Fusing Vegetation Data Sets to Provide a
Spatial Analysis of Sage Grouse Habitat on
the Army’s Yakima Training Center
C. J. Murray
L. L. Cadwell
J. L. Downs
M. A. Simmons
Abstract—Resource and land managers need better methods for
deriving detailed spatial environmental data to support resource
management decisions. This paper presents a case study for the
U.S. Army’s Yakima Training Center (YTC) that demonstrates the
fusion of plot-level field data with a detailed land/vegetation cover
map to describe habitat characteristics across cover types. An
advanced geostatistical algorithm, sequential Gaussian simulation
with locally varying means, was used to fuse field data on vegetation
parameters with Geographic Information System (GIS) data on
vegetation cover types present at the study site and provide spatial
interpolation of the data. The resulting vegetation data raster
layers were used to develop a spatial habitat suitability index (HSI)
for the western subspecies of sage grouse on the YTC. Using an HSI
allows ecologists and site managers to examine the spatial distribution of areas that provide suitable habitat for sage grouse.
This paper describes the application of a geostatistical
method used to fuse two different data types, a GIS map of
vegetation cover type and field data collected for five different vegetation variables from across the landscape.
Geostatistics is a specialized form of spatial statistics that
develops quantitative models for the spatial continuity of
variables using variogram analysis, then estimates or simulates the values at unsampled locations using either kriging
or a stochastic simulation technique based on kriging (Isaaks
and Srivastava 1989; Goovaerts 1997). This approach allows
effective estimation of the value of a variable at locations
where field data are not available. The products of the data
fusion are spatially explicit map layers that can then be used
as input to models or for ecosystem management decision
making. In this exercise, the geostatistical estimates were
used to create raster layers for vegetative variables, which
were then used as input to a spatial habitat suitability index
(HSI) model.
We adopted the basic framework of a draft HSI for sage
grouse in Washington State (Ashley 1998). After reviewing
extensive vegetation data sets available for the U.S. Army’s
In: McArthur, E. Durant; Ostler, W. Kent; Wambolt, Carl L., comps. 1999.
Proceedings: shrubland ecotones; 1998 August 12–14; Ephraim, UT. Proc.
RMRS-P-11. Ogden, UT: U.S. Department of Agriculture, Forest Service,
Rocky Mountain Research Station.
C. J. Murray, L. L. Cadwell, J. L. Downs, and M. A. Simmons are Research
Scientists in the Applied Geology and Geochemistry Group (CJM) and the
Ecology Group (LLC, JLD, and MAS), Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352.
200
Yakima Training Center (YTC), and based on our own
habitat-related studies of the species (Eberhardt and
Hofmann 1991; Cadwell and others 1994, 1997; Sveum
1995), we chose a subset of the Ashley variables to represent
current sage grouse nesting habitat for the YTC. The selected variables we used for the modified version of Ashley’s
model were grass cover, forb cover, exotic weed cover, sagebrush height, sagebrush cover, and sagebrush species.
The YTC is an important military training area used to
maintain readiness for wartime and contingency operations. The facility is also home to one of only two small
populations of western sage grouse (Centrocercus
urophasianus phaios) in the state, which the Washington
Department of Fish and Wildlife recently listed as “threatened.” Because of the potential for training activities to
destroy sage grouse habitat, the Army’s Directorate of Environment and Natural Resources identified the need for an
HSI model for sage grouse on the YTC. The HSI model,
displayed as a map layer, will be used to plan and schedule
training so impacts to sage grouse habitat are minimized.
Methods _______________________
Site
2
The YTC occupies 1,322 km in east-central Washington.
It is part of the shrub-steppe ecoregion of Washington State
and part of the big sagebrush (Artemisia tridentata)/
bluebunch wheatgrass (Pseudorgeneria spicata) vegetation
zone. Area climate is characterized by hot, dry summers and
cold, dry winters. Annual precipitation is approximately 20
cm, and temperatures range from –4°C in January to 40°C
2
in July. A 322 km portion of the YTC, which represents
prime sage grouse habitat, was used for this study.
Vegetation Data
A detailed vegetation and land cover map for the sage
grouse protection area on the YTC was reclassified to delineate 13 cover types with respect to sage grouse habitat
requirements (fig. 1A). The level of detail included in the
mapping efforts allowed us to separate cover classes not only
by plant community type or land use, but also to separate
units where shrub cover was sparse (<5%) or patchy, from
areas where shrub cover was continuous.
USDA Forest Service Proceedings RMRS-P-11. 1999
Figure 1—Maps of sage grouse protection area at Yakima Training Center: (A) vegetation
cover type; (B) spatial distribution of sagebrush cover at the site from geostatistical
mapping; (C) spatial distribution of habitat suitability index for sage grouse.
USDA Forest Service Proceedings RMRS-P-11. 1999
201
Several different data sets were used to quantify the
vegetation characteristics within each cover type at a number of points across the modeling landscape (fig. 1A and
table 1). The primary data set used was the Army’s Land
Condition Trend Analysis Data (LCTA) for the site for 1996.
The methodology used for LCTA data collection provided
information on the relative cover of grasses, forbs, exotic
weeds, and sagebrush height. Four other existing data sets
relevant to the sage grouse modeling area were used in
addition to the LCTA data. Two of those data sets included
transect data describing shrub characteristics (100-m lineintercept measurements), while a third data set was composed of data gathered along 100-m transects and quadrats
to describe both shrub and herbaceous vegetation (Canfield
1941; Daubenmire 1970). Ocular estimates of species cover
and height gathered through ground reconnaissance during
vegetation mapping constituted the fourth additional data
set. These five data sets were individually summarized to
provide numerical values for sagebrush cover, sagebrush
height, perennial forb cover, perennial grass cover, and
exotic weed cover at each field sampling point in the sage
grouse protection area. The sixth HSI variable, sagebrush
species, was determined for each cover type by evaluating
both the original mapped cover type and the shrub cover
data for all sampling points within that cover type.
Geostatistical Methods
Geostatistical methods were used to estimate the values of
five of the vegetation variables discussed above (sagebrush
cover, sagebrush height, perennial forb cover, perennial
grass cover, and exotic weed cover) on a 100-m-square grid
across the study area. For each variable, the geostatistical
estimations were conditioned on two different types of data:
the point data measurements of the variable and the vegetation cover map. The vegetation cover map allowed us to
constrain the geostatistical estimates based on the variation
in mean values for the variables among cover types. Sequential Gaussian simulation with a locally varying mean was
applied to fuse the data sets and ultimately generate the
habitat characteristic layers [implemented through the
GSLIB program SGSIM (Deutsch and Journel 1998)].
Several analysis steps were required for each vegetation
variable. Mean values of each variable for each cover type
were determined from the available field data from within
the respective cover types. Most variables had skewed distributions, so the median values for habitat variables, rather
than the means, were normally retained because they were
more representative of the central value of the data. To apply
the Gaussian simulation algorithm used in the study, point
measurements of each variable first were transformed to a
univariate normal distribution using a graphical normal
score transform (Deutsch and Journel 1998). Residuals from
the local means were calculated for each data point by
subtracting the normal score for the mean of all data measured for the relevant cover type from the normal score
transform of the value measured at a data point. The
variogram calculation and modeling (Isaaks and Srivastava
1989) for each variable were performed on the residual
normal scores of the point data using the GSLIB program
GAMV (Deutsch and Journel 1998). The variogram models
for each variable were then used with the locally varying
mean option of SGSIM to generate 100 simulations of the
spatial distribution of each variable. Because each simulation was conditioned on the point measurements and the
cover type present at each grid node, each simulation in the
suite of realization represents an equally probable map of
the variable that honors the conditioning data and the
spatial continuity model captured in the variogram model.
The suite of simulated values at each grid node can be used
as an estimate of the local, conditional-distribution function,
and statistics calculated on the suite of simulated values can
be used to estimate values at each grid node (Goovaerts
1997). For this study, the median simulated value at each
grid node was retained as the estimate for each variable and
used to generate the raster layers defining vegetation characteristics for numerical input to the HSI model.
Habitat Suitability Index
Habitat Suitability Index models are used to assess the
ability of a habitat to support a species. Variables for the
model are developed through an understanding of the biology of the species and characteristics of the habitat. For each
Table 1—Relative area of each cover type and number of samples of each variable by cover type.
Cover type
Relative area
Sage cover
Sage height
Perennial grass
Exotics
Perennial forbs
14.82
8.00
31.90
4.76
0.08
0.27
0.15
10.60
0.11
7.29
0.16
0.96
20.87
50
30
99
17
2
1
2
30
0
19
7
5
45
56
35
121
17
2
1
2
33
0
22
7
6
54
41
20
80
15
2
1
2
29
0
17
6
6
47
41
20
80
15
2
1
2
29
0
17
6
6
47
41
20
80
15
2
1
2
29
0
17
6
6
47
100.00
307
356
262
262
262
Percent
(Big sagebrush)/bunchgrass
[Big sage-bitterbrush]/bunchgr
Big sagebrush/bunchgrass
Bunchgrass
Cheatgrass
Facility
Greasewood/grass
Lithosol/goldenweed/sand. bluegrass
Quarry
Rabbitbrush/bunchgrass
Sand. bluegrass/Grays desertp
Stiff sagebrush/bunchgrass
Threetip sage/bunchgrass
Total
202
USDA Forest Service Proceedings RMRS-P-11. 1999
variable in the model, a relationship is constructed between
the level of the variable and a measure or index of how
different levels of that variable affect the suitability of the
habitat. The index ranges from 0 (for poor habitat) to 1 (for
excellent habitat). The overall HSI is generally calculated as
the geometric mean of all the parameters. The HSI model for
a species or population is represented as an equation of
several variables whose individual value may range between
“0” for the poorest habitats and “1” for the best habitats.
For the six variables in the HSI (grass cover, forb cover,
exotic weed cover, sagebrush height, sagebrush cover, and
sagebrush species), we used the habitat relationships developed by Ashley (1998) for the Washington State sage grouse
population (see appendix A):
(GC * FC * EC)1/3 *[ (SC * SH)1/2 * SS] 1/2
where:
GC = grass cover
FC = forb cover
EC = exotic weed cover
SC = sagebrush cover
SH = sagebrush height
SS = sagebrush species
Geostatistical analysis generated map layers for five of
the six variables needed by the HSI. The sixth map layer,
sagebrush species, was generated as a reclassification of the
vegetative land cover map. The HSI map layer was produced
using the six map layers as input to the model function in the
ERDAS IMAGINE GIS software package. The function
evaluates the model for each grid node in the study area and
produces a map (fig. 1C) showing the distribution of the HSI
across the landscape.
Results and Discussion __________
Analysis of the available plot-level vegetation data by
cover type revealed major differences in mean values for
each of the five variables between the different cover types
(table 2). These differences in mean values for sagebrush
cover and height and bunchgrass cover reflect differences
expected for the different habitat types and help substantiate the accuracy of the cover type map.
The spatial continuity of the five vegetation variables was
examined by calculating the experimental variograms for
the residual normal scores and fitting spherical variogram
models (Isaaks and Srivastava 1989) to the experimental
variograms of all variables. Figure 2 plots an example, the
variogram for the normal score residuals of the sagebrush
cover data. The range of spatial correlation of the variables
varied from 1,200 to 3,500 m (table 3). The relative nugget
effect, which is a measure of the proportion of the variability
in the data that cannot be accounted for by spatial variation
at the scale of the variogram models, ranged from 60 to 75%.
This indicates that a large proportion of the variability in the
normal score residuals occurs at distances less than 160 m,
the shortest lag interval that could be calculated for the
variograms. The magnitude of the relative nugget can be due
to several sources, the most common of which are variability
in the measurement process and spatial variability that
occurs over short distances. Both effects probably contribute
to the relative nugget in this case. No directional difference
in spatial continuity (i.e., anisotropy, see Isaaks and
Srivastava 1989), was identified in the variogram modeling.
The lack of spatial anisotropy and the high relative nugget
effects are both caused in part by transformation of the data
to normal score residuals, which removes some of the largescale trend information inherent in the data. This spatial
information is not “lost” though, because the trend information is recaptured in the simulation process when the cover
type data are fused with the point data using the locally
varying mean simulation algorithm.
Conditional Gaussian simulation with locally varying means
was used to generate raster layers depicting the spatial
distributions of each of the five variables. Figure 1B is an
example showing the spatial distribution of sagebrush cover
at the site. Statistical analysis over the set of cover types in the
map area showed that the median values estimated at each
grid node did an excellent job of reproducing the local means
for the cover types. Shifts in the local mean across cover type
boundaries can be seen by comparison of the sagebrush cover
map (fig. 1B) and the cover type map (fig. 1A).
Table 2—Means of vegetation data by cover type.
Cover type
(Big sagebrush)/bunchgrass
[Big sage-bitterbrush]/bunchgr
Big sagebrush/bunchgrass
Bunchgrass
Cheatgrass
Facility
Greasewood/grass
Lithosol/goldenweed
Quarry
Rabbitbrush/bunchgrass
Riparian
Sand. bluegrass/Grays desertp
Stiff sagebrush/bunchgrass
Threetip sage/bunchgrass
Water
Sage cover
Sage height
Percent
Cm
1
3.5
15.2
0.5
0
0
5
1
0
0.25
0
0.1
10
12
0
36.3
40
47.3
26.2
0
0
63
10
0
10.2
0
8.6
27.5
42
0
USDA Forest Service Proceedings RMRS-P-11. 1999
Perennial grass
Exotic
Perennial forbs
- - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - 36
4
4
20.5
4
12
40
2
4
42.1
4
15
0
100
0
0
23
1
23
23
1.5
20
1
14
0
0
0
41.9
5
2
0
0
0
20
2
27
16.5
3
4
50
1
9
0
0
0
203
Table 4—Percent of the sage grouse
study area within specified
ranges of HSI values.
HSI range
Percent of area
Percent
Gamma(h) = 0.65 + .35 Sph. 1,200(h)
0
2,500
5,000
7,500
10,000
12,500
Figure 2—Variogram of normal score residuals for
sagebrush cover data. The diamonds represent the
experimental variogram values calculated from the
data and the solid line represents the model fit to the
experimental variogram.
Table 3—Parameters of variogram models fit to residual normal
scores of vegetation data.
Variable
Sagebrush cover
Sagebrush height
Perennial forb cover
Perennial grass cover
Exotic weed cover
Range
Relative nugget
M
Percent
1,200
1,400
3,500
1,400
3,500
65
75
60
70
60
Residual normal scores for the point data exhibited spatial continuity ranging from 1 to 3 km, indicating that a
geostatistical mapping approach was suitable. Statistics
calculated for point measurements of habitat characteristics
by cover type (table 2) at the YTC site showed marked
differences between the different shrubland cover types.
This indicates that the cover type maps capture real differences in habitat characteristics, which can then be reproduced by the geostatistical simulations. The resulting raster
layers of habitat characteristics closely honor the GIS cover
type map and capture detail and information not possible
with other methods of gridding point data (e.g., inverse
distance or spline algorithms).
The HSI values from evaluation of the model (fig. 1C)
ranged from 0 to 1. The values (table 4) were in three
predominant groups, with 20% of the area having a low
range of HSI values (< = 0.2), another 20% having a middle
range (0.5 to 0.6), and 30% having a high range of HSI values
(>0.8).
Conclusions ____________________
Many resource management decisions require detailed
habitat or site information that is readily available only
through field inventory or sampling. Such sampling efforts
are often of limited scope because of budget constraints,
204
0 to <=0.1
>0.1 to <=0.2
>0.2 to <=0.3
>0.3 to <=0.4
>0.4 to <=0.5
>0.5 to <=0.6
>0.6 to <=0.7
>0.7 to <=0.8
>0.8 to <=0.9
>0.9 to <=1.0
10
10
5
7
7
19
8
3
12
19
time, and personnel. Neither field point data collection nor
vegetation cover mapping alone provide adequate information on which to base management decisions. Using
geostatistical methods allowed us to merge data sets collected at different scales to realistically represent landscape-scale habitat characteristics that could not otherwise
be mapped. Our simulations of spatial variation in habitat
characteristics across the study area were applied to habitat
suitability assessment. This combination of geostatistical
techniques and GIS-based methods of data integration could
also be applied to fuse data sets of different scales such as
remote sensing imagery from satellites or aerial sources
with field inventory data to address a number of other
ecological applications. Digital data sets describing spatial
habitat or vegetation characteristics such as those generated using this methodology are critical to landscape modeling efforts and management of resources.
Acknowledgments ______________
This work was funded by the U.S. Department of the Army
through a Related Services Agreement with the U.S. Department of Energy under Contract DE-AC06-76RLO 1830.
The authors wish to acknowledge Margaret Pounds of the
U.S. Army Directorate of Environment and Natural Resources, Yakima Training Center, whose vision and guidance made this work possible.
References _____________________
Ashley, P. R. 1998. Personal communication of draft sage grouse
HSI model. Spokane, WA: Washington Department of Fish and
Wildlife.
Cade, B. S.; Sousa, P. J. 1985. Habitat suitability index models:
ruffed grouse. Washington DC: Western Energy and Land Use
Team, Division of Biological Services, Research and Development, Fish and Wildlife Service. 42 p.
Cadwell, L. L.; Simmons, M. A.; Downs, J. L.; Sveum, C. M. 1994.
Sage grouse on the Yakima Training Center: a summary of
studies conducted during 1991 and 1992. Richland, WA: Pacific
Northwest Laboratory. 36 p.
Cadwell, L. L.; Simmons, M. A.; Nugent, J. J.; Cullinan, V. I. 1997.
Sage grouse habitat on the Yakima Training Center: Part II
Habitat modeling. Richland, WA: Pacific Northwest Laboratory.
54 p.
USDA Forest Service Proceedings RMRS-P-11. 1999
Canfield, R. 1941. Application of the line interception method in
sampling range vegetation. J. Forestrey 39:388-394.
Deutsch, C. V.; Journel, A. G. 1998. GSLIB: Geostatistical software
library and user’s guide. New York: Oxford University Press.
340 p.
Eberhardt, L. E.; Hofmann, L. A. 1991. Sage grouse on the Yakima
Training Center: a summary of studies conducted during 1989
and 1990. Richland, WA: Pacific Northwest Laboratory. 54 p.
Goovaerts, P. 1997. Geostatistics for natural resources evaluation.
New York: Oxford University Press. 483 p.
Isaaks, E. H.; Srivastava, R. M. 1989. An introduction to applied
geostatistics. New York: Oxford University Press. 561 p.
Prose, B. L. 1987. Habitat suitability index model. Plains sharptailed grouse. Washington DC: National Ecology Center, Fish
and Wildlife Service. 31 p.
Schroeder, R. L. 1984. Habitat suitability index models: blue grouse.
Washington DC: Western Energy and Land Use Team, Division
of Biological Services, Research and Development, Fish and
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Sveum, C. M. 1995. Habitat selection by sage grouse hens during the
breeding season in south-central Washington. Master’s Thesis.
Oregon State University, Corvallis, OR. 86 p.
Appendix A ____________________
Relationships between habitat variables and habitat suitability indices for sage grouse at the Yakima Training
Center from Ashley (1998). The first five variables (A-E)
came from the model for the nesting and brood rearing
population; the sixth variable was associated with the winter population. The variables are: (A) grass cover; (B) forb
cover; (C) exotic weed cover; (D) sagebrush cover; (E) sagebrush height, and (F) sagebrush species.
A
D
1
0.8
HSI Value
HSI Value
1
0.6
0.4
0.2
0
0.8
0.6
0.4
0.2
0
5
0
10
Percent
15
20
10
0
20
30
40
Percent
50
1
0.8
0.8
HSI Value
HSI Value
1
0.4
0.2
0.6
0.4
0.2
0
0
2
0
4
Percent
10
0
12
15
30
45
90
60 75
Height (cm)
105 120 135
F
C
1
1
0.8
HSI Value
HSI Value
70
E
B
0.6
60
0.6
0.4
0.2
0.8
0.6
0.4
0.2
0
0
0
40
Percent
USDA Forest Service Proceedings RMRS-P-11. 1999
100
None, A. rigida
A. tridentata
tridentata
A. tripartata
A. tridentata
wyomingensis
Species
205