Since a new raster map is created during the binning, the binning of points depends on the current computational region settings (extent and resolution) by default (see more about binning below). When using the -e flag, the binning will be done in the extent of the point cloud, so the resulting raster will have extent based on the input point cloud. When the resolution=value parameter is used, the binning is done using the provided resolution and the resulting raster will have that resolution (see more below for more information about extent and resolution management).
r.in.pdal is designed for processing massive point cloud datasets, for example raw LiDAR or sidescan sonar swath data.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.
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.
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:
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 examplee, 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.
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.
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.
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.
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 value is used only when calculating trimmed mean values. Attempt to use it with other statistical methods will result in an error.
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.
# set the computational region automatically, resol. for binning is 5m r.in.pdal -e -o input=points.las resolution=5 output=lidar_dem_mean g.region raster=lidar_dem_mean -p r.univar lidar_dem_mean
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
Further hints: how to calculate number of LiDAR points/square meter:
g.region -e # Metric location: # points_per_sq_m = n_points / (ns_extent * ew_extent) # Lat/Lon location: # points_per_sq_m = n_points / (ns_extent * ew_extent*cos(lat) * (1852*60)^2)
The sample LAS data are in the file "Serpent Mound Model LAS Data.las", available at appliedimagery.com:
# print LAS file info r.in.pdal -p input="Serpent Mound Model LAS Data.las" # using v.in.lidar to create a new location # create location with projection information of the LAS data v.in.lidar -i input="Serpent Mound Model LAS Data.las" location=Serpent_Mound # quit and restart GRASS in the newly created location "Serpent_Mound" # scan the extents of the LAS data r.in.pdal -g input="Serpent Mound Model LAS Data.las" # 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.las" \ output=Serpent_Mound_Model_LAS_Data method=mean
Figure: Elevation for the whole area of Serpent Mound dataset
g.region raster=elevation -p r.in.pdal input=points.las output=mean_height_above_ground base_raster=elevation method=mean
points1.laz points2.laz points3.laz
/home/user/data/points1.laz /home/user/data/points2.laz /home/user/data/points3.laz
ls /home/user/data/*.laz > /home/user/data/filelist.txt
dir /b c:\users\user\data\*.laz > c:\users\user\data\filelist.txt
g.region raster=elevation -p r.in.pdal file=/home/user/data/filelist.txt output=mean_height_above_ground base_raster=elevation method=mean
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')
Latest change: Thu Mar 24 20:47:52 2022 in commit: dc57aa34a77eb846a8675fc323b75dc7d3119fa1
© 2003-2022 GRASS Development Team, GRASS GIS 8.0.3dev Reference Manual