Flags:

-w

Reproject to project’s coordinate system if needed
Reprojects input dataset to the coordinate system of the GRASS project (by default only datasets with matching coordinate system can be imported

-e

Use the extent of the input for the raster extent
Set internally computational region extents based on the point cloud

-n

Set computation region to match the new raster map
Set computation region to match the 2D extent and resolution of the newly created new raster map

-o

Override projection check (use current project’s CRS)
Assume that the dataset has the same coordinate reference system as the current project

-d

Use base raster resolution instead of computational region
For getting values from base raster, use its actual resolution instead of computational region resolution

-p

Print LAS file info and exit

-g

Print data file extent in shell script style and then exit

--overwrite

Allow output files to overwrite existing files

--help

Print usage summary

--verbose

Verbose module output

--quiet

Quiet module output

--ui

Force launching GUI dialog

Parameters:

input=name

LAS input file
LiDAR input files in LAS format (*.las or *.laz)

output=name

Name for output raster map

file=name

File containing names of LAS input files
LiDAR input files in LAS format (*.las or *.laz)

method=string

Statistic to use for raster values
Options: n, min, max, range, sum, mean, stddev, variance, coeff_var, median, mode, percentile, skewness, trimmean, sidnmax, sidnmin, ev1, ev2, ev3
Default: mean
n: Number of points in cell
min: Minimum value of point values in cell
max: Maximum value of point values in cell
range: Range of point values in cell
sum: Sum of point values in cell
mean: Mean (average) value of point values in cell
stddev: Standard deviation of point values in cell
variance: Variance of point values in cell
coeff_var: Coefficient of variance of point values in cell
median: Median value of point values in cell
mode: Mode value of point values in cell
percentile: pth (nth) percentile of point values in cell
skewness: Skewness of point values in cell
trimmean: Trimmed mean of point values in cell
sidnmax: Maximum number of points in cell per source ID
sidnmin: Minimum number of points in cell per source ID
ev1: First eigenvalue of point x, y, z coordinates
ev2: Second eigenvalue of point x, y, z coordinates
ev3: Third eigenvalue of point x, y, z coordinates

type=string

Type of raster map to be created
Storage type for resultant raster map
Options: CELL, FCELL, DCELL
Default: FCELL
CELL: Integer
FCELL: Single precision floating point
DCELL: Double precision floating point

base_raster=name

Subtract raster values from the Z coordinates
The scale for Z is applied beforehand, the range filter for Z afterwards

zrange=min,max

Filter range for Z data (min,max)
Applied after base_raster transformation step

zscale=float

Scale to apply to Z data
Default: 1.0

irange=min,max

Filter range for intensity values (min,max)

iscale=float

Scale to apply to intensity values
Default: 1.0

drange=min,max

Filter range for output dimension values (min,max)

dscale=float

Scale to apply to output dimension values
Use if output dimension is not Z or intensity

input_srs=string

Input dataset projection (WKT or EPSG, e.g. EPSG:4326)
Override input dataset coordinate system using EPSG code or WKT definition

pth=integer

pth percentile of the values
Options: 1-100

trim=float

Discard given percentage of the smallest and largest values
Discard <trim> percent of the smallest and <trim> percent of the largest observations
Options: 0-50

resolution=float

Output raster resolution

return_filter=string

Only import points of selected return type
If not specified, all points are imported
Options: first, last, mid

class_filter=integer[,integer,...]

Only import points of selected class(es)
Input is comma separated integers. If not specified, all points are imported.

dimension=string

Dimension (variable) to use for raster values
Options: z, intensity, number, returns, direction, angle, class, source
Default: z
z: Z coordinate
intensity: Intensity
number: Return number
returns: Number of returns
direction: Scan direction
angle: Scan angle
class: Point class value
source: Source ID

user_dimension=string

Custom dimension (variable) to use for raster values
PDAL dimension name

Binning

The main difference between r.in.pdal and v.in.pdal is that r.in.pdal creates a raster instead of just importing the points into GRASS GIS. However, r.in.pdal does not merely rasterizes the points from the point cloud. r.in.pdal uses binning to derive values for individual raster cells, so the value of a cell is typically an aggregation of values of individual points falling into one cell. In general binning is the conversion of points into a regular grid. The binning of points with X and Y coordinates starts with the overlay of a grid of bins over the points.

In the basic case, binning is a method which counts the number of points which fall into one raster cell, i.e. bin. The number of points per cell (bin) indicates the density of points in the point cloud. The cell (bin) is always square or rectangular in case of r.in.pdal because the result is GRASS GIS 2D raster. The result of binning where the number of point per cell is counted is sometimes called 2D (two dimensional) histogram because a histogram is used in univariate statistics (in one dimension) to count the number samples falling into a given bin.

Figure: The binning on left was used to count number of points per (sometimes also called 2D histogram). The numbers in cells are examples of counts, the rest is represented by the color. The binning on right was used with mean to create a surface based on the values associated with the points. The numbers show examples of cell values. Note also the cells without any points which were assigned the NULL value. The basic concept of binning is extended when the points have another value associated with them. For LiDAR data this value can be the Z coordinate or intensity. The value for a given cell (bin) is computed using univariate statistics from the values of all points in the cell. For example, computing the mean value of Z coordinates can yield a raster representing the digital elevation model. Another example is the range of Z coordinates which can be used as a rough estimate of vegetation height.

Statistics

Available statistics for populating the output raster map are:

n

This computes the number (count) of points per cell. The result is a indicator of spatially variable density of points in the given area.

min

This finds the minimum of point values in each cell. It can be useful when finding topography in a forested or urban environment and there is a lot of points per one cells (terrain is oversampled considering the desired resolution). It can also create surfaces independent on the noise from premature hits as it will always select the lowest point.

max

This finds the maximum of point values in each cell. In connection with base_raster it can yield maximum vegetation of feature height per cell. For this purpose, it is usually much more appropriate than mean which would yield heights mostly influenced by the vertical distribution of points.

range

This computes the range of point values in each cell. The range of Z coordinates per cell can be used as a rough estimate of vegetation height when the cells are small enough, slopes low and the area is mostly vegetated. However, for more profound analysis, the base raster together with different statistics is recommended.

sum

This computes the sum of point values per cell. This is useful especially when intensity is used as a value.

mean

This is a mean (average) value of point values in cell. When used with Z coordinates (the default) and points from the ground class, the resulting raster is a digital elevation model. When intensity is used as a point value, the resulting raster contains mean intensity per cell. Note that mean gives heights influenced by the vertical distribution of points.

stddev

This computes the standard deviation of point values for each cell.

variance

This computes the variance of point values for each cell. Variance and derivatives use the biased estimator (n). It is calculated by Welford algorithm.

coeff_var

This computes the coefficient of variance of point values for each cell. Coefficient of variance is given in percentage and defined as 100 * sqrt(variance) / mean.

median

This computes the median of point values for each cell

mode

This computes the mode (the most common value) of point values for each cell. If there are more than one mode for a cell, an arbitrary one will be selected.

percentile

pth (nth) percentile of points in cell

skewness

This is a skewness of point values in cell

trimmean

This is a trimmed mean of point values in cell. Trimmed mean also know as truncated mean is a mean computed after discarding values at the low end and at the high end. How many values to discard is given by the trim option in percent. In statistics the usual percentage of trimmed values ranges from 5 to 25 percent.

sidnmax

Maximum number of points in cell per source ID. Points are counted for each source separately and largest count is used as the final cell value. Corresponds to majority density (Dmax) as defined by Smeeckaert et al., 2013.

sidnmin

Minimum number of points in cell per source ID. Points are counted for each source separately and smallest count is used as the final cell value. Corresponds to minority density (Dmin) as defined by Smeeckaert et al., 2013.

ev1, ev2, ev3

1st, 2nd and 3rd eigenvalue of point x, y and z coordinates within a cell (Mallet et al. 2011). Note that different statistics have different memory requirements (see below for details).

In varied terrain the user may find that min maps make for a good noise filter as most LIDAR noise is from premature hits. The min map may also be useful to find the underlying topography in a forested or urban environment if the cells are oversampled.

Filtering and selection

Points are always filtered by spatial extent (based on current computational region) unless -e flag is set. Afterwards import value scaling is performed, followed by filtering by having value in range.

Points falling outside the current computational region will be skipped. This includes points falling exactly on the southern or eastern region bound. To capture those adjust the region with:

g.region s=s-0.000001

The zrange parameter may be used for filtering the input data by vertical extent. Example uses include filtering out extreme outliers and outliers on relatively flat terrain. This parameter can be also used for cutting the point cloud into vertical sections preparing it for further processing by separate sections, together as if it would be an imagery group (see i.group), or combined into a 3D raster using r.to.rast3. In for these last examples, it might actually be more advantageous to use r3.in.lidar module. The zrange parameter is especially powerful when used together with the base_raster parameter. The zrange is applied to Z values after the base_raster reduction.

Figure: This is the principle of zrange filter. Points with the Z coordinate value below the lower value in the range (here 180) are filtered out (blue points) and same applies for points above higher value in the range (here 250). All other points are preserved (green points).

Filtering can be performed also on intensity values with irange parameter. If imported dimension is neither Z nor intensity, filtering can be performed with drange parameter. Filtering by Z values and intensity can be performed independently on imported dimension. If imported dimension is not Z nor intensity, filtering can be performed with all three parameters simultaneously (zrange, irange and drange) or any of their combination.

A LiDAR pulse can have multiple returns. The first return values can be used to obtain a digital surface model (DSM) where e.g. canopy cover is represented. The last return values can be used to obtain a digital terrain model (DTM) where e.g. the forest floor instead of canopy cover is represented. The return_filter option allows selecting one of first, mid, or last returns. Return number and number of returns in the pulse associated with each point are compared to determine if the point is first, mid, or last return.

LiDAR points often come as already classified into standardized classes. For example, class number 2 represents ground. For other classes see LAS format specification in references. The class_filter option allows selecting one or more classes using numbers (integers) separated by comma.

The user can use a combination of r.in.pdal output maps to create custom raster-based filters, for example, use r.mapcalc to create a mean-(2*stddev) map. (In this example the user may want to include a lower bound filter in r.mapcalc to remove highly variable points (small n) or run r.neighbors to smooth the stddev map before further use.)

Note that proper filtering of the input points in not only critical for the analysis itself but it can also speed up the processing.

LiDAR points can contain a lot of attributes besides their coordinates. By default r.in.pdal will use point Z coordinate as the output raster value source. The dimension option allows using different point attributes as an output instead of Z value. In case if other than Z value is chosen, output map type will be set to CELL. Explanation of various attributes can be found in LAS Specification chapter "Point Data Records". Be ware - not all attributes might be present and also their value ranges and meaning might vary between LAS file producers.

PDAL supports creation of custom dimensions (point attributes) in LAS files. Use user_dimension option to provide name of any dimension for import. This option supersedes one specified by dimension option.

Reduction to a base raster

For analysis of features on the terrain surface, especially vegetation it is advantageous to remove the influence of the terrain on heights because the height above the terrain is important (e.g. height of a tree) rather than height of the top of the tree above the see level. In this case, the base raster would be digital elevation model which can be one derived from the point cloud, or obtained in some other way. LiDAR data often come with precomputed DEMs (quality should be checked in this case) and there is often a DEM available for a given area (fit with the point cloud, especially vertical, and resolution should be checked).

Figure: This is a profile of base raster (in orange) representing digital elevation model and selected points, e.g. first return, from point cloud (green dots). By default the points would create a digital surface model (thin brown line) but after reducing the Z coordinates using the base raster, the created surface is a derived from the height of points relative to the base raster. The usage of base raster is not limited to digital elevation model. The base raster can be any surface which has some relation to the point values, for example digital surface model representing top of the canopy.

Setting extent and resolution

Since the creation of raster maps depends on the computational region settings (extent and resolution), as default the current region extents and resolution are used for the import. When using the -e flag along with the resolution=value parameter, the region used for the new raster will be based the point cloud extent and the provided resolution. It is therefore recommended to first use the -s flag to get the extents of the LiDAR point cloud to be imported, then adjust the current region extent and resolution accordingly, and only then proceed with the actual import. Another option is to automatically set the region extents based on the LAS dataset itself (-e flag) along with the desired raster resolution. The best option is to know the point cloud extent ahead, e.g. from tiling scheme, and use it. See below for details.

Since the r.in.pdal generates a raster map through binning from the original LiDAR points, the target computational region extent and resolution have to be determined. A typical workflow would involve the examination of the LAS data’s associated documentation or the scan of the LAS data file with r.in.pdal’s -g flag to find the input data’s bounds.

Another option is to automatically set the region extents based on the LAS dataset extent (-e flag) along with the desired raster resolution using the resolution parameter.

The -g flag prints extent of input data set(s) in a shell style suitable as command line parameters for g.region. Note, extent is determined from data set(s) metadata and without scanning whole dataset and thus selectros and filters are not applied.

A simpler option is to automatically set the region extents based on the LAS dataset (-e flag) along with the target raster resolution using the resolution parameter. Also here it is recommended to verify and optimize the resulting region settings with g.region prior to importing the dataset.

Transformation

Point Z axis values can be altered by scaling parameter set by zscale. Each point X axis value is multiplied by provided value before any filtering or further transformation takes place. In the same way act iscale and dscale parameters - values are multiplied before range filters irange or drnage are applied (if present).

Output value scaling can be performed independently of filtering by providing dscale parameter. This scaling parameter is applied only to output value independently of its dimension. Thus if zrange and dscale are set for Z output dimension, filtering by zrange will be done with unscaled values but output will be scaled by dscale. dscale takes precedence over zscale and iscale if more than one parameter is provided.

Format and projection support

The typical file extensions for the LAS format are .las and .laz (compressed). The compressed LAS (.laz) format can be imported only if libLAS has been compiled with LASzip support. It is also recommended to compile libLAS with GDAL which is used to test if the LAS coordinate reference system matches that of the GRASS project (previously called location).

Memory consumption

While the input file can be arbitrarily large, r.in.pdal will use a large amount of system memory (RAM) for large raster regions (> 10000x10000 pixels). If the module refuses to start complaining that there isn’t enough memory, use the percent parameter to run the module in several passes. In addition using a less precise map format (CELL [integer] or FCELL [floating point]) will use less memory than a DCELL [double precision floating point] output map. For methods=n, mode, sidnmin, sidnmax, the CELL format is used automatically.

The mean and range methods will use average amount of memory (comparing to other methods). Methods such as n, min, max, and sum will use less memory, while stddev, variance, and coeff_var will use more.

The memory usage for regular statistics mentioned above is based solely on region (raster) size. However, the aggregate functions median, mode, percentile, skewness and trimmean will use more memory and may not be appropriate for use with arbitrarily large input files without a small value for the percent option because unlike the other statistics memory use for these also depends on the number of data points.

The default map type=FCELL is intended as compromise between preserving data precision and limiting system resource consumption.

Trim option

Trim option value is used only when calculating trimmed mean values. Attempt to use it with other statistical methods will result in an error.

Print flag

The -p flag will use PDAL library built-in LAS file metadata printing function. It will also include a list of supported dimension names. Any of listed dimensions can be passed to the user_dimension option. Dimensions are not checked for presence of data for any of points in the file and thus importing a dimension without actual data will result in an empty map.

Finding suitable extent and resolution

For the output raster map, a suitable resolution can be found by dividing the number of input points by the area covered (this requires an iterative approach as outlined here):

# print LAS metadata (Number of Points)
r.in.pdal -p input=points.las
# Point count: 1287775
# scan for LAS points cloud extent
r.in.pdal -g input=points.las output=dummy -o
# n=2193507.740000 s=2190053.450000 e=6070237.920000 w=6066629.860000 b=-3.600000 t=906.000000
# set computation region to this extent
g.region n=2193507.740000 s=2190053.450000 e=6070237.920000 w=6066629.860000 -p
# print resulting extent
g.region -p
#  rows:       3454
#  cols:       3608
# points_per_cell = n_points / (rows * cols)
# Here: 1287775 / (3454 * 3608) = 0.1033359 LiDAR points/raster cell
# As this is too low, we need to select a lower raster resolution
g.region res=5 -ap
#  rows:       692
#  cols:       723
#  Now: 1287775 / (692 * 723) = 2.573923 LiDAR points/raster cell
# import as mean
r.in.pdal input=points.las output=lidar_dem_mean method=mean -o
# import as max
r.in.pdal input=points.las output=lidar_dem_max method=max -o
# import as p’th percentile of the values
r.in.pdal input=points.las output=lidar_dem_percentile_95 \


           method=percentile pth=95 -o

Mean value DEM in perspective view, imported from LAS file

Further hints: how to calculate number of LiDAR points/square meter:

g.region -e


  # Metric project:


  # points_per_sq_m = n_points / (ns_extent * ew_extent)


  # Lat/Lon project:


  # points_per_sq_m = n_points / (ns_extent * ew_extent*cos(lat) * (1852*60)^2)

Serpent Mound dataset

This example is analogous to the example used in the GRASS wiki page for importing LAS as raster DEM.

The sample LAS data are in the file "Serpent Mound Model LAS Data.laz", available at Serpent Mound Model LAS Data.laz:

# print LAS file info
r.in.pdal -p input="Serpent Mound Model LAS Data.laz"
# using v.in.lidar to create a new project
# create a project with CRS information of the LAS data
v.in.lidar -i input="Serpent Mound Model LAS Data.laz" project=Serpent_Mound
# quit and restart GRASS in the newly created project "Serpent_Mound"
# scan the extents of the LAS data
r.in.pdal -g input="Serpent Mound Model LAS Data.laz"
# set the region to the extents of the LAS data, align to resolution
g.region n=4323641.57 s=4320942.61 w=289020.90 e=290106.02 res=1 -ap
# import as raster DEM
r.in.pdal input="Serpent Mound Model LAS Data.laz" \


           output=Serpent_Mound_Model_LAS_Data method=mean

Figure: Elevation for the whole area of Serpent Mound dataset

Height above ground

The mean height above ground of the points can be computed for each raster cell (the ground elevation is given by the raster map elevation):

g.region raster=elevation -p
r.in.pdal input=points.las output=mean_height_above_ground base_raster=elevation method=mean

In this type of computation, it might be advantageous to change the resolution to match the precision of the points rather than deriving it from the base raster.

Multiple file input

The file option requires a file that contains a list of file names with the full path. For example, a list of files in the directory /home/user/data:

points1.laz
points2.laz
points3.laz

would be lised in the file as:

/home/user/data/points1.laz
/home/user/data/points2.laz
/home/user/data/points3.laz

On Linux and OSX, this file can be automatically generated with the command:

ls /home/user/data/*.laz > /home/user/data/filelist.txt

On Windows:

dir /b c:\users\user\data\*.laz > c:\users\user\data\filelist.txt

The mean height above ground example above would then be:

g.region raster=elevation -p
r.in.pdal file=/home/user/data/filelist.txt output=mean_height_above_ground base_raster=elevation method=mean

In Python, the list of files can be created using the glob Python module:

import glob
import gscript
file_list_name = ’/home/user/data/filelist.txt’
with open(, mode=’w’) as file_list:


    for path in glob.iglob(’/home/user/data/lidar/*.las’):


        file_list.write(path + "\n")
gscript.run_command(’r.in.pdal’, file=file_list_name,


                    output=’mean_height_above_ground’,


                    base_raster=’elevation’ method=’mean’)