default even if the CRS is empty.
  • In lastrees_dalponte the matching between the seeds and the canopy is more tolerant. Rasters can have different resolution and/or extent.
  • lasground uses (as an option) only the last and single returns to perform the segmentation.

OTHER CHANGES

  • catalog() displays a message when finding overlaps between files.
  • The LAScatalog class is more thoroughly documented.
  • Clusters now align on (0,0) by default when processing a LAScatalog by cluster.

BUG FIXES

  • lasscanline() did not compute the scanline because the conditional statement that checked if the field was properly populated was incorrect.
  • [#146] Fix matching between tree tops, raster and canopy raster.
  • tree_detection when used with a point cloud was not properly coded and tended to miss some trees.
  • In lasclip* if ofile was non empty, the function wrote properly the file but returned a non-expected error.
  • [#155] user supplied function was being analyzed by future and some function were missing. User supplied function is now manually analyzed.
  • [#156] Fix error when lasclip was used with a SpatialPolygonDataFrame.

lidR v1.5.1 (2018-06-14)

BUG FIXES

  • The area of a LAScatalog was wrongly computed for non square tiles because of a bad copy/paste in the code.
  • [#135] Fix NULL class objects returned by grid_* functions when processing a LAScatalog if the first cluster is empty.
  • [#143] rumple_index returns NA if not computable.

lidR v1.5.0 (2018-05-13)

SIGNIFICANT CHANGES

  • catalog_options() is formally deprecated. Use LAScatalog properties instead (see ?catalog).
  • The package magrittr is no longer loaded with lidR. Thus, piping operators are no longer usable by default. To use piping operators use library(magrittr).

NEW FEATURES

  • New lassmooth function. A point cloud-based smoothing function.
  • New lasfiltersurfacepoints function to filter surface points.
  • New grid_catalog function is a simplified and more powerful function like catalog_apply but specifically dedicated to grid_* outputs.
  • New functions lasadddata, lasaddextrabyte and lasaddextrabyte_manual to add new data in a LAS object.
  • lasclip can clip a SpatialPolygonsDataFrame
  • lasclipRectangle and lasclipCircle can clip multiple selections (non-documented feature).
  • The treeID computed with lastrees_* functions can now be written in a las/laz file by default.

OTHER CHANGES

  • LAScatalog objects are processed with a single core by default.
  • lasdecimate is formally deprecated. Use lasfilterdecimate
  • grid_density now returns both the point and the pulse density, where possible.
  • The option P is no longer set by default in readLAS.
  • The documentation of lastrees has been split into several pages.
  • When a catalog is processed using several cores, if an error is raised the process triggers an early signal to stop the loop. In previous releases the entire process was run and the error was raised at the end when the futures were evaluated.

BUG FIXES

  • grid_metrics(lidar, stdmetrics_i(Intensity)) returned and empty data.table
  • [#128] Fix raster data extraction using the slower and memory-greedy, but safer raster::extract function.
  • [#126] propagate the CRS in filter functions.
  • [#116] Fix clash between function area from lidR and from raster.
  • [#110] Fix out-of-bounds rasterization.

lidR v1.4.2 (2018-04-19)

BUG FIXES

  • [#103] fix user-defined function not exported in clusters on Windows
  • [#104] fix potential bin exclusion in entropy function
  • [#106] fix wrong count of points below 0
  • Fix wrong type attribution in lasclassify when using the shapefile table of attributes as data.
  • Fix column addition when field = NULL in lasclassify.
  • Fix NA return in entropy when negative value are found.

NEW FEATURES

  • Li et al algorithm has a new parameter Zu (see reference) that is no longer hard coded.

lidR v1.4.1 (2018-02-01)

OTHER CHANGES

  • Removed examples and unit tests that imply the watershed segmentation to make CRAN check happy with the new rules relative to bioconductor packages.

NEW FEATURES

  • Parameter start has been enabled in grid_metrics with catalogs.

lidR v1.4.0 (2018-01-24)

NEW FEATURES

  • lasclip and lasclip* can extract from a catalog.
  • lasclip supports sp::Polygon objects.
  • lastrees gains a new algorithm from Silva et al. (2016).
  • lastrees with the Li et al. (2012) algorithm gains a new parameter to prevent over-segmentation.
  • new function lassnags for classifying points as snag points or for segmenting snags.
  • new function tree_detection to detect individual trees. This feature has been extracted from lastrees's algorithms and it is now up to the users to use lidR's algos or other input sources.
  • plot supports natively the PointCloudViewer package available on github.

BUG FIXES

  • Fix missing pixel in DTM that made normalization impossible.
  • [#80] fix segfault.
  • [#84] fix bug in lasscanline.

ENHANCEMENTS

  • lastrees with the Li et al. (2012) algorithm is now 5-6 times faster and much more memory efficient.
  • lastrees with the Li et al. (2012) algorithm no longer sorts the original point cloud.
  • lastrees with the Dalponte et al (2016) algorithm is now computed in linear time and is therefore hundreds to millions times faster.
  • catalog_reshape() streams the data and uses virtually zero memory to run.
  • grid_canopy() has been rewritten entirely in C++ and is now 10 to 20 times faster both with the option subcircle or without it.
  • grid_canopy() with the option subcircle uses only 16 bytes of extra memory to run, while this feature previously required the equivalent of several copies of the point cloud (several hundreds of MB).
  • as.raster() is now three times faster.
  • lasclassify now uses a QuadTree and is therefore faster. This enables several algorithms to run faster, such as lastrees with Silva's algo.

OTHER CHANGES

  • lasground with the PMF algorithm now accepts user-defined sequences.
  • lasground with the PMF algorithm has simplified parameter names to make them easier to type and understand, and to prepare the package for new algorithms.
  • lasground documentation is more explicit about the actual algorithm used.
  • lasground now computes the windows size more closely in line with the original Zhang paper.
  • lastrees when used with raster-based methods now accepts a missing las object. In that case extra is turned to true.
  • new parameter p (for power) added to functions that enable spatial interpolation with IDW.

lidR v1.3.1 (Release date: 2017-09-20)

BUG FIXES

  • Fix a bug of computer precision leading to non interpolated pixels at the boundaries of the QuadTree.

lidR v1.3.0 (Release date: 2017-09-16)

This version is dedicated to extending functions and processes to entire catalogs in a continuous way. Major changes are:

  • How catalog_apply works. More powerful but no longer compatible with previous releases
  • Former existing functions that now natively support a Catalog
  • Management of buffered areas

NEW FEATURES

  • catalog_apply has been entirely re-designed. It is more flexible, more user-friendly and enables loading of buffered data.
  • catalog_queries has now an argument ... to pass any argument of readLAS.
  • catalog_queries has now an argument buffer to load extra buffered points around the region of interest.
  • grid_metrics accepts a catalog as input. It allows users to grid an entire catalog in a continuous way.
  • grid_density also inherits this new feature
  • grid_terrain also inherits this new feature
  • grid_canopy also inherits this new feature
  • grid_tincanopy also inherits this new feature
  • grid_metrics has now has an argument filter for streaming filters when used with a catalog
  • New function catalog_reshape

OTHER CHANGES

  • lasnormalize updates the point cloud by reference and avoids making deep copies. An option copy = TRUE is available for compatibility with former versions.
  • readLAS arguments changed. The new syntax is simpler. The previous syntax is still supported.
  • catalog_index is no longer an exported function. It is now an internal function.
  • plot.Catalog accepts the usual plot arguments
  • catalog_queries and catalog_apply do not expect a parameter mc.cores. This is now driven by global options in catalog_options().
  • grid_metrics and lasmetrics do not expect a parameter debug. This is now driven by global options in lidr_options.
  • catalog can build a catalog from a set of paths to files instead of a path to a folder.
  • removed $ access to LAS attribute (incredibly slow)
  • catalog_select is more pleasant an more interactive to use.
  • S3 Catalog class is now a S4 LAScatalog class
  • LAS and LAScatalog class gain a slot crs automatically filled with a proj4 string
  • plot.LAScatalog display a google map background if the catalog has a CRS.
  • plot.LAScatalog gains an argument y to display a either a terrain, road, satellite map.
  • lasarea is deprecated. Use the more generic function area

BUG FIXES

  • Computer precision errors lead to holes in raster computed from a Delaunay triangulation.
  • Message in writeLAS for skipped fields when no field is skipped is now correct.

ENHANCEMENTS

  • grid_terrain with delaunay allocates less memory, makes fewer deep copies and is 2 to 3 times faster
  • grid_terrain with knnidw allocates less memory, makes fewer deep copies and is 2 to 3 times faster
  • lasnormalize and lasclassify no longer rely on raster::extract but on internal fast_extract, which is memory efficient and more than 15 times faster.
  • catalog enables a LAScatalog to be built 8 times faster than previously.
  • removed dependencies to RANN package using internal k-nearest neighbor search (2 to 3 times faster)

lidR v1.2.1 (Release date: 2017-06-12)

NEW FEATURES

  • new function tree_metrics.
  • new function stdtreemetrics.
  • grid_tincanopy() gains a parameter subcircle like grid_canopy()
  • new function rumple_index for measuring roughness of a digital model (terrain or canopy)
  • global options to parameterize the package - available with lidr_options()

BUG FIXES

  • Installation fails if package sp is missing.
  • Memory leak in QuadTree algorithm. Memory is now free after QuadTree deletion.
  • Dalponte's algorithm had a bug due to the use of std::abs which works with integers. Replaced by std::fabs which works with doubles.
  • In grid_tincanopy x > 0 was replaced by x >= 0 to avoid errors in the canopy height models
  • Triangle boundaries are now taken into account in the rasterization of the Delaunay triangulation

OTHER CHANGES

  • lastrees Li et al. algorithm for tree segmentation is now ten to a thousand of times faster than in v1.2.0
  • grid_terrain, the interpolation is now done only within the convex hull of the point cloud
  • grid_tincanopy makes the triangulation only for highest return per grid cell.
  • grid_tincanopy and grid_terrain using Delaunay triangulation is now ten to a hundred times faster than in v1.2.0
  • as.raster now relies on sp and is more flexible
  • as.raster automatically returns a RasterStack if no layer is provided.
  • plot.lasmetrics inherits as.raster changes and can display a RasterStack

lidR v1.2.0 (Release date: 2017-03-26)

NEW FEATURES

  • new function lasground for ground segmentation.
  • new function grid_tincanopy. Canopy height model using Khosravipour et al. pit-free algorithm.
  • new function grid_hexametrics. Area-based approach in hexagonal cells.
  • lasnormalize allows for "non-discretized" normalization i.e interpolating each point instead of using a raster.
  • internally lascheck performs more tests to check if the header is in accordance with the data.

BUG FIXES

  • [#48] gap_fraction_profile() bug with negative values (thanks to Florian de Boissieu)
  • [#49] typo error leading to the wrong metric in stdmetric_i
  • [#50] typo error leading to the wrong metric in stdmetric
  • Fix bug in stdmetric_z when max(Z) = 0
  • [#54] better re-computation of the header of LAS objects.

OTHER CHANGES

  • Slightly faster point classification from shapefiles.
  • [#51] in grid_terrain, forcing the lowest point to be retained is now an option keep_lowest = FALSE

lidR v1.1.0 (Release date: 2017-02-05)

NEW FEATURES

  • lastree() for individual tree segmentation
  • readLAS() gains a parameter filter from rlas (>= 1.1.0)
  • catalog_queries() relies on rlas (>= 1.1.0). It saves a lot of memory, is 2 to 6 times faster and supports .lax files.

OTHER CHANGES

  • colorPalette parameter in plot.LAS() now expects a list of colors instead of a function. Use height.colors(50) instead of height.colors
  • The header of a LAS object is now an S4 class object called LASheader
  • The spatial interpolation method akima is now called delaunay because it corresponds to what is actually computed.
  • The spatial interpolation method akima lost its parameter linear.
  • The spatial interpolation method kriging now performs a KNN kriging.
  • catalog_queries() lost the parameter ... all the fields are loaded by default.
  • Removed lasterrain() which was not consistent with other functions and not useful.

BUG FIXES

  • The header of LAS objects automatically updates Number of point records and Number of nth return.
  • lasnormalize() updates the header and returns warnings for some behaviors
  • [#39] interpolation with duplicated ground points

lidR v1.0.2 (Release date: 2016-12-31)

Third submission

  • Change: explain LiDAR in the Description - requested by Kurt Hornik.

lidR v1.0.1 (Release date: 2016-12-30)

Second submission - rejected

  • Change: split the package in two parts. 'lidR' relies on 'rlas' to read binary files.

lidR v1.0.0 (Release date: 201-12-16)

First submission - rejected

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("lidR")

2.1.4 by Jean-Romain Roussel, a month ago


https://github.com/Jean-Romain/lidR


Report a bug at https://github.com/Jean-Romain/lidR/issues


Browse source code at https://github.com/cran/lidR


Authors: Jean-Romain Roussel [aut, cre, cph] , David Auty [aut, ctb] (Reviews the documentation) , Florian De Boissieu [ctb] (Fixed bugs and improved catalog features) , Andrew Sánchez Meador [ctb] (Implemented lassnags)


Documentation:   PDF Manual  


GPL-3 license


Imports data.table, future, gdalUtils, geometry, glue, grDevices, lazyeval, imager, Rcpp, RCSF, rgeos, rgdal, rgl, rlas, sf, stats, tools, utils

Depends on methods, raster, sp

Suggests EBImage, concaveman, crayon, gstat, hexbin, mapview, mapedit, progress, testthat, knitr, rmarkdown

Linking to BH, Rcpp, RcppArmadillo


Imported by leafR.

Depended on by TreeLS.


See at CRAN