Multiscale Sagebrush Rangeland Habitat Modeling in 

the Gunnison Basin of Colorado

Open-File Report 2013–1049

U.S. Department of the Interior

U.S. Geological Survey
Cover photograph. “Main Gulch” in Gunnison Basin, Colorado, 2007 (photograph by Lorie Brummer, U.S. Geological Survey).
Multiscale Sagebrush Rangeland Habitat 

Modeling in the Gunnison Basin of 

Colorado 

By Collin G. Homer, Cameron L. Aldridge, Debra K. Meyer, and Spencer J. Schell

Open-File Report 2013–1049

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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Suggested citation:

Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J., 2013, Multiscale sagebrush rangeland habitat modeling in 

the Gunnison Basin of Colorado: U.S. Geological Survey Open-File Report 2013–1049, 12 p., http://pubs.usgs.gov/

of/2013/1049/.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Purpose and Scope .......................................................................................................................................2

Study Area.......................................................................................................................................................2

Methods...........................................................................................................................................................3

Results .............................................................................................................................................................6

Discussion.......................................................................................................................................................7

Acknowledgments.......................................................................................................................................11

References Cited..........................................................................................................................................11

Figures

1. Map showing location and spatial extent of the study areas for mapping sagebrush

habitats in south-central Colorado in 2007...............................................................................3

2. Field protocol design for sampling sagebrush rangeland components using 14

quadrats within each sampled polygon....................................................................................4

3. Map showing location of the 41 sagebrush rangeland accuracy assessment plots

across the Landsat path 34 row 33 study area focused on the Gunnison Basin,

Colorado .........................................................................................................................................6

4. Example of percent bare ground predictions at two spatial scales, in QuickBird 

site 1 within the Gunnison Basin, Colorado..............................................................................7

5. Example predictions for four primary sagebrush rangeland components from

QuickBird imagery in QuickBird site 1 within the Gunnison Basin, Colorado ....................8

6. Example predictions for four primary components from Landsat (30-meter) imagery

in the Gunnison Basin, Colorado..............................................................................................10

Tables

1. Input layer number and type used to model components .....................................................5

2. Comparison of QuickBird acquisitions versus field sampling dates....................................6

3. Accuracy assessment (AA) of all sagebrush rangeland variable predictions for

each study area extent and sensor, from south-central Colorado.......................................9
iv

Conversion Factors and Datums

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2

) 0.0002471 acre 

hectare (ha) 2.471 acre

square kilometer (km2

) 247.1 acre

square meter (m2

) 10.76 square foot (ft2) 

hectare (ha) 0.003861 square mile (mi2

) 

square kilometer (km2

) 0.3861 square mile (mi2)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).
Multiscale Sagebrush Rangeland Habitat Modeling in the 

Gunnison Basin of Colorado

By Collin G. Homer1, Cameron L. Aldridge2, Debra K. Meyer3, and Spencer J. Schell1

1

U.S. Geological Survey 

2

NREL, Colorado State University and U.S. Geological Survey 

3

Stinger Ghaffarian Technologies, Inc. 

Abstract

North American sagebrush-steppe ecosystems have 

decreased by about 50 percent since European settlement. As 

a result, sagebrush-steppe dependent species, such as the Gunnison sage-grouse, have experienced drastic range contractions and population declines. Coordinated ecosystem-wide 

research, integrated with monitoring and management activities, is needed to help maintain existing sagebrush habitats; 

however, products that accurately model and map sagebrush 

habitats in detail over the Gunnison Basin in Colorado are still 

unavailable. The goal of this project is to provide a rigorous 

large-area sagebrush habitat classification and inventory with 

statistically validated products and estimates of precision 

across the Gunnison Basin. This research employs a combination of methods, including (1) modeling sagebrush rangeland 

as a series of independent objective components that can be 

combined and customized by any user at multiple spatial 

scales; (2) collecting ground measured plot data on 2.4-meter 

QuickBird satellite imagery in the same season the imagery is 

acquired; (3) modeling of ground measured data on 2.4-meter 

imagery to maximize subsequent extrapolation; (4) acquiring 

multiple seasons (spring, summer, and fall) of Landsat Thematic Mapper imagery (30-meter) for optimal modeling; (5) 

using regression tree classification technology that optimizes 

data mining of multiple image dates, ratios, and bands with 

ancillary data to extrapolate ground training data to coarser 

resolution Landsat Thematic Mapper; and 6) employing 

accuracy assessment of model predictions to enable users to 

understand their dependencies. Results include the prediction 

of four primary components including percent bare ground, 

percent herbaceous, percent shrub, and percent litter, and four 

secondary components including percent sagebrush (Artemisia

spp.), percent big sagebrush (Artemisia tridentata), percent 

Wyoming sagebrush (Artemisia tridentata wyomingensis), 

and shrub height (centimeters). Results were validated with 

an independent accuracy assessment, with root mean square 

error values ranging from 3.5 (percent big sagebrush) to 10.8 

(percent bare ground) at the QuickBird scale, and from 4.5 

(percent Wyoming sagebrush) to 12.4 (percent herbaceous) at 

the full Landsat scale. These results offer significant improvement in sagebrush ecosystem quantification across the Gunnison Basin, and also provide maximum flexibility to users to 

employ for a wide variety of applications. Further refinement 

of these remote sensing component predictions in the future 

will be most likely achieved by focusing on more extensive 

ground plot sampling, employing new high and moderate-resolution satellite sensors that offer additional spectral bands for 

vegetation discrimination, and capturing more dates of satellite 

imagery to better represent phenological variation.

Introduction 

Sagebrush ecosystems are important for sustaining a variety of landscape functions and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem 

services in arid and semiarid areas in the intermountain region 

of the western United States (Connelly and others, 2004; 

Davies and others, 2007; Anderson and McCuistion, 2008). 

However, North American sagebrush-steppe ecosystems have 

decreased by about 50 percent since European settlement 

(Schroeder and others, 2004). Habitat loss and fragmentation due to urban development, associated infrastructure, and 

conversion of habitats to other uses (Oyler-McCance and others, 2001; Aldridge and others, 2012), has caused sagebrushsteppe dependent species such as the Gunnison sage-grouse to 

experiance severe population declines (Gunnison Sage-Grouse 

Rangewide Steering Committee, 2005) which threaten the 

long-term viability of the species (Gunnison Sage-Grouse 

Rangewide Steering Committee, 2005; Aldridge and others, 

2012)

Gunnison sage-grouse (C. minimus) is the smaller of 

the two sage-grouse species, whose geographic distribution 

is limited to southwestern Colorado and southeastern Utah 

(Schroeder and others, 2004). There are an estimated 4,000 

birds remaining, distributed across 7 disjunction populations 

(GSGRSC, 2005; Aldridge and others, 2012). Approximately 

87 percent of birds are contained within the largest population, 
2 Multiscale Sagebrush Rangeland Habitat Modeling in the Gunnison Basin of Colorado 

located in the Gunnison Basin of Colorado (Aldridge and others, 2012). In 2010, Gunnison sage-grouse were considered a 

candidate species under the U.S. Endangered Species Act (65 

Federal Register 82310). Threats to species persistence, such 

as those for greater sage-grouse (C. urophasianus), include 

loss and degradation of habitats, especially as it relates to 

increasing human population growth within the area (Colorado 

Water Conservation Board, 2009; Aldridge and others, 2012). 

To ensure long-term persistence of this species, there is a need 

to quantify and monitor sagebrush habitats across the basin, 

allowing for best management practices to be identified and 

the spatial identification and protection of crucial resources 

on the landscape. Currently, such products do not exist for 

the basin, but have recently been developed for other regions. 

(Homer and others, 2008; Homer and others, 2012).

Currently, remote sensing offers the best data source 

to both efficiently and effectively characterize sagebrush 

landscapes across the range of spatial and temporal scales in 

the Gunnison Basin. Traditional remote sensing products in 

this region have attempted only a category based approaches, 

using generalized land cover or vegetation cover-type classes 

for delineation (Rollins, 2009; Schrupp and others, 2000). This 

more subjective approach can be adequate for general applications, but quickly breaks down when the attempt is made to 

either broaden the use or repeat the process. Recent advances 

in remote sensing modeling and mapping of rangeland vegetation characteristics using multi-scale resolution imagery 

now allow for more objective continuous cover component 

predictions to be made across large regions like the Gunnison 

Basin at 30-meter (m) resolution (Homer and others, 2008; 

Homer and others, 2012). This new approach also provides 

an improved foundation for monitoring and habitat characterization over traditional products, and would represent a 

useful tool for land and wildlife resource managers within the 

Gunnison Basin. Subsequently, we developed a continuous 

field method of sagebrush habitat component assessment to 

improve source products for sage-grouse habitat characterization across the Gunnison Basin.

Purpose and Scope

The goal of this project was to develop a rigorous habitat 

characterization that provides statistically validated products 

with estimates of precision for quantifying habitat components of the sagebrush ecosystem across the entire Gunnison 

Basin, Colorado. A combination of approaches was used to 

characterize sagebrush rangelands, expanding beyond traditional remote sensing methods (Homer and others, 2012). This 

combination includes (1) modeling rangeland as a series of 

independent objective components that can be synergistically 

customized by any user at multiple spatial scales; (2) collecting ground measured plot data on 2.4-m QuickBird (QB) 

satellite imagery in the same season the imagery is acquired; 

(3) effective modeling of ground measured data on 2.4-m 

imagery to maximize subsequent extrapolation; (4) acquiring 

multiple seasons (spring, summer, and fall) of Landsat Thematic Mapper (TM) imagery (30-m) for optimal modeling; (5) 

using regression tree classification technology that optimizes 

data mining of multiple image dates, ratios, and bands with 

ancillary data to extrapolate ground training data to a coarser 

resolution sensor (Homer and others, 2012); and 6) employing 

accuracy assessment of model predictions to enable users to 

understand their dependencies. 

Specific objectives included:

1. Develop, measure, and assess field sample protocols that 

support collection of training and testing (evaluation) 

data for optimal remote sensing predictions at two spatial 

scales.

2. Develop, predict, and assess eight rangeland components 

(including percent bare ground, percent shrub, percent 

herbaceous, percent litter, percent sagebrush, percent big 

sagebrush, percent Wyoming sagebrush, and shrub height) 

across four 64-kilometer-squared (km2) study sites using 

4-m QB imagery.

3. Develop, predict, and assess the extrapolation of 4-m QB 

predictions of eight rangeland components across the 

entire Gunnison Basin in Colorado using 30-m Landsat 

TM imagery.

Study Area

The operational scale of the study is the full extent of the 

Landsat TM scene, encompassing World Reference System-2 

path 34, row 33 (hereafter called 34/33), which falls in central 

Colorado (fig. 1). The focus was on the Gunnison Basin, an 

approximately 845-km2 area in the southwest corner of the 

scene. The basin is a high-elevation valley on the eastern side 

of the Colorado plateau, with elevations ranging from 2,180 

to 3,100 m. The average annual temperature and precipitation 

are 3.1 degrees centigrade and 27 centimeters, respectively. 

Sagebrush-steppe is common across the basin, with big sagebrush (Artemisia tridentata) being the dominant species. Narrowleaf cottonwood (Populus angustifolia), juniper (Juniperus 

scopulorum), gamble oak (Quercus gambelii), serviceberry 

(Amelanchier alnifolia) and wild rose (Rosa woodsii) occur in 

the riparian areas and drainages. Most of the valley bottoms 

along major drainages have been converted to hayfields and 

pastures. High elevation areas contain forests of ponderosa 

pine (Pinus ponderosa), Douglas fir (Pseudotsuga menziesii), 

spruce (Picea spp.), and aspen (Populus tremuloides).

 Prediction results are reported across four analyzed 

64-km2

 QB) scenes (QB sites 1–4), which were placed across 

the Gunnison Basin and provided a typical range of expected 

sagebrush habitat types and conditions. Prediction results also 

are reported for the entire 34/33 Landsat scene, excluding 
Methods 3

Figure 1. Location and spatial extent of the 

study areas for mapping sagebrush habitats in 

south-central Colorado in 2007. Initial models 

were developed for the four QuickBird sites 

(yellow squares, numbers 1–4) and predictions 

were extrapolated to Landsat Thematic Mapper 

path 34, row 33. Masked high elevation areas 

(above 3,100 meters) are in white. Red outlined 

area defines the Gunnison Basin.

areas more than 3,100-m in elevation. The full Landsat scene 

study area provided an opportunity to test the accuracy of 

predictions scaled up from the QuickBird scale to the Landsat 

scale under scenarios typical of future full scale operational 

products.

Methods

Methods were developed to combine 2.4-m QB and 

30-m Landsat TM imagery with rigorous ground sampling 

to produce continuous fractional vegetation predictions (for 

example, the percentage of a cell or pixel covered by the 

component) for eight sagebrush-steppe habitat components 

(similar to Homer and others, 2012). The four primary components, which represent 100 percent of all cover in a habitat 

without trees, include percent bare ground, percent herbaceous 

(grasses and forbs), percent shrub, and percent litter. The four 

secondary components are all subsets of the shrub primary 

component, and include percent sagebrush (Artemisia spp.), 

percent big sagebrush (A. tridentata), percent Wyoming sagebrush (A. t. wyomingensis) and mean shrub height (centimeters). Specific methodological descriptions are detailed below.

Field sampling protocols: Definiens eCognition® software was used to segment QB imagery into uniform polygon 

patches to identify sites for potential field sampling. Each 

image was also per-pixel classified into 30 unsupervised clusters using an isodata algorithm in Leica Geosystems ERDAS 

IMAGINE® software (similar to the method described in 

Homer and others, 2009). Segmented polygons were intersected with the 30 clusters to identify the majority cluster class 

in each polygon. Potential polygons were filtered based on the 

size of the patch (greater than 0.5 hectare), adjacency to roads 

(within 1 km), land ownership (public access), and spatial distribution across the image. Typically, if available, at least two 

polygons from each of the 30 cluster classes were selected, 

with a minimum of 70 selected polygons per QB scene. 

This process was designed to ensure that selected samples 

represented the spectral and ecological diversity within the 

QB footprint while also facilitating the field sampling effort. 

Selected polygons were supplied to field crews for ground 

sampling as near to the QB image acquisition date as logistically possible.

The vegetation characteristics of each selected polygon 

were sampled at 14 1-meter-squared (m2

) quadrats divided 

evenly between and along two 30-m transects (fig. 2). The 

start point for the first transect was located in a randomly 

chosen ordinal north, south, east, or west direction 10 m from 

the polygon centroid. The transect was extended at a 90° angle 

from the originally chosen direction (randomly picked from 
4 Multiscale Sagebrush Rangeland Habitat Modeling in the Gunnison Basin of Colorado 

Figure 2. Field protocol design for sampling sagebrush rangeland components using 14 quadrats within 

each sampled polygon.

the two possibilities). Individual quadrats were placed every 

5 m along the transect, beginning from the starting point. The 

second transect started 10 m from the polygon centroid but in 

the opposite direction from the first start point, and extended 

parallel to but in the opposite direction from the first transect 

(fig. 2).

Cover for each quadrat was estimated from an overhead 

perspective. Estimates were made in 5 percent increments, and 

the total of all vegetation cover components could not sum 

beyond 100 percent. Shrubs and trees were identified also and 

estimated at the species level, with sagebrush covers assessed 

at the sub-species level. All other components within the quadrat were combined into broad categories of herbaceous vegetation, litter, and bare ground. Cover measurements for shrubs 

were based on only parts of the canopy with live green vegetation. Cover measurements for herbaceous vegetation consisted 

of all grasses (live and residual standing) and forbs (including 

plants like sellaginella, phlox, and cacti). Litter was estimated 

as the combined cover of standing woody vegetation (including exposed shrub stems, alive or dead) and all detached plant 

and animal organic matter. Bare ground included any exposed 

soil or rocks. The height of each shrub or tree species was 

estimated by measuring the droop height of the tallest green 

vegetation (excluding seed stalks) for one representative plant 

within each quadrat.

For application to remotely sensed data, the mean value 

for each of the eight components was calculated across all 14 

1-m2

 quadrats within a sampled polygon. These values were 

assigned to all QB pixels occurring within the sampled plot, 

defined as a polygon connecting the start and end points of 

both transects.

QuickBird imagery predictions: The proportion of each of 

eight components occurring within all four QB footprints on a 

per-pixel basis was classified independently using commercial 

regression tree (RT) software called Cubist® (Quinlan, 1993), 

identifying empirical relations between each component and 

the QuickBird data. Typically, all four 2.4-m spectral bands 

(bands 1–4) were used directly, with an additional three bands 

of ratio indices derived for capturing the Green Normalized 

Difference Vegetation Index [NDVI; (band 4–band 2)/(band 

4+band 2)], Moisture Index [(band 4 – band1)/(band 4 + band 

1)], and Specific Leaf Area Vegetation Index [(band 4)/(band 

3 + band 2)] bringing the total to seven input bands. Training 

inputs for each component were developed using the average 

value for each sampled plot within each QuickBird footprint 

(typically 55 sample plots). Predictions quantifying the spatial 

distribution and per-pixel proportion of seven components 

as a continuous variable from 0 to 100 percent and shrub 

height (in centimeters) were then produced for all pixels in 

each QuickBird image. Model fit was assessed by comparing 

sample training data with model predictions using a Pearson’s 

correlation.

Landsat imagery predictions: Predictions of all eight 

rangeland components were classified independently for the 

full Landsat 34/33 study area to test procedures under operational scenarios. Each component prediction from the four 

QuickBird footprints (objective 2) was re-sampled from 2.4-m 

cells to 30-m cells to provide training data for the Landsat 

model predictions. The training data were then further refined 
Methods 5

using a filtering model to eliminate outlier pixels (Homer and 

others, 2012). Only those pixels with prediction summations 

for the four primary components (percent bare ground, shrub, 

herbaceous, and litter) that ranged from 90 percent to 110 percent were retained for training. All Landsat images were 

corrected using the Multi Resolution Land Characteristics 

Consortium (MRLC) image protocol, which standardized the 

imagery to at-satellite reflectance before using the imagery in 

the modeling (Chander and others, 2009). Input layer combinations were drawn from multiple image dates, ratios that 

targeted spectral content, ratio differences between these dates, 

and ancillary topographic data. A combination of 40 input layers (table 1) was used to derive model results for the Landsat 

predictions. These input layers represent the total data made 

available to Cubist for data mining to build model predictions 

for each component response variable. 

Because RT models are sensitive to proportional weighting of data, multiple trials were performed to determine the 

training data proportions required to provide the most accurate 

predictions. Analysis of each trial depended upon the crossvalidated model fit and visual inspection to determine the best 

predictions. Ultimately, we divided data for each of the eight 

component response variables into three roughly equal bins, 

defined by thresholds derived from the mean and RMSE values of predictions. Values less than the mean minus the RMSE 

were grouped into a low bin, values greater than the mean plus 

the RMSE were grouped into a high bin, and the remaining 

values were considered the middle bin. Because the re-sampling process inherently narrows the range of values and the 

RT models tend to concentrate on the most frequent values 

(in this case, the medium predictions in the middle bin were 

thinned), this approach attempted to ensure that higher and 

lower component predictions would carry more equal weighting in model development and reduce overall bias. Predictions 

were extrapolated for all 7 cover variables from 0 to 100 percent, and the shrub height variable, across all Landsat pixels 

in the 34/33 area. Ssecondary shrub components were reconciled (sagebrush, big sagebrush, and Wyoming sagebrush) 

to the primary shrub components to retain proper hierarchy, 

by ensuring the summation of secondary component canopy 

did not exceed shrub canopy and by removing any secondary 

shrub height value where there were 0 percent shrub estimates.

Accuracy Assessment: For QB scale predictions, an 

accuracy assessment evaluation was performed for the 4 QB 

images using 16 plots chosen from the pool of sampled plots 

for each image and excluded from model development. These 

evaluation plots were selected by targeting spectral categories 

(30 per image) that contained excess plots beyond the two 

required for model training and then randomly choosing the 

validation plot from the candidate plots within each targeted 

spectral category. For Landsat scale accuracy assessment, 

independent plot samples collected across the study area during the same year as the training data and predictions were 

used (fig. 3). Potential locations for assessment plots were 

selected in two stages, and were restricted to landscapes below 

3,100 m in elevation, on public land, within 1 km of a mapped 

Table 1. Input layer number and type used to model components.

Layer Description

 01 May 30, 2008 Landsat 5, band 1

 02 May 30, 2008 Landsat 5, band 2

 03 May 30, 2008 Landsat 5, band 3

 04 May 30, 2008 Landsat 5, band 4

 05 May 30, 2008 Landsat 5, band 5

 06 May 30, 2008 Landsat 5, band 6

 07 May 30, 2008 3 Ratio Index band 1

 08 May 30, 2008 3 Ratio Index band 2

 09 May 30, 2008 3 Ratio Index band 3

 10 June 15, 2008 Landsat 5, band 1

 11 June 15, 2008 Landsat 5, band 2

 12 June 15, 2008 Landsat 5, band 3

 13 June 15, 2008 Landsat 5, band 4

 14 June 15, 2008 Landsat 5, band 5

 15 June 15, 2008 Landsat 5, band 6

 16 June 15, 2008 3 Ratio Index band 1

 17 June 15, 2008 3 Ratio Index band 2

 18 June 15, 2008 3 Ratio Index band 3

 19 October 3, 2007 Landsat 5, band 1

 20 October 3, 2007 Landsat 5, band 2

 21 October 3, 2007 Landsat 5, band 3

 22 October 3, 2007 Landsat 5, band 4

 23 October 3, 2007 Landsat 5, band 5

 24 October 3, 2007 Landsat 5, band 6

 25 October 3, 2007 3 Ratio Index band 1

 26 October 3, 2007 3 Ratio Index band 2

 27 October 3, 2007 3 Ratio Index band3

 28 Aspect, 9 Direction

 29 Elevation, seven classes

 30 Slope Position Index

 31 Slope, Degrees

 32 May 30, 2008, June 15, 2008 Ratio Difference Index band 1

 33 May 30, 2008, June 15, 2008 Ratio Difference Index band 2

 34 May 30, 2008, June 15, 2008 Ratio Difference Index band 3

 35 May30, 2008, October 3, 2007 Ratio Difference Index band 1

 36 May 30, 2008, October 3, 2007 Ratio Difference Index band 2

 37 May 30, 2008, October 3, 2007 Ratio Difference Index band 3

 38 June 15, 2008, October 3, 2007 Ratio Difference Index band 1

 39 June 15, 2008, October 3, 2007 Ratio Difference Index band 2

 40 June 15, 2008, October 3, 2007 Ratio Difference Index band 3
6 Multiscale Sagebrush Rangeland Habitat Modeling in the Gunnison Basin of Colorado 

Figure 3. Location of the 41 sagebrush 

rangeland accuracy assessment plots across 

the Landsat path 34 row 33 study area focused 

on the Gunnison Basin, Colorado. The plots 

are in black and the higher elevation areas 

(greater than 3,100-m) are masked in white.

road or trail, and within the extent of the lumped shrub, grass, 

and barren classes in the U.S. National Land Cover Database 

2001 (Homer and others, 2007). For the first stage, five 10-km 

selection circles randomly were located within the Gunnison Basin to focus field validation efforts. For the second 

stage, seven to nine plot locations occurring within each of 

the 10-km circles randomly were selected from within the 

potential landscapes defined above. Evaluation plot sampling 

was completed using the same field protocols as those used for 

training plot data collection.

Independent accuracy assessment results are reported 

using the coefficient of determination (R2

), the root mean 

square error (RMSE), and the normalized root mean square 

error (NRMSE). RMSE represents an absolute measure of 

model fit and is in the same unit as the modeled variable. 

NRMSE is dimensionless and is calculated by dividing the 

RMSE by the range of observed values to allow comparisons 

among RMSE calculations for different variable predictions, 

and typically is expressed as a percentage.

Results

Continuous predictions for eight different sagebrushsteppe components at 2.4-m QuickBird and 30-m Landsat TM 

scales were generated (fig. 4). QuickBird results were generated across four 64-km2

 footprints in the Gunnison Basin, 

Colorado. Landsat predictions were generated across the Path 

34, Row 33 scene extent in south-central Colorado, which 

includes the Gunnison Basin.

Field sampling results: A total of 331 polygons were sampled in and around the Gunnison Basin, Colorado area during 

the summer of 2007. Vegetative characteristics at 290 sampled 

polygons across four QuickBird scenes were used for model 

development training data and QB model prediction evaluation. Sampling at an additional 41 randomly located plots was 

used to provide independent evaluation data for the Landsat 

34/33 study area model (fig. 3). Field collection of training 

data occurred on average within 26 days (5.49 standard error 

days) for all four sampled QB images (table 2).

Table 2. Comparison of QuickBird acquisitions versus field 

sampling dates.

Site

Number of days between 

QuickBird (QB) acquisition 

and field measurement

QB site 1 23

QB site 2 12

QB site 3 32

QB site 4 37

Average 26 
Discussion 7

Figure 4. Example of percent 

bare ground predictions at two 

spatial scales, in QuickBird site 

1 within the Gunnison Basin, 

Colorado.

Model predictions—QuickBird imagery: Predictions for 

all eight sagebrush-steppe components across each of the four 

QuickBird scenes were independently generated (fig. 5); but 

results are summarized across all four sites in table 3. Accuracy assessment was based on the correlation of independent 

data with modeled predictions from a sample of plots withheld for validation. R2

 values ranged from a low of 0.13 for 

Wyoming sagebrush (A.t. wyomingensis) to a high of 0.61 for 

bare ground (table 3). RMSE values ranged from 3.52 percent 

for big sagebrush (A. tridentata) to 10.82 percent for bare 

ground (table 3). Based on these RMSE results, a bare ground 

prediction of 20 percent from the model would typically range 

from 9.18 percent to 30.82 percent in measured results on the 

ground. A big sagebrush prediction of 20 percent from the 

model would typically range from 16.48 percent to 23.52 percent in measured results in the field (table 3). NRMSE values 

across all component QB predictions averaged 18 percent.

Model predictions—Landsat: Predictions for each of 

the eight rangeland components across the Landsat 34/33 

study area were generated using Landsat TM 30-m imagery 

(fig. 6). Landsat component prediction accuracy results were 

determined using 41 validation plots sampled independently.

R2

 values across the 34/33 study area ranged from a low of 

0.09 for herbaceous to a high of 0.45 for bare ground (table 

3). RMSE values ranged across the 34/33 study area ranged 

from 4.53 percent for Wyoming sagebrush cover to 12.44 

percent for herbaceous cover (table 3). Based on these RMSE 

results at the full Landsat scene scale, an herbaceous prediction of 20 percent from the model would typically range 

from 7.56 percent to 32.44 percent in measured results on the 

ground, and a Wyoming sagebrush prediction of 20 percent 

from our model would typically range from 15.47 percent to 

24.53 percent in measured results on the ground. NRMSE 

values for Landsat predictions averaged 22.9 percent, about 

5 percent higher than QB predictions.

Discussion

These products potentially offer the most current (2012) 

rigorous large-area attempt for remote sensing quantification 

of sagebrush habitat across the Gunnison Basin, and provide 

the opportunity to develop more extensive sage-grouse habitat 

modeling than has previously been available. Project specific 

objectives and outcomes are discussed below.

Field sampling: The field sampling protocol strived to 

achieve a reasonable balance among scale, effort, access, and 

precision across all components. The sample site selection 

protocol using QuickBird (QB) segmentation was especially 

effective at optimizing field collection to maximize sampling 

of variation in spectral signatures, and it facilitated successful application to larger imagery scales. The average time (26 

days) between the collection of the image and field sampling 

was phenolgically reasonable in this ecosystem, especially 
8 Multiscale Sagebrush Rangeland Habitat Modeling in the Gunnison Basin of Colorado 

Figure 5. Example predictions for four primary sagebrush rangeland components from QuickBird imagery in QuickBird site 1 

within the Gunnison Basin, Colorado. Elevations over 3,100 meters and nonsagebrush-steppe land covers are masked (white).

given the difficult logistical issues involved of synergizing 

field and image collection. The 1-m2 quadrats used for cover 

measurements overall offered a reasonable method for reliable 

and consistent measurement of all components; however, steep 

topography provided access challenges for some plots, and the 

presence of trees in a few plots made optimal measurement 

more difficult.

Model prediction, QuickBird imagery: Based on the 

modeled results at the QB scale, the stratification and field 

sampling approaches successfully measured the range of 

variability of the rangeland components. Multispectral QB 

imagery proved to be a useful extrapolation medium for the 

field sampling. The 2.4-m pixel scale allowed the RT enough 

spatial area for sub-pixel estimates to be relevant while still 

maintaining high resolution. QB component predictions averaged across all four QB sites displayed a mean R2

 value of 

0.41 for all eight components, and a mean RMSE across the 

seven cover predictions of 5.95 percent. These results were not 

only adequate to support further spatial extrapolation of these 

results at Landsat scales, but also are comparable to QB results 

from a similar study conducted across the State of Wyoming 

(Homer and others, 2012).

Model prediction, Landsat: Coarse scale imagery such as 

Landsat offers an ideal medium to further extrapolate component models from the QB scales to larger landscapes. As 

expected, moving to a lower resolution, even with an increase 

in the number of input variables for the regression tree, did 

reduce the prediction confidence. The component predictions at the Landsat scale displayed a mean value of 0.25 for 

all eight components, and a mean RMSE across the seven 

cover predictions of 7.96. One cause for the loss of precision 

between Quickbird and Landsat scales is the Modifiable Areal 

Unit Problem (MAUP), where the mean value does not change 

but variance declines with increasing aggregation. Thus, the 

modeled range of a given variable becomes compressed as 

the spatial size of the pixel increases, for example, the total 

possible range of sagebrush canopy density decreases as 

patch size increases [see Homer and others (2012) for further 

discussion]. Overall, accuracies were comparable to similar 
Discussion 9

Table 3. Accuracy assessment (AA) of all sagebrush rangeland variable predictions for each study area extent and sensor, from 

south-central Colorado. 

[Root mean square error (RMSE) values are in the units of model prediction (percent cover or centimeters for shrub height)].

Site Sensor Variable N R2 RMSE NRMSE Type

All Sites Quickbird Sage (percent) 64 0.47 3.67 0.20 Independent AA

All Sites Quickbird Shrub (percent) 64 0.19 5.10 0.20 Independent AA

All Sites Quickbird Bare ground (percent) 64 0.61 10.82 0.14 Independent AA

All Sites Quickbird Herbaceous (percent) 64 0.55 5.93 0.10 Independent AA

All Sites Quickbird Litter (percent) 64 0.43 8.10 0.17 Independent AA

All Sites Quickbird Big sage (percent) 64 0.52 3.52 0.20 Independent AA

All Sites Quickbird Wyomingensis (percent) 64 0.13 4.49 0.30 Independent AA

All Sites Quickbird Shrub (height) 64 0.40 8.15 0.14 Independent AA

COVER MEAN

(excluding shrub height)

.41 5.95 .19

Site 1 Quickbird Sage (percent) 18 0.40 2.95 0.23 Independent AA

Site 1 Quickbird Shrub (percent) 18 0.73 2.27 0.17 Independent AA

Site 1 Quickbird Bare (percent) 18 0.67 8.95 0.16 Independent AA

Site 1 Quickbird Herbaceous (percent) 18 0.31 5.97 0.23 Independent AA

Site 1 Quickbird Litter (percent) 18 0.45 5.23 0.28 Independent AA

Site 1 Quickbird Big sage (percent) 18 0.29 3.15 0.29 Independent AA

Site 1 Quickbird Wyomingensis (percent) 18 0.09 5.14 0.34 Independent AA

Site 1 Quickbird Shrub (height) 18 0.59 7.61 0.19 Independent AA

Site 2 Quickbird Sage (percent) 15 0.18 4.65 0.33 Independent AA

Site 2 Quickbird Shrub (percent) 15 0.15 5.60 0.29 Independent AA

Site 2 Quickbird Bare (percent) 15 0.18 10.18 0.36 Independent AA

Site 2 Quickbird Herbaceous (percent) 15 0.30 5.81 0.25 Independent AA

Site 2 Quickbird Litter (percent) 15 0.18 6.29 0.26 Independent AA

Site 2 Quickbird Big sage (percent) 15 0.26 4.07 0.29 Independent AA

Site 2 Quickbird Wyomingensis (percent) 15 0.09 4.29 0.31 Independent AA

Site 2 Quickbird Shrub (height) 15 0.48 5.35 0.17 Independent AA

Site 3 Quickbird Sage (percent) 15 0.43 3.26 0.25 Independent AA

Site 3 Quickbird Shrub (percent) 15 0.01 4.60 0.42 Independent AA

Site 3 Quickbird Bare (percent) 15 0.43 13.93 0.24 Independent AA

Site 3 Quickbird Herbaceous (percent) 15 0.93 3.71 0.07 Independent AA

Site 3 Quickbird Litter (percent) 15 0.06 11.32 0.32 Independent AA

Site 3 Quickbird Big sage (percent) 15 0.65 3.45 0.19 Independent AA

Site 3 Quickbird Wyomingensis (percent) 15 0.01 5.05 0.46 Independent AA

Site 3 Quickbird Shrub (height) 15 0.26 10.2 0.35 Independent AA

Site 4 Quickbird Sage (percent) 16 0.28 3.72 0.37 Independent AA

Site 4 Quickbird Shrub (percent) 16 0.01 6.98 0.29 Independent AA

Site 4 Quickbird Bare (percent) 16 0.80 9.97 0.14 Independent AA

Site 4 Quickbird Herbaceous (percent) 16 0.06 7.47 0.39 Independent AA
10 Multiscale Sagebrush Rangeland Habitat Modeling in the Gunnison Basin of Colorado 

Table 3. Accuracy assessment (AA) of all sagebrush rangeland variable predictions for each study area extent and sensor, from 

south-central Colorado.—Continued

[Root mean square error (RMSE) values are in the units of model prediction (percent cover or centimeters for shrub height)].

Site Sensor Variable N R2 RMSE NRMSE Type

Site 4 Quickbird Litter (percent) 16 0.63 8.63 0.18 Independent AA

Site 4 Quickbird Big sage (percent) 16 0.37 3.40 0.34 Independent AA

Site 4 Quickbird Wyomingensis (percent) 16 0.00 3.14 0.79 Independent AA

Site 4 Quickbird Shrub (height) 16 0.45 8.72 0.17 Independent AA

Path/Row 34/33 Landsat Sage (percent) 41 0.19 6.18 0.24 Independent AA

Path/Row 34/33 Landsat Shrub (percent) 41 0.25 6.51 0.23 Independent AA

Path/Row 34/33 Landsat Bare (percent) 41 0.45 12.41 0.17 Independent AA

Path/Row 34/33 Landsat Herbaceous (percent) 41 0.09 12.44 0.32 Independent AA

Path/Row 34/33 Landsat Litter (percent) 41 0.26 7.98 0.21 Independent AA

Path/Row 34/33 Landsat Big sage (percent) 41 0.17 6.03 0.23 Independent AA

Path/Row 34/33 Landsat Wyomingensis (percent) 41 0.21 4.53 0.23 Independent AA

Path/Row 34/33 Landsat Shrub (height) 41 0.35 7.61 0,20 Independent AA

COVER MEAN

(excluding shrub height)

.23 8.01 .23

Figure 6. Example 

predictions for four primary 

components from Landsat 

(30-meter) imagery in the 

Gunnison Basin, Colorado. 

Elevations more than 3,100 

meters and nonsagebrushsteppe land covers are 

masked (white).
References Cited 11

work already done in Wyoming (Homer and others, 2012). We 

caution users that although Landsat prediction products were 

extrapolated within the full area of path/row 34/33 (including areas outside of the Gunnison Basin), products were not 

calibrated for areas outside the Gunnison Basin and should be 

used with caution in those areas.

Project products demonstrate that significant incremental 

methodological improvements can be made in sagebrushsteppe habitat classification using remote sensing in the 

Gunnison Basin. These results offer significant improvement 

to sagebrush habitat quantification at multiple scales and 

offer users products that have been validated. The approach 

of using pre-sampling segmentation and landscape stratification, detailed ground sampling, and regression tree modeling 

of components resulted in reliable component predictions that 

offer sufficient RMSE accuracies to provide baseline products 

for a variety of potential applications. These methods have 

been specifically developed to be both operational and cost 

effective over large areas, enabling potential re-measurement 

to support future change monitoring. These modeled components can now provide maximum flexibility to users at various 

scales and for a wide variety of applications. Further refinement of these remote sensing component predictions in the 

future is possible, likely by focusing on more extensive ground 

plot sampling, employing new high and moderate-resolution 

satellite sensors that offer additional spectral bands for vegetation discrimination, and capturing more dates of satellite 

imagery to better represent phenological variation.

Acknowledgments

We thank Greg Wann and Lorie Brummer for their 

dedicated efforts in conducting field sampling, and Theresa 

Childers and Dan Neubaum for assisting with the coordination 

of field sampling efforts. 

Funding for this research was provided by the U.S. 

Geological Survey, Fort Collins Science Center, Black Canyon 

of the Gunnison National Park, Curecanti National Recreation 

Area, the U.S. Geological Survey Natural Resources Preservation Program, National Park Service Challenge Cost Share 

Program, and the Rocky Mountain Cooperative Ecosystem 

Studies Unit. We also thank the Gunnison sage-grouse Strategic Committee for their interest in our research and support in 

seeing these products developed.

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Homer and others—Multiscale Sagebrush Rangeland Habitat Modeling in the Gunnison Basin of Colorado—Open-File Report 2013–1049