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Remote Sensing of Environm

Identifying land cover variability distinct from land cover change:

Cheatgrass in the Great Basin

Bethany A. Bradley*, John F. Mustard

Brown University, Department of Geological Sciences, 324 Brook Street Providence, RI 02912, United States

Received 3 May 2004; received in revised form 24 August 2004; accepted 29 August 2004

Abstract

An understanding of land use/land cover change at local, regional, and global scales is important in an increasingly human-dominated

biosphere. Here, we report on an under-appreciated complexity in the analysis of land cover change important in arid and semi-arid

environments. In these environments, some land cover types show a high degree of inter-annual variability in productivity. In this study, we

show that ecosystems dominated by non-native cheatgrass (Bromus tectorum) show an inter-annual amplified response to rainfall distinct

from native shrub/bunch grass in the Great Basin, US. This response is apparent in time series of Landsat and Advanced Very High

Resolution Radiometer (AVHRR) that encompass enough time to include years with high and low rainfall. Based on areas showing a similar

amplified response elsewhere in the Great Basin, 20,000 km2, or 7% of land cover, are currently dominated by cheatgrass. Inter-annual

patterns, like the high variability seen in cheatgrass-dominated areas, should be considered for more accurate land cover classification. Land

cover change science should be aware that high inter-annual variability is inherent in annual dominated ecosystems and does not necessarily

correspond to active land cover change.

D 2004 Elsevier Inc. All rights reserved.

Keywords: Land use/land cover change; Great Basin; Cheatgrass; Bromus tectorum; Invasive species; Time series; NDVI; Inter-annual variability

1. Introduction

A challenge to land cover change science is distinguish-

ing change in land cover from variability within land cover

types. Change implies a shift in land cover type, frequently

associated with land use, while inter-annual variability in

growth is an inherent characteristic of some vegetated

communities. In the semi-arid Great Basin, US (Fig. 1),

inter-annual variability distinct from land cover change is

prominent in land cover types dominated by cheatgrass

(Bromus tectorum). Correctly identifying and interpreting

change in land cover and/or land use is of great interest in

the field of environmental change (Dale, 1997; Lambin et

al., 2001; Turner, 2003; Vitousek et al., 1997), so inter-

annual patterns caused by communities like cheatgrass

should be recognized.

0034-4257/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.rse.2004.08.016

* Corresponding author. Tel.: +1 401 863 9845.

E-mail address: [email protected] (B.A. Bradley).

In order to identify change, it is important to have a

baseline of land cover at national and international scales.

Global assessments of land cover have been performed

based on single year phenologies using Normalized Differ-

ence Vegetation Index (NDVI) data from Advanced Very

High Resolution Radiometer (AVHRR) and the Moderate

Resolution Imaging Spectroradiometer (MODIS) (Defries &

Townshend, 1994; DeFries et al., 1995; Friedl et al., 2002;

Loveland et al., 2000, 1999). Multiple phenology-based

mapping methods have been employed, including principle

component analysis, fourier analysis, and decision tree

approaches (Defries & Townshend, 1994; Homer et al.,

1997; Jakubauskas et al., 2002). However, because most

land cover algorithms rely on single year or averaged

phenologies to differentiate between land cover types, they

do not capture inter-annual variability. Land cover with high

inter-annual variability could distort the baseline from

which change is measured depending on growing conditions

during the baseline year (Lambin, 1996).

ent 94 (2005) 204–213

Fig. 1. Classifications of Great Basin land cover. (A) Native vegetation zones mapped by David Charlet and produced by the Biological Resources Research

Center, University of Nevada Reno. (B) AVHRR-based USGS classification of land cover in the Great Basin.

B.A. Bradley, J.F. Mustard / Remote Sensing of Environment 94 (2005) 204–213 205

Landsat-scale land cover change detection relies on an

accurate interpretation of baseline conditions and change in

surface spectral properties over time. For example, the

conversion of vegetated surfaces to man-made materials

through urbanization can be readily identified and mapped

using remotely sensed tools (Masek et al., 2000). Similarly,

land cover dynamics in tropical regions such as cutting of

forest for pasture and secondary regrowth have well

documented trajectories of spectral properties over time

tied to land cover (Adams et al., 1995; Skole & Tucker,

1993). In the Great Basin, baseline conditions from which to

measure change are difficult to define. Here, changes in

remotely sensed surface properties can be a result of a high

degree of species elasticity in response to rainfall, not land

cover change. A comparison of NDVI for this land cover

type between low and high rainfall periods could suggest a

significant increase in vegetation, or the reverse, a dramatic

loss. Inter-annual variability makes identification of a

baseline increasingly complex.

Regional scale change detection typically involves

interpretation of NDVI time series from sensors such as

AVHRR and MODIS. Time series analyses have been used

to identify changes in the Sahel (Tucker et al., 1991), and to

estimate changes to growing season attributed to climate in

the northern latitudes (Myneni et al., 1997, 1998; Shabanov

et al., 2002; Zhou et al., 2003, 2001) and in China (Young &

Wang, 2001). Potter et al. (2003) use AVHRR derived

fraction absorbed of photosynthetically active radiation

(FPAR) estimates to identify global disturbance events

during an 18-year record. With an increasing interest in

change detection over large spatial scales, it is important to

understand how a range of ecosystems are affected by short-

term climate patterns. In the Amazon, AVHRR NDVI time

series have been shown to be greatly influenced by

precipitation, and thus coupled to the El Nino cycle (Asner

et al., 2000). As we will show, an even more pronounced

effect exists in cheatgrass-dominated annual grasslands in

the Great Basin.

Cheatgrass’ inter-annual variability is distinct in the

Great Basin due to its link to rainfall. Precipitation patterns

in the Great Basin show a high degree of temporal

variability. Twenty six distributed National Climatic Data

Center rain gages with continuous records from 1990 to

2002 averaged 18 cm of rainfall per year with an inter-

annual variance about that mean of 26 cm. Rainfall during

wet years is frequently three to four times higher than during

dry years. This pattern of extreme wet and dry years causes

a dramatic response in cheatgrass. Rangeland studies have

shown that during wet years cheatgrass can be 10 times

taller and denser than during dry years (Hull & Pechanec,

1947; Stewart & Hull, 1949; Stewart & Young, 1939).

High inter-annual variability is unique to annual grass-

lands in the Great Basin. Shrub/perennial bunch grass

ecosystems are adapted to rainfall patterns. While they show

some change in productivity coupled with rainfall, the range

of variance is limited (Prince et al., 1998). Elmore et al.

(2003) showed that for a 20-cm variance in rainfall, native

perennial communities exhibited a 5% variance in live

cover, while invasive annuals had an inter-annual variance

in live cover up to five times higher. Remote sensing is a

good candidate for identifying a similar pattern in the Great

Basin because regionally, cheatgrass monocultures have

become increasingly prominent in formerly shrub/bunch

grass ecosystems (Mack, 1981; Young et al., 1972).

Our goals in this analysis are two-fold. First, we

demonstrate that annual grassland in the Great Basin, US

B.A. Bradley, J.F. Mustard / Remote Sensing of Environment 94 (2005) 204–213206

exhibits inter-annual variability of greenness in response to

rainfall that exceeds the range expected for shrub-dominated

land cover types. Second, we show that this signal of

amplified response to environmental conditions can be used

to map a land cover type not previously captured by

standard land cover mapping methods. This land cover type

is dominated by the invasive annual cheatgrass. It is

important to note that the extreme variability in inter-annual

NDVI resulting from cheatgrass dominance is not caused by

land cover change during the period of record. Rather, the

variability is an effect of cheatgrass presence. These goals

are accomplished through coordinated analysis of coarse

spatial, high temporal resolution AVHRR data and high

spatial resolution Landsat data.

2. Great Basin vegetation

Native vegetation in the Great Basin is adapted to the

desert’s highly variable precipitation. The Great Basin

frequently has years of persistent drought followed by

extremely wet years. Native perennial shrubs and grasses

have adapted to these conditions by setting deep roots (in

some cases up to 30 ft), growing photosynthetically active

leaves judiciously, and investing resources in seed produc-

tion only in years when conditions are optimal (Grayson,

1993; Meyer & Monsen, 1992). Perennial shrub and bunch

grass communities typically green up in early to mid-May

and remain photosynthetically active through the hot

summer months.

Cheatgrass, an invasive annual, germinates in mid-April

in the Great Basin (except in rare years when wet conditions

permit fall germination), but remains green for only a few

Fig. 2. Location map of field sites with known land cover types. Green rectangles a

orange rectangles are salt flats. Sites A, B, and C correspond to the photographs

weeks (Rice et al., 1992). It invests all of its resources in

growing larger to support the production of a maximum

number of seeds (Knapp, 1996; Young & Allen, 1997).

Cheatgrass growth peaks in mid-May, after which point, it

quickly senesces and remains dormant until seeds germinate

the following season. The distinct inter-annual response of

perennial vs. annual communities presented here is caused

by these differences in plant physiology.

3. Datasets

Three types of data were used in this study: field

observations of land cover, a time series of Landsat data

centred on the lower Humboldt River, Nevada, and a time

series of AVHRR weekly and biweekly data. All three were

used to assess temporal vegetation patterns in the Great

Basin.

Field observations were made in May 2003 in areas

surrounding the lower Humboldt River, Nevada (40–418N,117.5–1198W). Thirteen sites dominated by either cheat-

grass monoculture, or native shadscale (Atriplex spp.) and

bunch grass (Poa secunda, Agropyron spicatum) were

identified and geolocated. Polygons were then created

surrounding the observed areas. Five sites, each spanning

z4 km2, contained cheatgrass monoculture. The remaining

eight sites, also spanning z4 km2, contained native

shadscale/bunch grass and no cheatgrass. An additional

three sites, each spanning z9 km2, were selected on non-

vegetated salt flats. A location map and pictures of

representative land cover types are shown in Fig. 2.

Field observations were repeated in 2004 at 659

distributed localities in order to validate the predicted

re cheatgrass monoculture, blue rectangles are native shadscale/bunch grass,

shown at right.

Fig. 3. Locations of validation points collected in May, 2004.


distribution of high density cheatgrass (Fig. 3). Validation

localities span the Landsat scene in areas that are accessible

by road. Validation points were selected to include three

types of non-cheatgrass-dominated native vegetation: dry

desert shrubs, sagebrush steppe, and pinyon-juniper wood-

land. Cheatgrass-dominated validation points included

monoculture and dense understories of cheatgrass in dry

desert shrub and sagebrush steppe ecosystems. The incor-

poration of validation points in the study allows us to assess

the accuracy of the distribution maps created with the

remotely sensed data.

A series of 10 Landsat TM and ETM+ scenes (Path 42

Row 32) were acquired for the lower Humboldt River,

Nevada between 5/15/88 and 5/11/2001 (Table 1). The 30 m

resolution of Landsat captures local, landscape-scale varia-

bility over time. Dates of the scenes were as close to May 15

as possible. By this time, residual snow has melted from the

valleys, and Great Basin vegetation, both shrubs and

cheatgrass, is near peak greenness. Landsat data were

converted to reflectance based on the spectral signatures of

five sites whose reflectance spectra were measured with an

ASD FieldSpec spectrometer (Elmore et al., 2000; Hall et al.,

1991; Schott et al., 1988). The use of invariant targets rather

than Landsat calibration coefficients increases the accuracy

Table 1

Dates of Landsat Scenes acquired for p42, r32 in western Nevada

Sensor type Date

Landsat TM 5/15/1988


Landsat TM 6/9/1991



Landsat TM 6/4/1995

Landsat TM 6/6/1996

Landsat TM 5/8/1997


Landsat ETM+ 5/11/2001

of the reflectance conversion by better accounting for

atmospheric aerosols for each observation. The sites con-

tained no vegetation, and were therefore assumed to be

spectrally invariant over time. The five spectrally invariant

targets encompass a range of reflectance values from dark

(Pyramid Lake) to bright (salt flat) (Fig. 4). Themean spectral

signature of 25 spectra acquired at each of the five sites was

used to convert Landsat-measured radiance to reflectance. A

linear regression fit to DN vs. reflectance had an R2 value

greater than 0.98 in all cases. NDVI values for each scene

were then calculated using Landsat bands 3 and 4.

Weekly and biweekly AVHRR NDVI composites for the

conterminous US were used for the regional analysis

(Eidenshink, 1992; Teillet & Holben, 1994). The 1-km

resolution of AVHRR allows for regional detection of

prominent processes found at the landscape scale. The

dataset spans 1/1/1990–12/31/2001 and was clipped to

include only the Great Basin (boundaries in Fig. 1). The

visible band for every scene was checked for snow and/or

cloud cover. Parts of scenes containing snow or clouds

(primarily in winter scenes) were discarded, creating a final

NDVI dataset consisting of 394 scenes. Signal variations

caused by orbital drift and sensor degradation have

previously been recognized (Privette et al., 1995) and

removed by subtracting NDVI time series over unvegetated

surfaces from vegetation NDVI time series (Myneni et al.,

1998). In order to account for long-term sensor bias in the

Great Basin, we removed a curve fit to the mean NDVI time

series of the unvegetated Bonneville Salt Flat, UT and Black

Rock Desert, NV. A smooth curve fit rather than the mean

signal is used to prevent any propagation of stochastic error

in the data. A linear continuum was fit to the NOAA-11

NDVI data, and a 3rd order polynomial fit to the NOAA-14

NDVI data after (Kastens et al., 2003). This continuum was

subtracted from every pixel.

Fig. 4. Reflectance in Landsat TM bands 3 and 4 of non-vegetated,

spectrally invariant targets used to calibrate Landsat scenes to reflectance.


4. Amplified response of cheatgrass-dominated land

cover

Previous work has shown that Landsat time series can

detect an amplified response to rainfall characteristic of

invasive annuals in Owens Valley, CA (Elmore et al., 2003).

Cheatgrass has also been observed to have a similar

amplified response following years with above average

rainfall (Hull & Pechanec, 1947; Stewart & Hull, 1949).

Therefore, we predict that time-series of NDVI determined

from remotely sensed data will show an inter-annual

amplified response to rainfall in cheatgrass-dominated areas.

We expect to see this pattern in time series of Landsat as

well as AVHRR data.

In order to identify amplified response in the Landsat

time series, we determine the DNDVI, or the difference in

NDVI values between a high rainfall year and a low rainfall

year for each pixel. By subtracting the low rainfall NDVI

values, we normalize the high rainfall scene across

vegetation types to target areas showing an amplified

response. Native perennial communities should have low

DNDVI values because they do not respond strongly to

inter-annual rainfall variability (Elmore et al., 2003).

However, cheatgrass-dominated areas should have high

DNDVI values as a result of their amplified response to

rainfall. We selected 1995 as the high rainfall scene and

1991 as the low rainfall scene. The 1995 scene was chosen

for DNDVI calculation because its timing, June 4th, was

closer to cheatgrass peak greenness than the June 28, 1998

scene (Table 1). The low rainfall scene was selected due to

its seasonal proximity, June 9, 1991, to the high rainfall

1995 scene.

In the case of AVHRR, 2 years are not adequate for

identification of regional amplified response because rainfall

patterns are heterogeneous throughout the Great Basin. We

identified regionally high and low rainfall years based on 26

rain gages with continuous records from 1990 to 2001

distributed through Utah, Nevada, and Oregon (Fig. 5A). To

Fig. 5. Rainfall patterns in the Great Basin. (A) Distribution of 26 Great Basin rai

patterns for those gages. 1993, 1995, and 1998 were the wettest years, while the

account for differences in rainfall patterns between stations,

rainfall is normalized by the decadal mean at individual

stations, making mean rainfall equal to 1. The resulting

normalized October–May rainfall patterns for all 26 gages

were then averaged to identify regionally high rainfall years

(Fig. 5B). The 3 years of regionally high rainfall were 1993,

1995, and 1998. Annual rainfall was above the station

decadal average at 65%, 96%, and 88% of the rain gages

during 1993, 1995, and 1998, respectively. This indicates

that these years were regionally wet, rather than affecting

only localized areas. The remaining years were below

average at between 62% and 92% of the stations, indicating

that these years were regionally dry. To identify amplified

response, we found the maximum NDVI between April 15

and June 15 for each year. Spring timing was chosen to

coincide with peak cheatgrass productivity. DNDVI was

calculated by subtracting the median of the spring maxima

during the 8 low rainfall years from the median of the spring

maxima during the 3 high rainfall years. By using medians

rather than means, the result is not skewed by anomalously

low or high values caused by local rainfall variability,

agriculture, or sensor error. The method used to identify

regional amplified response with AVHRR assumes that

cheatgrass was present during all 3 high rainfall years in the

1990s. Changes in the distribution of cheatgrass during this

time period via expansion or decline add uncertainty to the

regional estimate.

5. Results

Fig. 6 shows mean Landsat and AVHRR time series for

the three land cover type localities identified in Fig. 2:

cheatgrass monoculture, native shadscale/bunchgrass, and

non-vegetated salt flats. In the Landsat time series,

cheatgrass shows a highly amplified response to rainfall

compared to the other land cover types following the wet

years of 1988, 1995, and 1998. Likewise, in the AVHRR

n gages with continuous 1990–2001 records. (B) Mean-normalized rainfall

remaining years were average or below average.

Fig. 6. Temporal NDVI responses for three land cover types. The mean time

series for the land cover type areas from Fig. 2 are shown. Due to its smaller

pixel size, many more samples from the type localities are available for

Landsat than for AVHRR. Cheatgrass shows an amplified NDVI response

following high rainfall years in both (A) Landsat and (B) AVHRR time series.

Fig. 7. Box plots and histograms indicate the distribution of DNDVI values

at three land cover types. Boxes encompass the central 50% of values, lines

encompass 95%. Cheatgrass-dominated areas show higher DNDVI values,

a result of their amplified response to rainfall. (A) Landsat box plots. (B)

AVHRR box plots. (C) Landsat histograms.


time series, cheatgrass shows an amplified response

following 1995 and 1998. The amplified response of

cheatgrass is also apparent when DNDVI values are

compared (Fig. 7). With Landsat, cheatgrass-dominated

areas have a mean DNDVI of 0.202F0.091 (1r), nativeshadscale/bunch grass ecosystems have a mean DNDVI of

0.055F0.032 (1r), and salt flats have a mean DNDVI of

0F0.015 (1r). Histograms of DNDVI values for the three

land cover types show that salt flats and native ecosystems

are normally distributed. Cheatgrass, however, shows a

bimodal distribution of DNDVI values with the lower peak

covering a similar range of values as native vegetation (Fig.

7C). This suggests that the cheatgrass-dominated field sites

contained patches of native vegetation in 1995. These areas

may have since been converted to cheatgrass monoculture,

accounting for the discrepancy between the remotely sensed

data and the field observations.

With AVHRR data, we see a similar separation between

land cover types. Cheatgrass-dominated areas have a mean

DNDVI of 0.12F0.045 (1r), native shadscale/bunch grass

ecosystems have a mean DNDVI of 0.049F0.021 (1r),and salt flats have a mean DNDVI of 0.004F0.005 (1r).


All three land cover types appear normally distributed,

although there are fewer samples because of the 1-km2

AVHRR pixel size. The mean DNDVI value for cheatgrass

land cover calculated from AVHRR is lower than the mean

DNDVI calculated from Landsat. Mixtures of shrub/

bunchgrass and cheatgrass are more likely within the 1-

km2 spatial resolution of AVHRR, resulting in a lower

mean. The AVHRR calculation also combines multiple

years to identify DNDVI, which may account for the lower

values.

5.1. Regional amplified response

Using the distributions of DNDVI values at known land

cover types, we estimate regional occurrence of amplified

response in the Great Basin. We chose to increase the

sensitivity, or the likelihood that cheatgrass will be present

given a positive detection, by setting a detection level above

the 95% confidence interval (C.I.) for the native DNDVI

distribution. Localities with DNDVI values above this

detection level are predicted to contain cheatgrass. Predicted

cheatgrass-dominated areas are well separated from the

native population while still capturing most of the cheatgrass

population (Fig. 7). To demonstrate that populations showing

an amplified response are distinct despite changes to the

threshold level, the distributions of areas with DNDVI values

above both the 95% and 99%C.I.s are presented in Fig. 8. For

Landsat, the 95% C.I. for native shadscale/bunch grass is

N0.103 DNDVI and the 99% C.I. is N0.124 DNDVI. For

AVHRR, the 95% C.I. is N0.082 DNDVI and the 99% C.I. is

N0.102 DNDVI.

In the Great Basin, the AVHRR 95% C.I. amplified

response map predicts 20,000 km2, or 7% of the total land

cover to be dominated by cheatgrass. The Landsat 95% C.I.

amplified response map predicts 5000 km2, or 17% of the

total land cover within the Landsat scene to be dominated by

cheatgrass. In the Landsat scene, AVHRR predicts 4500 km2

to be dominated by cheatgrass. Areas of overlap between the

two sensors are shown in Fig. 8C. Both sensors identify

similar spatial patterns of cheatgrass presence in valleys of

the eastern half of the Landsat scene. The inter-annual

variability of cheatgrass is distinct enough that it can be

identified over spatial resolutions ranging from 30 m to 1 km.

Any discrepancy between Landsat and AVHRR distributions

of DNDVI values may be due to differences in timing of

scene acquisition, differences in band width at the red and

infra-red wavelengths, and the difference in sensor resolution

mentioned previously.

Fig. 8. Predicted distribution of cheatgrass-dominated areas based on an

amplified response to rainfall. (A) Landsat distribution of amplified response

higher than the 95th (blue) and 99th (red) percentile for native vegetation.

Cultivated areas and peaks above 1800 m are masked. (B) AVHRR

distribution of amplified response higher than the 95th (blue) and 99th

(red) percentile for native vegetation. Landsat extents are outlined in black.

(C) Overlap of Landsat and AVHRR distributions of amplified response.


5.2. Validation

The accuracy of both Landsat and AVHRR cheatgrass

distributions was validated through field observations at

659 localities in May 2004 (Fig. 3). These localities are

distributed along accessible roadways within the Landsat

boundaries. Sites containing no cheatgrass or trace

cheatgrass (only along roadsides or under shrub canopies)

were considered to have no cheatgrass and account for 250

of the 659 observations. Sites containing cheatrass mono-

culture or a dense understory of cheatgrass in shrub

interspaces were considered to have cheatgrass and

account for 409 of the 659 observations. Localities with

a sparse understory of cheatgrass in shrub interspaces were

not considered.

Receiver operator calibration (ROC) curves for both

the Landsat and AVHRR predictions are shown in Fig.

9. Based on the validation results, a threshold of N0.103

DNDVI for Landsat accurately identifies 72% of

cheatgrass cover while misidentifying 17% of other land

cover types. A threshold of N0.082 DNDVI for AVHRR

accurately identifies 64% of cheatgrass cover while

misidentifying 15% of other land cover types. By

combining the two sensors, we identify a higher

percentage of cheatgrass occurrence. Sites identified by

either Landsat or AVHRR using the previous threshold

levels accurately identify 81% of cheatgrass cover while

misidentifying 25% of other land cover types. The

validation results show a decreased sensitivity compared

to the original 13 field sites. The higher estimate derived

from the original field sites is likely a result of spatial

autocorrelation. By using validation points distributed

across the Landsat scene, we can more precisely define

the accuracy of the maps.

Fig. 9. Receiver operator characteristic (ROC) curves for predictive

capacity of the Landsat and AVHRR cheatgrass maps. Landsat accurately

identifies 72% of cheatgrass cover with an error of 17%. AVHRR

accurately identifies 64% of cheatgrass cover with an error of 15%.

6. Discussion

This study shows that surfaces with high inter-annual

variability, a response linked to cheatgrass presence, are

widespread throughout the Great Basin. This pattern is

distinct from other land cover types and can be used to

identify areas where cheatgrass dominates. Cheatgrass,

native shadscale/bunch grass, and salt flats are distinct in

both time series (Fig. 6) and DNDVI values (Fig. 7).

When the spatial pattern of amplified response is

mapped (Fig. 8), the areas identified by AVHRR are

similar to those identified by Landsat. Changing the

detection confidence interval does not affect the con-

tiguous areas of amplified response seen in the eastern

half of the Landsat scene. The presence of areas

identified as cheatgrass by only one of the two sensors

is consistent with the identification rates of 64% and

71% by AVHRR and Landsat, respectively. Additionally,

areas identified in AVHRR by the DNDVI method

almost all show the targeted amplified response to

rainfall over time. The only exception being scattered

pixels in southwestern Utah. There, time series show an

amplitude increase during the late 1990s which may be

attributable to slight misregistration along steep mountain

gradients of the Fish Lake and Dixie National Forests.

Everywhere else, positively identified pixels show an

amplified response in 1993, 1995, 1998, or a combina-

tion of the three. Because cheatgrass is the primary

invasive annual into native perennial communities,

amplified response is likely caused by cheatgrass

dominance.

The inter-annual pattern caused by cheatgrass’ amplified

response should not be confused with land cover change.

There is some risk of this type of pattern being erroneously

labeled change, particularly because several of the cheat-

grass-dominated time series show a secondary amplified

response the year following a wet year. This is particularly

notable in 1996 and 1999, despite the fact that rainfall

during these 2 years was below average. This effect may

be a result of an increased seed bank the year following

wet years (Prince et al., 1998), or, in the case of 1999, an

exceptionally wet preceding September/October which

allowed for fall germination of cheatgrass. A time series

spanning the 1990s could be misinterpreted as land cover

change due to this secondary response (Fig. 10). However,

the pattern is entirely attributable to cheatgrass variability.

Land cover change research, particularly studies at regional

scales, should be aware that this type of rainfall-driven

pattern is a temporal signature of an unchanging land

cover type.

Based on the regional map (Fig. 8B), cheatgrass-

dominated ecosystems are widespread in northern

Nevada and western Utah. The counties most affected

by invasion include Pershing, Humboldt, and northern

Lander and Eureka in Nevada. Tooele and Box Elder

counties are most affected in Utah, and Harney County

Fig. 10. Mean cheatgrass time series of 6 km2 in Buena Vista Valley

(4085VN, 1188W) for 1990–2001. While suggestive of land cover change

between 1994–1995, this pattern is entirely attributable to cheatgrass

variability.


in Oregon also has a concentrated invasion. In all,

20,000 km2 of detected amplified response is apparent in

several concentrated localities in the Great Basin. Despite

the large extent of cheatgrass dominance, the land cover

type is not apparent in single year phenology-based

classifications of US land cover, like the North American

Land Cover Characteristics Map (Fig. 1B) (Loveland et

al., 1999). Inter-annual variability in time series encom-

passing wet and dry years should be considered for more

accurate classification.

The broad spatial extent of cheatgrass invasion allows for

easy scaling between field observations, Landsat, and

AVHRR resolutions. However, the amplified response of

cheatgrass is not apparent without a time series of data

encompassing both dry and wet years. Additionally,

cheatgrass phenology is short, lasting only 1–2 spring

months. As a result, Landsat scenes must be chosen with

care to capture peak productivity. Analysis of a single scene

is not enough to differentiate cheatgrass from native land

cover types. While cheatgrass detection based on its early

green-up has been performed using Landsat scenes from

2001 (Peterson, 2003), identification of cheatgrass is

optimal during wet years. Time series analysis should be

considered for distinguishing between land cover types,

particularly when they have different responses to climatic

patterns on an inter-annual basis.

Variability over time does not necessarily imply active

land cover change. Cheatgrass-dominated areas show

changing productivity over time, but the land cover itself

is not changing. For example, a cheatgrass time series from

northern Nevada may at first suggest a dramatic shift in land

cover between 1994 and 1995, particularly in areas

exhibiting a second year of rainfall-related amplified

response (Fig. 10). Likewise, a comparison of NDVI based

productivity for 1998 and 2000 would indicate a marked

loss of biomass that could be erroneously attributed to land

cover change. In fact, the same land cover has been in place

at these localities for at least the past decade. Land Use/

Land Cover Change science, particularly regional and

global models, must be aware that some responses that

look like change are caused by inherent variability within

ecosystems. Strong correlation to precipitation may be of

particular concern in semi-arid ecosystems; however, differ-

ent environmental variables may affect inter-annual vegeta-

tion patterns in other ecosystems. Variability over time,

particularly when it can be correlated to climatic patterns, is

likely a response of a particular land cover type rather than

an indication of land cover change. Land cover change

scientists should use caution when interpreting signals like

the one presented here.

Acknowledgement

The work was funded by the NASA Land Use Land

Cover Change Program and the American Society for

Engineering Education. We are grateful for the insight and

expertise of Jeff Albert, Jeremy Fisher, Erica Fleishman,

Steven Hamburg, and Robin Tausch.

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