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Journal of
Arid
Environments
Journal of Arid Environments 58 (2004) 249–272
www.elsevier.com/locate/jnlabr/yjare
Long-term vegetation monitoring with
NDVI in a diverse semi-arid setting,
central New Mexico, USA
Jeremy L. Weissa,b,*, David S. Gutzlera,
Julia E. Allred Coonrodc, Clifford N. Dahmd
b
a
Department of Earth & Planetary Sciences, University of New Mexico, Albuquerque, NM, USA
Department of Geosciences Environmental Studies Laboratory, University of Arizona, Tucson, AZ, USA
c
Department of Civil Engineering, University of New Mexico, Albuquerque, NM, USA
d
Department of Biology, University of New Mexico, Albuquerque, NM, USA
Received 6 August 2002; accepted 9 July 2003
Abstract
Time-series of normalized difference vegetation index (NDVI) are shown to capture
essential features of seasonal and inter-annual vegetation variability at six nearby yet distinct
vegetation communities in semi-arid New Mexico, USA NDVI values tend to follow a
uniform order across communities, related directly to local vegetation. All communities
exhibit a bimodal growing season on average, with peaks in springtime and summer. NDVI
fluctuations are more spatially uniform in spring than in summer. NDVI variability
corresponds to precipitation variability from the North American monsoon and El NiñoSouthern Oscillation, and shows agreement with regional ground measurements.
r 2003 Elsevier Ltd. All rights reserved.
Keywords: AVHRR; NDVI; Remote sensing; Vegetation; Phenology; Climate; Time-series; Southwestern North America
1. Introduction
Land surface monitoring over long time-scales is necessary to discern ecosystem
responses to climate variability. Arid and semi-arid ecosystems provide unique
*Corresponding author. Department of Geosciences Environmental Studies Laboratory, University of
Arizona, Tucson, AZ, USA. Tel.: +1 520 621 80255.
E-mail address: [email protected] (J.L. Weiss).
0140-1963/$ - see front matter r 2003 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jaridenv.2003.07.001
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environments for detecting climate variation realized through changes in precipitation, as water is the most limiting resource in these environments (Hadley and
Szarek, 1981). Shortly after rainfall events, annual and perennial plants usually
respond with germination, growth, and reproduction (Morgan Ernest et al., 2000).
Multi-year drought and pluvial periods also affect these environments, influencing
plant community composition, physiology, and growth (Ehleringer et al., 1991;
Swetnam and Betancourt, 1998).
Remote sensing images from an advanced very high resolution radiometer
(AVHRR) were originally intended to observe cloud and ocean parameters (Peters
and Eve, 1995; United States Geological Survey, 2001), but have subsequently been
used to monitor and characterize Earth’s land surface. Vegetation indices such as the
normalized difference vegetation index (NDVI) have been derived from AVHRR
images, allowing a coarse (1 km2) spatial scale land surface characterization with
relatively high temporal resolution (global coverage twice daily).
NDVI is the ratio of the amounts of reflectance in the near infrared (NIR) and red
(RED) portions of the electromagnetic spectrum (ranges 0.72–1.10 and 0.58–
0.68 mm, respectively), calculated using the formula
NDVI ¼ ðNIR REDÞ=ðNIR þ REDÞ:
NDVI thus theoretically takes values ranging from –1.0 to +1.0. Positive NDVI
values (NIR>RED) indicate green, vegetated surfaces, and higher values indicate
increases in green vegetation. Reflectance of the red portion of the spectrum
decreases as solar radiation is absorbed, largely by chlorophyll, whereas reflectance
of the near infrared portion is caused by leaf mesophyll structure (Kremer and
Running, 1993). Negative NDVI values indicate non-vegetated surfaces such as
water, ice, and snow. Studies have related NDVI to biophysical variables such as leaf
area, canopy coverage, productivity, and chlorophyll density as well as to vegetation
phenology (Goward et al., 1985; Justice et al., 1985; Tucker et al., 1985; Townshend
and Justice, 1986; Spanner et al., 1990; Yoder and Waring, 1994; Peters and Eve,
1995; Prince et al., 1995). Uncertainties in relating NDVI to vegetation are
associated with the effects of atmospheric variations, sensor calibration, and sensor
degradation over time on AVHRR measurements (James and Kullari, 1994;
Townshend, 1995). Surface heterogeneity also complicates interpretation of NDVI.
Due to low values that result from sparse vegetation, uncertainties in interpreting
NDVI can increase in arid and semi-arid environments. Vegetation canopies in arid
and semi-arid environments do not achieve complete coverage, making NDVI
susceptible to the spectral influence of the soil and soil moisture in gaps between
vegetation (Kremer and Running, 1993; Peters and Eve, 1995).
Nonetheless, studies examining seasonal and inter-annual behavior of different
vegetation types have demonstrated usefulness of NDVI in arid and semi-arid
environments. Malo and Nicholson (1990) concluded that phenology of six
vegetation types in the Sahel in western Africa as measured by NDVI is affected
by soil moisture availability. Peters and Eve (1995) and Peters et al. (1997)
differentiated shrub, grass, and mixed shrub and grass vegetation of incomplete
canopy coverage in a Chihuahuan Desert site in southern New Mexico. Through
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interpretation of the temporal differences of NDVI time-series with respect to
seasonal precipitation, these authors concluded that fixed measurement areas
normalize the soil background influence so that meaningful vegetation signals can be
distinguished. Over a 2-year time period in the Negev Desert in Israel, Schmidt and
Karnieli (2000) found peaks in NDVI values coinciding with field observations of
annual and perennial vegetation response to rainfall.
The objective of this study is to examine 11 years (1990–2000) of seasonal and
inter-annual variability of NDVI in a diverse semi-arid setting in central New
Mexico, USA, that includes six different vegetation communities: Great Plains/
desert grassland (GPGrslnd), Chihuahuan Desert (ChiDes), piñon-juniper woodland
(PJWdlnd), juniper savanna (JunSav), Colorado Plateau shrub-steppe (CPShbStp),
and Colorado Plateau grassland (CPGrslnd) (Moore, 1989–2001). Principal
vegetation classes found at each community are listed in Table 1. Analysis areas
are separated from each other by just 10–20 km but feature distinct vegetation
compositions related to sharp gradients of elevation, aspect, and soil.
The south-western United States exhibits a summer season precipitation
maximum associated with the North American monsoon system (Douglas et al.,
1993; Higgins et al., 1997). On inter-annual time-scales the area is influenced by
precipitation variability associated with El Niño-Southern Oscillation (ENSO) cycles
recurring on average several times per decade (Andrade and Sellers, 1988; Sheppard
et al., 2002). Qualitative evaluation of a long-term NDVI dataset with respect to
seasonal and inter-annual precipitation variability at the community level from
south-western North America is currently absent from the literature. This study thus
provides a needed understanding of effects on vegetation resulting from climate
variability.
2. Data and analysis techniques
2.1. Site description
The Sevilleta National Wildlife Refuge and Long-term Ecological Research
(LTER) site (hereafter Sevilleta) is located approximately 100 km south of
Albuquerque, New Mexico, USA (Fig. 1). Established in 1989 for examining
ecosystem responses to natural and anthropogenic disturbances at various spatial
and temporal scales, the Sevilleta covers approximately 1000 km2 of the middle Rio
Grande basin, a varied landscape ranging from the riverside riparian corridor and
Chihuahuan Desert to flat grassland plains and mountainous evergreen forests
(http://sevilleta.unm.edu). It is a transition area for several biomes, providing a good
sample of vegetation communities that are extensive across south-western North
America.
A network of meteorological stations monitors large spatial and temporal
variations in weather, facilitating studies of joint climate and vegetation variability.
All six vegetation communities in this study have a respective meteorological station
(Table 1).
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Table 1
Meteorological station properties (Moore, 1989–2001) and principal vegetation classes (Muldavin and
Milne, 1993; Shore, 1989–2001a) of the six vegetation communities.
Vegetation community
Meteorological station,
elevation and location
Principal vegetation classes
Great Plains/desert grassland
1596 m: 34.36 N, 106.69 W
Chihuahuan Desert
1538 m: 34.22 N, 106.80 W
Piñon-juniper woodland
1971 m: 34.37 N, 106.54 W
Juniper savanna
1766 m: 34.40 N, 107.04 W
Colorado Plateau shrub-steppe
1503 m: 34.30 N, 106.93 W
Colorado Plateau grassland
1547 m: 34.41 N, 106.93 W
Transition Chihuahuan and
Great Basin grasslands
Transition Chihuahuan and
Plains grasslands
Chihuahuan Desert grasslands
Great Basin grasslands
Chihuahuan or Great Basin
lowland/swale grasslands
Chihuahuan Desert shrublands
Transition Chihuahuan and
Great Basin grasslands
Rocky Mountain conifer
woodlands
Rocky Mountain conifer
savanna
Rocky Mountain conifer
savanna
Great Basin grasslands
Chihuahuan Desert grasslands
Rocky Mountain conifer
woodlands
Great Basin shrublands
Great Basin grasslands
Great Basin grasslands
Transition Chihuahuan and
Great Basin grasslands
Principal vegetation classes comprise at least 80% of the analysis area of each vegetation community.
Average seasonal cycles of temperature at the six communities show constant
order associated with elevation (Fig. 2). Higher elevation communities (juniper
savanna and piñon-juniper woodland) are significantly cooler than the others.
Among the four lower elevation communities, the Chihuahuan Desert and Colorado
Plateau shrub-steppe are slightly warmer than the Great Plains/desert grassland and
Colorado Plateau grassland. Highest values consistently occur in June and July and
lowest values in December and January. Inter-annual temperature variability is
greatest in November, December, January, and February (Weiss, 2002). May and
September also display notable variability.
As is the case throughout the south-western United States, precipitation is mostly
generated by large-scale frontal systems from October to May, whereas thunderstorm activity associated with the North American monsoon is responsible for most
precipitation from late June to September (Douglas et al., 1993; Sheppard et al.,
2002). Monthly precipitation variability during the 1990–2000 analysis period is
shown in Fig. 3. As previously mentioned, ENSO cycles affect inter-annual
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Utah Colorado
Arizona New Mexico
253
Colorado Kansas
New Mexico Oklahoma
Texas
Albuquerque
35N
105W
Rio
Gra
nd
e
110W
SEVILLETA
100W
35N
UNITED STATES
MEXICO
New Mexico
Arizona
Sonora
Chihuahua
Texas
N
MEXICO
100
0
100
200
300 km
UNITED STATES
E
W
S
Fig. 1. Location of the Sevilleta LTER site, approximately 100 km south of Albuquerque, NM, USA.
precipitation variability during the winter and spring (Andrade and Sellers,
1988; Sheppard et al., 2002). El Niño events typically result in above average
precipitation during these seasons while La Niña events typically provide
below average precipitation. El Niño conditions were present during 1992,
1993, and 1998, and those of La Niña in 1999. Inter-annual variability during the
summer is less coherent and more difficult to associate with large-scale climate
variability.
On average, highest monthly precipitation amounts occur in July, August, and
September (these 3 months accounting for about half the annual total) with
lower amounts in the other months (Fig. 4). With the exception of April, order
among the analysis areas is nearly constant, precipitation generally increasing
with elevation (Table 1). The piñon-juniper woodland records the highest
precipitation amounts, followed by the remaining vegetation communities. The
juniper savanna and Chihuahuan Desert receive the greatest amounts, followed by
the Colorado Plateau shrub-steppe, Great Plains/desert grassland, and Colorado
Plateau grassland. Average monthly precipitation amounts during the analysis
period ranged from 5.1 to 68.3 mm considering all six vegetation communities
(Moore, 1989–2001).
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AVHRR Biweekly Composite Period
40
35
1 2 3 4 5 6 7 8 9 10 1112 13 14 15 16 17
GPGrslnd
ChiDes
PJWdlnd
JunSav
CPShbStp
CPGrslnd
Temperature
Degrees Celsius
30
25
20
15
10
5
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
Month
Fig. 2. Monthly average (1990–2000) maximum temperature ( C) at the meteorological stations at each of
the six vegetation communities (see Table 1).
120
Precipitation
100
Amount (mm)
80
60
40
20
0
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Year
Fig. 3. Monthly precipitation amounts for the analysis period (1990–2000). Values are averaged over all
six vegetation communities.
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AVHRR Biweekly Composite Period
80
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
70
Precipitation
Amount (m m)
60
50
GPGrslnd
ChiDes
PJW dlnd
JunSav
CPShbStp
CPGrslnd
40
30
20
10
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Fig. 4. Like Fig. 2, but for monthly average precipitation (mm).
2.2. NDVI
NDVI time-series for the six vegetation communities were derived from the
AVHRR biweekly composite images available from the Sevilleta (Shore, 1989–
2001b). These images were originally obtained from the EROS Data Center of the
US Geological Survey (United States Geological Survey, 2001). NDVI can be
calculated at several points during processing of AVHRR data. For biweekly
composite images, NDVI is calculated using reflectance values after calibration of
the NIR and RED channels and before byte range scaling, thereby retaining the
most precision. The composite procedure selects the image for an individual pixel
that minimizes atmospheric contamination such as haze or cloud cover. This
procedure also mitigates effects of directional reflectance, off-nadir (i.e., nonperpendicular to surface) viewing, sun angle, and shadows (Holben, 1986). For
Sevilleta online access purposes, AVHRR images of the conterminous US are
georegistered and clipped to an area extending 50 km beyond the state border of
New Mexico (Shore, 1989–2001b). NDVI values were rescaled to range from 0 to
200 (United States Geological Survey, 2001). Non-vegetated surfaces take values
from 0 to 100 and green, vegetated surfaces from 100 to 200.
Complete annual coverage with AVHRR images did not occur until 1995, with
portions of October–March missing in the years from 1989 to 1994. Previous work
has shown, however, that NDVI time-series of different vegetation during the nongrowing season do not provide clear phenological separation (Ramsey et al., 1995;
Senay and Elliott, 2000). Therefore only images representing the growing season
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(i.e., greatest vegetative activity), approximately from March to October, were
utilized in this study. AVHRR images for the 1994 growing season ended in midSeptember due to the failure of the NOAA-11 satellite (Minor et al., 1999).
A 5-km-diameter circle surrounding a meteorological station, including approximately 20 1 km2 AVHRR pixels, delineates the analysis area for each vegetation
community (Fig. 5a). The size of each area is limited to account for the localized
rainfall from convective thunderstorms during the summer. Gosz et al. (1995) used a
3-km-radius circle to study the relationship between summer rainfall and lightning at
the Sevilleta. Sensitivity analyses to determine effects of analysis area size on NDVI
mean values were done for this study, comparing diameters of 3, 4, and 5 km. Mean
NDVI values for each were comparable and the 5-km-diameter area was chosen to
increase the statistical robustness of each biweekly average. The six vegetation
communities used in this analysis represent a substantial fraction of the vegetation
communities at the Sevilleta.
Using ArcView and ArcInfo geographic information system (GIS) software,
1 km 1 km NDVI values were extracted in grid format from each AVHRR image
(ESRI, 1999; Fig. 5b). An example NDVI grid from the biweekly composite period
2–15 July 1999 can be seen in Fig. 5b. The highest NDVI values occur in the
agricultural and riparian areas along the Rio Grande through the center of the grid,
and areas at higher elevations near the north-west and eastern boundaries of the
Sevilleta. Smaller patches of greenness in the interior of the Sevilleta are likely due to
vegetation responding to localized rainfall from convective thunderstorms during the
monsoon season.
Within the six analysis areas the range, mean, and standard deviation of 1 km2
NDVI values were calculated for each composite period. Mean biweekly NDVI
values for an analysis area are used in the construction of the time-series. Despite
utilization of the biweekly composite procedure, outliers with very unrealistic low
values still occurred, most likely due to atmospheric contamination from haze and
cloud cover. These values were subjectively omitted from the analysis. In 2000, for
example, outliers occurred at both the Great Plains/desert grassland and piñonjuniper woodland during the ninth biweekly composite period, and at the juniper
savanna during the eleventh (Fig. 6). Considering all six analysis areas, 22 out of a
total of 1104 NDVI data points were removed during the study period (Weiss, 2002).
Individual analysis areas had no more than one omission per growing season.
Techniques to create cloud masks with which to filter NDVI data, such as using
AVHRR thermal data (Peters et al., 1997), or to model time-series data for estimates
to replace outliers were not used in this study.
3. Results and interpretation
3.1. Annual and average characteristics
Considerable uniform time-series behavior among the vegetation communities
exists each year. In 1990 for example, all communities display increases in NDVI
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Fig. 5. Maps of the Sevilleta (see Fig. 1) showing the six vegetation community analysis areas
superimposed on (a) elevation above sea level and (b) a representative biweekly NDVI image. Locations of
the six analysis areas are outlined by 5-km-diameter circles that surround a meteorological station. Actual
spatial extent of the areas is further limited by the Sevilleta boundary for all areas except the Great Plains/
desert grassland. The NDVI image in (b) is from the biweekly composite period 2 July–15 July 1999. Each
pixel is 1 km 1 km.
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Mar
135
130
Apr
Jun
May
Jul
Aug
Sep
Oct
GPGrslnd
ChiDes
PJWdlnd
JunSav
CPShbStp
CPGrslnd
NDVI 2000
NDVI Value
125
120
115
110
105
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18
Biweekly Composite Period
Fig. 6. NDVI time-series of each of the six vegetation communities during 2000. Months in which the
biweekly composite periods approximately fall are given at the top of the graph.
from March into May, followed by decreases that culminate in a pre-monsoon
minimum in July (Fig. 7). At this point, NDVI increases again during the growing
season for all communities and continues to increase to the end of the analysis period
in October. The piñon-juniper woodland always exhibits the highest NDVI values,
followed by the juniper savanna. The remaining communities display variably
ordered NDVI values.
NDVI values computed for the different vegetation communities are reasonably
close to corresponding vegetation in other studies performed in southern New
Mexico. Peters and Eve (1995) and Peters et al. (1997) report NDVI values for grass,
grass/shrub, and shrub regions from approximately 105 to 130 (on the scale of 0–200
used in this study). These regions also showed ordinal consistency, with grass regions
highest, grass/shrub regions second highest, and shrub regions lowest, in agreement
with values in this study. No literature explicitly describing NDVI values in savanna
and woodland communities in New Mexico has been published to date. Nonetheless,
it is expected that this vegetation will exhibit higher NDVI values, as grasslands and
shrublands are often of lower plant density and stature than savanna and woodland
vegetation, resulting in lower canopy coverage and hence lower NDVI values
(Nemani and Running, 1997).
The 11-year average NDVI time-series of the vegetation communities generally
display uniform behavior through the growing season (Fig. 8). The piñon-juniper
woodland always records the highest NDVI values, followed by the juniper savanna
except during the summer when the Great Plains/desert grassland exhibits similar
values. The Great Plains/desert grassland, Chihuahuan Desert, Colorado Plateau
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Mar
Apr
Jun
May
Jul
Aug
Sep
Oct
GPGrslnd
ChiDes
PJWdlnd
JunSav
CPShbStp
CPGrslnd
NDVI 1990
130
125
NDVI Value
259
120
115
110
105
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18
Biweekly Composite Period
Fig. 7. Like Fig. 6, but for 1990.
shrub-steppe, and Colorado Plateau grassland have similar values during the first
half of the growing season while separating (higher values at Great Plains/desert
grassland and Chihuahuan Desert) during the second half. NDVI time-series values
for the 11-year averages range from 109.8 to 126.5 for the six communities during the
analysis period.
Despite similar NDVI values during the spring, the Colorado Plateau shrubsteppe and grassland exhibit lower NDVI values than the Great Plains/desert
grassland and Chihuahuan Desert during the summer (Fig. 8). This difference may
be attributable to lower summertime leaf area, canopy coverage, chlorophyll density,
or productivity occurring in the two Colorado Plateau vegetation communities
compared to the Great Plains/desert grassland and Chihuahuan Desert. This
seasonal divergence may also relate to effects of soils on moisture availability found
at the four analysis areas, although some soils at the Great Plains/desert grassland,
Chihuahuan Desert, and Colorado Plateau grassland are similar (Johnson, 1988).
Additionally, the divergence may suggest differential vegetation responses to
summer climate conditions such as responsiveness to precipitation or limitations
from high temperatures. Such differential responses have been observed at intra- and
interspecific levels in the south-western United States (Ehleringer et al., 1991;
Flanagan et al., 1992; Donovan and Ehleringer, 1994; Lin et al., 1996; Weltzin and
McPherson, 1997; Williams and Ehleringer, 2000).
The 11-year average seasonal cycle of NDVI provides a clear distinction between
spring and summer components of the growing season (Fig. 8). Local maxima are in
the periods April–May and August–September–October. Local minima are in the
March and June–July periods. The former minimum coincides with the beginning of
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260
135
Apr
May
SPRING
Mar
Jun
Jul
Aug
Sep
Oct
SUMMER
130
NDVI Value
125
GPGrslnd
ChiDes
PJWdlnd
JunSav
CPShbStp
CPGrslnd
120
115
110
105
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18
Biweekly Composite Period
Fig. 8. NDVI average (1990–2000) time-series of each of the six vegetation communities. Months in which
the biweekly composite periods approximately fall are given at the top of the graph.
the growing season and the latter minimum is promoted by high temperatures
(Fig. 2) and low precipitation (Fig. 4) that are typical during this time of the year,
immediately before the climatological onset of the monsoon. These climatic
conditions may signal a transition between optimal conditions for plants with C3
and C4 photosynthetic pathways, ways in which carbon dioxide is incorporated and
carbohydrates made. Plants with the C3 photosynthetic pathway have optimum
temperatures for photosynthesis from 20 C to 25 C, a low light saturation
threshold, and begin reducing photosynthesis under moderate water stress (Smith,
1992; Hopkins, 1995). C4 plants have optimum temperatures for photosynthesis
from 30 C to 45 C, difficulty in reaching light saturation, and the capability to
continue photosynthesis under moderate water stress. Despite these seemingly clear
differences, arbitrarily assigning behavioral characteristics of a particular pathway to
vegetation can be very difficult (Smith, 1992). Some C3 plants, for instance, do not
have a low light saturation threshold. Peters and Eve (1995) and Peters et al. (1997)
used similar reasoning for interpreting a similar decline in NDVI time-series of grass,
shrub, and grass/shrub vegetation regions in southern New Mexico.
Other potential mechanisms may also contribute to the pre-monsoon NDVI
minimum in late spring and early summer. Standing dead material of senescent
vegetation (e.g., grasses) is subject to photodegradation from exposure to high
temperatures and high solar radiation during this part of the growing season.
Photodegradation has been observed to result in considerable surface litter losses in
the Chihuahuan Desert of southern New Mexico (Moorhead and Reynolds, 1989).
Such losses may cause decreases in NDVI, as senescent vegetation displays a higher
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NDVI value than bare soil (Kremer and Running, 1993). Malo and Nicholson (1990)
observed a similar NDVI decrease in western Africa before the rainy season and
attributed the decline to atmospheric contamination from increases in water vapor
and aerosols. Analogous conditions occur in south-western North America as humid
monsoon air moves northward into the region and dust emissions may increase due
to decreased soil moisture (Holcombe et al., 1997). Spectral influences from bare soil
and decreasing soil moisture may also partially produce this NDVI minimum.
Bimodal growing seasons alter nutrient dynamics of plant communities in southwestern North America, creating interactions across seasons. Gutierrez and
Whitford (1987) stated that lower than expected growth of Chihuahuan Desert
annuals was most likely due to soil nitrogen deficiencies resulting from two
successive seasons of high annual growth. Using an 18-year record of winter and
summer annuals in south-eastern Arizona, Guo and Brown (1997) noticed a negative
relationship between temporally separated annuals, as large numbers of plants never
occurred in successive seasons. The authors hypothesized that plant growth of one
season depletes and immobilizes nutrients long enough to affect growth in the
following season. Such cross-seasonal interactions are less known with respect to
perennial vegetation and study of these conditional dynamics is warranted.
3.2. Inter-annual characteristics
The NDVI time-series for each vegetation community displays inter-annual
variability from 1990 to 2000 throughout the growing season. For example, the
Great Plains/desert grassland exhibits spring NDVI time-series increases in 1990,
1992, and 1998 and little to no spring NDVI increases in the remaining years (Fig. 9).
Although increases in the NDVI time-series happen more often during the second
half of the growing season, timing varies from year to year. In 1999, for instance, the
NDVI peaks in August while in 1997 the peak occurs in late September. Minimum
values during the growing season occur at the beginning or end of the growing
season or immediately before the start of the monsoon, while maximum values occur
during the monsoon season in all years except in 1998 when the maximum occurs
in May.
High variability of precipitation results in high variability of vegetation
production within arid and semi-arid ecosystems (Ludwig, 1986, pp. 5–18). High
inter-annual and interseasonal variability have been found in previous studies of
abundance of ephemerals in south-eastern Arizona (Guo and Brown, 1997), net
primary production (NPP) in grasslands of southern New Mexico (Huenneke et al.,
2002), and grassland cover at the Sevilleta (Morgan Ernest et al., 2000). Thus it is not
surprising that NDVI time-series of these vegetation communities display large interannual and interseasonal variability and that NDVI behavior of individual years and
the 11-year average have poor similarity. NDVI time-series behavior of the Great
Plains/desert grassland and Colorado Plateau shrub-steppe are in agreement with
time-series of spring and late summer percent cover measurements of nearby plots
reported by Morgan Ernest et al. (2000). In each of these locales, all time-series show
general increases from 1990 to 1992–1993, decreases from 1992–1993 to 1996, and
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Mar
135
May
Jun
Jul
Aug
Sep
Oct
NDVI Great Plains Grassland
130
NDVI Value
Apr
125
120
115
110
105
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
0
1
2
3
4
5
6
7
8
9 10 11 12
Biweekly Composite Period
13
14
15
16
17
18
135
NDVI Value
130
125
120
115
110
105
135
NDVI Value
130
125
120
115
110
105
Fig. 9. NDVI time-series from the Great Plains/desert grassland community. Months in which the
biweekly composite periods approximately fall are given at the top of the graph.
increases from 1996 to 1997. Examination of relationships between NDVI and
grassland biomass and NPP are currently underway at the Sevilleta.
Ability of NDVI to represent vegetation variability in arid and semi-arid
ecosystems is also evident through use of the bimodal seasonal cycle to distinguish
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cool and warm growing seasons. The Great Plains/desert grassland, Chihuahuan
Desert, piñon-juniper woodland, Colorado Plateau shrub-steppe, and Colorado
Plateau grassland communities each have a local minimum in late June and early
July (Fig. 8). For the juniper savanna, this local minimum is during the middle of
July. Using this average local minimum value to separate the two parts of the
growing season, the cool season (‘‘spring’’) is henceforth defined as the first nine
biweekly composite periods of a particular growing season (approximately from
March to June), while the warm season (‘‘summer’’) is defined as the last eight
biweekly composite periods (approximately from July to middle October). The slight
temporal deviation of the local minimum at the juniper savanna does not warrant
concern in these seasonal prescriptions since the NDVI time-series values for the
10th and 11th biweekly periods are reasonably similar for this vegetation
community.
Average NDVI values for the spring (NDVISP) and summer (NDVISU) growing
seasons were calculated for each vegetation community for each year. The NDVISP
values computed for the analysis period once again display the ordinal consistency
and strong uniform behavior observed previously (Fig. 10). The piñon-juniper
woodland exhibits the highest NDVISP value every year. The juniper savanna shows
the second highest values except in 1991 and 1995 when NDVISP values of the Great
Plains/desert grassland are similar. Excepting these 2 years, values at the Great
Plains/desert grassland are lower and similar to values computed for the Chihuahuan
Desert, Colorado Plateau shrub-steppe, and Colorado Plateau grassland. General
increases in NDVISP for all communities occur from 1990 to 1992, 1994 to 1995, and
1996 to 1998, while general decreases happen from 1992 to 1994, 1995 to 1996, and
1998 to 2000.
135
130
GPGrslnd
ChiDes
PJWdlnd
JunSav
CPShbStp
CPG rslnd
NDVISP
NDVI Value
125
120
115
110
105
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
Fig. 10. NDVISP time-series (1990–2000) of each of the six vegetation communities.
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The coherence of inter-annual variability across the Sevilleta in springtime was
quantified by calculating the linear correlation between NDVISP values for each pair
of vegetation communities (Fig. 11). The highest correlation is between the Colorado
Plateau shrub-steppe and Colorado Plateau grassland (r ¼ 0:99), and the lowest
correlation is between the Great Plains/desert grassland and juniper savanna
(r ¼ 0:91). Increased scatter mostly occurs with the lower NDVISP values. NDVISP
values range from 108.4 to 127.4 across all vegetation communities.
NDVISU values computed for the analysis period display less ordinal consistency
and uniform behavior than those observed for NDVISP (Fig. 12). The piñon-juniper
woodland again exhibits the highest NDVISU value each year while the juniper
savanna usually displays the second highest. In 1991 the Great Plains/desert
grassland and Chihuahuan Desert record a higher NDVISU value whereas in 1996,
only the Great Plains/desert grassland is higher. In 1995, the NDVISU value for the
juniper savanna is similar to that of the Great Plains/desert grassland. Compared to
NDVISP values, NDVISU values showed greater separation between the Great
Plains/desert grassland, Chihuahuan Desert, Colorado Plateau shrub-steppe, and
Colorado Plateau grassland, as values were typically higher at the Great Plains/
desert grassland and Chihuahuan Desert. General increases in NDVISU mostly occur
from 1990 to 1992 and 1995 to 1998, while general decreases happen from 1992 to
1995 and 1999 to 2000. Notable exceptions are the decrease from 1996 to 1997 at the
Great Plains/desert grassland and the truncated increase/early decrease at the piñonjuniper woodland in 1998.
GPGrslnd
116
ChiDes
111
r = 0.96
125
PJWdlnd
NDVISP
120
r = 0.95
JunSav NDVISP
122
120
118
116
114
r = 0.92
112
116 118 120 122 124 126 128
PJWdlnd NDVISP
JunSav
119
115
r = 0.91
r = 0.92
r = 0.95
r = 0.97
r = 0.97
r = 0.96
r = 0.96
r = 0.96
r = 0.96
r = 0.93
r = 0.96
115
CPShbStp
111
115
CPGrslnd
111
112
116
111
116
120
125
115
119
r = 0.99
111
115
NDVISP
Fig. 11. Scatter plots of annual values of NDVISP for each pair of vegetation communities. Correlation
statistics are calculated from the 11-year record.
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135
130
GPGrslnd
ChiDes
PJWdlnd
JunSav
CPShbStp
CPGrslnd
NDVISU
NDVI Value
125
120
115
110
105
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
Fig. 12. NDVISU time-series (1990–2000) of each of the six vegetation communities. NDVISU for 1994 are
omitted due to the shortened observation period of the growing season.
The reduction in the uniform behavior of NDVISU is illustrated by lower linear
correlations between values for each pair of vegetation communities (Fig. 13). The
Colorado Plateau shrub-steppe and Colorado Plateau grassland still exhibit the
highest correlation (r ¼ 0:92) while the lowest correlation is between the Great
Plains/desert grassland and Colorado Plateau grassland (r ¼ 0:36). The pattern
noted previously of more scatter at the lower values is not observed in summer.
NDVISU values range from 108.1 to 130.9 across all vegetation communities.
The order of NDVISP values across the vegetation communities is more constant
than NDVISU (Figs. 10 and 12). Behavior of NDVI at the vegetation communities is
more uniform during the first part of the growing season than during the second
(Figs. 11 and 13). These characteristics reveal large-scale, regional quiescence or
large-scale, regional ‘greening’ in spring and more small-scale, localized quiescence
and ‘greening’ in summer. Seasonal differences in the spatial scale of quiescence and
‘greening’ can be attributable to the dynamics of regional climate and moisture
availability. Precipitation in the south-west from October to May is mostly generated
by westerly low-pressure systems of large spatial extent and low spatial variability
(Betancourt et al., 1993, pp. 42–62; Gosz et al., 1995). Convective thunderstorms
during the monsoon season produce heavy, localized precipitation with high spatial
variability.
Due to the temporal length of the study period, inter-annual variability of NDVI
can be evaluated to both seasonal and inter-annual precipitation variability. NDVISP
and NDVISU values of each community are combined into time-series and
juxtaposed in Fig. 14 with time-series of the southern oscillation index (SOI,
obtained from the NOAA Climate Prediction Center) and monthly precipitation
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GPGrslnd
JunSav NDVISU
124
117
ChiDes
113
r = 0.76
r = 0.74
128
PJWdlnd
NDVISU
123
r = 0.78
116
120
118
116
114
112
118 120 122 124 126 128 130 132
PJWdlnd NDVISU
JunSav
r = 0.70
121
122
r = 0.64
r = 0.83
r = 0.76
115
CPShbStp
112
r = 0.51
r = 0.77
r = 0.88
r = 0.61
r = 0.77
114
CPGrslnd
110
r = 0.36
116
121
r = 0.80
113
117
123 128
NDVISU
116
121
r = 0.92
112
115
Fig. 13. Scatter plots of annual values of NDVISU for each pair of vegetation communities. Correlation
statistics are calculated from a 10-year record since NDVISU for 1994 are omitted due to the shortened
observation period of the growing season.
averaged over all six vegetation communities. The SOI is a measure of ENSO
variability, with negative values associated with El Niño (warm) conditions along the
equatorial Pacific and positive values associated with La Niña (cold) conditions. The
general increase in NDVI from 1990 to 1993 coincides with El Niño conditions
(above average winter and spring precipitation). Lower NDVI values in 1995 and the
spring of 1996 match below average precipitation during this time. Although La
Niña conditions (below average winter and spring precipitation) exist in 1996, a
marked increase in NDVI values occurs with substantial monsoon precipitation. As
previously stated, Morgan Ernest et al. (2000) also observed this notable increase
with percent cover measurements at the Sevilleta. Combined effects from El Niño
conditions and favorable monsoon precipitation maintain high NDVI values
through 1998. Lower precipitation due to both La Niña conditions and less
monsoon precipitation during the end of the study period concur with decreases in
NDVI. This transition from El Niño to La Niña conditions was also observed over
semi-arid eastern and southern Africa, where related changes in precipitation
patterns significantly affected NDVI (Anyamba et al., 2002).
4. Discussion
Results indicate that the locations and spatial extent of the six analysis areas are
adequate to characterize and differentiate distinct phenologies of major vegetation
communities at the Sevilleta. Due to spatial uniformity in spring, differently located
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GPGrslnd
ChiDes
PJWdlnd
JunSav
CPShbStp
CPGrslnd
135
130
NDVI Value
267
125
12 0
115
110
105
3
2
SOI
1
0
-1
-2
-3
-4
120
Amount (mm)
100
80
60
40
20
0
1990
1991
19 92
1993
1994
1995
1996
Year
1997
1998
1999
2000
2001
Fig. 14. Time-series plots of seasonally averaged NDVI values of each of the six vegetation communities,
monthly SOI, and monthly average precipitation. Monthly precipitation values are averaged over all six
vegetation communities.
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analysis areas of the same vegetation community should yield similar NDVI values
for a particular year to those reported in this study (Figs. 10 and 11). Similar
ordering of NDVI values among different vegetation communities should also occur.
NDVI values for a given vegetation community and ordinal consistency among the
communities for differently located analysis areas may not display as much similarity
during the summer, however, as spatial uniformity is weaker (Figs. 12 and 13).
Weaker spatial uniformity suggests that analysis areas delineated by 5-km-diameter
circles are sufficient to capture the high spatial variability of convective thunderstorms during the monsoon season.
Influences of biological soil crusts and microphytic vegetation such as lichens on
NDVI in arid and semi-arid regions have been reported (Karnieli et al., 1996, 2002;
Schmidt and Karnieli, 2000). As with perennial and ephemeral vegetation, this type
of vegetation also responds to precipitation variability, demonstrating the most
rapid response to rainfall of the three. Possible influence of biological soil crusts and
microphytic vegetation on NDVI values and variability in this study are perceived to
be greater at the biweekly time-scale than at the seasonal and inter-annual timescales, as well as during periods when NDVI values are lower (e.g., 1993–1995).
Effects of biological soil crusts on multi-spectral remote sensing data is under study
at the Sevilleta and should be considered in future work, especially studies linking
NDVI to quantitative ground measurements as biological soil crusts and
microphytic vegetation may confound such correlations (Karnieli et al., 1996).
Applying these characteristics of NDVI beyond the six analysis areas at the
Sevilleta will help provide a better understanding of vegetation across south-western
North America because the vegetation communities and climate dynamics reported
in this study cover extensive parts of the region (Brown, 1994). For monitoring
purposes, the property of strong uniform behavior in spring may permit the
aggregation of several different vegetation communities in order to ascertain
vegetation activity at larger spatial scales. Strictly limiting such aggregation is
necessary to accomplish this task in the summer.
Understanding the time-dependent effects of precipitation, temperature, and other
meteorological variables on these NDVI data would contribute to assessments of
possible environmental change resulting from climatic phenomena such as drought
and global warming. The results of this study indicate that NDVI can provide a
useful index of vegetation variability on seasonal and inter-annual time-scales. For
the six vegetation communities studied here, the results also suggest that inter-annual
variability of NDVI could show meaningful relationships with inter-annual climate
variability, since the 11-year average, seasonal cycle, and inter-annual variability of
NDVI reflect characteristics of the regional climate. Such correlations are the subject
of a subsequent study.
5. Summary
The results of this study confirm the utility of NDVI for characterizing vegetation
variability in arid and semi-arid environments and allow decade-scale analysis of
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vegetation behavior in response to climate variability. Computing 11-year average
NDVI time-series for each of the six different vegetation communities in central New
Mexico shows a bimodal growing season with a pre-monsoon low that temporally
matches the change in precipitation patterns and high summer temperatures.
Individual growing season time-series for all communities display high large interannual variability during the 11-year period, effected by both the North American
monsoon and ENSO-forced climate variability.
The order of NDVI values is more constant and uniform behavior is stronger
among the communities during the spring than the summer, indicating an
interseasonal shift from large-scale, regional vegetation quiescence or vegetation
‘greening’ to more small-scale, localized vegetation quiescence and ‘greening’. The
apparent summertime decrease in the spatial scale of vegetation variability is
consistent with seasonal changes in precipitation across the region. From October to
May, precipitation mostly comes from spatially extensive frontal systems, which are
replaced by convective thunderstorms that produce heavy, localized precipitation
with high spatial variability from June to September.
Despite problems of utilizing NDVI in arid and semi-arid environments,
seasonal and inter-annual variability of NDVI reported in this study displays
realistic and readily explained behavior. In addition to ties between vegetation
and precipitation variability already mentioned, NDVI variability agrees with
observed ground measurements in this region. Ordering of NDVI values
corresponds to characteristics of the vegetation communities. Results thus
indicate that NDVI can provide a useful index of vegetation variability on
seasonal and inter-annual time-scales, and that long-term monitoring of NDVI
will help elucidate relationships between interannual fluctuations of vegetation
and climate.
Acknowledgements
Greg Shore and Doug Moore of the Sevilleta provided invaluable assistance and
comments. Two anonymous reviewers provided suggestions that improved the
manuscript. Research was funded by NOAA Office of Global Programs Grant
NA06GP0377 for North American Monsoon Studies and NSF EAR-0083752
Biocomplexity Incubation Grant for Studies of Drought and Climate–Vegetation
Interactions. AVHRR NDVI and meteorological data from the Sevilleta were
collected during Sevilleta LTER I Grant BSR 88-11906, LTER II Grant DEB
9411976, and LTER III Grant DEB 0080529 from NSF. This is Sevilleta LTER
publication number 267.
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