Discussion: Lidar and vegetation mapping

The available descriptions of vegetation mapping with lidar seem to rest on the assumption that lower vegetation height equates with slower vegetation. Has anyone developed a reliable method of detecting thicker understory beneath a forest canopy?

So far in my limited experience I've been making the grid using all the last returns and rendering a half-meter resolution relief image using OL Laser, and creating a mask from that by manually painting over in gimp, then exporting into OCAD using a custom tool.

You can find plenty of articles publishing various ways to estimate near-ground vegetative characteristics for forestry and ecological purposes. Almost all bin the number of data points within a given 3D cube (sometimes called a voxel) or other shape above the bare earth (ground) surface as calculated. But it's bound to be tricky, and to depend on plenty of factors including the overall point density and the vegetative density, and the exact nature of the vegetation in a given area. Results obtained in one habitat may have little applicability in another.

Anyway, it's not lower vegetation height that may correlate with "slower" vegetation; it's the density of the near-ground (0 - 2 meters) vegetation that may correlate, at least for some terrains.

"Anyway, it's not lower vegetation height that may correlate with "slower" vegetation; it's the density of the near-ground (0 - 2 meters) vegetation that may correlate, at least for some terrains."
That has been my approach so far. I have selected the returns above ground and less than 2 metres and taken the density function of these. Being highly uninformed in this area, wondered if there was a better approach.

Other than varying your parameters until they give results closest to what a good O'map of the area shows, so you can then apply those settings to a similar but as-yet-unmapped area, I'm not aware of any better approach using (aerial) LiDAR on its own.

It is possible to combine aerial LiDAR in various ways with other datasets, including infrared data, and laser scanning itself is also possible horizontally using side-scanning systems to get density measurements. I'm not aware of anyone who's gone to that much trouble for an O'map, however.

I select all returns between 0.5-2.5m above bare earth and then count the number (measure the density of those returns) in a 6x6m spatial bin. I've played a bit with the binsize - its just a trade between S/N and resolution. Will depend some on your post spacing. Then I resample back to my original grid spacing - usually 1m/pix - and use a green color scale (white to green, tuned to roughly match the O colors of corresponding density). I write this image to a .bmp and use it manually as a template - its more useful in the field than in the armchair. Trying to draw in areas from the template alone is less fruitful except for the highest density blobs, but its quite nice to have while fieldchecking. An example with existing stereophoto produced O-map to compare to on the last few pages of this .ppt presentation. Most of the understory under canopy here is Mt. Laurel, which is a broadleaf evergreen shrub.

Thanks for that. The binning might be what I need.

Thanks for sharing that ppt eddie, a nice collection of examples.

Eddie, did you try the kernel density estimator? It kinda takes the guesswork out of the bin size. You can then evaluate the density function on a grid of your choosing.