1. Individual Snag Detection
Intro:
All three of these applications utilize an additional lidar data analysis step “Neighborhood point cloud filtering”, which is focused on identifying what forest attribute each lidar point is associated with (e.g., live tree, dead tree, understory woody vegetation, cwd, tree boles, stumps, etc.). The filtering methods use both the intensity and location attributes of the lidar data to help determine what forest attribute each point is most likely associated with. After the filtering is completed the filtered data is used to generate predictor metrics that are more targeted towards the individual attribute(s) of interest (e.g., snags, understory vegetation, cwd, and live tree metrics). These metrics improve models (live tree metrics as well (since the dead tree points are removed…)).
Key to filtering lidar data:
Intensity must be normalized or meaningful and in NIR spectrum for these applications. If it’s Quantum (WSI) data you are good (95% of the time), if its another vendor you are usually not so good (conversions can be applied but are difficult). We now require vendors to normalize intensity in all acquisition contracts.
In the future filtering lidar data will improve with the incorporation additional spectral information.
Individual Snag Detection:
The method filters through overstory lidar and assigns each point to be associated with a live or dead tree. Once the filter is applied, an individual tree segmentation is used on the snag-filtered lidar points to identify individual snags.
In a nutshell:
The method gets about 60% of the snags in natural stand conditions (hasn’t been tried in coastal forests, but guessing the number is lower in these forest types), and 90% in post-fire stand conditions.
Correction factors can be used to generate accurate snag density estimates where necessary.
Overall it identifies the snags that are exposed to the sky, and has a hard time getting the snags fully intermixing with, or underneath, live trees.

2. Lidar Intensity Individual Tree Trends
Not sure if you want to use this slide, but this explains the intensity relationship the snag filtering algorithm utilizes.
If a lidar point intersects solid wood it usually has a lower intensity value, if it intersects foliage it typically has a mid-range intensity value.
We’ve found that higher intensity values are often associated with new growth (shiny leaves or needles) and solid wood with no bark and a hardened white surface.

3. Neighborhood Filtering Variables
Not sure if you want to use this one as well, but thought I’d throw it in…
These are the three neighborhood filtering variables used to filter between live and dead lidar points. If you want more information read the pub…

4. Individual Snag Detection
Pre-Fire Live and Dead
Pre-Fire Snags
Depiction of the snag filtering algorithm applied to a recent pre and post-wildfire acquisitions (not part of the original publication data). The top shows the entire lidar point cloud pre-fire. Lidar points are colored by intensity.
The middle shows the same point cloud after the snag algorithm is applied. Notice the pocket of snags, this is a bark beetle mortality area.
The bottom shows the snag-filtered post-fire lidar data.
We are now applying the snag algorithm across entire acquisition areas, so if anyone is interested in applying the algorithm to their lidar data can contact Brian
Post-Fire Snags

5. Individual Tree Crown Health
There are a number of spin-offs from the snag algorithm as well. This is a depiction of a new one we are currently working on. The lidar points in this movie are colored by the most sensitive of the three snag filtering neighborhood variables (sphere). With this variable we are able to look at individual tree crown health.
This is an area with laminated root disease, the trees comprised of red points are dead, yellow points are associated with sparse crowns, and green and blue points are associated with healthy trees. With this value, we can make out dead top trees, less vigorous trees, etc…
We have also noticed differences between pine and fir species with this variable.

6. Understory Vegetation Cover
This application developed a prediction model for understory vegetation cover. Predictor variables included traditional overstory and understory lidar metrics, but also a new lidar metric “understory cover density” which significantly increased the ability to predict understory vegetation.
The metric is created by segmenting the understory lidar points and then filtering out the understory lidar points associated with non-vegetation (i.e., cwd, stumps, tree boles, etc.) using a simple intensity threshold filter.

7. Understory Vegetation Cover
Understory Lidar Points are Filtered using Intensity
Filter helps remove non-vegetation lidar points.
Improves Ability to Predict Understory Vegetation Cover
Works Better with Shade Intolerant Shrub Species
The metric is created by segmenting the understory lidar points and then filtering out the understory lidar points associated with non-vegetation (i.e., cwd, stumps, tree boles, etc.) using a simple intensity threshold filter.
The method improves our ability to predict understory vegetation cover, but it works much better in forests with shade intolerant shrub species. The method has not been used in coastal forests, and my hypothesis is that it wouldn’t work nearly as well. That said, it’s still better than any thing currently being used…

8. Coarse Woody Debris 450 Plots with Georeferenced Stumps and CWD
Covering Wide-Range of Forested Conditions
Currently working on this application and soliciting help if anyone is interested . We have over 450 plots (225 sqm: ¼ of a 30x30 meter grid cell) throughout Oregon and California where we’ve mapped every piece of CWD (>= 1 foot in height (anywhere along the piece), and all stumps (i.e., snags < DBH)).
We are using overstory lidar metrics coupled with filtered understory lidar metrics to develop a presence vs. absence prediction model.