v4.0.3

lidR v4.0.3 (Release date: 2023-03-11)

• Add a function add_lasnir().
• Replace rg::rgl.* by rgl::*3d functions #651
• Fix: readLAS no longer checks for attribute names validity as they are necessarily correct #659
• Fix: plot_metrics() no longer fails with a single plot #664

v4.0.2

lidR v4.0.2 (Release date: 2022-11-28)

• Fix: #638. unormalize_height() removes extra_bytes in VLR.
• Fix: #637. print(las) works even when the CRS is not recognized by sf.
• New: dsmtin and pitfree gain an argument highest. This option was enabled by default in previous releases. There is now an option to disable it.
• Fix: #580 and #622 normalize_height() and segment_trees work in parallel with SpatRaster.
• Fix: #586.
• Fix: #587. crown_metrics() now triggers a warning when invalid geometries are created and delineate_crowns() remove these geometries before to convert to sp.
• Fix: #594. crown_metrics() now works with func = NULL and a LAScatalog.
• Fix: #608. The C++ function used to compute the range between a point and the sensor from the sensor positions was re-based to resolve a bug when a single sensor position was found for a given flightline. New warnings were added.
• Fix: #609. *_metrics() functions always returned NAs for lastofmany.
• Fix: #614. Manual tree detection preserves the CRS.
• Doc: dalponte2016 doc updated to use terra.

v4.0.1

lidR v4.0.1 (Release date: 2022-05-03)

• Fix: plot(ctg, chunk = TRUE) does not fail if an invalid output file template is registered #537
• Enhance: locate_trees() throws an informative error if called with an on-disk raster. The former error was cryptic. If the raster is small enough it is loaded on-the-fly.
• Fix: merge_spatial() with RGB and SpatRaster was not working properly #545
• Enhance: st_area() better estimates the area of small point-clouds and is faster
• Fix: #548
• Enhance: Scale factors are better estimated in interpret_waveform #549.
• Fix: plot_metrics() returns NA if 0 points available #551.
• Fix: floating point accuracy error with rasterize_canopy may generate error or messed-up CHM #552.
• Fix: print() and st_area() were not working for point cloud with no CRS
• Fix: track_sensor() does not fail with a LAScatalog when no sensor position is found. It also triggers a warning. #556.
• Fix: The LAScatalog processing engine works with a single file #558.
• Fix: rasterize_terrain() now works with a LAScatalog and shape = sfc_object #558.
• Fix: catalog_retile() now works when some tiles are empty #563.
• Fix: crown_metrics() messed up tree IDs with a hull geometry #554.
• Fix: merge_spatial() crops large vectors to the extent of the point cloud before to perform the merge. This has for consequences to sometime transform polygons into multipolygons. When polygons and multipolygons were mixed the functions stopped with an error. It now works.
• Fix: normalize_height() now sets the Z offset to 0 #571.
• Fix: smaller rasters stored on-disk are better handled and loaded if needed

Changes related to rlas 1.6.0

We are currently developing rlas 1.6.0 that uses the ALTREP framework to load compact representation of non populated attributes. For example UserData is usually populated with zeros (not populated). Yet it takes 32 bits per point to store each 0. With rlas 1.6.0 it will only uses 644 bits no matter the number of points loaded for non populated attributes. This applies to each attribute populated with a single repeated value. This allows for saving approximately 30% of memory usage depending on the number of non-populated attributes that are present in the file. rlas 1.6.0 is compatible will all versions of lidR but lidR 4.0.1 introduced some internal optimization, internal fixes and new functions to fully take advantage of rlas 1.6.0. lidR v<= 4.0.0 will work with rlas 1.6.0 but won't take advantage of the new compression feature.

1. the function LAS() no longer call data.table::setDT() if the input is already a data.table. Indeed data.table::setDT() materializes the compressed ALTREP vectors and this is not what we want. One consequence of this change is that readLAS() now preserve the ALTREPness (i.e. the compression) of the output of rlas::read.las().

2. Subsetting a LAS object no longer call data.table native subset. We previously used something like las@data[indx] to subset the point cloud. Sadly data.table tries to materialized the ALTREPed vector whenever it can. We implemented internally a smart_subset() function that subset and preserves the compression of the vectors. One consequence of such change is that all filter_*() and clip_*() functions preserve the compression of the point-cloud if any.

3. las_check() has been slightly modified to ensure it does not materialize ALTREPed object. One side effect of las_check() was to decompress the point cloud unexpectedly. Such a pity! We also change las_check() to print information about the compression.

4. We changed the way *_metrics() functions evaluates the user defined expression because we found that it had the side effect of materializing all the attributes instead of materializing only those needed. For example pixel_metrics(las, mean(Z)) only needs the attribute Z. No need to allocate and copy memory for Intensity, ScanAngle and so on. In previous version all attributes where inspected with the side effect to materialize all compressed vectors. The *_metrics() functions now properly detect which attributes are actually necessary for the evaluation of func. Two consequences: (1)*_metrics() functions are 20 to 40% faster, (2) the compression is preserved if no compressed attribute is used in the evaluation and e.g. pixel_metrics(las, mean(UserData)) uncompresses only UserData.

5. New functions las_is_compressed() that tells which attributes are compressed and las_size() that returns the true size of a LAS objects taking into account the compression. las_size() should returns something similar to pryr::object_size() but different to object.size() that is not ALTREP aware. We also changed the print function so it uses las_size() instead of object.size().

On overall lidR's functions are expected to almost never decompress a LAS object. However other R packages and R functions may do it. For example data.table::print do materializes the ALTREP vectors. base::range() too but not base::mean() or base::var().

las@data                    # Full decompression (print data.table)
range(las$Userdata)         # Decompression of UserData
las@data[2, UserData := 1]  # Decompression of UserData
las@data[1:10]              # Full decompression

v4.0.0

lidR v4.0.0 (Release date: 2022-02-17)

rgdal and rgeos will be retired on Jan 1st 2024. see twitter, youtube, or see the respective package descriptions on CRAN. Packages raster and sp are based on rgdal/rgeos and lidR was based on raster and sp because it was created before sf, terra and stars. This means that sooner or later lidR will run into trouble (actually it is more or less already the case). Consequently, we modernized lidR by moving to sf, terra/stars and we are no longer depending on sp and raster (see also Older R Spatial Package for more insight). It is time for everybody to stop using sp and raster and to embrace sf and stars/terra.

In version 4 lidR now no longer uses sp, it uses sf and it no longer uses raster. It is now raster agnostic and works transparently with rasters from raster, terra and stars. These two changes meant we had to rewrite a large portion of the code base, which implies few backward incompatibilities. The backward incompatibilities are very small compared to the huge internal changes we implemented in the foundations of the code and should not even be visible for most users.

Backward inconpatibilites

1. lidR no longer loads raster and sp. To manipulate Raster* and Spatial* objects returned by lidR users need to load sp and raster with:

   library(sp)
   library(raster)
   library(lidR)

2. The formal class LAS no longer inherits the class Spatial from sp. It means, among other things, that a LAS object no longer has a slot @proj4string with a CRS from sp, or a slot @bbox. The CRS is now stored in the slot @crs in a crs object from sf. Former functions crs() and projection() inherited from raster are backward compatible and return a CRS or a proj4string from sp. However code that accesses these slots manually are no longer valid (but nobody was supposed to do that anyway because it was the purpose of the function projection()):

   las@proj4string # No longer works
   las@bbox        # No longer works
   inherits(las, "Spatial") # Now returns FALSE

3. The formal class LAScatalog no longer inherits the class SpatialPolygonDataFrame from sp. It means, among other things, that a LAScatalog object no longer has a slot @proj4string, or @bbox, or @polygons. The slot @data is preserved and contains an sf,data.frame instead of a data.frame allowing backward compatibility of data access to be maintained. The syntax ctg$attribute is the way to access data, but statement like ctg@data$attribute are backward compatible. However, code that accesses other slots manually is no longer valid, like for the LAS class:

   ctg@proj4string # No longer works
   ctg@bbox        # No longer works
   ctg@polygons    # No longer works
   inherits(ctg, "Spatial") # Now returns FALSE

4. sp::spplot() no longer works on a LAScatalog because a LAScatalog is no longer a SpatialPolygonDataFrame

   spplot(ctg, "Max.Z")
   # becomes
   plot(ctg["Max.Z"])

5. raster::projection() no longer works on LAS* objects because they no longer inherit Spatial. Moreover, lidR no longer Depends on raster which means that raster::projection() and lidR::projection can mask each other. Users should use st_crs() preferentially. To use projection users can either load raster before lidR or call lidR::projection() with the explicit namespace.

   library(lidR)
   projection(las) # works
   library(raster)
   projection(las) # no longer works

6. Serialized LAS/LAScatalog objects (i.e. stored in .rds or .Rdata files) saved with lidR v3.x.y are no longer compatible with lidR v4.x.y. Indeed, the structure of a LAS/LAScatalog object is now different mainly because the slot @crs replaces the slot @proj4string. Users may get errors when using e.g. readRDS(las.rds) to load back an R object. However we put safeguards in place so, in practice, it should be backward compatible transparently, and even repaired automatically in some circumstances. Consequently we are not sure it is a backward incompatibility because we handled and fixed all warnings and errors we found. In the worst case it is possible to repair a LAS object v3 with:

   las <- LAS(las)

7. track_sensor() is not backward compatible because it is a very specific function used by probably just 10 people in the world. We chose not to rename it. It now returns an sf object instead of a SpatialPointsDataFrame.

New modern functions

Former functions that return Spatial* objects from package sp should no longer be used. It is time for everybody to embrace sf. However, these functions are still in lidR for backward compatibility. They won't be removed except if package sp is removed from CRAN. It might happen on Jan 1st 2024, it might happen later. We do not know. New functions return sf or sfc objects. Old functions are not documented so new users won't be able to use them.

• tree_metrics() and delineate_crowns() are replaced by a single function crown_metrics() that has the same functionality, and more.
• find_trees() is replaced by locate_trees().

Older functions that return Raster* objects from the raster package should no longer be used. It is time for everybody to embrace terra/stars. However, these functions are still in lidR for backward compatibility. They won't be removed except if package raster is removed from CRAN. New functions return either a Raster*, a SpatRaster, or a stars object, according to user preference.

• grid_metrics() is replaced by pixel_metrics()
• grid_terrain(), grid_canopy(), grid_density() are replaced by rasterize_terrain(), rasterize_canopy(), rasterize_density()

New features

New functions are mostly convenient features that simplify some workflow aspects without introducing a lot of brand new functionality that did not already exist in lidR v3.

1. New geometry functions st_convex_hull() and st_concave_hull() that return sfc

2. New modern functions st_area(), st_bbox(), st_transform() and st_crs() inherited from sf for LAS* objects.

3. New convenient functions nrow(), ncol(), dim(), names() inherited from base for LAS* objects

4. New operators $, [[, $<- and [[<- on LASheader. The following are now valid statements:

   header[["Version Major"]]
   header[["Z scale factor"]] <- 0.001

5. Operators $, [[, $<- and [[<- on LAS can now access the LASheader metadata. The following are now valid statements:

   las[["Version Major"]]
   las[["Z scale factor"]] <- 0.001

6. RStudio now supports auto completion for operator $ in LAS objects. Yay!

7. New functions template_metrics(), hexagon_metrics(), polygon_metrics() that extend the concept of metrics further to any kind of template.

8. Functions that used to accept spatial vector or spatial raster as input now consistently accept any of Spatial*, sf, sfc, Raster*, SpatRaster and stars objects. This include merge_spatial(), normalize_intensity(), normalize_height(), rasterize_*(), segment_trees(), plot_dtm3d() and several others. We plan to support SpatVector in future releases.

9. Every function that supports a raster as input now accept an "on-disk" raster from raster, terra and stars i.e. a raster not loaded in memory. This includes rasterization functions, individual tree segmentation functions, merge_spatial and others, in particular plot_dtm3d() and add_dtm3d() that now downsample on-disk rasters on-the-fly to display very large DTMs. On-disk rasters were already generally supported in previous versions but not every function was properly optimized to handle such objects.

10. All the functions that return a raster (pixel_metrics() and rasterize_*()) are raster agnostic and can return rasters from raster, terra or stars. They have an argument pkg = "raster|terra|stars" to choose. The default is terra but this can be changed globally using:

    options(lidR.raster.default = "stars")

11. New function catalog_map() that simplifies catalog_apply() to a large degree. Yet it is not as versatile as catalog_apply() but well suits around 80% of use cases. Applying a user-defined function to a collection of LAS files is now as simple as:

    my_fun <- function(las, ...) {
      # do something with the point cloud
      return(something)
    }
    res <- catalog_map(ctg, my_fun, param1 = 2, param2 = 5)

12. Operator [ on LAS object has been overloaded to clip a point-cloud using a bbox or a sfc

    sub <- las[sfc]

13. rasterize_terrain() accepts an sfc as argument to force interpolation within a defined area.

14. normalize_height() now always interpolates all points. It is no longer possible to get an error that some points cannot be interpolated. The problem of interpolating the DTM where there is no data is still present but we opted for a nearest neighbour approach with a warning instead of a failure. This prevents the method from failing after hours of computation for special cases somewhere in the file collection. This also means we removed the na.rm option that is no longer relevant.

15. New functions header(), payload(), phb(), vlr(), evlr() to get the corresponding data from a LAS object.

16. New algorithm shp_hline and shp_vline for segment_shapes() [#499](https://gith...

v3.2.2

lidR v3.2.2 (Release date: 2021-10-20)

• Enhance: grid_*() functions support a RasterLayer smaller than the point cloud (#483)
• Fix: las_check() with a LAScatalog and with deep = TRUE failed with a output file template (#484).
• Fix: readLAS() no longer reads LAS files on some Windows/Mac machine (#485). It seems it is an issue with CRAN binaries. By releasing 3.2.2 we hope to trigger a new build.
• Enhance: get_range() and consequently range_correction() no longer throw high range error for highly variable range sensor like TLS (#490).

v3.2.0

lidR v3.2.0 (Release date: 2021-09-26)

ANNOUCEMENT

rgdal and rgeos will be retired on Jan 1st 2024. raster and sp are based on rgdal/rgeos. lidR is based on raster and sp because it was created before sf, terra and stars. This means that sooner or later lidR will run into trouble (actually it has already started to be the case). So, it is time to fully embrace sf, terra/stars and to leave sp and raster. This will require an in-depth rebase of lidR. We have started the work and we plan to release lidR 4.0.0 that will no longer have any internal code that uses sp and raster. This version already no longer uses rgdal. We hope make these changes with minimal breakage in backward compatibility by maintaining the conversion to sp/raster for functions from v < 4.0.0, but some backward incompatibilities will necessarily arise. In particular, LAS will no longer inherit the sp::Spatial class and will no longer contain a sp::CRS but a sf::crs and LAScatalog will no longer be sp::SpatialPolygonDataFrame. Our plan is (hopefully) to rebase lidR in such a way that nobody will notice the changes expect users who dig a little deeper into the objects.

CHANGES

1. hexbin_metrics() was an unused function and has been removed from lidR. It can be retrieved in lidRplugins

2. Functions using the former namespace such as lassomething() that were renamed into verb_noun() in version 3.0.0 now throw a warning. In v3.0.0 they were still usable for backward compatibility but not documented. In v3.1.0 they printed a message saying to move on to the new namespace. Now in 3.2.0 they throw a formal warning saying to move on to the new namespace. They will throw an error in the next version.

NEW FEATURES

1. classify_poi(). New function capable of attributing a class of choice to any points that meet a logical criterion (e.g. Z > 2) and/or a spatial criterion (e.g. inside a polygon). For example, the following will attribute the class "high vegetation" to each non-ground point that is not in the lake polygon.

   las <- classify_poi(las, LASHIGHVEGETATION, poi = ~Classification != 2, roi = lakes, inverse = TRUE)

2. LAScatalog

   • New function rbind() for LAScatalog.
   • New functions projection()<- and crs()<- for LAScatalog. Those two functions were already working in previous versions but in absence of dedicated functions in lidR the functions that were actually called were raster::projection() and raster::crs() thanks to class inheritance. However the functions from raster do not support crs from sf or numbers as input. Adding a dedicated function in lidR brings consistency between LAS and LAScatalog (#405):

     projection(ctg) <- st_crs(3625)
     # or
     projection(ctg) <- 3625

   • The processing engine has a new option to drop some chunks under ctg@chunk_options$drop. This generates regions that won't be processed. This option accepts a vector of chunk IDs that are dropped and is thus versatile, but its main role is to allow restarting a computation that failed. We consequently introduced the function opt_restart(). Let's assume that the computation failed after few hours at 80% in chunk number 800. Users get a partial output for the first 799 chunks but chunk 800 has a problem that can be solved. It is now possible to restart at 800 and get the second part of the output without restarting from 0:

     output <-    catlog_apply(ctg, myfun, param)
     # Failed after 80%, 'output' contains a partial output
     # Fix the trouble
     
     opt_restart(ctg) <- 800
     output2 <- catlog_apply(ctg, myfun, param)
     
     # Merge 'output' and 'output2'

   • The vignette LAScatalog engine and the manual LAScatalog-class were updated to reflect these features

3. LASheader

   • The function LASheader() can now create a LASheader object from a data.frame. This addition aims to facilitate the creation of valid LAS objects from external data.
   • las_check() can now check a standalone LASheader

     las_check(las@header)

4. LAS

   • The function LAS now automatically fixes the font case of attributes names to match the naming convention of the rlas package. This simplifies the creation of compatible objects from non-LAS file sources.

     data <- data.frame(x = runif(10), Y = runif(10), z = runif(10), pointsourceid = 1:10)
     las <- LAS(data)
     #> Attribute 'x' renamed 'X' to match with default attribute names.
     #> Attribute 'z' renamed 'Z' to match with default attribute names.
     #> Attribute 'pointsourceid' renamed 'PointSourceID' to match with default attribute names.
     las$PointSourceID
     #> [1]  1  2  3  4  5  6  7  8  9 10

5. Full waveform: with most recent versions of the rlas package, full waveform (FWF) can be read and lidR provides some compatible functions. However the support of FWF is still a work in progress in the rlas package. How it is read, interpreted and represented in R may change. Consequently, tools provided by lidR may also change until the support of FWF becomes mature and stable in rlas.

   • New function interpret_waveform() to transform waveform into a regular point cloud
   • New supported flag W for parameter select in readLAS()
   • New automatic colouring scheme for attribute Amplitude in plot(las, color = "Amplitude") that aims to be used with FWF.

6. catalog_intersect() now supports sf, sfc, Extent and bbox objects

7. Concave hull: lidR now includes its own C++ code to compute concave hulls using concaveman-cpp.

   • New function concaveman() to compute concave hulls
   • delineate_crowns() using concave hulls is now between 10 to 50 times faster.

     LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
     las = readLAS(LASfile, select = "xyz0")
     concave_hulls <- delineate_crowns(las, "concave")
     # Before v3.2.0: 7.1 seconds
     # From v3.2.0  : 0.2 seconds

   • grid_terrain() with is_concave = TRUE should also be faster.

8. New function catalog_boundary() to compute the actual shape of the point-cloud

9. In find_trees() and segment_trees() the bitmerge strategy to generate robust unique IDs was not actually a valid and robust procedure. It had the advantage of generating integers but was not 100% unique. The probability to generate duplicates was low but we changed the strategy to use a true bit-merging procedure anyway. The new IDs thus generated are weird decimal number such as 5.001120e-310 but are guaranteed to be unique. The documentation has been updated to explain the method.

10. New algorithm random_per_voxel() for decimate_points that keep n points per voxel (#406).

11. 3D rendering:

    • plot() gains a new parameter voxels = TRUE or voxels = 0.5 to render a point cloud with voxels. This is useful to render the output of voxelize_points() or voxel_metrics(), for example. This is computationally demanding and takes time so it should be reserved to small scenes with 30,000 or 40,000 voxels maximum, but note that there is no hard coded limit.

      vm <- voxel_metrics(las, ~list(N = length(Z)), 8)
      plot(vm, color = "V1", voxels = T)

    • specular reflections are now disable in plot().

12. New function plot_metrics() that wraps several other functions into one seamless function that extracts ground inventory plots, computes metrics for each plot and returns a ready to use data.frame for statistical modelling.

13. New function point_eigenvalue() that is equivalent to point_metrics(las, .stdshapemetrics) but specialized, optimized and parallelized to be 10 times faster.

14. grid_metrics() gains a new parameters by_echo allowing users to compute the metrics for different types of echos independently. It is now possible to map e.g. mean(Intensity) for first returns only + multiple return only + single return only. All metrics are computed in a single run and returned in a raster stack.

15. merge_spatial() supports sfc

ENHANCEMENTS

1. grid_density() is 10 times faster

FIXES

1. Fix: quantize() now preserves NaN values instead of converting them into minus infinity (#460).
2. Fix: stdmetrics_i() now fails with an informative message when the sum of intensities is greater than .Machine$integer.max and becomes double (#463)
3. Fix: find_localmaxima() respects the filter argument. It was previously not considered.

MISCELLANEOUS

1. Remove crayon and hexbin dependencies
2. Packages RCSF and rgeos are now only suggested and they are consequently no longer installed by default with lidR
3. Change: rgdal will be retired in 2024. Code using rgdal internally has been removed. In many cases this will not change anything for users but in some cases it may fail when assigning an EPSG code to the LAS file. Also, old versions of rgdal built with old versions of gdal and proj are no longer supported (#466)

v3.1.4

lidR v3.1.4 (Release date: 2021-06-22)

• Change: manual() now uses the middle button to perform the selection. Historically the button was "right" but later the right button was added in lidR and attributed to the dragging action. By using "right" in this function this disabled the possibility to drag the scene. Consequently we changed the default to use the middle button. (#442).
• Change: manual() now removes all apices in the selection rectangle when removing some false positive (#445).
• Doc: fix some code block rendering in catalog_apply man page
• Fix: fix catalog processing engine edge case when the last chunks fail (#435).
• Fix: voxel_metrics() with all_voxels = TRUE did not work as expected. The insertion of empty voxels corrupted some of the real voxels. This bug lead to invalid output and some floating points precision errors lead to supernumerary voxels (#437, #439).
• Fix: grid_terrain() used with a LAScatalog no longer propagated the options. For example when using use_class = c(2L, 8L, 9L, 10L) this was not propagated and the option was actually the default one i.e. use_class = c(2L, 9L). This bug was introduced in 3.1.0
• Fix: delineate_crowns() now returns NULL if the input point-cloud has only points with treeID = NA. It also triggers a warning. (#438).
• Fix: manual() the function that allow for finding the trees manually was no longer working probably because of some slight modifications in the rgl package.
• Enhance: the plot function used to display the output of voxel_metrics() now internally uses the same function than LAS objects. This enhances the rendering using the clear_artifact option by default and allows for a lot more flexibility in the rendering.
• Enhance: new parameter button in manual() to choose which button to use.
• Enhance: segment_trees() now print a message if all points are NA to suggest to use other parameters

v3.1.3

lidR v3.1.3 (Release date: 2021-05-20)

• Fix: las_check(..., deep = TRUE) was not working in parallel (#411).
• Fix: the LAScatalog processing engine printed the outputs twice for rare functions that print something like las_check() (#414)
• Fix: the internal way lidR is checking for nested parallelism has been reworked in depth fixing some bugs and allowing to support more strategies thanks to @Lenostatos (#418, #421)
• Fix: merge_spatial() did not work with sf objects.
• New: las_check() introduces a new type of message called "message". Some message previously classified as "warning" are now classified as "message". Warnings are now displayed in orange and messages in yellow. The output of las_check() has now 3 items instead of 2.
• New: stdmetrics_z gains a new parameter zmin = 0 to control the lower bound of the integration for metrics zpcumx (#424).
• Enhance: max_cr_factor in silva2019() is now allowed to be in [0, inf[ instead of [0,1] (#417)
• Enhance: added a workaround to avoid sp printing proj_create: crs not found for non recognized EPSG codes and avoid throwing warning Discarded datum [...] in Proj4 definition
• Enhance: readLAScatalog() throws a more informative error when attempting to read an non-existing folder.
• Enhance: readXXXLAS() now throws an error for LAScluster (#430).
• Doc: Updates and clarifications in the doc of stdmetrics.
• Misc: removed LazyData in DESCRIPTION

v3.1.2

lidR v3.1.2 (Release date: 2021-03-11)

• New: the class LASheader has a new slot @EVLR for the extended variable length records. print() has been extended to display EVLR. While this change is compatible with rlas <= 1.3.9 it is only used with version of rlas >= 1.4.0.
• New: algorithm lowest() for decimate_points()
• Fix: usban outside the range of representable values of type 'char' for spatial indexes built with 0 point.
• Fix: build failure with GCC 4.x
• Fix: catalog_apply() now works with cluster plan plan(cluster) meaning that it can be used on HPC e.g. with MDPI. We took advantage of this bug to better detect the parallel strategy used and disable or not OpenMP. When lidR is not able to figure out if the strategy involves multiple machines or multiple cores of a single machine, then a warning is thrown and OpenMP is disabled by security.

  The parallel evaluation strategy was no recognized and lidR does not know if OpenMP should be disabled.
  OpenMP has been disabled by security. 
  Use options(lidR.check.nested.parallelism = FALSE) and set_lidr_threads() for a fine control of parallelism.
  

• Fix: incorrect offset computation in spTransform() have for consequences to make the function failing with error: Non quantizable value outside the range of representable values of type 'int'.
• Fix: attribution of a WKT string with projection() when using an epsg code as input (projection(las) <- 12345).
• Fix: partial processing mode now respects the raster alignment when processed by file
• Fix: readLAScatalog() now reads the WKT CRS of LAS files format 1.4. To support both EPSG and WKT the table of attribute of a LAScatalog now has a column named CRS that replace former column EPSG.
• Fix: print() for a LAScatalog now prints the CRS exactly like print for LAS.
• Doc: documentation of options(lidR.check.nested.parallelism = FALSE) was missing. Information can now be found in ?lidR-package and ?lidR-parallelism
• Enhance: in catalog_apply() if lidR.check.nested.parallelism = FALSE it now respects the input of set_lidr_thread() instead of the output of get_lidr_threads(). For example if set_lidr_thread(0) it now propagates the information 0 (all cores) instead of the output of get_lidr_thread() which might be e.g. 4 on the master worker but might be different on the slave workers. Similarly set_lidr_thread(20) will request 20 cores to the workers even if get_lidr_thread() returns 4 on the local machine.
• Enhance: set_lidr_thread() accepts inputs < 1 such as 0.5 or 0.25 to mean 'half' or 'quarter' of available cores.
• Enhance: grid_density() now returns 0 for pixels with 0 points instead of NA which make more sense and corresponds to what should be expected.

v3.1.1

lidR v3.1.1 (Release date: 2021-01-22)

• Fix usban issue: outside the range of representable values of type 'int' for spatial indexes built with 0 point.
• Fix usban issue: outside the range of representable values of type 'int' when quantizing or counting non quantized values that are not quantizable according the the given scale and offset.
• Remove lax files in example data.