Hello Luka,
I assume you mean the pulse density (just counting one return per shot) that is usually computed as the last-return point density? There is an (undocumented ... shame on my head) option to directly compute the density per cell in lasgrid:
lasgrid -i mapua\strips_raw\strip000001.laz ^
-last_only ^
-step 10 -point_density_32bit ^
-odix _dns -obil
You can quickly get a histogram by using the ASC (or better BIL) rasters as input for lasinfo and by creating a histogram over the z value (which stores the area-normalized point density).
lasinfo -i mapua\strips_raw\strips_000001.asc ^
-histo z 0.5 ^
-nh -nv -nmm
lasinfo for mapua\strips_raw\strips_000001.asc
WARNING: there is coordinate resolution fluff (x10) in XY
WARNING: there is serious coordinate resolution fluff (x100) in XY
z coordinate histogram with bin size 0.5
bin [0,0.5) has 430
bin [0.5,1) has 425
bin [1,1.5) has 1670
bin [1.5,2) has 29560
bin [2,2.5) has 82947
bin [2.5,3) has 5049
bin [3,3.5) has 1677
bin [3.5,4) has 1003
bin [4,4.5) has 649
bin [4.5,5) has 537
bin [5,5.5) has 491
bin [5.5,6) has 557
bin [6,6.5) has 527
bin [6.5,7) has 284
bin [7,7.5) has 91
bin [7.5,8) has 37
bin [8,8.5) has 6
bin [8.5,9) has 3
bin [9,9.5) has 2
bin [9.5,10) has 2
average z coordinate 2.21584
And of course you can run this in parallel over a whole folder of strilps:
lasgrid -i mapua\strips_raw\strip*.laz ^
-last_only ^
-step 10 -point_density_32bit ^
-odix _dns -oasc ^
-cores 4
and visualize it in QGIS (see attached picture) and notice how the densities increase towards the edges of the scan because an oscillating mirror was used. More on LiDAR scan patterns here:
Regards,
Martin @rapidlasso