A Sense of Scale
Authors: Vermillion Monica 
Published: September 2020

The native vegetation communities in the sagebrush steppe, a semi-arid ecosystem type, are under threat
from exotic annual grasses. Exotic annual grasses increase fire severity and frequency, decrease biodiversity,
and reduce soil carbon storage amongst other ecosystem services. The invasion of exotic annual grasses is
causing detrimental impacts to land use by eliminating forage for livestock and creating a huge economic
cost from fire control and post-fire restoration. To combat invasion, land managers need to know what exotic
annual grasses are present, where they are invading, and estimates of their biomass. Mapping exotic annual
grasses is challenging because many areas in the sagebrush steppe are difficult to access; yet field
measurements are the main method to identify and quantify their existence. In this study, we address this
challenge by exploring the use of both landscape-scale and plot-scale observations with remote sensing.
First, we use satellite imagery to map where exotic annual grasses are invading and identify the native
species which are being encroached upon. Second, we investigate the use of fine-scale imagery for
non-destructive measurements of biomass of exotic annual grasses.

Understanding the location of exotic annual grasses is important for restoration efforts, e.g. large
swath (~100m) herbicide spraying. Restoration efforts are expensive and often ineffective in areas
already dominated by exotic annual grasses. Early detection of exotic annual grasses in sagebrush and
native grasses communities will increase the chances of effective ecosystem restoration. We used
Sentinel-2 satellite imagery in Google Earth Engine, a cloud computing platform, to train a random
forest (RF) machine learning algorithm to map vegetation in ~150,000 acres in the sagebrush steppe
in southeast Idaho. The result is a classification map of vegetation (overall accuracy of 72%) and a
map of percent cover of annual grass (R2 = 0.58). The combination of these two maps will
allow land managers to target areas of restoration and make informed decisions about where to allow grazing.

In addition to knowing what exotic annual grasses exist and their percent cover, detailed information
about their biomass is important for understanding fuel loads and forage quality. Structure from Motion
(SfM) is a photogrammetry technique that uses digital images to develop 3-dimensional point clouds that
can be transformed into volumetric measurements of biomass. The SfM technique has the potential to
quantify biomass estimates across multiple plots while minimizing field work. We developed allometric
equations relating SfM-derived volume (m3) to biomass (g/m2) for a study area in
southeast Oregon. The resulting equation showed a positive relationship (R2 = 0.51) between
the log transformed SfM-derived volume and log transformed biomass when litter was removed. This relationship
shows promise in being upscaled to larger surveys using aerial platforms. This method can reduce the
need for destructively harvesting biomass, and thus allow field work to cover a greater spatial extent.
Ultimately, increasing spatial coverage for biomass will improve accuracy in quantifying fuel loads and
carbon storage, providing insights to how these exotic plants are altering ecosystem services.