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NAME - Creates a 3D raster map from LAS LiDAR points using univariate statistics.


raster3d, import, LIDAR, statistics, conversion, aggregation, binning

SYNOPSIS --help [-dvopsg] [input=name] [file=name] [n=name] [sum=name] [mean=name] [proportional_n=name] [proportional_sum=name] [return_filter=string] [class_filter=integer[,integer,...]] [base_raster=name] [zscale=float] [intensity_range=min,max] [intensity_scale=float] [--overwrite] [--help] [--verbose] [--quiet] [--ui]


Use base raster actual resolution instead of computational region
Use only valid points
Points invalid according to APSRS LAS specification will be filtered out
Override projection check (use current location's projection)
Assume that the dataset has same projection as the current location
Print LAS file info and exit
Scan data file for extent then exit
In scan mode, print using shell script style
Allow output files to overwrite existing files
Print usage summary
Verbose module output
Quiet module output
Force launching GUI dialog


LAS input file
LiDAR input file in LAS format (*.las or *.laz)
File containing names of LAS input files
LiDAR input files in LAS format (*.las or *.laz)
Count of points per cell
Name for output 3D raster map
Sum of values of point intensities per cell
Name for output 3D raster map
Mean of point intensities per cell
Name for output 3D raster map
3D raster map of proportional point count
Point count per 3D cell divided by point count per vertical column
3D raster map of proportional sum of values
Sum of values per 3D cell divided by sum of values per vertical column
Only import points of selected return type
If not specified, all points are imported
Options: first, last, mid
Only import points of selected class(es)
Input is comma separated integers. If not specified, all points are imported.
Subtract raster values from the z coordinates
The scale for z is applied beforehand, the filter afterwards
Scale to apply to Z data
Default: 1.0
Filter range for intensity values (min,max)
Scale to apply to intensity values
Default: 1.0

Table of contents


The module is very similar to the module and many parts of its documentation apply also for

Figure: Proportional count of points per 3D cell. When 50% of all points in a vertical column fall into a given 3D cell, the value is 0.5. Here, the green color was assigned to 0.5, red to 1 and yellow to 0. The figure shows vertical slices and green color indicates high vegetation while red color indicates bare ground.



Basic import of the data

Set the region according to a 2D raster and adding 3D minimum (bottom), maximum (top) and vertical (top-bottom) resolution.
g.region rast=secref b=80 t=160 tbres=5 -p3
Now, will create the 3D raster of the size given by the computation region: input=points.las n=points_n sum=points_sum \
    mean=points_mean proportional_n=points_n_prop \

Point density vertical structure reduced to the terrain

Create ground raster: input=points.las output=ground method=mean class_filter=2
Set vertical extent of computational region to (relative) coordinates above ground:
g.region rast=secref b=0 t=47 -p3
Compute point density: input=points.las n=points_n sum=points_sum \
    mean=points_mean proportional_n=points_n_prop \
    proportional_sum=points_sum_prop \

Complete workflow for vertical structure analysis

Compute the point density of points in 2D while setting the output extent to be based on the data (-e) and the resolution set to a relatively high number (here 10 map units, i.e. meters for metric projections). input=points.las output=points_n method=n -e resolution=10
This step can be repeated with using different resolutions (and the --overwrite flag) to determine the resolution for the further analysis.

The class_filter option should be also provided if only part of the points is analyzed, for example class_filter=3,4,5 would be used for low, medium, and high vegetation if the LAS file follows the usedstandard ASPRS class numbers.

The resolution should be suitable for computing digital elevation model which will be computed in the next steps. Once you decided on the resolution, you can use the 2D raster to set the computational region extent and resolution:

g.region raster=points_n -p3
class_filter=2 is used to limit input=points.las output=ground_mean method=mean class_filter=2
The following steps are not necessary if the point density is high enough to fill the raster continuously.

Convert the raster to vector point resulting in a decimated point cloud: input=ground_mean type=point output=ground_decimated use=z
Interpolate the ground surface from the decimated ground points: input=ground_decimated elevation=ground
Now we need to determine upper vertical limit for the 3D raster (the top value from g.region -p3). This can be potentially done with lower resolution. The -d flag ensures that the ground raster will be used in its actual resolution regardless of the resolution of the output. input=points.las method=max output=veg_max class_filter=3,4,5 base_raster=ground -d
With that, we can finally set up the 3D extent and resolution:
g.region rast=secref b=0 t=40 res=1 res3=1 -p3
Note that the 2D and 3D resolutions are separate so that user can perform the 2D calculations on a finer resolution than the 3D calculations. The vertical resolution can be and often is set to a different value as well. Here we use the same value for all resolutions for simplicity.

Finally, we perform the 3D binning where we count number of points per cell (voxel): input=points.las n=n class_filter=3,4,5 base_raster=ground -d


SEE ALSO,,,,, r3.mapcalc, g.region


Vaclav Petras, NCSU GeoForAll Lab


Available at: source code (history)

Latest change: Monday Feb 27 13:45:25 2023 in commit: a82501dc85294207e8e58437c3ec7f1056465ed4

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