Voxel Volumes and Biomass: estimating vegetation volume and litter
accumulation of exotic annual grasses using automated ultra-high
resolution SfM and advanced classification techniques
Abstract
Rangelands and semi-arid ecosystems are subject to increasing changes in
ecologic makeup from a collection of factors. In much of the northern
Great Basin, rangelands invaded by exotic annual grasses such as
cheatgrass (Broumus tectorum) and medusahead (Taeniatherum
caput-medusae) are experiencing an increasingly short fire cycle which
is compounding and persistent. Improving and expanding ground-based
field methods for measuring above-ground biomass (AGB) may enable more
sample collections across a landscape and over succession regimes, and
better harmonize with other remote sensing techniques. Developments and
increased adoption of uncrewed aerial vehicles and instrumentation for
vegetation monitoring are enabling greater understanding of vegetation
in many ecosystems. Research towards understanding the relationship of
traditional field measurements with newer aerial platforms in rangeland
environments is growing rapidly, and there is increasing interest in
exploring the potential use both to quantify AGB and fine fuel load at
pasture scales. Our study here uses relatively inexpensive handheld
photography with custom sampling frames to collect and automatically
reconstruct 3D-models of the vegetation within 0.2 m2 quatrats (n =
288). Next, we examine the relationship between volumetric estimates of
vegetation to compare with biomass. We found that volumes calculated
with 0.5 cm voxel sizes (0.125 cm3) most closely represented the range
of biomass weights. We further develop methods to classify ground
points, finding a 2% reduction in predictive ability compared to using
the true ground surface. Overall, our reconstruction workflow had an R2
of 0.42, further emphasizing the importance of high-resolution imagery
and reconstruction techniques. Ultimately, we conclude that more work is
needed of increasing extents (such as from UAS) to better understand and
constrain uncertainties in volumetric estimations of biomass in
ecosystems with high amounts of invasive annual grasses and fine fuel
litter.