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Assessing ecosystem services in high-shrub rangelands using two herbaceous cover sampling methods: Photography-based grid point intercept (GPI) and quadrat sampling.

  • Working Lands Conservation

Abstract and Figures

• We used photography-based grid point intercept (GPI) analysis and Daubenmire to assess ecosystem services in high-shrub rangelands. • Cover estimates were higher for some functional groups when using Daubenmire, likely because Daubenmire frames were situated below the shrub canopy and thus included subcanopy cover, whereas GPI photographs taken above the canopy could not eliminate shrubs that obscured subcanopy attributes. • Choice of methods affected assessment of two ecosystem services: sage-grouse habitat quality and site biodiversity; each was higher when using Daubenmire. • Understanding cover-estimate differences that stem from using GPI photo plots versus Daubenmire will allow practitioners to decide if GPI methods address project objectives.
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Technical Notes
A Comparison of Two
Herbaceous Cover Sampling
Methods to Assess Ecosystem
Services in High-Shrub
Rangelands: Photography-Based
Grid Point Intercept (GPI) Versus
Quadrat Sampling
By Kristin B. Hulvey, Katherine Thomas, and Eric Thacker
On the Ground
We used photography-based grid point intercept
(GPI) analysis and Daubenmire to assess ecosys-
tem services in high-shrub rangelands.
Cover estimates were higher for some functional
groups when using Daubenmire, likely because
Daubenmire frames were situated below the shrub
canopy and thus included subcanopy cover, whereas
GPI photographs taken above the canopy could not
eliminate shrubs that obscured subcanopy attributes.
Choice of methods affected assessment of two
ecosystem services: sage-grouse habitat quality and
site biodiversity; each was higher when using
Understanding cover-estimate differences that stem
from using GPI photo plots versus Daubenmire will
allow practitioners to decide if GPI methods address
project objectives.
Keywords: canopy cover, daubenmire, ecosystem
service, great basin, herbaceous cover, methods
Rangelands 40(5):152159
doi 10.1016/j.rala.2018.08.004
©2018 The Authors. Published by Elsevier Inc. on
behalf of The Society for Range Management. This is an
open access article under the CC BY-NC-ND license
angelands in the Intermountain West provide
multiple ecosystem services including forage for
livestock, habitat for sage-grouse, and reservoirs
of plant biodiversity.
One way to assess these
services is by monitoring the cover of herbaceous species.
Because cover can be assessed using many methods,
researchers often choose those best paired with project
Common methods to assess cover include ocular estimates
of cover in quadrats and point intercept.
Because these
methods have been used for over 50 years in rangeland
monitoring, their benefits and drawbacks are well
New techniques for monitoring cover, on
the other hand, require field testing to determine how they
compare and when they may be preferable to established
A method increasingly being used in rangeland monitoring
is photography-based, grid point intercept (GPI) analysis.
Developed with the advent of high-resolution digital cameras,
this method is similar to and builds upon point-frame methods.
As with traditional point-frames, the GPI method entails
identifying plants touched by a grid of pins.However, rather
than cover and composition data being collected directly from
quadrats placed across sampling sites in the field, researchers
take nadir photographs of plots, which are analyzed at a later
time in the lab. These photographs are uploaded into a
computer program that imposes a cross-hair pixel grid across
the photo plot. The pixels act as the pinsused in traditional
point intercept sampling, with intersected species identified
and counted.
Previous studies highlight advantages of GPI photo plot
methods including time savings in the field, reduced need for field
labor and thus lower monitoring costs, and the ability to analyze
data hours to months after originally collected.
Despite such
benefits, it is possible that the value of using GPI methods varies by
Evaluating how GPI compares with other commonly
used sampling methods to achieve project objectives can help
managers understand similarities and differences among the
methods. Managers and researchers can use this information to
choose methods that most closely meet monitoring needs.
Our goal was to examine whether three rangeland ecosystem
services that can be estimated by measuring herbaceous cover and
presence would be assessed similarly when using GPI photo plots
and Daubenmire quadrats. These two methods for measuring cover
can lead to different estimates for a number of reasons. First,
attributes of the vegetation found in the study area might affect
estimates. This is particularly important for GPI because the
photographs used to assess cover are taken from a distance above
quadrats. Landscapes with shrubs that block the view of herbaceous
understory may lead to unreliable understory cover estimates.
Second, quadrat and point intercept methods measure slightly
different types of cover. Quadrat methods commonly measure
canopy coverdefined as the vertical projection of vegetation on
the ground that includes the area of small canopy gaps.
In contrast,
point intercept methods often measure foliar coverdefined as the
vertical cover projection of plantsthatexcludescanopygaps.
When employing a single methodology to measure
multiple ecosystem attributes, it is important to ensure that
the method can do so adequately.
We aimed to estimate the
following: 1) sage-grouse habitat quality via cover of perennial
forbs, 2) livestock forage measured as perennial grass cover,
and 3) herbaceous species diversity by identifying total
number of plant species across plots. For GPI sampling we
used photo plots analyzed with SamplePoint,
a computer-
based GPI analysis software. We used Daubenmire plots for
our quadrat-based analysis because this method historically
has been used to assess herbaceous cover of sage-grouse
habitat in the Intermountain West rangelands.
The rangelands we worked in had high shrub cover; over 75%
of sites had at least 25% shrub cover. We thus anticipated the two
methods might produce significantly different herbaceous cover
estimates. In particular, we expected the quadrat-based method
would allow access to herbaceous cover beneath the shrub canopy,
whereas the GPI method, which was based on photographs
taken above shrubs, would restrict understory quantification
leading to underestimation of sub-shrub cover. Because all three
of our ecosystem services (sage-grouse habitat, livestock forage,
and species richness) are potentially affected by the herbaceous
cover both under shrubs and in the inter-shrub space, our
comparison provides a test of whether GPI and Daubenmire
methods lead to similar assessments of these ecosystem services.
Rangeland Sites
Our study took place in Rich County, northeastern Utah
(41°24N; 111°13W) during the spring and summer of 2015.
The area is sagebrush-steppe, semiarid cold desert, with
elevation ~1,915 m. Annual precipitation is ~ 34.2 cm (30
year average 19812010)
with the majority arriving as snow.
Temperatures range from an average of 9°C in winter to
17°C in summer.
Shrubs are common, and consist mainly of
sagebrushes (e.g., Big, Wyoming, Mountain, Black, and Low
[Artemisia tridentata, A. tridentata ssp. Wyomingensis, A.
tridentata ssp. vaseyana, A. nova, A. arbuscula ssp. arbuscula,
respectively]), but also rabbitbrush (Chrysothamnus viscidi-
florus,Ericameria nauseosa), snowberry (Symphoricarpos albus),
serviceberry (Amelanchier alnifolia), and Gray Horsebrush
(Tetradymia canescens). Grasses are mostly perennial species
and include Mutton grass (Poa fendleriana), sandberg
bluegrass (Poa secunda), western wheatgrass (Pascopyrum
smithii), and bluebunch wheatgrass (Pseudoroegneria spicata).
The non-native, invasive cheatgrass (Bromus tectorum) is rare,
but spreading. It can increase fire risk
and reduce rangeland
plant diversity, which may have negative consequences for
wildlife such as sage-grouse.
Its detection is important to
local managers and ranchers. There is also significant forb
diversity, with common forbs including pussytoes (Antennaria
spp.), spiny phlox (Phlox hoodii), silvery lupine (Lupinus
argenteus), and sulfur buckwheat (Eriogonum umbellatum).
Data Collection
We collected vegetation data from 20 May to 6 July 2015 on
51 transects located across 5,650 ha of public rangelands
managed by the Bureau of Land Management. The location of
transects was determined by first stratifying the landscape into
five categories based on elevation, aspect, vegetation type, and
soil type. Then, 9 to 12 transects were randomly located within
each landscape category and marked with fiberglass posts.
Data for both Daubenmire and GPI methods were
collected at the same time along the same transects. To
collect Daubenmire data, we ran a 50-m tape between posts
and placed a 50 x 20 cm Daubenmire frame at 10-m intervals
along the transect (n = 4 per transect). We then estimated
canopy cover of herbaceous species, bare ground, litter, rock,
and woody debris in frames using cover classes: b1, 15,
615, 1625, 2635, 3645, 4655, 5665, 6675, 7685,
8695, N95%. Shrubs were excluded from cover estimates
when using Daubenmire method. The field crew received
detailed field training supervised by an experienced field
ecologist to ensure calibration of ocular cover estimates.
Next, to collect GPI photo plots, we took photographs
from 1.1 m above each Daubenmire plot using a monopod to
position the camera directly above the sample area. This
resulted in images that frequently included shrubs (Fig. 1A
and B). After the field season ended, we used the computer
program SamplePoint
to estimate cover in photo plots in
the lab. We first used the program to generate a 100-point
pixel grid on photographs (Fig. 2A and B), then manually
identified plants intersected by the grid, recording the species
of each plant and number of hitsin the program interface.
We also recorded the number of hits on bare ground, litter,
rock, and woody debris. Because photos included shrubs, we
could not exclude these from our estimates.
October 2018 153
To reduce observer bias, one crew member interpreted
SamplePoint data with supervision of the field crew leader.
The crew member also collected Daubenmire data along
transects, which provided working knowledge of local plants.
We do not think Daubenmire sampling biased the Sample-
Point cover analysis because the time elapsed between field
data collection and SamplePoint analysis was at least 1 month,
and the large number of quadrats sampled would make
remembering cover in any single subplot difficult.
We calculated cover of all species, bare ground, litter, rock,
and woody debris using the transect as the sampling unit for
both GPI and Daubenmire methods. For Daubenmire, we
used standard methods to calculate these transect cover
values from quadrat subsampling units as summarized in
Coulloudon et al.
For GPI analysis, the SamplePoint
program used the recorded number of hits on each species
or attribute to generate plot cover percentages, which we
converted to transect values by averaging across plots
belonging to the same transect (n = 4).
To determine the cover of functional groups including total
herbaceous vegetation, perennial forb, and perennial grass using
Daubenmire methods, we summed the midpoint value per
cover class across all species belonging to individual functional
groups within a quadrat, then averaged these values across
quadrats per transect. We acknowledge that this procedure
introduces error, especially if cover values of individual species
are not well represented by the cover classesmidpoint when
summed within plots and averaged across transects.
procedure, however, is used in the field,
and we wanted to
understand how binned functional group cover values compared
with cover values generated via GPI methods.
To determine GPI functional group cover per photo frame,
we summed the total number of hits on species belonging to
each functional group and divided this value by the total
number of pins. We then determined transect cover values by
averaging subplot values per transect. Because GPI photos are
Figure 1. Representative sampling plots with (A) low- and (B) high-shrub cover. Photos taken along transects in Rich County, UT, 16 and 22 June and
2015, respectively.
Figure 2. Screen shot of grid point intercept photo-plot program SamplePoint, showing (A) the entire photograph loaded into the program (no digital pins
on photo), and (B) the image zoomed to a single pixel (i.e., digital pin).
nadir photos taken above vegetation, the method can only
sample the top layer of vegetation, excluding below-shrub
cover. We assessed GPI cover as absolute cover (including all
points, whether hitting shrub or nonshrub attributes) rather
than relative cover (using only points hitting nonshrub
attributes) because functional group cover can be different
under shrubs and in shrub interspaces,
and because other
studies examining cover methods have similarly used absolute
cover in analyses.
In transects with high-shrub cover, this can
result in lower values of other functional groups but accurately
reflects what technicians see when analyzing photographs.
Perennial forbs and perennial grasses contribute directly to
our target ecosystem services (e.g., sage-grouse habitat quality
and forage availability for livestock, respectively). We thus
examined differences in their cover as sampled by Daubenmire
versus GPI methods. We used separate general linear model
(GLM) analyses in SPSS,
with sampling methodology
(Daubenmire/GPI) and shrub level (low/mid/high) as categor-
ical fixed factors, plus an interaction term that revealed how
Daubenmire and GPI assessments differed across levels of
shrub cover. When the interaction term was insignificant, we
reran analyses without the term.
We determined shrub levels
from GPI sampling and included the following three binned
levels: low: 021%, mid: 2242%, and high: 4363% cover.
Shrub All Bare Rock Woody
herbaceous ground debris
Cover (%)
Funconal Group
Grid point intercept
Daubenmire frames
* *
Figure 3. Mean cover (%) and standard error (1 SE) for total herbaceous cover (forbs + grasses + sedges), bare ground, rock, and woody debris, estimated
using Daubenmire versus photography-based grid point intercept plots across 51 transects. Separate general linear model analyses were conducted for
each functional group, with sampling methodology included as a categorical fixed factor. Significant Pvalues for this factor are noted with an asterisk (*).
Lier cover (%)
Shrub Level
Low Mid High
Grid point intercept
Daubenmire frames
Figure 4. Mean cover (%) and standard error (1 SE) for litter at low-, mid-,
and high-shrub levels, estimated using Daubenmire versus photography-
based grid point intercept plots across 51 transects. General linear model
analysis was conducted with sampling methodology and shrub level
included as categorical fixed factors plus an interaction term.
October 2018 155
We used separate GLM analyses to examine differences
between sampling methods for other commonly measured
functional groups and abiotic factors, including total herba-
ceous vegetation (grasses + forbs + sedges), litter, bare ground,
rock, and woody debris. We included sampling method
(Daubenmire/GPI) and shrub level (low/mid/high) as
categorical fixed factors, plus an interaction term that was
dropped from the analysis when insignificant.
For all
analyses, we arcsine square root transformed data and used a
Bonferroni correction for post hoc tests.
We determined species richness by summing the total
number of unique species encountered using each method.
Functional Group Differences per Method
No shrub cover was recorded when using Daubenmire, whereas
an average of 33.5% cover was estimated via GPI methods (Fig. 3).
Twelve transects had low shrub cover, 25 transects had mid-shrub
cover levels, and 14 transects had high-shrub cover.
The interaction between sampling method and shrub cover
was not significant for herbaceous vegetation, bare ground,
woody debris, or rock cover. Daubenmire detected more
herbaceous cover (F
1, 98
= 8.69, P= 0.004; Fig. 3) and bare
ground (F
1, 98
= 57.23, Pb0.001) than GPI methods,
whereas sampling method did not affect rock (F
1, 98
= 0.021,
P= 0.885) or woody debris (F
1, 98
= 3.82, P= 0.054)
detection. Shrub cover level did not affect herbaceous cover
detection (F
2, 98
=0.95,P= 0.389), but did affect bare ground
2, 98
=3.72,P= 0.028; Fig. S1), rock (F
2, 98
= 13.86, Pb
0.001), and woody debris (F
2, 98
=3.38,P= 0.038) detection.
See Supplemental Figure S1 (available online at https://www.
more details on these shrub effects.
Litter cover assessment was influenced by an interaction
between sampling method and shrub level (F
2, 96
= 5.78, P=
0.004). Litter measured using Daubenmire increased as shrub
levels increased, whereas litter measured using GPI declined
as shrub levels increased (Fig. 4).
The interaction between sampling method and shrub cover
was not significant for perennial forbs or grasses. Daubenmire
detected more perennial forb cover than GPI methods (F
= 10.69, P= 0.001) and was not affected by shrub level (F
= 0.19, P= 0.831; Fig. 5). In contrast, sampling method did
not influence perennial grass detection (F
1, 98
= 3.13, P=
0.080), but shrub level did (F
2, 98
= 4.43, P= 0.014; Fig. 6).
Grass cover was higher at low- and mid-shrub levels than high-
shrub levels (post hoc test: P= 0.027 and 0.035, respectively),
and similar at low- and mid-shrub levels (P=1.0).
Species Differences per Method
Across 51 transects, we found 53 herbaceous species
including 41 forbs, 11 grasses, and 1 sedge (Table S1; available
online at
S0190052818300233). A total of 31.7% of forbs (13 of 41)
and 27.3% of grasses (3 of 11) were found only when using
Daubenmire, whereas 7.3% of forbs (3 of 41) and 9% of grasses
(1 of 11) were found only using GPI. Species detection using
GPI methods was more likely when the species cover calculated
by Daubenmire was N1% in any subplot. For example, all but
three of the 36 GPI-detected species had at least one
Daubenmire subplot with N1% cover, whereas 8 of 17 species
not detected using GPI never had N1% cover in any subplot.
The monitoring methods chosen to collect rangeland cover
data often depend on a number of factors including study
historical methods used,
and vegetation
Cover (%)
Funconal Group
Grid point intercept
Daubenmire frames
Perennial forbs Perennial grasses
Figure 5. Mean cover (%) and standard error (1 SE) for perennial forbs and
perennial grass, estimated using Daubenmire versus photography-based grid
point intercept plots across 51 transects. Separate general linear model
analyses were conducted for each functional group, with sam pling methodology
and shrub level included as a categorical fixed factors. Significant differences
between sampling methodologies (P0.05) are noted with an asterisk (*).
Perennial grass cover (%)
Shrub Level
a a b
Low Mid High
Figure 6. Mean cover (%) and standard error (1 SE) for perennial grass at
low-, mid-, and high-shrub levels across 51 transects. A general linear
model analysis was conducted with sampling methodology and shrub level
included as a categorical fixed factors. Significant differences between
shrub levels (P0.05) are noted with letters.
characteristics of the study area.
We were interested in
understanding how a photograph-based GPI method com-
pared with Daubenmire methods to monitor a number of
ecosystem services including sage-grouse habitat quality,
livestock forage, and herbaceous species diversity, which
included detection of cheatgrassa species targeted for
management in the Intermountain West. Key additional
considerations were that our study was located in a high-
density shrub area, and that Daubenmire has historically been
used to assess habitat suitability for sage-grouse.
Differences in Cover Sampling Methods
We found differences in cover estimates obtained via GPI
versus Daubenmire methods that could potentially affect
assessment of target ecosystem services. At the functional
group scale, the most striking difference resulted from the
presence of shrubs in transects. Daubenmire frames were set
below the shrub canopy, whereas GPI photo plots required
photos taken above the canopy, which obstructed our view of
vegetation beneath shrubs. Thus, only Daubenmire allowed
us to assess ground cover without shrub interference. Other
method-comparison studies have noted shrub canopy reduces
estimates of additional functional groups in proportion to
measured canopy cover.
Our results support this idea, with
Daubenmire detecting about 5% more herbaceous cover, 17%
more bare ground, 8% more litter, and 1.5% more woody
debris than GPI methods. Summed together, these approx-
imately equaled the area covered by shrubs when using GPI
methods (33.5%).
Cover assessed using GPI and Daubenmire methods may
also differ if relative proportions of functional groups under
shrubs differ from those found between shrubs. For example,
in our study, litter cover increased with increasing shrub level
when using Daubenmire, but not GPI methods. Because
Daubenmire frames capture the area under shrubs, this
increasing litter suggests that litter levels were higher under
shrubs than in intershrub spaces, something not detectable
with GPI analysis.
Ecosystem Service Assessment
Two functional groups were especially important for
assessing our target ecosystem services: perennial forbs for
sage-grouse habitat and perennial grasses for livestock forage.
Perennial forb cover was ~5% higher across our study area
when using Daubenmire methods. Estimates of perennial
grass cover, in contrast, were similar when assessed by each
method, although they were affected by shrub levelsgrass
cover was lowest in high-shrub areas. Past studies of forb and
grass distribution in shrub dominated areas found forbs to be
more common under shrubs, whereas grasses were more
common in intershrub areas.
It is thus possible that our
different forb cover estimates are due to Daubenmires ability
to assess forb cover beneath the shrub canopy, whereas similar
grass estimates generated by each method are due to both
methods similarly gauging intershrub cover where grasses are
commonly located.
Alternatively, any differences between methods when
measuring forb cover could stem from the well-known
measurement biases of canopy or foliar cover methods.
However, because higher cover estimates are only found for
forbs but not for grasses, we do not believe this is the case.
Rather, it seems more likely that any potential methodological
biases found in our study stem from quadrat methods
overestimating forb cover because of the use of cover bins.
This occurs because cover is assigned a mid-bin value when
averaged across replicates or summed into functional groups.
When plots include many species with low cover, but the real
cover value of each species is less than the mid-point assigned for
the bin, this can lead to the cover of these sparse individuals being
overestimated. Because our sites included many rare perennial
forbs (30 out of 34 had b1% cover), this bias may have been
particularly important. Although perennial grasses were also rare,
the total number of species was small (nine species) likely
resulting in a less-pronounced mid-point value bias.
Regardless of the reason for cover differences between
methods, in at least one casethat of perennial forbseven
small differences in cover could have implications for
rangeland management. Perennial forb cover is one of many
factors related to sage-grouse habitat quality. Sage-grouse rely
on perennial forbs and the insects on forbs for food both
during their nesting and brooding stages.
According to
habitat management standards released by the Bureau of Land
Management, upland areas like those included in our study
that have 5% perennial forb cover are classified as suitable
those with 3% to 5% cover are classified as
marginal habitat, and those with b3% cover are classified as
unsuitable habitat.
In our study, perennial forb cover
estimates generated by Daubenmire (13.5%) and GPI
methods (8.9%) both indicated suitable sage-grouse habitat.
The difference between cover values, however, was large
enough to straddle habitat suitability boundaries at lower
cover levels (i.e., a difference of 4.6%). It is thus possible that
differences in perennial forb cover gained via these methods
could contribute to different determinations of habitat
suitability in some situations. In a worst case scenario, this
difference could lead to management decisions that limit
other rangeland uses, such as grazing, in an effort to increase
perennial forb populations. Because suitable habitat depends
on a combination of vegetative factors,
rather than only the
single factor of perennial forb cover, such cases may be
limited. One way to avoid such possible conflicts, is to avoid
switching from one method to the other, particularly when
forb cover is low, because detected changes in cover may be an
artifact of the methods shift. Additional studies that compare
how well monitoring methods estimate vegetative factors
contributing to sage-grouse habitat suitability, such as Di
Stefano et al.s study of vegetation height,
will also help
managers understand when using different monitoring
methods to assess vegetative factors is important for
determining sage-grouse habitat suitability.
A second ecosystem service of importance in our study area
is the production of forage for livestock, which we measured as
the cover of perennial grasses. There was no difference in
October 2018 157
perennial grass cover between the two examined methods. As
such, both methods performed similarly when assessing this
ecosystem service.
The last of our three target ecosystem services depended on
the detection of individual species. We were interested in
assessing overall grassland richness and determining if the
target invasive species, cheatgrass, was present at sites. We
found large differences in the number of species detected
using Daubenmire versus GPI methods, with Daubenmire
detecting ~33% more herbaceous species (49 vs. 37), ~ 36%
more forbs (38 vs. 28), and 25% more grasses (10 vs. 8
species). We also only detected cheatgrass, which was rare
across our site, when using Daubenmire. Past studies have
similarly found total and rare species detection to be higher
when using ocular methods such as Daubenmire.
on our results and past studies, if GPI methods are chosen to
save time or cost, adding a quick ocular scan of the affected
area or including Daubenmire assessment only for target
species may lead to better species detection without sacrificing
the benefits of GPI photo plots.
Management Implications
Although the advent of new cover estimation technologies
can lead to savings in cost and time,
it is also important to
understand the limitations of these methods. In shrub-
dominated rangelands, GPI methods at times provided cover
estimates that were lower than those estimated using
Daubenmire. Although estimation biases commonly con-
nected to quadrat and point intercept methods, such as
differences in cover stemming from canopy versus foliar
measures, may be driving some of this divergence, we expect
differences were driven by two other causes. These include
GPIs use of nadir photos, which prevent researchers from
assessing cover under shrubs and biases stemming from
combining species into functional groups using mid-point
estimates of cover-classes.
Importantly, differences in cover estimates using GPI
versus Daubenmire methods could lead managers to draw
different conclusions about the production of key rangeland
ecosystem services. In our case, two of three target ecosystem
services were found to be higher when using Daubenmire
compared with GPI methods. Future work that conducts
similar GPI and quadrat method comparisons in other
rangeland ecosystems will help managers understand when
GPI can be used in place of quadrat methods to assess
rangeland ecosystem services effectively.
Finally, a unique drawback of using GPI versus Daubenmire
methods is that it can be difficult to identify species from a
Being able to hold a plant in-hand is a benefit to
field-based quadrat methods. Agencies and universities often hire
seasonal employees to monitor rangelands. Having these
employees sit at a computer days to months after collecting
field data may lead to identification errors that reduce accuracy.
One way to avoid this error is to assign species to easily
distinguishable functional groups, or train technicians to
recognize key study-relevant species. Alternatively, prior experi-
ence with the sites plants may increase efficient and effective use
of GPI methods. Ultimately, understanding potential cover-
estimate differences that can stem from vegetation characteristics
when using different cover sampling methodologies, will allow
project managers to decide if photography-based GPI methods
can address their project objectives.
Supplementary data to this article can be found online at
This research was supported by a Utah Agricultural
Experiment Station Seed Grant. This research was also
supported by the Utah Agricultural Experiment Station, Utah
State University, and approved as journal paper number 9076.
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Authors are Assistant Professor, Department of Wildland
Resources, Utah State University, Logan, UT, 84322, USA and
The Ecology Center, Utah State University, Logan, UT, 84322,
USA (Hulvey,; Undergraduate research
assistant, Department of Wildland Resources, Utah State
University, Logan, UT, 84322, USA (Thomas); and Assistant
Professor, Department of Wildland Resources, Utah State
University, Logan, UT, 84322, USA (Thacker). This research
was supported by the Utah Agricultural Experiment Station, Utah
State University, and approved as journal paper number 9076.
October 2018 159
... Studies have attempted to gauge the sampling error associated with the point-intercept technique by comparing it to different methodological approaches; for example, Dethier et al. (1993) compared random point quadrats with visual estimates, while Lam et al. (2006) compared pointintercept transects with video-based estimates from randomly sampled frames in videos. Other studies compared field intercept sampling with a computer-based sampling of images in which quadrats are superimposed on digital images of the area (Hulvey et al., 2018). Etchberger and Krausman (1997) compared different methods to their "known" cover, which was based on counts of all individual plants and their calculated percentage occurrence. ...
... Etchberger and Krausman (1997) compared different methods to their "known" cover, which was based on counts of all individual plants and their calculated percentage occurrence. Booth et al. (2006) compared traditional and automated methods and compared their results with "known" values, which they described as being derived from pictures taken 2 m above the ground; however, Hulvey et al. (2018) observed that layers of vegetation are poorly sampled using photography as lower-lying vegetation may be missed. Point-intercept techniques have also been used in conjunction with or compared with remotely sensed data (e.g., Karl et al., 2017); however, we question whether estimates from different field techniques should even be compared. ...
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Premise: The point-intercept method is one of the most commonly used approaches to measure species cover in ecosystems worldwide. In this approach, multiple points are sampled for presence/absence of a species, and the number of present points divided by the total number of sampled points provides an estimate of percent cover. Our purpose is to mathematically analyze the accuracy of the point-intercept approach and establish guidelines for its use. Methods: We developed formulas that analyze the point-intercept method and confirmed their effectiveness using simulations. Results: We find that a point-intercept spacing of at least 80% of the largest plant diameter provides the most reliable results. We present a user-friendly spreadsheet that calculates the number of intercepts needed for fieldwork, as well as the standard deviation, expected deviation, and confidence interval of the collected data. Discussion: We provide a variety of guidelines for establishing field protocols based on our results, including dealing with rare species and combining results for multiple species. Quadrat characteristics (intercept spacing, number of point intercepts) can now be easily calculated to guide research design prior to fieldwork; after fieldwork is complete, the accuracy of this technique can (and should) be reported in all future ecological studies in which it is used.
... However, DIA may be unreliable in complex, multi-strata vegetation. For instance, in rangelands of the western US, Hulvey et al. (2018) reported plant cover that was underestimated by DIA compared to visual methods, which they attributed to plant cover concealed by shrubs in the digital imagery. ...
... Therefore, we conclude that post-fire conifer regeneration at Mt. Washburn severely hindered the efficacy of DIA in estimating plant cover at this study area. Interference by tall vegetation strata in digital estimation is not limited to seral forest communities and has been reported in shrublands (Hulvey et al., 2018) and ...
Question Reliable estimates of understory (non-tree) plant cover following fire are essential to assess early forest community recovery. Photographic digital image analysis (DIA) is frequently used in seral, single-strata vegetation, given its greater objectivity and repeatability compared to observer visual estimation; however, its efficacy in multi-strata forest vegetation may be compromised, where various visual obstructions (coarse downed wood—CDW—conifer regeneration, and shadows) may conceal plant cover in the digital imagery. We asked whether vegetation complexity influences plant cover estimated by DIA relative to two visual methods: plot-level (20 m²) estimation (PLE) and quadrat-level (1 m²) estimation (QLE)? Location Greater Yellowstone Ecosystem, U.S.A. Methods We estimated understory plant cover in subalpine forest vegetation on permanent plots (n = 141) at two study areas ~30 years after the 1988 Yellowstone fires to 1) assess differences in visual obstructions between study areas in our digital imagery, 2) compare digital to visual estimates of plant cover, and 3) determine relationships between estimated plant cover and visual obstructions measured in situ. Results Percent conifer regeneration pixels differed significantly (odds ratio=8.34) between study areas, which represented the greatest difference in visual obstructions. At the study area with lower conifer pixels, DIA estimated 9% (95% confidence interval (CI)=3–14%) and 16% (95% CI=10–21%) more understory plant cover than PLE or QLE, respectively, but had comparable variability. At the study area with higher conifer pixels, DIA estimated 28% (95% CI=24–32%) and 22% (95% CI=18–26%) less understory plant cover than PLE or QLE, respectively, and had more variability. Furthermore, plot-level subcanopy regeneration (height>137 cm) density was negatively associated with digitally-derived plant cover but showed no relationship with visually-derived plant cover. Conclusions Post-fire conifer regeneration hindered the efficacy of DIA in estimating understory plant cover. Digital estimation is advantageous in single-strata vegetation but should not be used in complex, multi-strata vegetation.
... For example, Curran et al. (2019) found a 10-fold increase in field sampling efficiency for nadir photography when compared with LPI methods. Studies to compare field surveys and photographic data have found varying degrees of correspondence among methods, and difficulties can arise in measuring cover of lower canopy levels using digital images ( Cagney et al., 2011 ;Pilliod and Arkle, 2013 ;Hulvey et al., 2018 ). However, the advantages of image analysis may outweigh any disadvantages if vegetation structure is not a primary objective. ...
... The lack of agreement between the two vegetation assessment protocols is consistent with previous studies ( Cagney et al., 2011 ;Hulvey et al., 2018 ). Cagney et al. (2011) suggested that differences in cover and bare ground estimates result from differences in the contact point used to measure cover categories. ...
Geomorphic reclamation creates variable topography and surface architecture, including rolling hillslopes and drainages. In contrast, traditional methods of reclamation result in landscapes susceptible to erosion due to steep, linear gradients. Geomorphic approaches to surface mine reclamation are relatively new, and hypotheses suggest the use of geomorphic principles in reclamation will improve vegetation outcomes relative to traditional methods. Topographic variability created by geomorphic reclamation likely results in more environmental heterogeneity, which should correlate with greater plant diversity. We examined revegetation outcomes of traditional and geomorphic reclamation on two reclaimed surface mines in Wyoming using nadir image sampling. Functional group diversity and measures of cover were compared between reclamation methods and undisturbed rangeland. Geomorphic reclamation supported greater total richness and greater native functional group richness relative to traditional reclamation. Native species cover on geomorphic reclamation, particularly for native perennial grasses, was either similar to undisturbed rangeland or greater than undisturbed rangeland and traditional reclamation. Reclamation shrub cover differed significantly from undisturbed sites, but was greater in geomorphic treatments. Results of nadir image analysis are compared to line-point intercept data from the same locations and outcomes are discussed in light of different reclamation techniques and sampling methods. Significant differences in cover categories were observed between nadir image and line-point intercept methods, however both methods revealed similar patterns between study sites.
... For example, Curran et al. (2019) found a 10-fold increase in field sampling efficiency for nadir photography when compared with LPI methods. Studies to compare field surveys and photographic data have found varying degrees of correspondence among methods, and difficulties can arise in measuring cover of lower canopy levels using digital images ( Cagney et al., 2011 ;Pilliod and Arkle, 2013 ;Hulvey et al., 2018 ). However, the advantages of image analysis may outweigh any disadvantages if vegetation structure is not a primary objective. ...
... The lack of agreement between the two vegetation assessment protocols is consistent with previous studies ( Cagney et al., 2011 ;Hulvey et al., 2018 ). Cagney et al. (2011) suggested that differences in cover and bare ground estimates result from differences in the contact point used to measure cover categories. ...
Traditional reclamation consists of landform reconstruction characterized by uniform topography and linear slopes. Geomorphic reclamation improves on traditional reclamation by recreating heterogeneous landforms that blend into surrounding landscapes. Environmental heterogeneity created by geomorphic design is expected to increase the number of available ecological niches, and thus increase plant species diversity when compared with traditional reclamation practices. We sampled plant communities at two reclaimed surface mines in Wyoming using line-point intercept transects to compare vegetative diversity, composition, and structure between sites reclaimed using geomorphic and traditional methods. Greater species richness and Simpson's diversity were observed in geomorphic reclamation at the first site, but did not differ significantly at the second site, although geomorphic reclamation was more likely to resemble undisturbed controls. Shrub abundance was up to 10 times greater on geomorphic reclamation compared to traditional reclamation. Neither reclamation method achieved levels of vegetative diversity observed on nearby, undisturbed rangeland. Geomorphic methods have potential benefits for restoration of vegetative diversity and foundation species such as Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis). Our results suggest geomorphic reclamation may improve plant community diversity and wildlife habitat as a practical method for landscape-level restoration in post-mining sites.
... Akidi, melon and sweet potato Percent ground cover of akidi, melon or sweet potato vines was determined at 4 and 10 WAP using a 50 cm x 50 cm quadrat subdivided into 100 units by means of strings. The quadrat was thrown randomly twice in each plot and the average area of the quadrat covered by the vines was estimated and recorded (Godínez-Alvarez et al., 2009;Kristin et al., 2018). ...
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Indiscriminate usage of herbicides with attendant hazardous effects on soil, water, plants, environment, and humans necessitates the quest for weed management strategies that could enhance the effectiveness of lower-than-labeled dose of herbicide. A field experiment was conducted at the College of Agriculture Teaching and Research Farm, Jalingo, Taraba State, Nigeria in 2008 and 2009, to evaluate the integrated use of 25% rates of selected herbicides mixtures (atrazine-pendimethalin (AP1) or primextra (atrazine + metolachlor) (PX1) and cover crops (a vegetable cowpea, "Akidi" (A),Melon (M) and Sweet potato (S) planted sole or mixed at 20,000 stands ha-1 (1) or 40,000 stands ha-1 (3) under manual (MT) and tractor tillage (TT) methods on weed control and performance of maize using split plot arrangement in a randomized complete block design. TT and MT were the main treatments. The herbicide-covercrop integrated weed management (IWM) sub-plot factors were AP1AI, AP1AS1, AP1S3, AP1MS3, AP1AMS3, PX1A1, PX1AS1, PX1S3, PX1MS3, PX1AMS3. Weeding 3+6 Weeks After Planting (WAP) and unweeded were controls. Data collected include Ground Coverage (GC) of cover crops, weed control rating, Weed Density (WD), biomass at 10 WAP and Maize Grain Yield (MGY). These were analyzed using descriptive statistics and ANOVA (p = 0.05). Weed control from IWM treatments was 11.0 % higher in MT than TT. The GC in TT (43.2%) > MT (35.5%). The MGY in AP1A1, AP1MS3, PX1AS1, PX1MS3 were significantly higher than unweeded by 118.4, 85.4, 78.5 and 78.4%, respectively.
... The plant species were selected based on their medicinal and cultural potential mentioned by informants, citation frequency, occurrence in alpine meadows, and the palatability as described by local inhabitants (Table 1). In each 1 x 1 m subplot, the percent cover for each plant species was estimated to the nearest 10% for each species rooted inside the plot (1 x 1 m %%, 10cm grid points) using the Daubenmire method [57]. Implementing this method, we measured the cover; defined as vertical projections of vegetation that include the area of a quadrat. ...
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The relative effects of climate warming with grazing on medicinally important plants are not fully understood in Hindukush-Himalaya (HKH) region. Therefore, we combined the indigenous knowledge about culturally important therapeutic plants and climate change with experimental warming (open-top chambers) and manual clipping (simulated grazing effect) and compared the relative difference on aboveground biomass and percent cover of plant species at five alpine meadow sites on an elevation gradient (4696 m-3346 m) from 2016-2018. Experimental warming increased biomass and percent cover throughout the experiment. However, the interactive treatment effect (warming x clipping) was significant on biomass but not on percent cover. These responses were taxa specific. Warming induced an increase of 1 ± 0.6% in Bistorta officinalis percent cover while for Poa alpina it was 18.7 ± 4.9%. Contrastingly, clipping had a marginally significant effect in reducing the biomass and cover of all plant species. Clipping treatment reduced vegetation cover & biomass by 2.3% and 6.26%, respectively, but that was not significant due to the high variability among taxa response at different sites. It was found that clipping decreased the effects of warming in interactive plots. Thus, warming may increase the availability of therapeutic plants for indigenous people while overgrazing would have deteriorating effects locally. The findings of this research illustrate that vegetation sensitivity to warming and overgrazing is likely to affect man-environment relationships, and traditional knowledge on a regional scale.
... Both height and canopy gap may be metrics which need only be monitored when the reclaimed area is nearing bond release. It has also been reported that image analysis may result in difficulties capturing vegetation under dense canopy cover (Hulvey et al. 2018). Our study areas were not prone to this issue and it is unlikely for reclaimed sites in semi-arid sagebrush-steppe ecosystems to have dense canopy cover during the interim reclamation phase. ...
... To investigate the accurate CC estimation using the RGB-based sensor, which is equivalent to CC estimation using NDVI, a pixel-wise classification method was implemented; this is presented in Figure 4. As found in the literature, pixel classification methods are considered highly accurate for separating the canopy and non-canopy classes, and they are mainly used to calibrate RGB-based methods [35,38,45]. A pixel classification method based on K-means clustering was used to compare the RGB-based methods that use vegetation indices to separate canopy areas from non-canopy areas. ...
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This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.
... Both height and canopy gap may be metrics which need only be monitored when the reclaimed area is nearing bond release. It has also been reported that image analysis may result in difficulties capturing vegetation under dense canopy cover (Hulvey et al. 2018). Our study areas were not prone to this issue and it is unlikely for reclaimed sites in semi-arid sagebrush-steppe ecosystems to have dense canopy cover during the interim reclamation phase. ...
Full-text available
Land reclamation associated with natural gas development has become increasingly important to mitigate land surface disturbance in western North America. Since well pads occur on sites with multiple land use and ownership, the progress and outcomes of these efforts are of interest to multiple stakeholders including industry, practitioners and consultants, regulatory agents, private landowners and the scientific community. Reclamation success criteria often vary within, and among, government agencies and across land ownership type. Typically, reclamation success of a well pad is judged by comparing vegetation cover from a single transect on the pad to a single transect in an adjacent reference site and data are collected by a large number of technicians with various field monitoring skills. We utilized ‘SamplePoint’ image analysis software and a spatially balanced sampling design, called balanced acceptance sampling, to demonstrate how spatially‐explicit quantitative data can be used to determine if sites are meeting various reclamation success criteria and use chi‐square tests to show how sites in vegetation percent cover differ from a statistical standpoint. This method collects field data faster than traditional methods. We demonstrate how quantitative and spatially‐explicit data can be utilized by multiple stakeholders, how it can improve upon current reference site selection, how it can satisfy reclamation monitoring requirements for multiple regulatory agencies, how it may help improve future seed mix selection, and discuss how it may reduce costs for operations responsible for reclamation and how it may reduce observer bias. This article is protected by copyright. All rights reserved.
Global plant production faces the major challenge of sustainability under the constraint of a rapidly growing world population and the gradual depletion of natural resources. Remote sensing can play a fundamental role in changing the production model through the development and implementation of new technologies for vegetation monitoring (e.g., advanced sensors and remote platforms, powerful algorithms, etc.) that will lead to higher yields and more sustainable and environmentally friendly food and plant products. Unmanned aerial vehicles (UAVs) or drones have been highlighted as one of the most suitable tools for timely tracking and assessment of vegetation status. They can operate at low altitudes, providing an ultra-high spatial resolution image, have great flexibility of flight schedules for data collection at critical and desired moments, and, also, the generation of digital surface models (DSMs) using highly overlapped images and photo-reconstruction techniques or artificial vision. Therefore, it is essential to advance the research for the technical configuration of UAVs, as well as to improve processing and analysis of the UAV imagery of agricultural and forest scenarios in order to strengthen the knowledge of ecosystems and thereby improve farmers’ decision-making processes. This book compiles a set of original and innovative papers included in the Special Issue on UAVs for vegetation monitoring, which proposes new methods and techniques applied to diverse agricultural and forestry scenarios. It is addressed to all members involved in the development and applications of UAVs on vegetation. The editors of this book would like to thank the authors for selecting this special issue to publish their most recent findings, as well as the editor team members for their help in this process.
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Monitoring and adaptive management are fundamental concepts to rangeland management across land management agencies and embodied as best management practices for private landowners. Historically, rangeland monitoring was limited to determining impacts or maximizing the potential of specific land uses—typically grazing. Over the past several decades, though, the uses of and disturbances to rangelands have increased dramatically against a backdrop of global climate change that adds uncertainty to predictions of future rangeland conditions. Thus, today’s monitoring needs are more complex (or multidimensional) and yet still must be reconciled with the realities of costs to collect requisite data. However, conceptual advances in rangeland ecology and management and changes in natural resource policies and societal values over the past 25 years have facilitated new approaches to monitoring that can support rangeland management’s diverse information needs. Additionally, advances in sensor technologies and remote-sensing techniques have broadened the suite of rangeland attributes that can be monitored and the temporal and spatial scales at which they can be monitored. We review some of the conceptual and technological advancements and provide examples of how they have influenced rangeland monitoring. We then discuss implications of these developments for rangeland management and highlight what we see as challenges and opportunities for implementing effective rangeland monitoring. We conclude with a vision for how monitoring can contribute to rangeland information needs in the future.
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Plant cover is measured by different methods and it is important to be able to estimate mean cover and to compare estimates of plant cover across different sampling methods in a coherent statistical framework. Here, a framework that incorporates (1) pin-point cover data, (2) visually determined cover data, and (3) ordinal cover classification systems (e.g., Braun-Blanquet cover data) is presented and tested on simulated plant cover data. The effect of measurement error when applying a visual determination of plant cover is considered. Generally, the estimation of the mean plant cover was well-behaved and unbiased for all the three methods, whereas the estimate of the intra-plot correlation tended to be upward biased and especially so if the plant cover data was collected using the Braun-Blanquet method. It was surprising that the Braun-Blanquet sampling procedure provided mean plant cover estimates that were comparable to the other sampling schemes. This method shows promise in the attempt to use the large amount of historic Braun-Blanquet plant cover data in the investigation of the underlying causes for observed vegetation changes.
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On the Ground Evaluation of range/habitat projects for sage-grouse require careful monitoring to measure their impact. Daubenmire canopy cover and line-point intercept did not yield similar results. As herbaceous canopy cover increased, the differences between the cover estimates increased. Adoption of both techniques by both groups may be the only feasible solution since institutional constraints limit either group from changing monitoring techniques.
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Rangeland ecosystems are capable of providing an array of ecosystem services important to the wellbeing of society. Some of these services (e.g. meat, fibre) are transported to markets and their quantity, quality and value are established via a set of widely accepted standards. Other services (e.g. climate mitigation, water quality, wildlife habitat) do not leave the land, but are, in fact, most valuable when they remain in situ. Determining their quantity, quality and value presents a challenge that must be met if there is to be a credible, accessible ecosystem services market for rangelands. In this paper we describe some of the ecosystem services that may be extracted from rangelands, discuss their unique ecological nature and relate those unique ecological properties to soil and vegetation attributes that can serve as a basis for measurement, both quality and quantity. We suggest the use of a soil/vegetation-based system in which similar climate, geomorphology and edaphic properties are grouped into ecological sites based on their response to disturbance. Within each ecological site, a unique state and transition model describes the dynamics of vegetation and soil surface properties, provides state indicators (vegetation structure, soil properties), predicts ecosystem services that may be derived at multiple scales, and organises information related to management to achieve ecosystem service objectives, including sustainability.
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There is global recognition that sustainable land use requires monitoring that will detect change on a scale that protects the resource. That fundamental necessity is threatened where labor-intensive methods and high labor costs cause sampling deficiencies and increased Type-II error rates (false negatives). Ground-based imaging is a monitoring method that reduces monitoring labor costs. Nadir (vertical) images acquired with common digital cameras can be manually analyzed for cover using free software. We used an innovative field protocol to acquire standardized, freehand, nadir images (samples) of rangeland, then compared point intercept (PI) and image-analysis techniques. Between methods, precision (repeatability) across users was equivalent; cover measurements were often different, and the image-analysis technique took only a third as long to complete. Image analysis has several advantages over PI besides the reduced labor cost: Images are permanent resource records available for reanalysis if data are questioned, if software improves, or if management objectives change; and image analysis is less biased by moving vegetation, moving pointing devices, and bright vegetation color.
The 2015 Sage‐Grouse Habitat Assessment Framework (HAF) was developed to evaluate habitat quality for sage‐grouse (Centrocercus spp.), with the greater sage‐grouse (C. urophasianus) as the primary focus of HAF evaluations and basis of the indicators in the HAF. Site‐scale assessments of sage‐grouse habitat can be completed using either data collection methods described in the HAF or core methods adopted by the U.S. Bureau of Land Management's Assessment, Inventory, and Monitoring (AIM) program. However, there is a discrepancy in how vegetation height is measured between HAF and AIM methods, which has led to confusion as to which protocol should be used and if the AIM height method is compatible with the HAF for habitat assessments. Our objective was to use simulations and data from multiple study areas to determine how often differences between the 2 methods would result in a different determination of quality for the vegetation‐height habitat indicator. We confirmed that the AIM method generally yields lower estimates of height than the HAF method because it estimates mean vegetation height whereas the HAF method estimates mean maximum height (d = 0.031). However, differences between methods at the plot level most often were not substantial enough to lead to a different conclusion about the HAF vegetation‐height indicator for habitat quality. There is value in implementing the AIM method because it is widely used for other monitoring purposes, and slight modifications to the AIM technique (i.e., increasing measurement frequency, adding measurements for both grasses and forbs) could improve usefulness for sage‐grouse habitat assessments. © 2018 This article is a U.S. Government work and is in the public domain in the USA.
On The Ground • Method comparison studies are necessary to reconcile monitoring methods that have arisen among disparate programs; however, we find that Thacker et al.’s study comparing Daubenmire frame (DF) and line-point intercept (LPI) methods for estimating vegetation cover is not adequate to support their conclusions. • Because the DF and LPI methods estimate different aspects of vegetation cover (total canopy vs. foliar cover), there should be no a priori expectation that the two techniques would produce the same results. • Thacker et al. omit critical information about their methods (sampling design, training and calibration, indicator calculations) that could have a large impact on their results and how they can be interpreted. • Differences in results between different vegetation cover measurement techniques can also be attributable to factors like observer training and calibration, plot heterogeneity and complexity, spatial distribution of vegetation, plant morphology, and plot size; thus it is difficult to draw strong conclusions from a single study. • Rather than implementing both DF and LPI techniques in sage-grouse studies as Thacker et al. recommend, effort should instead be invested in ensuring that sampling for one selected method is adequate. • Critical evaluations of vegetation measurement methods to advance the science of rangeland monitoring should be conducted and reported in a rigorous manner, provide a thorough review of previous studies, and discuss how new results contribute to existing knowledge.
(1) Cover is among the most widely used measures of abundance of plant species because it is not biased by the size or distribution of individuals. This study compared cover estimates obtained by line interception, point interception, and cover-class estimation from 136 sample lines located systematically in sagebrush steppe in western U.S.A. (2) Line interception estimates of shrub cover were significantly higher than those obtained by point interception; estimates of `bare ground and litter' by point interception were higher than those of line interception by the same amount. These offsetting differences resulted from the assumption, used in line interception sampling, that shrubs completely cover the areas within the outlines of their canopies. Otherwise, point interception and line interception estimates were very similar. (3) Cover-class estimation provided reliable estimates only for the dominant shrub species. Typically, estimates for grasses and other species with small or rare individuals were high in comparison with the other techniques. This is a consequence of the assumption that cover values are uniformly distributed about the mid-points of the cover classes. (4) Point interception achieved about the same degree of precision as line interception in one-third less sampling time. Point interception is the most efficient of the three methods where estimates for most of the species in a community are needed. (5) Optimal combinations of numbers of lines and numbers of sample units per line in relation to the time involved for a particular level of precision were investigated. In general, increased precision was achieved by sampling more lines rather than more sample units per line.