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GREAT BASIN ANNUAL VEGETATION
PATTERNS ASSESSED BY REMOTE
SENSING
Paul T. Tueller
ABSTRACT
CIIEATGRASS
Cheatgrass-infested wildfire scars are readily identifiable on various scales of aerial photography including
Landsat images if the bums are recent. Measurements
were made from four Landsat MSS images and 11 NASA
high-altitude color infrared aerial photographs of a scale
of 1:29,000. Individual Landsat bands and various ratio
and coeffwient-based vegetation indices can separate
burned from unburned sites using image processing techniques. Several vegetation indices were useful in linear
models describing range sites with increasing amounts
of cheatgrass.
There is ample evidence in the literature that cheatgrass is strongly competitive in the sagebrush/grass vegetation in the Great Basin and Intermountain region.
Young and others (1971) state that annual species, like
cheatgrass, are highly evolved to occupy a low seral niche
caused by fire or grazing. Native species have never
evolved to occupy these niches. These niches are not
present in pristine big sagebrush-perennial bunchgrass
communities, but when a perturbation occurs such as fire,
alien annuals readily invade. Part of their success is attributable to their highly developed reproductive system,
which makes them tough competitors after they have
been established. Cheatgrass, sometimes called downy
brome, readily invades a low seral niche created by a disturbance. The presence of plant litter and a rough microtopography are key seedbed characteristics permitting
cheatgrass establishment, although success on bare
ground is not high (Young and Evans 1972).
Even though cheatgrass caryopses are greatly reduced
after fire, the surviving caryopses respond dynamically
to the released environment potential. The response may
include hybridization and recombination. Cheatgrass has
the inherent competitive ability to close the new seral
community to seedlings of native perennial grasses and
annuals, and its dominance predisposes the site to recurring wildfire perturbation and cyclic environmental degradation (Young and Evans 1977). Cheatgrass is able to
rapidly occupy the belowground space and actively utilize
soil resources competing with native species for soil
water. This negatively affects their soil-water status,
aboveground biomass production, and root-length density,
giving a strong competitive advantage to the cheatgrass,
which then dominates the site (Melgoza 1989).
Young and others (1969) conclude that the important
factors in the dynamics of downy brome populations are:
(a) large numbers of viable caryopses are carried from one
year to the next in the litter and soil; (b) downy brome
caryopses production is density dependent; and (c) the
simultaneous germination characteristics of freshly harvested downy brome caryopses can be conditioned environmentally to continuous germination.
Wright and Klemmedson (1965) compared the experimental burning of four species of grasses-Sandberg bluegrass CPoa secunda), bottlebrush squirreltail (Sitanion
hystrix), needle-and-thread (Stipa comata), and Thurber
needlegrass (Stipa thurberiana)-to understand their
compatibility with cheatgrass. Sandberg bluegrass and
squirreltail proved to be fairly resistant to fire, with
squirreltail being damaged only in June and the bluegrass
INTRODUCTION
In recorded history, many hundreds of wildfires have
occurred throughout the Great Basin and Intermountain
region. The majority of these have occurred in the sagebrush vegetation that covers an estimated 19.5 million
hectares throughout the region (Tueller 1989a). These
wildfires vary considerably as to number and acreage involved. Many thousands of hectares have been impacted.
The result has generally been the creation of an annual
grass type dominated by cheatgrass (Bromus tectorum).
The number, extent, and location have not generally been
recorded for future reference.
This paper considers the potential uses of remote sensing technology for examining the extent of and changes in
the annual cheatgrass vegetation types found in the Great
Basin. Consideration has been given to the measurement
of these sometimes monospecific plant communities where
cheatgrass is the dominant or codominant species on both
small and large areas. Lengthy ecotones have been formed.
These have been evaluated by interpretation and measurements from aerial photographs and the measurement
of these same attributes on satellite-derived computer
compatible data with image processing. Procedures for
monitoring successional trajectories are discussed. The
spectral signatures associated with burned sagebrush or
pinyon/juniper plant communities have been examined.
Paper presented at the symposium on Ecology. Management. and Restoration of Intermountain Annual Rangelands, Boise, ID, May 18-22, 1992.
Paul T. Tueller is Professor of Range Ecology. Department of Range,
Wildlife and Forestry, University of Nevada. Reno.
126
not at all. Needle-and-thread appeared to be the most
susceptible to fire. Burning in June and July caused extreme damage to needle-and-thread, but in August it was
relatively resistant. Thurber needlegrass had a similar
response as needle-and-thread but less severe. Season
of burn determined the extent of damage to both needlegrass species, and size of plant became increasingly important in determining damage in late summer.
DeFlon (1986) describes positive aspects of cheatgrass.
Cheatgrass is one of the few grasses that will invade and
flourish in the alkaline soils associated with the marginal
rainfall of the Great Basin. He goes on to say that it
should not be grazed in the summer, but in the winter
it can be grazed when it is more palatable and nutritious
than crested wheatgrass. He feels the case of cheatgrass
as a grazeable species should be reexamined.
Raison (1979) has drawn several conclusions relative
to the management of controlled burns. Effects of fire
vary with each ecological situation, and currently insufficient data exist to allow accurate prediction of its full
long-term effects on many ecosystems that are regularly
burned Furthermore, there needs to be an integrated
regional study of the long-term effects of fire on plant
communities; regularly burned areas need to be established and maintained so that fire ecology can be better
researched and understood. Remote sensing technology
has a role to play in this endeavor.
parameters such as vegetation biomass, species composition, phenology, species cover, and vegetation changes
(plant succession) based on Landsat TM data (Tueller
1989). Most vegetation indices have been developed under
humid conditions. In arid zones the ability to interpret
spectral information based on vegetation indices is restricted because of the usual small portions of green phytomass and generally low percentages of shrub cover. In
semi-arid regions with large areas of bare soil (50-100 percent) most ratio-based indices are known to be adversely
affected by differences in soil brightness (Elvidge and
Lyon 1985; Huete 1986). Differing spectral responses on
changing soil types significantly limit the accuracy of vegetation estimates (Heilman and Boyd 1986; Jackson 1983).
Huete and Jackson (1987) found both ratio and orthogonal vegetation indices to be unreliable for detecting green
phytomass of range canopies. Site-specific soil lines and
coefficients can reduce the soil background influence and
thus improve the estimate of greenness. Huete (1988)
developed a soil-a(ljusted vegetation index (SAVI) to minimize the soil brightness spectral influence.
Ratio-based indices include the Ratio Vegetation Index
(RVI), the Normalized Difference Vegetation Index (NDVI),
the Transformed Normalized Difference Vegetation Index
(TNDVI), the Modified Normalized Difference Vegetation
Index (MNDVI), the Soil A(ljusted Vegetation Index
(SAVI), and the Transformed Soil Adjusted Vegetation
Index (TSAVI).
Paltridge and Barber (1988) applied a modified normalized difference vegetation index (MNDVI) (NIR-1.2*RED)/
(NIR+RED). They used a 1.2 factor to a(ljust the NDVI to
arid rangeland conditions in Australia. N-space indices
include the Greenness Vegetation Index (GVI), the Soil
Brightness Index (SBI), and the Perpendicular Vegetation
Index (PVI). Jackson (1983) gives a clear example of how
to calculate the coefficients of n-space vegetation indices,
GVI, and SBI using the Gram-Schmitt orthogonalization
process (Frieberger 1960) to obtain vectors. Four linear
combinations of Landsat MSS bands known as "brightness," "greenness," "yellowness," and "nonsuch" originate
from a four-band Landsat MSS orthogonal linear combination as described by Kauth and Thomas (1976).
The two-dimensional Perpendicular Vegetation Index
(PVI) measures the orthogonal distance of a pixel back
to a pre-established soil line (Richardson and Wiegand
1977). The farther a pixel is away from the soil line, the
higher the green vegetation contribution to the pixel. We
found that the PVI is highly correlated to shrub cover on
salt desert and sagebrush-dominated rangelands (r =0.91).
Other indices to consider include the WDVI (Weighted
Difference Vegetation Index) (Clevers 1989) and the
TSAVI (Transformed Soil Adjusted Vegetation Index)
(Baret and Guyot 1991).
Corrections for atmospheric variability are important
to normalize spectral data recorded from high altitudes.
Atmospheric corrections can be made for satellite data by
using linear regressions of light and dark standard reflectance targets on the ground (Marsh and Lyon 1980). Path
radiance corrections can increase the classification accuracy of the data (Kowalik and others 1983). Elvidge and
Lyon (1985) also describe a useful method for defining the
atmospheric influence on digital counts for the TM bands.
REMOTE SENSING
Remote sensing techniques hold considerable promise
for the inventory and monitoring of natural resources in
arid regions. They have the potential to integrate various systems for rangeland management (Tueller 1989b)
leading to more efficient utilization of forage resources
from rangelands by wildlife species as well as domestic
livestock and wild horses. However, there has been a
fundamental reason why applications have not been
forthcoming-a significant lack of information concerning
basic spectral characteristics of rangeland/aridland vegetation and soils. Without such basic information relevant to the sensors that are now available to rangeland
resource managers, it has been and will remain difficult
to develop the desired applications.
Soil substrate plays a significant role in vegetation assessments from spectral data. Similar plant communities
can appear different if they have different soil types. Vegetation biomass tends to be overestimated on dark substrates and underestimated on light substrates (Eividge
and Lyon 1984). The typical spectra from rangeland
scenes are closely correlated with the soil line developed
by Kauth and Thomas (1976). Huete and Jackson (1987)
found that senesced grass and weathered litter significantly altered the spectral response of the range vegetation canopy in the first four Thematic Mapper wavebands
and thus seriously hamper tn.e use of spectral vegetation
indices in assessing green phytomass levels. However,
pixel modeling may allow phytomass estimations (Huete
1986).
Data transformations involving such things as differences, sums, and ratios both with the red and infrared
spectral space can be evaluated to determine if such data
manipulation procedures will have predictor value for
127
Table 1-landsat TM bands acquired and vegetation indices
computed in this study
A modified AVHRR vegetation index was found to be
related to "grassland" fuel-moisture content in a study
near Victoria, Australia. The AVHRR system as a whole
proved extremely valuable for monitoring potential fire
danger areas of the State. The relationship was based on
the dominant control of leaf moisture status on satelliteobserved vegetation indices (Paltridge and Barber 1988).
Yue-Hong and others (1990) compared spatial weighting functions and found that the contiguity weight can
be used to define the spatial term of neighborhood effects
on wildfire. Data overlays of multiple GIS layers derive
the explanatory variables for modeling the distribution
of wildfires from logistic regressions. The model improves
when the spatial term is included. Their results suggest
that neighborhood effects are a primary factor in the distribution of wildfires.
Burned and unburned grass canopies had distinctly different diurnal surface radiative temperatures, and measurements of surface energy balance components revealed
a difference in partitioning of the available energy between the two canopies, which resulted in the difference
of their measured surface temperatures. Additionally, the
timing of measurements and topographic conditions affect
the magnitude of the difference in surface temperatures
(Asrar and others 1988).
A geographic information system (GIS) can be used to
identify and analyze not only the type and amount of
change on a burn site, but also those classes which did
not change. A GIS restricts change analysis to the fireaffected area by masking out unaffected vegetation
through overlay operations. Furthermore, the flexibility
afforded by a GIS allows additional data sets from preceding or succeeding dates to be added, creating a database
for the study of long-term change within the study site
(Jakubauski and others 1990).
TM1 Thematic Mapper Band1
TM2 Thematic Mapper Band2
TM3 Thematic Mapper Band3
TM4 Thematic Mapper Band4
Shrub Cover Percent
BATE Cover Percent
DVI
= Difference Vegetation Index
RVI
= Ratio Vegetation Index
TNDVI = Transformed Normalized Difference Vegetation Index
Bl
== Normalized Difference Vegetation Index
PVI
Perpendicular Vegetation Index
MNDVI = Modified Normalized Vegetation Index
SBI
Soil Brightness Index
GVI
= Greenness Vegetation Index
SAVI = Soil Adjusted Vegetation Index
TSAVI = Transformed Soil Adjusted Vegetation Index
WDVI = Weighted Difference Vegetation Index
amounts of cheatgrass mixed in the stand were sampled.
The data set consisted of ground measurements of cover
and both reflectance and radiance data from a 20-pixel
sample from the same ground locations of western
Nevada shrub-dominated range sites. The 20 radiance
values for each TM band were used to· compute several
vegetation indices as described earlier in the paper. For
this paper I have examined only the linear relationships
with ground-measured dominant shrub (usually sagebrush) cover, bare ground, and cheatgrass cover as the
dependent variable against a number of independent variables (vegetation indices).
A third data set consisted of interpreting and studying
four Landsat MSS images and 11 NASA high-altitude
frames of a scale of 1:29,000. These were analyzed by
photo interpretation techniques. For these scenes we
identified and measured each bum in the scene (most
were in the sagebrush vegetation) and determined the
acreage and length of perimeter of the bum.
METHODS
Three data sets were developed for this study. First
we examined 10 burns in the sagebrush or pinyon/juniper
woodland vegetation from three Landsat subscenes in
western and central Nevada. Homogeneous areas were
selected for burned and unburned sites at these 10 burn
locations. From these homogeneous sites 20 pixels of
Landsat Thematic Mapper data were obtained for analysis. Data for each of the TM bands (one through five and
seven) except the thermal band were summarized (table 1).
Then 11 vegetation indices were computed for each site
based on TM Band four (Near Infrared) and TM Band
three (red). Formulas for these vegetation indices are
listed in table 2. No ground data were available for these
10 bum-unburn comparisons. The various vegetation indices were chosen and interpreted based on their potential to distinguish between burned and unburned vegetation sites during June. Means and standard deviations
were calculated along with a Student's t statistic in order
to compare burned with unburned sites.
A second data set was analyzed as part of another study
we are working on designed to model pixels for their various components based on various vegetation indices. Ten
unburned shrub-dominated range sites in western Nevada
(or they had not been recently burned) with differing
Table 2-Vegetation indices used in this study
Vegetation
Index
RVI 1
NDVI
TNDVI
MNDVI
PVI
SBI
GBI
DVI
WDVI
SAVI
TSAVI
1
128
Formula
TM4/TM3
TM4-TM3/TM4+TM3
~ (TM4-TM3/TM4+TM3)+0.5
TM4-1.20*TM3/TM4+TM3
~ (Rgg5-TM3)2+(Rgg7-TM4)2
(0.664*TM3)+(0.747*TM4)
(-o.747*TM3)+(0.664*TM4)
TM4-TM3
TM4-0.08*TM3
TM4-TM3/(TM4+TM3+0.5) • (1.0+0.5)
a1 (TM4-a1 *TM3-b1 )/
[a1-TM4+TM3-a1*b1 +0.08(1 +a12)]
For variable names see table 1.
RESULTS AND DISCUSSION
Table 4-A comparison of Landsat TM bands as simple comparative signatures for burned and unburned pixel samples
from the Fort Sage study site in western Nevada
p
Site: Fort Sage Mean
S.D.
S.E.
T
Increasing amounts of cheatgrass are usually associated with site degradation by overgrazing, wildfire, or
other potential perturbations. Burns that are only a few
years old are reasonably easy to identify on the Landsat
Thematic Mapper images displayed on the image processing monitor. Tables 3 and 4 show the results from two of
the 10 sites, a fire on the Gund Ranch in central Nevada
and one on Fort Sage Mountain in western Nevada. Statistical comparisons with each band comparing the burned
and unburned sites easily record significant differences
using a simple t test.
While it is easy to record the difference using such a
procedure, not much is really learned about the spectral
characteristics of these rather simple ecosystems. Rather
there is a need to carefully do some pixel modeling in order to determine the information content of the individual
pixels or pixel samples for the site.
Data comparing these same 10 burns using several
vegetation indices described earlier show that some indices show significant differences and some do not. Data
from four of these indices, RVI, GVI, NDVI, and TNDVI,
showed potential for describing differences among burned
and unburned sites (table 5). In this case the RVI and the
PVI were not successful in differentiating among the
burned and unburned sites. However, the NDVI and the
TNDVI were useful vegetation indices in this regard.
These two indices among several others seem to be sensitive to the small amounts of vegetation reflectance from
these cold desert sites.
For predicting the level of cheatgrass amount on a site
I refer the reader to the second data set that was not completed on burned and unburned sites (fig. 1). The adjustedR2 value of0.9661 for this data set is somewhat
suspect since two seeming outliers accounted for the
strength of the relationship. Further sites with cheatgrass will be required to determine the real value of this
relationship. However, cheatgrass tends to be abundant
on a site or not abundant depending on disturbance, and
TM1
Burned
Unburned
Mean
S.D.
S.E.
T
p
TM1
Burned
Unburned
79.85
73.10
2.796
1.483
0.6252
.3317
9.54
9.54
0.0000
.0000
42.50
35.50
1.638
.607
.3663
.1357
17.92
17.92
.0000
.0000
Burned
Unburned
74.65
59.00
2.207
.858
.4935
.1919
29.55
29.55
.0000
.0000
TM4
Burned
Unburned
72.50
54.05
1.960
.887
.4383
.1983
38.35
38.35
.0000
.0000
TM5
Burned
Unburned
147.60
121.50
1.932
2.089
.4321
.4672
41.01
41.01
.0000
.0000
0.3293
.4672
10.06
10.06
0.0000
.0000
Burned
Unburned
40.35
36.70
.7452
1.302()
.1666
.2911
10.88
10.88
.0000
.0000
TM3
Bumed
Unburned
66.50
57.75
1.0000
2.6730
.2236
.5977
13.71
13.71
.0000
.0000
TM4
Bumed
Unburned
67.95
58.75
.8256
1.0700
.1846
.2392
30.44
30.44
.0000
.0000
TM5
Bumed
Unburned
147.50
126.80
1.6700
2.9610
.3735
.6620
27.17
27.17
.0000
.0000
the two outliers reflect disturbed sites with high cheatgrass cover.
The percentage of the dominant shrub on our 10 westem Nevada study sites was predictable based on a combination of the two vegetation indices, the NDVI and
MNDVI, giving an acljusted R 2 of 0.8066. A prediction
based on TM4A and TM2A gave an even higher acljusted
R 2 (0.9002) value. It is gratifying to find that a reasonable estimation of the dominant shrub on these cold
desert sites can be made with Landsat data alone. The
increased acljusted R2 based on ground reflectance of the
site-dominant shrub in bands two and five is difficult to
explain and requires further research.
The third data set involved the photo interpretation of
several burn sites in the sagebrush grass. Photo interpretation criteria used to identify these sites include such
factors as lighter tones associated with the burned area
when compared with the darker tones of the sagebrush
vegetation and the irregular shape of the burns. Some of
the burns were bounded by tracks made by heavy equipment used in the suppression process. Table 6 and table 7
summarize these data. For the 11 NASA high-flight color
infrared photographs we found that the burn size varied
from 26.8 acres to 113.2 acres. Perimeters for these burns
varied from 0.9 to 10.98 miles in length. Ratios of size
to perimeter were from 30/1 to 138/1. In other words for
each mile of perimeter there were from 30 to 138 acres of
burned ground. Each of the 11 frames represented about
50,000 acres. The recent burns identified and studied
represented only 1.9 percent of the total area.
The four Landsat MSS images that we studied represented approximately 6,400,000 acres per frame. With
an average of 16,446 acres of recently burned ground per
frame the area burned represented only 0.25 percent of
the total. With periodic analysis of new imagery, monitoring would provide good data on the amount of burned
ground possibly on an every 2 or 3 year basis. On these
four frames the ratios of acreage to perimeter in miles
varied from 4111 to 83/1.
TM2
Burned
Unburned
1.4730
2.0890
TM2
Table 3-A comparison of Landsat TM bands as simple comparative signatures for burned and unburned pixel samples
from the Gund Ranch study site in central Nevada
Site: Gund
74.80
69.05
TM3
129
Table ~elected vegetation index values for 10 northern Nevada burned and unburned range sltes1
RVI
x
B
u
0.461
.444
.457
.491
.504
.466
.505
.583
.512
.695
.512
.024
0.463
.4n
.505
.445
.462
.525
.668
.797
.475
.586
.540
.036
t
p
PVI
B
5.381
9.938
6.583
3.183
7.619
3.920
6.734
16.542
2.821
10.189
7.287
1.307
-o.91
0.3873
""NDVI
u
4.573
8.226
10.119
.275
5.975
6.628
14.974
19.734
3.644
13.597
8.n4
1.864
TNDVI
B
u
B
u
0.010
.054
.019
.015
.025
.005
.026
.199
.022
.064
.044
.018
0.008
.064
.056
.044
.023
.033
.1n
.281
.008
.130
.083
.028
0.715
.745
.511
.697
.725
.704
.725
.836
.692
.751
.710
.026
0.713
.751
.746
.675
.725
.725
.823
.884
.701
.794
.754
.020
-1.39
'0.1979
-2.45
0.0350
-1.85
0.0976
8. burned; U • unburned.
1
50
information about burns in the sagebrush/grass resulting
in annual grassland. The difficulty extends to the concept
of extending the signature to different sites (spatially)
and different dates (temporally) and still have a signature
based on a vegetation index or some other satellite dataderived index, that can describe specified environmental
parameters and follow changes in them for monitoring
purposes. This will require enhanced procedures for assessing the quantitative aspects of pixel components for
the sites to be studied and monitored. Also, analysis and
Actual =16.83-1.01 GVI +
65.73 NDVI-35.03 TNDVI
R 2 =.9661
40
w
t-
a:
m 30
(ij
:I
~ 20
10
Table 6-Bum aaeage to bum perimeter miles and the ratio of
aaeage to perimeter for wildfires in northern Nevada
of 11 NASA color infrared photographs (panoramic)1
0,
0
10
20
30
40
Frame
No.
50
Estimated BATE
Figure 1-The linear relationship between actual
cheatgrass and estimated cheatgrass for 10
western Nevada shrub-dominated range sites.
CONCLUSIONS
Perimeter
PM
180
26.8
.9
19.78:1
162
85.8
4.7
18.26:1
111
106
64.4
333.8
1.4
8.0
46.0:1
41.73:1
88
50
648.
1,132.
579.
33.5
391.
5.5
8.2
4.5
.9
5.5
117.82:1
138.05:1
128.67:1
37.22:1
85.0:1
37
30
344.6
911.
4.6
10.98
74.91:1
82.97:1
Ex
4,549.9
413.63
55.18
5.0
71:1
85
81
72
Efforts directed toward using satellite remote sensing
data to measure burned sites in the sagebrush/grass vegetation will eventually be routine data inputs to resource
management and GIS. Also, such data can be used to
monitor changes in rangelands resulting from management or from various perturbations.
Satellite radiance data alone can provide information
relative to burned vs. unburned sagebrush/grass sites in
the Great Basin and Intermountain region. However, the
difficult task is the business of establishing satellitebased signatures that can be used to extract meaningful
Acre/
Acreage
x
1
Scale-1:29,000; date: June 21, 1979.
130
Plant
community
Pinyon-Juniper/
Sagebrush
Pinyon-Juniper/
Sagebrush
Sagebrush
Sage/Mountain
Mahogany
Sagebrush
Sagebrush
Sagebrush
Sagebrush
Pinyon-Juniper/
Sage
Sagebrush
Salt Desert
Shrub
Table 7-Total average total burn perimeter and the ratio of perimeter to acreage for wildfires in northern Nevada on four
MSS images from 1973'
Total bum
acreage
Frame
Ruby Mountains
Santa Rosa
Coils Creek
Black Rock Desert
b
i
26,344.5
12,840.1
15,206.1
11 1393.1
65,783.9
16,446.0
Total
perimeter
miles
317.59
202.384
365.274
210.028
b 1,095.3
i 273.8
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J.D.; Dezzani, R. J.1990. Spatial autocorrelation of wildfire distribution in the Idyllwild Quadrangle, San Jacinto
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Young, J. A.; Evans, R. A. 1972. Downy brome-intruder in
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Ratio
acreage/PM
82.951:1
63.444:1
41.629:1
54.246:1
i
60.57:1
'0.25 percent of the area burned.
interpretation of remote sensing systems with higher
spectral resolution may be helpful.
Predictions of changes in shrub cover, perennial grass
cover, annual grass cover, bare ground, and other surface
soil parameters with satellite-derived remote sensing data
will allow better management of these cold desert ecosystems. Establishment of descriptive signatures based on
satellite data alone may be the most useful. However, the
signatures for describing scene components may be improved in some cases by acquiring ground-based reflectance data to include in the model.
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