Keywords: rangeland, fine fuels, medusahead, SfM, biomass
Introduction
Semi-arid ecosystems act as carbon sinks and are thought to play a major
role in global interannual carbon variations (Ahlström et al. 2015). In
addition to soil carbon sequestration, semi-arid ecosystems at regional
scales provide important ecosystem services such as wildlife habitat and
mesic refugia. Semi-arid ecosystems, such as in the western U.S. are
water-limited and pressures from climate change and population growth
make these lands vulnerable to degradation. Specifically, the
sagebrush-steppe ecosystem is currently under threat from the invasion
of exotic annual grasses, such as cheatgrass (Bromus tectorum L.)
and medusahead (Taeniatherum caput-medusae (L.) Nevski), which
are decreasing biodiversity (Knapp, 1996), altering the fire cycle
(Bradley et al., 2018), and reducing carbon storage (Bradley et al.,
2006).
Quantifying vegetation biomass is critical for assessing ecosystem
structure, tracking vegetation growth, and quantifying carbon storage
(Houghton et al., 2009). Above ground biomass (AGB) in semi-arid
ecosystems helps scientists and land managers better understand the
contribution of these ecosystems to the global carbon flux, and the
impacts of ecosystem shifts towards desertification (Chambers et al.,
2014). In particular, AGB is defined as the dried weight of vegetation
above the ground including both alive and dead components, is difficult
to accurately measure in semi-arid ecosystems because of the
heterogeneity and fine-scale structure of vegetation (Fern et al., 2018;
Wijesingha et al., 2019). This is especially so for invasive annual
grasses such as medusahead and cheatgrass, where previous growth can
become a mat of dry litter, decreasing native vegetation growth and
further promoting the invasive annual grass species (Evans & Young
1970). As a result, an increase in the continuity of fine fuel loads
occurs. This build-up of fine fuels, coupled with senescence early in
the growing season promote ignition and increased fire spread. This
feedback loop, ‘invasive grass-fire cycle’ further results in
degradation of semi-arid ecosystems (Fernández-Guisuraga et al. 2022).
Management approaches to the invasive grass-fire cycle are varied across
techniques and spatial and temporal scales. Approaches such as
re-seeding, grazing, and fuel breaks, aim to effect and observe changes
in a plot, pasture, or regional scales (100s-1000s
km2). Where the sites may be relatively small in
extent (e.g. grazing exclosure scales, or m to km), there is a benefit
of reducing the need to destructively harvest samples to observe changes
while reducing impact on the vegetation. Monitoring data at high spatial
resolutions and with low uncertainty are needed to provide baseline
information to both of these scales to be able to extrapolate
measurements with greater certainty, and where changes over time and in
response to treatments and climate are valuable to observe.
How AGB is Measured in the Field, Destructively and
Non-Destructively
Current methods for collecting AGB can be described as a combination of
site-specific and extrapolated measurements. An example of site-specific
measurements is the destructive harvesting of biomass. Often
quantification of biomass at the plot scale is extrapolated to larger
scales (Clark et al., 2008). Further, in the field plot-level data
collection of plants are destructively harvested, dried, and weighed to
obtain a biomass metric, or point frame data is used to relate to
biomass. The manual process itself has uncertainty in the collection
from human error. Often these plots are small, and the biomass can vary
significantly across spatial scales, even within 1 m or less. The act of
removing plants itself alters the landscape and impacts future studies
of those plots. Advances in remote sensing systems like lidar, unmanned
aerial systems (UAS), and structure from motion (SfM) software have led
to advances in quantifying biomass in dryland ecosystems (Anderson et
al., 2018; Cunliffe et al., 2016). Fine (spatial/structural) resolution
remote sensing has potential to help solve the above rangeland
vegetation challenges. SfM is photogrammetry, the method of using 2D
stereoscopic images and detecting common points, resulting in a 3D
reconstruction in the form of a point cloud. SfM provides a similar data
product to lidar: a point cloud in which vegetation structure can be
interpreted. However since SfM is based on optical passive imagery it
does not penetrate the canopy (Salamí et al., 2014). SfM and lidar are
similar in their point cloud reconstruction, however because lidar is
active, it has an intensity associated with each point. Additionally,
lidar can be collected as full-waveform or in discrete returns which can
characterize the understory and ground surface (Wallace et al. 2017).
In contrast, SfM is derived from passive remote sensing relying on
optical imagery to create point clouds where each point has an
associated color value for the spectra of the optical image. This value
is rarely radiometrically calibrated. SfM point clouds are not
discretized from a waveform but developed based on the overlap of
imagery taken. Vegetation parameters such as volume, can be derived from
point clouds to develop allometric relationships between vegetation and
destructively harvested biomass. SfM offers a low-cost, time efficient,
and in some cases, more accurate method compared to terrestrial laser
scanning (TLS) for estimating vegetation structure in dryland ecosystems
(Cooper et al. 2017, Olsoy et al., 2018; Wallace et al., 2017).
Extremely close-range SfM, defined by the use of hand-held instruments
to collect the imagery for the SfM method, has shown success comparable
to TLS in deriving parameters such as height and volume from the
reconstruction of individual trees (Miller et al., 2015). In grasslands,
extremely close-range SfM was shown to outperform TLS and traditional
height measurements in developing allometric equations, in part because
SfM can capture finer details compared to TLS, dependent upon the
experimental setup (Cooper et al., 2017). TLS at plot scale (Anderson et
al. 2017) and TLS and hand-held camera photos at 0.5 m x 0.5 m
(0.46*0.46, Cooper et al. 2017) had high correlations between SfM and
TLS and AGB. SfM has been shown to successfully capture grasses and
reconstruct fine features such as leaves and stems (Cooper et al. 2017,
Kröhnert et al., 2018). These previous studies demonstrate that SfM
shows promise in providing accurate information for allometric equations
used to extrapolate biomass of rangeland grass to a landscape-scale.
Field work for capturing extremely close-range SfM imagery requires less
training for field crews compared to TLS and UAS data collection and
eliminates the need for specialized equipment. Developing an allometric
relationship at a plot scale (cm) will inform future SfM studies that
utilize UAS imagery (Gillan et al., 2019). Furthermore, close range SfM
can be complementary and used as a precursor for extrapolating to scales
that are amenable to commonly available UAS (e.g., m to km). However,
vegetation and soil classification in such a fine resolution dataset
remains challenging. Spectral reflectance in the visible spectrum and
near-infrared may be able to better separate vegetation and dry biomass
litter from one-another, or from the mineral soil or ground surface.
Although differences in the soil composition can cause different types
of confusion with vegetation and litter, possibly reducing
transferability of methods between study sites (Huang et al., 2007), in
areas where the ground surface can be largely obscured may benefit from
other approaches such as structural analyses from either SfM or active
remote sensing technologies may be more successful (Calders et al.,
2015).
A common technique for estimation of AGB relies on Digital Terrain Model
(DTM) and Digital Surface Model (DSM) computation and the retrieved
Canopy Height Model (CHM) and Leaf Area Index (LAI) from them (Ota et
al., 2015, Zhang et al., 2018, Xu et al., 2019). The biomass from lidar
and UAS-derived point clouds CHM is generally classified into ground and
vegetation by setting a height threshold (Cunliffe et al., 2016,
Viljanen et al., 2018, Näsi et al., 2018, Navarro et al., 2020).
However, the accuracy of CHM mostly exceeds the height of short shrubs
and grasses, and thus CHM models are less suitable for aboveground
biomass estimation in dryland ecosystems (Zahawi et al., 2015).
Schulze‐Brüninghoff et al. (2021) suggested a method combining
lidar-derived metrics (sum of voxels, canopy height model, and canopy
surface structure) and vegetation spectral properties to estimate fresh
and dry biomass in a machine learning approach. In addition, by
combining ground camera and UAS, Taugourdeau et al. (2022) extracted
vegetation metrics from RGB bands and height to estimate aboveground
biomass in a random forest model. Therefore, the uncertainty of biomass
estimation is limited to the uncertainty in CHM, data resolution, and
the type of land treatments (grazed or ungrazed). In this regard, the
discrimination between vegetation and bare ground structural properties
(smoothness of the surface, randomness of vegetation foliage, and
curvature) could improve AGB estimation from high-resolution ground
camera and UAS SfM-derived point clouds.
The objectives of this work are to explore the potential for close-range
SfM to quantify fine-fuel AGB, with the thought of replacing
destructively harvested sampling with the methodology, or to utilize the
close-range SfM volumetric estimations to inform potential UAS data
collection. To accomplish this, we first developed a method to acquire
and process field data from photos to point clouds. Second, we explored
how to separate and classify the ground surface through the thick litter
layer found in the study area. Third, we examined the relationship
between the measurement unit of volumetric calculation and the biomass
of the study area’s vegetation. Lastly, we discuss some observations on
how the SfM point frame might be used, and how this method may inform
UAS data seeking to estimate biomass or vegetation volume.
Our study area is the Three Fingers allotment located in southeastern
Oregon (Fig. 1), approximately 80 km west of Boise, Idaho. The Vale
District U.S. Bureau of Land Management (BLM) manages the allotment,
which has a history of disturbance from grazing, recreation, and fire.
The area has experienced significant departure from native vegetation
communities, with few remaining native shrubs and an extensive cover of
cheatgrass and medusahead, and other annual grasses (>80%
in all experimental exclosures).
This study was part of a larger study focused on evaluating the effect
of dormant season grazing on fine fuels (Arispe et al. 2022). As part of
the project, data were collected across two years and on three different
pastures on the Three Fingers allotment—approximately 55,000 hectares.
The three pastures within the project included, McIntyre (MCI; 3100 ha),
South Camp Kettle (SCK; 2500 ha), and Saddle Butte (SB; 3800 ha). Within
each pasture a northern and southern exclosure was randomly placed, for
a total of six treatment areas. Each exclosure contained four paddocks
150 m x 150 m in size, for a total of 24 paddocks.