Flags:¶

-d

Use base raster actual resolution instead of computational region

-v

Use only valid points
Points invalid according to APSRS LAS specification will be filtered out

-o

Override projection check (use current location’s projection)
Assume that the dataset has same projection as the current location

-p

Print LAS file info and exit

-s

Scan data file for extent then exit

-g

In scan mode, print using shell script style

--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 file in LAS format (*.las or *.laz)

file=name

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

n=name

Count of points per cell
Name for output 3D raster map

sum=name

Sum of values of point intensities per cell
Name for output 3D raster map

mean=name

Mean of point intensities per cell
Name for output 3D raster map

proportional_n=name

3D raster map of proportional point count
Point count per 3D cell divided by point count per vertical column

proportional_sum=name

3D raster map of proportional sum of values
Sum of values per 3D cell divided by sum of values per vertical column

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.

base_raster=name

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

zscale=float

Scale to apply to Z data
Default: 1.0

intensity_range=min,max

Filter range for intensity values (min,max)

intensity_scale=float

Scale to apply to intensity values
Default: 1.0

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, r3.in.lidar will create the 3D raster of the size given by the computation region:

r3.in.lidar input=points.las n=points_n sum=points_sum \


    mean=points_mean proportional_n=points_n_prop \


    proportional_sum=points_sum_prop

Point density vertical structure reduced to the terrain¶

Create ground raster:

r.in.lidar 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:

r3.in.lidar input=points.las n=points_n sum=points_sum \


    mean=points_mean proportional_n=points_n_prop \


    proportional_sum=points_sum_prop \


    base_raster=ground

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).

r.in.lidar 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

r.in.lidar input=points.las output=ground_mean method=mean class_filter=2

Convert the raster to vector point resulting in a decimated point cloud:

v.to.rast input=ground_mean type=point output=ground_decimated use=z

v.surf.rst input=ground_decimated elevation=ground

r.in.lidar input=points.las method=max output=veg_max class_filter=3,4,5 base_raster=ground -d

g.region rast=secref b=0 t=40 res=1 res3=1 -p3

Finally, we perform the 3D binning where we count number of points per cell (voxel):

r3.in.lidar input=points.las n=n class_filter=3,4,5 base_raster=ground -d