Fig. 3: Point cloud processing workflow.
Evaluating Volumetric Measurement Methods
Once the data were classified into ground and vegetation, we
investigated several methods to compare volume with above-ground
biomass. We assessed the data based on a per site (n = 6) and as
combined sites per year, and by combining years. Our interest in
assessing on a per-site basis was to consider possible effects of
changing photography conditions (one site photographed per day), or
influences of the site location such as general vegetation type. Similar
logic was applied to the per year versus both years combined datasets.
We examined how we accounted for voxel volume by using
vegetation-classified points as the volumetric measure (vegetation voxel
volume), and by using the total plot voxel volume with no classification
of vegetation and ground. We performed the latter to test whether litter
or other obstructions had a detrimental effect on the ability to detect
the ground surface. Ultimately, in this model, litter was considered as
part of the biomass volume.
We also computed a vegetation voxel volume above the surface, by fitting
a surface to the ground and computing the vegetation volume above the
surface. Additionally, we examined the relationships between volumes and
vegetation type (e.g. perennial and annual grasses, forbs). Linear
models and logarithmic models were both investigated for the
relationships between voxel volume and biomass.
Finally, we also assessed the scale at which volumetric data is best
reconstructed for sagebrush-steppe vegetation (e.g., fine annual
grasses) and to inform the possibility for using volumes reconstructed
from UAS imagery. This included voxelizing the point clouds at several
resolutions ranging from 2 mm to 100 mm, comparing volumes of all
reconstructed points and volumes of vegetation-classified points only.
Results
- Volume and Biomass Allometry Modeling and Sensitivity
Analysis
Table 1 depicts the models we tested that had a significant
(> 0.30 R2) relationship between voxel
volume and field biomass. We found the best correlation
(R2 = 0.48) with a logarithmic regression between 5 mm
voxel volumes of vegetation-classified points with above ground biomass
for all sites across years (Table 1). We generally found that
logarithmic models performed better than linear models when all plots
were grouped together across years.
A linear regression of plots (n = 36) from both years from South Camp
Kettle (South site) attained an R2 of 0.62 between
classified vegetation points at 5 mm voxel sizes with the biomass of
only annual and perennial grasses (i.e. no litter biomass was included).
However, generally there was no pattern of linear or logarithmic
modeling performing better for any of the comparisons when modeling by
site or by year.
Generally, our vegetation voxel volume calculated from classified
vegetation points (from Fig. 3, workflow) performed better than using a
fitted surface to identify vegetation points and calculate a volume.
Using a total plot voxel volume along with only annual and perennial
grasses had a relatively high R2. No relationship with
forb biomass was found due to their relative rarity in the study area.