U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2012–1239

Producing Fractional Rangeland Component Predictions 

in a Sagebrush Ecosystem, a Wyoming Sensitivity 

Analysis

Producing Fractional Rangeland 

Component Predictions in a Sagebrush 

Ecosystem, a Wyoming Sensitivity 

Analysis

By George Xian, Collin Homer, Brian Granneman, and Debbie Meyer

Open-File Report 2012–1239

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod

To order other USGS information products, visit http://store.usgs.gov

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Xian, George, Homer, Collin, Granneman, Brian, and Meyer, Debbie, 2012, Producing fractional rangeland component 

predictions in a sagebrush ecosystem, a Wyoming sensitivity analysis: U.S. Geological Survey Open-File Report 

2012–1239, 18 p.
iii

Contents

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

Design of the Sensitivity Tests.....................................................................................................................1

Background on Component Measurement Methods .....................................................................1

QuickBird Scene Training Sensitivity ................................................................................................2

QuickBird Field Training Sample Size Sensitivity ............................................................................2

Landsat Mosaic Evaluation.................................................................................................................2

Landsat Mosaic Evaluation Across Different Ecological Zones...................................................4

Results .............................................................................................................................................................5

QuickBird Scene Test...........................................................................................................................5

Training Data Sensitivity ......................................................................................................................5

Multiple Landsat Scenes Test.............................................................................................................7

Ecoregion Sensitivity Test .................................................................................................................10

Summary........................................................................................................................................................17

References Cited..........................................................................................................................................18

Figures

1. Map showing the extent of Landsat p37/r31 and locations of QuickBird images .............3

2. Map showing the spatial extent of sensitivity tests with 2 (p37/r31 and p37/r30)

(MT-2 test); 3 (p37/r31, p37/r30, and p36/r31) (MT-3 test); 4 (p37/r31, p37/r30, p36/r31,

and p36/r30) (MT-4 test); and 6 (p37/r31, p37/r30, P36/R31, p36/r30, p35/r31, and

p36/r29) (MT-6 test) Landsat scenes mosaic ...........................................................................4

3. Map showing the Landsat scenes used for different ecological zone tests......................6

4. Mosaic showing percent covers of sagebrush components obtained using

different numbers of QuickBird imagery...................................................................................8

5. Graphs showing field sample sensitivity test for three Landsat footprints. Root

mean square error and square of correlation coefficient are calculated

using re-estimate and 2006 products ........................................................................................9

6. Graphs showing average root mean square error (a) and (b) by comparing 

reference and re-estimate datasets for three Landsat path/rows.....................................10

7. Mosaic showing percent covers of sagebrush components derived from the 

MT-2 and MT-3 tests...................................................................................................................11

8. Mosaic showing percent covers of sagebrush components derived from the 

MT-4 and MT-6 tests...................................................................................................................14

9. Graphs showing mean percent cover of four sagebrush components with 

different numbers of Landsat scenes......................................................................................15

10. Graphs showing mean root mean square error and square of correlation

coefficient values for four sagebrush component predictions derived 

from extrapolating training data from a single Landsat scene to different 

numbers of Landsat scenes......................................................................................................16
iv

Tables

1. Landsat scenes and acquisition dates......................................................................................5

2. QuickBird scenes used in the 2006 sagebrush products.......................................................5

3. QuickBird test for percent cover of bare-ground....................................................................6

4. QuickBird test for percent cover of herbaceous.....................................................................7

5. QuickBird test for percent cover of sagebrush.......................................................................7

6. QuickBird test for percent cover of shrub................................................................................7

7. Multiple Landsat scene test for bare-ground in each mosaic that has different

number of paths and rows ........................................................................................................12

8. Multiple Landsat scene tests for herbaceous.......................................................................12

9. Multiple Landsat scene test for sagebrush............................................................................13

10. Multiple Landsat scene tests for herbaceous.......................................................................13

11. Bare-ground comparison using two Landsat scenes across multiple ecological

zones.............................................................................................................................................16

12. Herbaceous comparison using two Landsat scenes across multiple ecological

zones.............................................................................................................................................16

13. Sagebrush comparison using two Landsat scenes across multiple ecological 

zones.............................................................................................................................................17

14. Shrub comparison using two Landsat scenes across multiple ecological zones..........17
Producing Fractional Rangeland Component Predictions in 

a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

By George Xian1, Collin Homer2, Brian Granneman3, and Debbie Meyer3

1

 ARTS, contractor to the U.S. Geological Survey (USGS) Earth Resources 

Observation and Science Center, Sioux Falls, SD 57198, USA. 

Work performed under USGS contract G08PC91508.

2

 USGS Earth Resources Observation and Science Center, Sioux Falls, 

SD 57198, USA.

3

 SGT, contractor to the USGS Earth Resources Observation and Science 

Center, Sioux Falls, SD 57198, USA. Work performed under USGS 

contract G10PC00044.

Introduction

Remote sensing information has been widely used to 

monitor vegetation condition and variations in a variety of 

ecosystems, including shrublands. Careful application of 

remotely sensed imagery can provide additional spatially 

explicit, continuous, and extensive data on the composition 

and condition of shrubland ecosystems. Historically, the most 

widely available remote sensing information has been collected by Landsat, which has offered large spatial coverage 

and moderate spatial resolution data globally for nearly three 

decades. Such medium-resolution satellite remote sensing 

information can quantify the distribution and variation of 

terrestrial ecosystems. Landsat imagery has been frequently 

used with other high-resolution remote sensing data to classify 

sagebrush components and quantify their spatial distributions 

(Ramsey and others, 2004; Seefeldt and Booth, 2004; Stow

and others, 2008; Underwood and others, 2007). Modeling 

algorithms have been developed to use field measurements 

and satellite remote sensing data to quantify the extent and 

evaluate the quality of shrub ecosystem components in large 

geographic areas (Homer and others, 2009). The percent cover 

of sagebrush ecosystem components, including bare-ground, 

herbaceous, litter, sagebrush, and shrub, have been quantified 

for entire western states (Homer and others, 2012). Furthermore, research has demonstrated the use of current measurements with historical archives of Landsat imagery to quantify 

the variations of these components for the last two decades 

(Xian and others, 2012). 

The modeling method used to quantify the extent and 

spatial distribution of sagebrush components over a large area 

also has required considerable amounts of training data to 

meet targeted accuracy requirements. These training data have 

maintained product accuracy by ensuring that they are derived 

from good quality field measurements collected during appropriate ecosystem phenology and subsequently maximized by 

extrapolation on high-resolution remote sensing data (Homer 

and others, 2012). This method has proven its utility; however, 

to develop these products across even larger areas will require 

additional cost efficiencies to ensure that an adequate product 

can be developed for the lowest cost possible. Given the vast 

geographic extent of shrubland ecosystems in the western 

United States, identifying cost efficiencies with optimal training data development and subsequent application to medium 

resolution satellite imagery provide the most likely areas for 

methodological efficiency gains. 

The primary objective of this research was to conduct 

a series of sensitivity tests to evaluate the most optimal 

and practical way to develop Landsat scale information for 

estimating the extent and distribution of sagebrush ecosystem components over large areas in the conterminous United 

States. An existing dataset of sagebrush components developed from extensive field measurements, high-resolution 

satellite imagery, and medium resolution Landsat imagery in 

Wyoming was used as the reference database (Homer and others, 2012). Statistical analysis was performed to analyze the 

relation between the accuracy of sagebrush components and 

the amount and distribution of training data on Landsat scenes 

needed to obtain accurate predictions. 

Design of the Sensitivity Tests

Background on Component Measurement 

Methods

The sagebrush ecosystem components were estimated 

in Wyoming using 2006 Landsat imagery supported by 

many QuickBird images, which have a spatial resolution of 

2.4 meters (m), (Homer and others, 2012). Field measurements 

were first collected from selected sites, and then combined into 

a first phase training dataset with a resolution sufficient for 

training QuickBird imagery. Generally, 60–75 field measurements were collected within one QuickBird footprint, which 

has a spatial extent of approximately 100 square kilometers 

(km2

), for the 2006 Wyoming product. Field measurements 
2 Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

representing the percent cover of each component (the portion 

of a ground unit covered by the component) were upscaled for 

creating regression models to derive a component prediction 

for each pixel of the QuickBird imagery for each component 

with regression tree algorithms. QuickBird image predictions 

were then rescaled to match Landsat resolution and serve as 

second phase training data to train the regression tree models 

with Landsat imagery. Unique Landsat regression tree models 

are required for each sagebrush component. Landsat imagery 

from spring, summer, and fall along with other derivatives 

including digital elevation model (DEM), slope, aspect, and 

three spectral indexes were used as independent variables 

to create the regression tree models. Four to seven QuickBird scenes were used to train one Landsat scene, and a total 

of seven Landsat images were used to cover the extent of 

Wyoming.

The accuracy of the sagebrush component prediction in 

Wyoming depends highly on the quality of training datasets 

used for the regression tree models; however, the quantities of 

training data needed to obtain accurate estimates for the sagebrush components are not certain. It is important, therefore, 

to understand the sensitivity of the size and spatial distribution of training data on the accuracy of sagebrush component 

distributions. The requirements of the size of field measurement were discussed in a previous study (Homer and others, 

2009). The main focus of this sensitivity testing is to explore 

the quantities of both field data and high-resolution images 

required to achieve relatively accurate sagebrush distribution 

estimates with the use of medium resolution Landsat data. 

The first focus was to measure the sensitivity of the first phase 

training data including field dataset collection and high-resolution training data size. The second focus was to evaluate the 

sensitivity of using multiple instead of single Landsat scenes 

to extrapolate sagebrush component predictions derived with 

training datasets from high-resolution imagery. Four sensitivity tests were done: (1) the optimal number of high-resolution 

QuickBird images needed to create the training dataset in one 

Landsat scene; (2) the number of Landsat scenes in a mosaic 

that could be used in one ecological zone with limited training 

datasets; (3) the sensitivity of using Landsat scene mosaics 

across different ecological zones with limited training datasets; and (4) the sensitivity of the QuickBird prediction to the 

number of field plots used for training datasets.

QuickBird Scene Training Sensitivity

In the Wyoming sagebrush component prediction 

research, high-resolution QuickBird imagery was used with 

field observations to build the second phase training data for 

Landsat regression tree models. The fractional cover of four 

sagebrush components including bare-ground, herbaceous, 

sagebrush, and shrub was estimated. A series of tests was 

done to determine the minimum number of QuickBird scenes 

that would be needed as training datasets to achieve satisfactory predictions on one Landsat scene. For each sagebrush 

component, the prediction derived using all available QuickBird scenes (Qn

) in the footprint of a Landsat scene were 

used as the reference database. The percent cover of the same 

component was then reproduced with the number of QuickBird scenes varying from 1 to Qn

–1 using regression tree 

algorithms and the same independent inputs as in the 2006 

product. By using a randomly stratified approach, 1,000 points 

were selected to acquire the percent cover of the component 

from the reference dataset and the reproduced dataset to 

enable comparison. Statistical analyses including regression 

and a statistical hypothesis test (t-test) for pairing of two 

sample means were performed to compare the difference in 

the mean of the two datasets. Root mean square error (RMSE), 

square of correlation coefficient r2 value, and significance 

level were included in the statistical analysis. Similar analyses 

were carried out for all four components. The Landsat scene 

of path 37/row 31 (p37/r31) was selected for the test, in which 

Qn equals 4 for the original estimates. The locations of the 

QuickBird scenes used in the Landsat footprint are shown in 

figure 1.

QuickBird Field Training Sample Size Sensitivity

The method to evaluate the sensitivity of numbers of 

high-resolution imagery used for the second phase training 

dataset was discussed in QuickBird Scene Training Sensitivity section. This test was designed to determine the number 

of field plots required for optimal first phase training of the 

QuickBird predictions. Training samples were acquired from 

p37/r31, p36/r30, and p36/r29 to produce estimates for all 

four sagebrush components and were compared with the 2006 

products. In p37/31, the 2006 product was produced by using 

four QuickBird scenes. In p36/r30, five QuickBird scenes were 

used to form the second phase training dataset. In p36/r29, 

three QuickBird scenes were selected to produce the second 

phase training dataset. In every QuickBird footprint, more than 

60 field measurements were collected, except for two QuickBird scenes in p36/r29 and p36/r30, where only 50 samples 

were collected to produce 2006 products. Three QuickBird 

scenes were selected in each Landsat footprint for this test. 

The three selected QuickBird scenes contain the second phase 

training datasets derived by using regression models with 

the best, worst, and average modeling performances. In each 

QuickBird footprint, 10 to 50 field samples were selected to 

re-estimate percent covers of sagebrush components, except 

in p36/r28, where 10 to 40 samples were acquired. Statistical 

analyses were conducted to evaluate the similarity and difference between the re-estimate and 2006 products for bareground and shrub.

Landsat Mosaic Evaluation

The percent covers of sagebrush components were 

predicted using Landsat imagery with different numbers of 

QuickBird scenes as the second phase training datasets within 
Design of the Sensitivity Tests 3

Figure 1. The extent of Landsat p37/r31 and locations of QuickBird images. The four QuickBird images that are 

labeled as Qb1, Qb2, Qb3, and Qb4 are used as second phase training datasets.

a Landsat footprint in the 2006 product. Because sagebrush 

components have many similar characteristics in the same 

ecological region, including the type, density, height, and age, 

this test was designed to evaluate the possibility of using the 

second phase training data in one Landsat scene to extrapolate 

predictions of sagebrush components in a larger area covered 

by multiple adjacent Landsat scenes.

The sensitivity analyses consisted of four different tests 

named MT-2, MT-3, MT-4, and MT-6 representing 2, 3, 4, 

and 6, Landsat scene mosaics. The spatial extents of these 

tests are shown in figure 2 and acquisition dates for Landsat 

scenes used for the test are listed in table 1. In the MT-2 test, 

the second phase training datasets derived from four QuickBird images within the footprint of Landsat p37/r31 were 

selected as the reference training data. The reference datasets 

were then used with a two-scene Landsat mosaic to create 

regression models. Other input data were the same as used in 

the 2006 product; therefore, the percent covers of sagebrush 

components were reproduced in the area covered by two 

Landsat footprints. The MT-3 test is similar to the MT-2, with 

the use of the reference training data limited in only Landsat 

p37/r31. The re-estimates of the percent cover of sagebrush 

components were conducted in a larger area covered by a 

three-scene Landsat mosaic. Similarly, the MT-4 and MT-6 

tests were performed using the same approach but with fourand six-scene Landsat mosaics, respectively. The number of 

QuickBird scenes used in each Landsat path and row in the 

2006 products is shown in table 2. Statistical analyses were 

conducted for all tests by comparing the re-estimate products 

with the 2006 reference products. There were 1,000 samples 

collected from the 2006 product and the reproduced estimate 

that were used to acquire magnitudes of the percent cover of 

sagebrush components. These randomly selected points distributed in each individual Landsat footprint were produced by 

using the randomly stratified approach. Statistical parameters, 

including RMSE, r2

, and significant level of correlation, were 
4 Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

Figure 2. The spatial extent of sensitivity tests with 2 (p37/r31 and p37/r30) (MT-2 test); 3 (p37/r31, p37/r30, and p36/r31) (MT-3 

test); 4 (p37/r31, p37/r30, p36/r31, and p36/r30) (MT-4 test); and 6 (p37/r31, p37/r30, P36/R31, p36/r30, p35/r31, and p36/r29) (MT-6 

test) Landsat scenes mosaic.

calculated and analyzed to measure the similarity and difference of the reference and re-estimate products. The scenes 

used were restricted to the same ecological zone as much as 

possible.

Landsat Mosaic Evaluation Across Different 

Ecological Zones

This test was designed to use a two-scene Landsat mosaic 

that stratifies different ecological zones. The intent was to test 

how well prediction extrapolation would succeed across multiscene mosaics that cover multiple ecological zones, whereas 

prediction models were being trained using the second phase 

dataset in just one Landsat footprint. For this testing, two 
Results 5

Table 1. Landsat scenes and acquisition dates. 

Path/row Spring acquisition date Summer acquisition date Fall acquisition date

p35/r31 5/16/2006 7/06/2007 9/05/2006

p36/r29 4/21/2006 6/29/2008 9/15/2007

p36/r30 4/21/2006 7/31/2008 9/12/2006

p36/r31 4/21/2006 7/31/2008 9/12/2006

p37/r30 5/14/2006 6/18/2007 9/03/2006

p37/r31 5/14/2006 7/17/2006 9/03/2006

Table 2. QuickBird scenes used in the 2006 sagebrush products.

Path/row p35/r31 p36/r29 p36/r30 p36/r31 p37/r30 p37/r31

Number of 

QuickBird 

scenes

6 4 7 6 6 4

separate two-scene mosaics were created. For the first test 

(ET-1), two scenes, p35/r30, and p35/r31 were mosaicked. 

Training data from p35/r31 were used to make the predictions. 

For the second test (ET-2), p36/r29, was merged with p37/r29 

with training data coming from the former scene. The Landsat scene extents and ecological zones are shown in figure 3. 

In the ET-1 test, the second phase training data were used 

with the two-scene mosaics to reproduce the percent cover of 

sagebrush components for the area of two Landsat footprints. 

Similarly, in the ET-2 test, the second phase training data were 

used with the two-scene mosaics to extrapolate the percent 

cover for components within the two Landsat footprints. 

Statistical analyses were conducted for both ET-1 and 

ET-2 products. These analyses used 1,000 samples that were 

acquired from randomly selected points. Magnitudes of 

RMSE, r2, and significant level were derived by comparing 

cover values of different components from the 2006 products 

against the reproduced estimates from the ET-1 and ET-2 

predictions.

Results

QuickBird Scene Test

The sensitivities from using various numbers of second 

phase training datasets derived from QuickBird scenes to estimate the percent cover of sagebrush components with Landsat 

scenes p37/r31 and p36/r30 are presented in tables 3–6. Generally, the means obtained from using less than all reference 

QuickBird scenes are slightly less than the reference values for 

bare-ground cover and larger for other components. The correlations show that bare-ground and sagebrush have relatively 

large r2 values, whereas shrub and herbaceous have relatively 

smaller r2 values. The comparison of the mean percent cover 

between the 2006 products and the re-estimates suggests those 

with fewer QuickBird scenes may be slightly overestimating 

herbaceous, sagebrush, and shrub. However, the magnitudes 

of RMSE and r2

 derived from using fewer reference QuickBird 

scenes do not show substantial differences between re-estimate 

and reference products for all sagebrush components. Furthermore, the t-test results indicate that the means of the reference 

and re-estimate products have no significant differences for all 

components.

Spatial distributions of the percent cover of four sagebrush components from re-estimates are shown in figure 4, 

which were produced using one to three QuickBird scenes 

(fig. 4a-ll), and from 2006 products (fig. 4 (m)–(p)), which 

were produced using the training data from four QuickBird 

scenes. Compared with the 2006 products, the re-estimates 

capture most of the fundamental features of the sagebrush 

components. For example, the low percent cover of bareground and the high cover of shrub in higher elevation areas 

are captured in the re-estimates, and the distribution patterns 

are very similar to the 2006 products.

Training Data Sensitivity

The magnitudes of RMSE and r2 for each Landsat path 

and row are evaluated by comparing field samples from the 

reference and re-estimate products. Figure 5 depicts variations 

of RMSE and r2 for all four sagebrush components as field 

samples increase from 10 to 50 in p37/r31 (fig. 5a–b), p36/r30 

(fig. 5c–d), and p36/r29 (fig. 5e–f). In p37/r31, RMSE apparently decreases after field samples increase to 30 for all components (fig. 5a). The magnitudes of r2

 substantially increase 

after field samples increased beyond 20 (fig. 5b). Increasing the 

number of field samples causes r2 values to increase for all components in all sites except in site 2 for shrub. In p36/r30, magnitudes of RMSE for bare-ground and shrub steadily decrease in
6 Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

Figure 3. Landsat scenes used for different ecological zone tests. Landsat scenes of p35/r30 and p35/r31 

located in the Wyoming Basin, South Rockies, and Northwestern Great Plains ecological zones are used 

for the first test (ET-1). Landsat scenes of p37/r29 and p36/r29 located in Wyoming Basin, Middle Rockies, 

and Northwestern Great Plains ecological zones are used for the second test (ET-2).

Table 3. QuickBird (QB) test for percent cover of bare-ground. 

[Values of square of correlation coefficient (r2) and root mean square error (RMSE) are listed; N/A, represents data are not available]

r2 RMSE Mean

1 QB to 4 QB 0.696 12.64 65.52

2 QB to 4 QB 0.624 14.36 52.73

3 QB to 4 QB 0.665 13.17 56.05

4 QB – reference N/A N/A 61.96
Results 7

Table 4. QuickBird test for percent cover of herbaceous.

r2 RMSE Mean

1 QB to 4 QB 0.123 8.85 15.92

2 QB to 4 QB 0.252 8.33 15.26

3 QB to 4 QB 0.245 9.20 17.14

4 QB – reference N/A N/A 13.23

Table 5. QuickBird test for percent cover of sagebrush.

r2 RMSE Mean

1 QB to 4 QB 0.506 3.88 9.14

2 QB to 4 QB 0.667 3.56 9.32

3 QB to 4 QB 0.612 3.82 9.10

4 QB – reference N/A N/A 8.10

Table 6. QuickBird test for percent cover of shrub.

r

2 RMSE Mean

1 QB to 4 QB 0.400 3.66 9.88

2 QB to 4 QB 0.250 5.48 13.06

3 QB to 4 QB 0.371 4.28 11.56

4 QB – reference N/A N/A 10.49

all sites and become stable after field samples increase beyond 

40 samples. Additionally, r2 values for these two components 

also increase as field samples are increased and become 

relatively stable with more than 40 samples. Similar trends 

in both RMSE and r2 are also observed in p36/r29, although 

less than 50 samples were analyzed in some sites because the 

maximum samples used to produce the reference data were 50 

in these sites.

The variations of RMSE and r2

 in different paths and 

rows are averaged to review the overall effect of field sample 

selection. The average RMSE (fig. 6a) and r2 (fig. 6b) for bareground and shrub distributions from three scenes’ sensitivity 

tests is depicted in figure 10. The overall average RMSE and r2

follow approximately the same trends as observed in individual path and rows. For bare-ground, RMSE at the 10 field 

sample mark is 169 percent of the value of the 50 field sample 

mark, and it declines to 111 percent when 40 field samples are 

added. The r2 at the 10 field sample mark is 56 percent of the 

value at the 50 field sample mark, and it increases to 87 percent of the value at the 40 field sample mark. Similarly, ratios 

of RMSE and r2 at the 10 field sample mark to the 50 field 

sample mark vary from 160 percent to 100 percent and from 

45 percent to 90 percent, respectively, after the 40 field sample 

mark.

Multiple Landsat Scenes Test

Spatial distributions are displayed in figures 6 and 7of 

four sagebrush components for the 2006 products and reestimates using limited training datasets from the footprint 

of p37r31 with different Landsat mosaics. All re-estimate 

sagebrush components in the MT-2 test (fig. 6e–(h) have high 

agreement with the reference products (fig. 6a–d) in terms of 

spatial distribution patterns and cover intensities. The similarities are also observed in the MT-3 test (fig. 6i–p), except for 

the bare-ground re-estimate, which has relatively smaller percent covers. No apparent differences between reference (fig. 

7a–d)) and re-estimate (fig. 7e–h) are observed in the MT-4 

test. Apparent differences, especially for shrub distribution, 

are observed in the MT-6 test (fig. 6i–p), in which the reference product has relatively larger magnitudes in shrub percent 

cover (fig. 6l) than those in the re-estimate (fig. 6p). 

The RMSE and r2

 of all components from tests in each 

path and row are shown in tables 7 though 10. The 95-percent 

significance level of a t-test for two pairs of mean is indicated 

by a * after r2 values. The MT-2 tests have larger r2 values and 

smaller RMSE for bare-ground, sagebrush, and shrub, but not 

for herbaceous; also, t-tests for all components that show correlations of mean values between the reference and re-estimate 
8 Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

Figure 4. Percent covers of sagebrush components obtained using different numbers of QuickBird imagery. Panels from left to right 

are bare-ground, herbaceous, sagebrush, and shrub. Panels from top to bottom depict estimates by using one QuickBird scene (a–d), 

two QuickBird scenes (e–h), three QuickBird scenes (i–l), and four QuickBird scenes (m-p). Numbers in legends represent percent

covers of difference components.
Results 9

Figure 5. Field sample sensitivity test for three Landsat footprints. Root mean square error (RMSE) and square of correlation 

coefficient (r2

) are calculated using re-estimate and 2006 products. The 2006 product was produced by using all training 

sample numbers between 60 and 50. RMSE and r2

 values are displayed for p37/r31 (a–b), p36/r30 (c–d), and p36/r29 (e–f).
10 Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

Figure 6. Average root mean square error (RMSE) (a) and r2 (b) by comparing reference and re-estimate datasets for three Landsat 

path/rows.

products are significant in the MT-2 test. In the MT-3 test, 

most r2

 values are still large and RMSE values are small. All 

t-tests are significant except for the shrub. In the MT-4 test, 

all r2 values are reduced from the MT-2 and MT-3 tests. The 

t-tests still show most paths and rows are significant for all 

components. In the MT-6 test, r2

 values are smaller and RMSE 

values are larger than those in the area that is close to the 

path 37 and row 31, which contains the second phase training 

data.

The RMSE and r2 obtained by using different Landsat 

scene mosaics are averaged for each Landsat mosaic. These 

means are used to determine the general correlation between 

the reference and re-estimate products. The mean covers for 

all components calculated from each test are shown in figure 8. 

The difference trends are modestly higher as the numbers of 

scenes are increased to more than four but still remain within 

a reasonable range when comparing the mean percent cover. 

Mean RMSE and r2

 values for all components in the MT test 

are shown in figure 9. The RMSE shows that herbaceous has

a substantial increase after the number of scenes exceeds four 

(fig. 9a). Similar increasing trends are also demonstrated for 

sagebrush and shrub. The mean r2 values of bare-ground and 

herbaceous show substantial decreases as the numbers of 

scenes are increased by more than two (fig. 10 b). The mean 

r2 for shrub decreased most dramatically after the mosaic had 

grown to four images in the test while the mean r2 for sagebrush declined linearly.

Ecoregion Sensitivity Test

Values of r2, RMSE, and mean for all components in the 

ET-1 and ET-2 tests are shown in tables 11 through 14. The r2

values are small except for bare-ground, but the correlations 

are still significant for all components. The t-tests are significant for all components except for sagebrush in the ET-2 test. 

The RMSE values suggest that the accuracies of new estimates 

are relatively high for all components except for sagebrush in 

the ET-2 test and herbaceous in both ET-1 and ET-2 tests.
Results 11

Figure 7. Percent covers of sagebrush components derived from the MT-2 and MT-3 tests. Panels from left to right depict bareground, herbaceous, sagebrush, and shrub. Panels of (a–d) and (i–l) are reference sagebrush components. Panels of (e–h) and 

(m–p) are sagebrush components obtained by using two (MT-2) and three (MT-3) Landsat scene mosaics estimated with training 

data from one Landsat scene. The color legends are same as in Figure 4.
12 Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

Table 7. Multiple Landsat scene test (MT) for bare-ground in each mosaic that has different number of paths and rows (p/r).

[*, indicates the 95-percent significance level of t-test of two pairs of mean]

Path/row Statistics MT-2 MT-3 MT-4 MT-6

p35/r31 r

2 N/A N/A N/A 0.388

RMSE N/A N/A N/A 22.94

p36/r29 r

2 N/A N/A N/A 0.177

RMSE N/A N/A N/A 16.73

p36/r30 r

2 N/A N/A 0.134* 0.137*

RMSE N/A N/A 17.30 27.01

p36/r31 r

2 N/A 0.542* 0.443 0.486*

RMSE N/A 18.28 20.58 18.96

p37/r30 r

2 0.625* 0.572* 0.473* 0.477*

RMSE 14.31 14.67 15.35 15.30

p37/r31 r

2 0.701* 0.685* 0.667* 0.685*

RMSE 12.40 12.25 13.08 12.30

Table 8. Multiple Landsat scene tests for herbaceous.

[*, indicates the 95-percent significance level of t-test of two pairs of mean]

Path/row Statistics MT-2 MT-3 MT-4 MT-6

p35/r31 r

2 N/A N/A N/A 0.167*

RMSE N/A N/A N/A 12.30

p36/r29 r

2 N/A N/A N/A 0.177*

RMSE N/A N/A N/A 41.05

p36/r30 r

2 N/A N/A 0.126 0.149*

RMSE N/A N/A 11.60 16.73

p36/r31 r2 N/A 0.006* 0.004* 0.013*

RMSE N/A 10.09 9.05 9.50

p37/r30 r

2 0.031* 0.248* 0.066* 0.033*

RMSE 0.52 9.66 12.36 11.53

p37/r31 r

2 0.348* 0.293* 0.367 0.294

RMSE 8.31 9.84 8.63 9.00
Results 13

Table 9. Multiple Landsat scene test for sagebrush.

[*, indicates the 95-percent significance level of t-test of two pairs of mean]

Path/row Statistics MT-2 MT-3 MT-4 MT-6

p35/r31 r

2 N/A N/A N/A 0.068*

RMSE N/A N/A N/A 8.02

p36/r29 r

2 N/A N/A N/A 0.176*

RMSE N/A N/A N/A 6.30

p36/r30 r

2 N/A N/A 0.095* 0.014

RMSE N/A N/A 6.46 8.91

p36/r31 r

2 N/A 0.571* 0.536* 0.332*

RMSE N/A 3.69 3.83 4.85

p37/r30 r

2 0.526* 0.506* 0.454* 0.412*

RMSE 5.04 4.98 5.11 5.12

p37/r31 r

2 0.514* 0.481* 0.476* 0.594*

RMSE 4.12 4.66 4.75 3.89

Table 10. Multiple Landsat scene tests for herbaceous.

[*, indicates the 95-percent significance level of t-test of two pairs of mean]

Path/row Statistics MT-2 MT-3 MT-4 MT-6

p35/r31 r

2 N/A N/A N/A 0.093*

RMSE N/A N/A N/A 6.82

p36/r29 r

2 N/A N/A N/A 0.176*

RMSE N/A N/A N/A 6.61

p36/r30 r

2 N/A N/A 0.134* 0.046

RMSE N/A N/A 7.29 8.62

p36/r31 r

2 N/A 0.414 0.412 0.216

RMSE N/A 4.18 4.25 5.46

p37/r30 r

2 0.456* 0.413* 0.441* 0.434*

RMSE 5.19 4.48 4.12 5.45

p37/r31 r

2 0.571* 0.572* 0.555* 0.572*

RMSE 4.32 4.31 4.44 4.25
14 Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

Figure 8. Percent covers of sagebrush components derived from the MT-4 and MT-6 tests. Panels from left to right depict bareground, herbaceous, sagebrush, and shrub. Panels of (a–d) and (i–l) are reference sagebrush components. Panels of (e–h) and (m–p) 

are sagebrush components obtained by using four (MT-4) and six (MT-6) Landsat scene mosaics estimated with training data from one 

Landsat scene. The color legends are same as in Figure 4.
Results 15

Figure 9. Mean percent cover of four sagebrush components with different numbers of Landsat scenes. The x-axis is the number of 

Landsat scenes. The y-axis is the mean value. The reference is for the 2006 products and test is for the new estimates.
16 Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

Figure 10. Mean root mean square error (RMSE) and square of correlation coefficient (r2) values for four sagebrush component 

predictions derived from extrapolating training data from a single Landsat scene to different numbers of Landsat scenes.

Table 11. Bare-ground comparison using two Landsat scenes across multiple ecological zones. 

[*, indicates the 95-percent significance level of t-test of two pairs of mean; ET-1 and ET-2 represents tests in one and two ecological zones]

ET-1 ET-2

Statistics p35/r30 p35/r31 p37/r29 p36/r29

r

2 0.367* N/A 0.528* N/A

RMSE 17.65 N/A 12.77 N/A

Mean 49.16 54.96 63.09 61.53

Table 12. Herbaceous comparison using two Landsat scenes across multiple ecological zones 

[*, indicates the 95-percent significance level of t-test of two pairs of mean; ET-1 and ET-2 represents tests in one and two ecological zones]

ET-1 ET-2

Statistics p35/r30 p35/r31 p37/r29 p36/r29

r

2 0.204* N/A 0.286* N/A

RMSE 14.87 N/A 11.4 N/A

Mean 24.23 13.71 14.47 15.32
Summary 17

Table 13. Sagebrush comparison using two Landsat scenes across multiple ecological zones 

[*, indicates the 95-percent significance level of t-test of two pairs of mean; ET-1 and ET-2 represents tests in one and two ecological zones]

ET-1 ET-2

Statistics p35/r30 p35/r31 p37/r29 p36/r29

r

2 0.204* N/A 0.471 N/A

RMSE 5.19 N/A 2.43 N/A

Mean 8.69 10.54 4.56 4.48

Table 14. Shrub comparison using two Landsat scenes across multiple ecological zones

[*, indicates the 95-percent significance level of t-test of two pairs of mean; ET-1 and ET-2 represents tests in one and two ecological zones]

ET-1 ET-2

Statistics p35/r30 p35/r31 p37/r29 p36/r29

r

2 0.206* N/A 0.242* N/A

RMSE 5.49 N/A 2.97 N/A

Mean 9.64 11.67 6.66 6.42

Summary

The sensitivity analysis compared the 2006 sagebrush 

distributions with various iterations of re-estimates in Wyoming. The comparisons ascertain the most optimal and practical way to further develop training data and satellite imagery 

for estimating extents and distribution of sagebrush ecosystem 

components over a large area. Results have the potential to 

provide recommendations for future large-area attempts to 

quantify rangeland ecosystems by using remote-sensing information. Several conclusions are listed below.

Number of high-resolution images for Landsat estimation.—The accuracy of sagebrush component estimates did not 

demonstrate significant differences when different numbers of 

QuickBird scenes were used as training data within the extent 

of a Landsat scene. One QuickBird scene could be used to provide percent cover estimates for sagebrush ecosystem components in the extent of a Landsat scene without losing significant accuracy. However, this is dependent upon the quality 

of training data derived from the QuickBird imagery being 

reasonably accurate and representing the range of expected 

habitats in the Landsat footprint. 

Training data sensitivity.—This test explored the sensitivities of sagebrush component accuracies based on the total 

number of field plots used in the prediction. Results indicated 

that approximately 40 field samples provide the minimum 

training samples needed to achieve a stable and relatively 

high accuracy prediction on high-resolution imagery. For 

example, the RMSE value for bare-ground could be reduced 

by 169 percent at the 10 field sample level and to 111 percent 

at the 40 field sample level based on the estimates created 

using 50 field samples. The r2 value increased from 56 percent 

with 10 field samples to 87 percent with 40 field samples. The 

shrub estimate also has a similar pattern.

Estimation across multiple Landsat scenes.—The 

prediction accuracy of sagebrush components within a 

single ecoregion suggests that reasonable predictions can be 

extrapolated from one Landsat scene to an adjacent scene. 

Adequate training data can be generated from a single Landsat 

scene and successfully extrapolated for creating distributions 

of components on up to four adjacent Landsat scene paths/

rows; however, when scene mosaics expand beyond the four 

adjacent scenes, accuracy will drop, but not significantly. The 

herbaceous estimate had the lowest accuracy from using more 

than four Landsat scene mosaics.

Multiple Landsat scene estimation across ecological zones.—The test results suggest that most estimates of 

sagebrush components across ecoregions maintain relatively 

high accuracies despite the additional extrapolation. The bareground estimate had the largest r2

 and lowest RMSE values 

when compared to other components, whereas the herbaceous 

estimate had the lowest accuracy and smallest r2 values. 

Overall, the ET-2 tests have better modeling performance than 

the ET-1 tests do; however, the model estimates using cross 

ecological zone Landsat scene mosaics could not achieve 

better accuracies than the four Landsat scene mosaics did in 

the same ecological zone. The cross ecological zone test result 

suggests that training data extrapolation should be contained 

within only similar ecoregions.
18 Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis

References Cited

Homer, C.G., Aldridge, C.L., Meyer, D.K., Coan, M.J., and 

Bowen, Z.H., 2009, Multiscale sagebrush rangeland habitat 

modeling in southwest Wyoming: U.S. Geological Survey 

Open-File Report 2009–1027, 14 p.

Homer, C., Aldridge, C., Meyer, D., and Schell, S., 2012, 

Implementing a multi-scale remote sensing sagebrush habitat monitoring framework: International Journal of Applied 

Earth Observation and Geoinformation, v. 14, p. 233–244.

Ramsey, R.D., Wright, D.L. Jr., and McGinty, C., 2004, Evaluating the use of Landsat 30m Enhanced Thematic Mapper 

to monitor vegetation cover in shrub-steppe environments: 

Geocarto International, v. 19, p. 39–47.

Seefeldt, S.S., and Booth, D.T., 2004, Measuring plant cover 

in sagebrush steppe rangelands: a comparison of methods: 

Environmental Management, v. 37, p. 703–711.

Stow, D., Hamada, Y., Coulter, L., and Anguelova, Z., 2008, 

Monitoring shrubland habitat changes through object-based 

change identification with airborne multispectral imagery: 

Remote Sensing of Environment, v. 112, p. 1,051–1,061.

Underwood, E.C., Ustin, S.L., and Ramirez, C.M., 2007, A 

comparison of spatial and spectral image resolution for 

mapping invasive plants in coastal California: Environmental Management, v. 39, p. 63–83.

Xian, G., Homer, C., and Aldridge, C., 2012, Assessing 

long-term variations in sagebrush habitat-characterization 

of spatial extents and distribution patterns using multi-temporal satellite remote-sensing data; International Journal of 

Remote Sensing, v. 33, p. 2,034–2,058.

Publishing support provided by:

Rolla Publishing Service Center

For more information concerning this publication, contact:

U.S. Geological Survey Earth Resources Observation

and Science (EROS) Center

47914 252nd Street

Sioux Falls, South Dakota 57198

(605) 594-6151

Or visit the EROS Center Web site at:

http://eros.usgs.gov/

Xian and others—Producing Fractional Rangeland Component Predictions in a Sagebrush Ecosystem, a Wyoming Sensitivity Analysis—OFR 2012–1239