forest_structure
Borrowed from: http://irwantoshut.com

Continuing my series on slicing LiDAR data in order to analyze a forest, one of the objectives of the current project is to understand the habitats that particular species of birds prefer. This will be accomplished using field info from breeding bird surveys combined with LiDAR data of forest structure to help predict what habitats are necessary for particular species of breeding birds.

There are a number of studies doing just this for a variety of regions which I will detail later, but suffice it to say, structure of vegetation matters a lot to birds, so using LiDAR to map out structure can be an important predictive tool for mapping bird habitat. Being able to do that at scale across entire ecoregions—well, that’s just an exciting prospect.
Let’s get to some coding. I would like to take a few slices through our forest based on functional height groups:

Forest Canopy >15 meters
Sub-Canopy 10-15 meters
Tall Shrub 2-10 meters
Short Shrub 0.5-2 meters

(For the record, we don’t have to get these perfectly right. Sub-canopy, for example could be taller or shorter depending on how tall a forest it is in. This is just a convenience for dividing and reviewing the data for the time being. Also, we’ll split a little finer than the above numbers just for giggles.)

We’ll start by just echoing our pdal translate to make sure we like what we are getting for output:

for START in 0:0.5 0.5:1 1:2 2:5 5:10 10:15 15:20 20:35 35:50 50:75 75:100 100:200
 do
  nameend=`echo $START | sed s/:/-/g`
  echo pdal translate 54001640PAN_heightasz.bpf 54001640PAN_heightasz_$nameend.bpf -f range --filters.range.limits="Height[$START)"
done

Thus we get the following output:

pdal translate 54001640PAN_heightasz.bpf 54001640PAN_heightasz_0-0.5.bpf -f range --filters.range.limits=Height[0:0.5)
pdal translate 54001640PAN_heightasz.bpf 54001640PAN_heightasz_0.5-1.bpf -f range --filters.range.limits=Height[0.5:1)
pdal translate 54001640PAN_heightasz.bpf 54001640PAN_heightasz_1-2.bpf -f range --filters.range.limits=Height[1:2)
pdal translate 54001640PAN_heightasz.bpf 54001640PAN_heightasz_2-5.bpf -f range --filters.range.limits=Height[2:5)
... etc.

Let’s remove our echo statement so this actually runs:

for START in 0:0.5 0.5:1 1:2 2:5 5:10 10:15 15:20 20:35 35:50 50:75 75:100 100:200
 do
  nameend=`echo $START | sed s/:/-/g`
  pdal translate 54001640PAN_heightasz.bpf 54001640PAN_heightasz_$nameend.bpf -f range --filters.range.limits="Height[$START)"
done

We should generalize this a bit too. Let’s make this a script to which we can pass our filenames and ranges:

#!/bin/bash
namer=`basename $1 .bpf`
for START in $2
 do
  nameend=`echo $START | sed s/:/-/g`
  pdal translate $namer.bpf $namer"_"$nameend".bpf" -f range --filters.range.limits="Height[$START)"
done

Which to run, we use a statement as follows:

./tree_slicer.sh 54001640PAN_heightasz.bpf "0:0.5 0.5:1 1:2 2:5 5:10 10:15 15:20 20:35 35:50 50:75 75:100 100:200"

I forgot all my data is in feet, but my height classes in meters, so we’ll just multiply our input values by a factor of 3 to get in the same ballpark (future refinement likely):

./tree_slicer.sh 54001640PAN_heightasz.bpf "0:1.5 1.5:3 3:6 6:15 15:30 30:45 45:60 60:105 105:150 150:200"

We could alternatively stick to our original categories (short shrub, tall shrub, sub-canopy, canopy) as our break points:

./tree_slicer.sh 54001640PAN_heightasz.bpf "0:1.5 1.5:6 6:30 30:45 45:200"

Finally, we can convert to laz, and load all our slices of point clouds in plas.io an animate between the slices

for OUTPUT in $(ls *.bpf); do docker run -v /home/gisuser/test/test:/data pdal/master pdal translate //data/$OUTPUT //data/$OUTPUT.laz; done

If you look closely, you should be able to see where a tornado in the 80s knocked down much of the forest here. It’s signature is in the tall shrub / sub-canopy layer: