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Remote Sensing of Environment 87 (2003) 42–54

Response of seasonal vegetation development to climatic

variations in eastern central Asia

Fangfang Yua,b,*, Kevin P. Pricea,b, James Ellisc, Peijun Shid

aKansas Applied Remote Sensing (KARS) Program, University of Kansas, 2335 Irving Hill Dr., Lawrence, KS 66045, USAbDepartment of Geography, University of Kansas, Lawrence, USA

cNatural Resources Ecology Laboratory, Colorado State University, Fort Collins, USAd Institute of Resource Sciences, Beijing Normal University, Beijing, China

Received 5 October 2002; received in revised form 16 May 2003; accepted 1 June 2003

Abstract

Meteorological records show that central Asia has experienced one of the strongest warming signals in the world over the last 30 years. The

objective of this study was to examine the seasonal vegetation response to the recent climatic variation on the Mongolian steppes, the third

largest grassland in the world. The onset date of green-up for central Asia was estimated using time-series analysis of advanced very high

resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data collected between January 1982 and

December 1991. Monthly precipitation and mean temperature data (1982–1990) were acquired from 19 meteorological stations throughout the

grasslands of the eastern Mongolian steppes in China. Our results showed that while the taiga forest north of the Mongolian steppes (>50jN)experienced an earlier onset of green-up during the study period, a later onset was observed at the eastern and northern edges of the Gobi Desert

(40jN–50jN). Responses of different vegetation types to climatic variability appeared to vary with vegetation characteristics and spring soil

moisture availability of specific sites. Plant stress caused by drought was themost significant contributor to later vegetation green-up as observed

from satellite imagery over the desert steppe. Areas with greater seasonal soil moisture greened up earlier in the growing season. Our results

suggested that water budget limitations determine the pattern of vegetation responses to atmospheric warming.

D 2003 Elsevier Inc. All rights reserved.

Keywords: Central Asia; Mongolian steppe; Vegetation

1. Introduction Myneni, Tucker, Asrar, & Keeling, 1998; Myneni, Keeling,

Hansen, Ruedy, Gloscoe, and Sato (1999) have shown

over the past 30 years an unprecedented rate of global

temperature increase. This warming signal is strongest over

the subpolar land areas of Alaska, northwestern Canada, and

northern Eurasia, and is especially notable in the winter and

spring (Chapman & Walsh, 1993; Oechel et al., 2000).

Ecosystem response in the higher northern latitudes to this

warming trend include a reduction of annual snow cover and

earlier melting of spring snow (Groisman, Karl, & Knight,

1994; Parkinson, Cavalieri, Gloersen, Zwally, & Comiso,

1999), an earlier draw-down of atmospheric CO2 in the

spring (Keeling, Chin, & Whorf, 1996), advances in the

timing of spring plant emergence (Menzel and Fabian, 1999;

0034-4257/03/$ - see front matter D 2003 Elsevier Inc. All rights reserved.

doi:10.1016/S0034-4257(03)00144-5

* Corresponding author. Kansas Applied Remote Sensing (KARS)

Program, University of Kansas, 2335 Irving Hill Dr., Lawrence, KS 66045,

USA.

E-mail address: fyu@ku.edu (F. Yu).

Tucker, Asrar, & Nemani, 1997; Schwartz, 1998; Zhou et

al., 2001), and poleward migration of butterflies (Thomas &

Lenon, 1999). The studies cited above focus in areas north

of 45j latitude where temperature is normally the most

important climatic factor limiting plant photosynthesis.

The climate of northeast Asia (China, Russia, and Mon-

golia) has undergone significant changes over the last 30

years (1979–1997, +f 1.5 jC; Chase, Pielke, Knaff,

Kittel, & Eastman, 2000). Recent findings (Chase et al.,

2000; Mongolian Action Program, 2000) show that this

region has one of the strongest warming signals on the earth.

The center of the warming zone appears to lie just southeast

of Lake Baikal, putting the drylands of northern China and

Mongolia near the center of this hot spot. Changes in

ecosystem dynamics for this region can directly affect land

use, biodiversity, and human socioeconomics. Few studies,

however, have investigated the effects and implications of

global warming on the lower latitude grasslands of central

Asia (35j–50jN) where both temperature and precipitation

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–54 43

play important roles in limiting plant biological processes

(Ellis, Price, Boone, Yu, Christensen, & Yu, 2001).

Recent studies have explored the use of time-series

remotely sensed measurements to investigate ecosystem

response to climatic variation (Myneni et al., 1997, 1998;

Tucker et al., 2001; Zhou et al., 2001). The normalized

difference vegetation index (NDVI), which is the most

commonly used remote sensing derived measurement, has

been linked in numerous studies to such plant properties as

leaf chlorophyll content, leaf area, leaf biomass, and net

primary productivity (e.g., Goward, Waring, Dye, & Yang,

1994; Tieszen, Reed, Bliss, Wylie, & Dejong, 1997;

Tucker, 1979; Tucker & Sellers, 1986). Analyses of the

time-series NDVI data are therefore used as the evidence

for a biotic response to climatic variation in the changing

of growing season and greening patterns at high-latitudes

(Lutch et al., 2002; Myneni et al., 1997; Zhou et al.,

2001).

Fig. 1. The study area in central Asia. The land cover map of the central IMAR, Ch

Vegetation, IMNRS, 1991. The meteorological stations used for the statistical analy

in the typical steppe, and 5 in the desert steppe).

The overall goal of this study is to determine how

vegetation in central Asia is responding to recent changes

in climatic conditions. More specifically, we wanted to (1)

investigate whether vegetation phenological changes ob-

served in the higher northern latitudes are also present in

the lower mid-latitudes where such changes could impact a

greater number of the world’s human population; (2) iden-

tify the predominant vegetation communities undergoing

these changes; and (3) determine the climatic factors on the

changing onset of green-up patterns.

2. Study area

The Mongolian steppes is one of the world’s largest

grasslands, extending across the nation of Mongolia and the

Inner Mongolia Autonomous Region (IMAR) in northern

China (Fig. 1). It is an arid to semiarid land with a strong

ina is after Series Resources Maps of Inner Mongolia Autonomous Region:

sis are also shown in this figure (3 stations in forest and meadow steppe, 11

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–5444

climate gradient. In the east (IMAR) and in the north

(Mongolia), annual precipitation exceeds 400 mm and the

annual rainfall coefficient of variance (CV) is less than 0.25

(Ellis, 1992). In the eastern IMAR, the steppes grade into

what was formerly deciduous forest; presently, the land use

is dominated by rainfed and irrigated agriculture. Along the

northern edge of the steppes (Mongolia), there is an inter-

face with coniferous forest (taiga biome). The southern and

western parts of the steppes grade into the Gobi Desert

where annual rainfall is less than 100 mm/year and the

annual rainfall CV is greater than 0.40 (Ellis, 1992). When

one moves from the forest and grassland to the Gobi Desert,

mean annual temperature increases as annual precipitation

decreases. The mean annual temperature in the study area

ranges from less than � 4 jC in the north forest area to over

8 jC in the central Gobi Desert (Shi et al., 1989).

Between the desert and the forest, annual rainfall ranges

from 100 to 400 mm/year. These zones are covered by a

variety of types of grassland and shrubland. Three types of

grassland (meadow steppe, typical steppe or dry steppe, and

desert steppe) and desert were identified along this climate

gradient. Meadow steppe is the most productive of all the

grass steppes, with typical steppe the next productive. Plant

biomass is least for the desert steppe and desert areas. The

forest is composed primarily of deciduous trees, including

Betula platyphylla, Populus davidiana, and Quercus spp.

The dominant coniferous tree is Larix gmelinii. The dom-

inant vegetation types for the meadow steppe include

Filifolium sibiricum, Festuca ovina, and Stipa baicalensis,

and at lower elevations, Aneurolepidium chinense. The

dominant vegetation types of the typical steppes include

Stipa grandis, Aneurolepidium Chinense, and Agropyron

michnoi. The desert steppe is dominated by short grasses

including Stipa krylovii, Stipa bungeana, and Thymus ser-

pyllum (Ellis, 1992). Soil types associated with the three

grasslands (meadow steppe, typical steppe, and desert

steppe) in this region are chernozems, castanozems, and

brown desert, respectively (Li et al., 1990).

The Mongolian steppes has a strong continental climate

characterized by short hot summers and long cold winters.

In this region, most of the annual precipitation falls in

summer, coinciding with peak temperatures. Winter and

spring are typically windy and cold. Severe drought fre-

quently occurs in spring and early summer, which can cause

low productivity of vegetation across the study area (Zhang,

1992).

3. Data and methods

3.1. AVHRR NDVI data

For this study, a 10-year (1982–1991) 15-day maximum

value composite advanced very high resolution radiometer

(AVHRR) NDVI data set with a spatial resolution of about

4.0 km was used. This data set was processed and archived

by the Global Inventory Mapping and Monitoring Study

(GIMMS) group at NASA/Goddard Space Flight Center.

The continental GIMMS NDVI data set was derived

from Global Area Coverage (GAC) data, which were

collected from 1982 to 1991 by three different on-board

sensors; (1) National Oceanic and Atmospheric Adminis-

tration (NOAA)-7 from January 1982 to February 1985, (2)

NOAA-9 from February 1985 to October 1988, and (3)

NOAA-11 from October 1988 to December 1991. These

images were processed using methods described by Holben

(1986). It is widely accepted that composite NDVI images

can greatly reduce cloud and other atmospheric noise while

retaining dynamic vegetation information. Prior to compos-

iting, the data were preprocessed to eliminate the large

viewing-angle and cloud-affected pixels. Detailed informa-

tion on the processing of this NDVI data can be found in

Los, Justice, and Tucker (1994).

The 15-day composite data set was geometrically trans-

formed to a Lambert Azimuthal Equal Area projection using

ground control points and a nearest neighbor spectral resam-

pling approach. During the georectification process, the

pixels were resampled to 4.2� 4.2 km. Since radiometric

values among the three sensors varied due to differences in

detector calibration and orbital drift, each NDVI value was

adjusted based on offset coefficients derived through the

analysis of NDVI values obtained over an invariant target in a

hyper-arid area of the Gobi Desert (Tucker, Vanprate, &

Newcomm, 1991 and Tucker, Newcomb, & Dregne, 1994;

Myneni et al., 1998). A smoothing algorithm was also

applied to the NDVI data set to minimize the effects of

anomalous values caused by atmospheric haze and cloud

contamination (van Dijk, Callis, Sakamoto, & Decker, 1987).

Subsequent remote sensing data processing for this study was

based on the smoothed NDVI time-series data set.

3.2. Estimating the onset timing of vegetation green-up in

Mongolian steppes

Previous studies have described two ways of calculating

onset of green-up using time-series NDVI datasets. The first

method uses an NDVI ‘‘threshold’’ to identify the beginning

of photosynthetic activity in the spring, and the second

method identifies the time period when there is a ‘‘sudden

increase’’ in NDVI. The NDVI threshold approach more

specifically identifies the beginning of photosynthetic ac-

tivity in the spring (e.g., Fischer, 1994; Lloyd, 1990;

Markon, Fleming, & Binnian, 1995; Myneni et al., 1998;

White, Thornton, & Running, 1997), while the sudden

increase approach essentially identifies the time period

when there is an abrupt increase in NDVI (e.g., Badhwar,

1984; Badhwar, Austin, & Carnes, 1982; Kaduk & Hei-

mann, 1996; Reed et al., 1994).

A limitation associated with the ‘‘threshold’’ approach is

that various land cover types require the use of different

thresholds (Reed et al., 1994). Since most land cover types

are a mixture of plant types, determining the optimal

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–54 45

threshold value for an area can be difficult, if not impossi-

ble. Changing solar zenith and azimuth viewing angles can

influence the NDVI values, also complicating the use of the

threshold approach.

The most popular method for identifying a sudden

change in NDVI uses a ‘‘moving average’’ approach by

Reed et al. (1994). The ‘‘moving average’’ is used to

estimate the departure from the actual smoothed NDVI data

by running a moving average of NDVI values over a certain

time interval. The smoothed and ‘‘moving average’’ time-

series NDVI data are then superimposed to identify the

time-period when the two curves cross, which is defined as

the time when onset of green-up occurs.

The ‘‘moving average’’ approach also has a limitation that

made it difficult to use in this study. This limitation stems

from the fact that the biweekly GIMSS GAC NDVI values

are not computed for higher latitude regions of the earth in

winter periods (Los et al., 1994). During the data processing

for GIMSS NDVI compositing, low surface brightness tem-

peratures in the AVHRR thermal-infrared channel are used

for cloud screening. Pixels with less than a certain brightness

temperature in the AVHRR thermal-infrared channel are

considered as cloud contaminated and replaced with zero

values. An inherent problem with this cloud-screening algo-

rithm is that much continental data from higher northern

latitudes and arid regions at mid-latitudes are eliminated

during the winter periods. Due to this problem, NDVI data

for the areas above 45j and the central Gobi Desert for our

study area were not available between late October and early

March. The lack of data during the early spring and late

winter made it impossible to calculate a moving average early

in the year just prior to the green-up event.

Due to the limitations of the two approaches described

above, it was necessary to develop a new method for

extracting estimates of onset of green-up (Yu, Price, Ellis,

& Kastens, 2003). Our new approach for calculating onset

of green-up considers the modality of the NDVI multi-

temporal curve and is therefore able to account for spring

snowfall events that temporally decrease the NDVI values

(Reed et al., 1994). The method also constrains the estimate

of onset date to a logical time interval, which reduces gross

miscalculations for sparsely vegetation areas that do not

always experience a major onset of green-up event.

We assumed that the onset date of green-up represents

the period when vegetation growth begins to increase

rapidly in the spring or early summer. To avoid non-

vegetation NDVI increases caused by snow-melt events or

occurrence of NDVI after a missing winter period, a set of

thresholds was used to constrain the estimates of onset date

(Fig. 2). First, if biweekly period t is the onset period, then

Dht must be positive and represent the maximum change in

the NDVI slope angle. The maximum change in NDVI

slope angle corresponds to the maximum second derivative

value approximately by the second difference over the

smoothed NDVI curves (Fig. 2, step 1). Second, the NDVI

value at the abrupt increase period should be above 0.05, the

mean value for the Gobi desert at late March. Third, NDVI

values after the estimated onset time should successively

increase in the following two time-intervals (1 month). This

increasing trend after the onset event is to ensure elimination

of the influence of snow events (Fig. 2, step 3). Finally, the

onset event must occur within a logical time interval, which

in our study area was determined to be between late March

and late August (Fig. 2, step 4). This time interval is in

accordance with the temperature requirement for the onset

event. Late March was selected as the beginning of this time

interval because Kaduk and Heimann (1996) found that

onset of vegetation growth begins after mean temperature

reaches 5 jC, and work by Shi et al. (1989) shows that the 5

jC mean temperature normally occurs by late March within

the Mongolian Plateau. Late August was selected as the end

of the logical time interval because both precipitation and

temperatures normally peak in summer and start to decrease

dramatically in September (Shi et al., 1989), making it

difficult for newly emerging plants to survive under the

thereafter stressful conditions. A more detailed description

of this methodology is provided by Yu et al. (in press).

The Julian day of onset of green-up was then established

as the day mid-way through the biweekly period in which

the green-up event occurred. In sparsely vegetated areas

such as the Gobi desert region, onset of green-up may not

occur in years of low precipitation. If the green-up event

could not be detected before late August within the time

interval constraint, a randomly selected pseudo onset date in

the fall season, Julian day 250 in early September, was

assigned to the specific pixels at the particular year for

further multiple linear regression analysis.

3.3. Climatic data

Mean monthly temperature and total monthly precipita-

tion from 1982 to 1990 were obtained from 19 meteorolog-

ical stations located in the eastern Mongolian region (central

IMAR) of China, 3 in the meadow steppe and the forest, 11

in the typical steppe, and 5 in the desert steppe (Fig. 1). We

used the monthly mean temperature of 5 jC to define winter

period (Kaduk & Heimann, 1996). As the result, the winter

season in the desert steppe starts from November through

March.

In the arid and semi-arid region, although there is a

correlation between mean rainfall and vegetation productiv-

ity over the growing season and the soil moisture is regarded

as the determining factor in vegetation conditions, consid-

erable uncertainty of the vegetation response to climate

change still remains (Goward & Prince, 1995). This uncer-

tainty is mainly due to our current limited understanding of

the forcing/feedback surface–atmosphere interactions,

which usually have complex temporal lag effects (Braswell,

Schimel, Linder, & Moore, 1997; Tian et al., 2000; Zhou et

al., 2001). For example, warming temperature, combined

with changes in precipitation, can affect vegetation growth

through influencing soil moisture and nutrient availability

273175

Fig. 2. A flowchart showing the method for calculating onset dates from the time-series NDVI. Let yt � 1 be the pixel NDVI in period t� 1, yt the NDVI in

period t, and so on. Dyt= yt� yt � 1; ht= arctg(Dyt); {h} is the collection of hs where s is the period within the specific modality.

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–5446

(Kindermann, Wurth, Kohlmaier, & Badeck, 1996; Tian,

Melillo, Kicklighter, McGuire, & Helfrich, 1999; Tian et al.,

2000). Potter, Klooster, and Brooks (1999) found that, in the

arid and semi-arid mid-latitude areas of the northern hemi-

sphere, vegetation net primary production can be affected by

temperatures preceding the current period by up to 1 year.

Di, Rundquist, and Han (1994) found a 1–2-month lag-

period effect of precipitation on NDVI values derived

during the growing season. To evaluate the lag effects of

temperature and rainfall within our study area, we used

seasonal climate data from previous winters through the

mean onset dates, which were then used to analyze the

relationships between climatic variability and onset data for

different land cover types. Our estimate of the mean onset of

green-up was in early May for the forests, early June for

typical steppe and late July for desert steppe in central

IMAR, respectively. The time interval used for these anal-

yses therefore ranged from the previous October through

May for the forest ecosystem, from the previous October

through June for the typical steppe, and from the previous

November through July for the desert steppe. We use the

term ‘‘preseason’’ to refer to the period before the mean

onset date.

3.4. Statistical analysis

A set of linear multiple regression models was used to

evaluate the effects of climatic variation on the onset dates

of green-up for the three different land cover types in central

IMAR. Due to the coarse spatial resolution of AVHRR GAC

data, misregistration between years may exist to generate

noises that complicate the interpretation of year-to-year

differences in onset date. To minimize the noise, we derived

the estimates of onset date by using the median value of a

3� 3-pixel window. We also assumed that the spatial

variation of monthly climate data was relatively consistent

Table 1

Multiple regression analysis between the standardized onset date of green-

up and intra-annual climate data (monthly precipitation and mean

temperature of the months proceeding the average date of onset of green-

up) at desert steppe and typical steppe (only months with significant results

are listed)

Time of

the year

March May June The entire

model for

Eq. (1)

Typical precipitation b � 0.22 � 0.40

steppe Sig. 0.05* 0.00*

temperature b 0.09 0.05

Sig. 0.41 0.65

R2 0.14* 0.22* 0.54*

Desert precipitation b � 0.15 � 0.49

steppe Sig. 0.27 0.00*

temperature b 0.67 0.17

Sig. 0.00* 0.26

R2 0.42* 0.32* 0.87*

The entire model stands for the regression between the onset dates and all

the preseason climate variables (as described in Eq. (1)).

b = standardized regression coefficient and R2 = determination of

coefficient.

*P< 0.05.

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–54 47

within an area of about 10-km radium of each meteorolog-

ical station, corresponding to the 3� 3-pixel window size

area (Yang, Wylie, Tieszen, & Reed, 1998). All the depen-

dent variables in the regression models were the median

onset date and the independent variables were the preseason

monthly climate data for each meteorological station from

1983 to 1990. The general relation between onset date and

climate was described as:

Onset ¼ a þX�n

i¼0

ðbi1Ti þ bi2PiÞ þ e; ð1Þ

where onset is the timing of onset of green-up in Julian

days; T is the monthly mean temperature; P is the monthly

precipitation; a, bi1, and bi2 are coefficients that are esti-

mated statistically; e is the unexplained error from this

model; n is the number of periods before the onset event,

varying for different land cover types (e.g., n = 8 (October–

May) for the forest ecosystem, n = 9 (Oct–June) for the

typical steppe, and n = 9 (November–July) for the desert

steppe).1 This relationship between onset and climate data is

based on the grassland phenological physiology that onset

of green-up in the grassland was strongly influenced by

temperature and precipitation (French & Saucer, 1974; Pitt

& Wikeem, 1990).

One problem associated with regression models is multi-

collinearity among independent variables, which typically

inflates the estimation of the standard errors for regression

coefficients and hence renders them statistically insignifi-

cant. Variance inflation factor (VIF) analysis is used to test

for multicollinearity among independent variables (Weso-

lowsky, 1976). A close examination of the VIF values

revealed the existence of high multicollinearity. Most of

the multicollinearity among variable is attributable to the

strong correlation among monthly temperatures and the

correlation between monthly temperature and precipitation.

Multicollinearity, however, does not affect the significance

tests of the R2 for the assessment of the whole model.

Besides these three models designated as ‘‘the entire

model’’ in Table 1, we also performed a series of regression

analyses of onset date against preseason monthly climate

factors (two in each model). The equation for these single

monthly models is described as:

Onset ¼ a þ bk1Tk þ bk2Pk þ e ð2Þ

where k ranges from month from the end of previous

growing season (October and November for the typical

1 For the forest ecosystem, there were three meteorological stations,

each with eight annual onset events, providing a total of 24 observations.

There were also eight pairs of monthly climate factors, providing a total of

16 independent variables. For the typical steppe, there were 11

meteorological stations and 9 pairs of monthly climatic factors, giving 88

observations of dependent variables and 18 independent variables. For the

desert steppe, there were 5 meteorological stations and 9 pairs of monthly

climatic factors, which provided 40 onset observations and 18 independent

variables.

steppe and the desert steppe, respectively) to the mean onset

month (June and July for the typical and desert steppes,

respectively) for each land cover type. These models were

designed to determine which month(s)’s temperature and

precipitation can best predict the green-up date. Since the

general model for the forest ecosystem shows insignificant

results, we decided exclude the forest ecosystem from the

single month analyses. Significant results were reported in

Table 1 along with the R2 statistics of the general models

described with Eq. (1).

Temporal autocorrelation of the 8-year data set was

tested using the Durbin–Watson test (Durbin & Watson,

1971). Low to moderate Durbin–Watson values indicate

non-significant temporal autocorrelation for the selected

climate data in the models (Durbin–Watson ranging from

1.54 to 2.08). Since the meteorological stations are sparsely

distributed across the study area (Fig. 1), there is no reason

to suspect any spatial autocorrelation among the climate

variables.

4. Results

4.1. General onset pattern in eastern central Asia

The distribution of average onset dates is shown in Fig.

3. The date of onset of green-up varies considerably across

the study area. This variation is linked to ecosystem (i.e.,

forest, meadow steppe, typical steppe, and desert steppe)

distribution that is strongly influenced by geographic fea-

tures and their impacts on climate. The average date of onset

of green-up between 1982 and 1991 corresponds well with

the temperature and precipitation gradients described earlier.

The mixed forest in the Da Xingan Mountains and taiga

Fig. 3. Averaged onset date of green-up in the central Asia from 1982 to 1991.

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–5448

forest at the northern edge of the Mongolian steppes have

the earliest dates of onset, usually in late April or early May.

Onset of green-up becomes progressively later with increas-

ing distance from the eastern border moving toward the

Gobi Desert. In general, the typical steppe experiences

green-up in late May to early June. The date of onset of

green-up for the desert steppe normally occurs in late June

to early July and is the most variable among the grassland

ecosystems in this study area (Yu et al., in press). This

spatial distribution of mean timing of onset of green-up in

IMAR generally agrees with the pattern of onset of the rainy

season in this region. For the meadow steppe, the typical

steppe, and the desert steppe, the summer rainy season

usually arrives in May, June and early July, respectively

(Shi et al., 1989; Xue, 1996). Onset events were undetect-

able in the Gobi Desert over the 10-year study period.

4.2. Interannual variation of onset in the Mongolian steppes

The change rate in onset dates of green-up, averaged over

the 10-year study, is shown in Fig. 4. The region of

advanced green-up patterns (negative slopes for the fitting

line) covers much of the eastern steppe of Mongolia,

extending south from the forest zone to near the northern

edge of the Gobi Desert (Fig. 4a). This is the region on the

Mongolia steppe, with precipitation ranging from about 200

mm per annum in the south to more than 400 mm near the

ecotone of the forest zone (Ellis et al., 2001). Although most

of the region did not experience significant change in onset

date of green-up from 1982 to 1991, a significant earlier

onset date can be observed in the taiga zone north to the

Mongolian steppes (Fig. 4b). This earlier onset is consistent

with the observation reported by Myneni et al. (1997, 1998)

for high latitudes (>45jN).

A contiguous band of areas that are experiencing a later

date of onset can be observed along the northern and eastern

edges of the Gobi Desert at 40–50jN latitude (Fig. 4a). In

IMAR, the later green-up areas are associated with the

desert steppe, the transition between the Gobi Desert and

the typical grassland, and part of the typical steppe (Fig. 1).

Mean annual rainfall in most of this delayed green-up zone

is 100–200 mm per annum. Another area showing later

onset date in IMAR appears to be associated with the forest/

shrub ecosystem in the southern Da Xingan Mountains.

Since we have no meteorological data for this forested

region, our analyses of the climate-onset interaction are

focused on the IMAR grasslands bounding the Gobi Desert

only.

Fig. 5 displays the average onset dates of green-up for the

pixels experiencing a significant change (P < 0.10) from

1982 to 1991. Onset dates in the desert steppe were highly

variable with a significant late trend during this period. In

the taiga forest, progressive early onset can be observed

from 1982 to 1991 except for 1983 and 1988. Both of these

two profiles indicate late onset dates in 1983 and the

relatively late onset dates may be associated with the strong

El-Nino effect in the 1982–1983 period (Myneni et al.,

1998).

Two reasons attribute to the possibly overestimated onset

trends, about + 35 days for the taiga and about � 25 days

for the desert steppe in IMAR. First, the Mongolian steppes

were subjected to one of the strongest warming signals,

especially in the winter and early spring, over the last two

decades (Chase et al., 2000; Mongolian Action Program,

2001). Dramatic changes in the onset of plant growth are

expected as consequences of the warming trend at the

beginning of growing season (Ellis et al., 2002). Zhou

et al. (2001) reported the mean growing season for vegeta-

Fig. 4. (a) Mean change rate of onset date of green-up in central Asia (1982–1991). (b) Areas with significant changes in onset date during 1982–1991. Warm

colors stand for the areas experiencing later onset pattern during this period and cold colors for the earlier onset pattern.

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–54 49

Fig. 6. The relationship between the onset dates and the spring precipitation

(in May and June) for meteorological stations in the typical steppe in central

IMAR.

Fig. 5. Average onset date of green-up for the pixels in the Taiga forest and

the desert steppe in IMAR that experienced a significant onset changes

(a< 0.1) from 1982 to 1991.

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–5450

tion at the northwest Asia increased about 18 days over the

past two decades. Secondly, although the 15-day GAC

NDVI composites do have the advantage of using a longer

time period to reduce cloud and aerosol contamination,

effects of bidirectional reflectance, and data volume (Hol-

ben, 1986), the temporal resolution is scarified (White et al.,

1997). Since our method for estimating onset date uses the

mid-date of the 15-day NDVI composite (Holben, 1986), we

estimate the maximum expected error of the onset date to be

7–8 days for each year. This error, when averaged over the

area undergoing significant onset changes, may cause a bias

toward a higher onset trend values.

4.3. Relationships between onset and climate data in central

Imar, China

4.3.1. Meadow steppe

No significant relationship between onset dates and the

preseason climate could be found in the meadow steppe. An

examination of the onset dates showed that there was little

year-to-year variation in this spring phenology (Yu et al., in

press). Several reasons may account for this consistent

onset. First, the meadow steppe is mainly distributed in

higher latitudes with low interannual variability in annual

precipitation. The interannual variability in precipitation,

calculated as the coefficient of variance (standard devia-

tion/mean) was 0.14, 0.18, and 0.21 for meteorological

stations 1, 2, and 3 (Fig. 1), respectively, indicating a

relatively stable or equilibrium environment in this region

(Ellis, 1994). Secondly, the coarse temporal resolution of the

NDVI composite data (15-day interval for compositing)

may somehow mask the subtle response of vegetation to

the late winter and spring warming effects in the tempera-

ture-limited ecosystems such as forest. Therefore, we dis-

cuss only the onset-climate relation for the typical and desert

steppes where interannual variation of onset dates is highly

variable.

4.3.2. Typical steppe

Monthly model results show that precipitation in May

and June is the most import factor determining the onset

date of green-up (R2 = 0.14 and 0.22, respectively; Table 1).

Accumulation of precipitation in May and June can explain

about 30% of the changes in observed onset dates in the

typical steppe (Fig. 6). The entire monthly preseason climate

(precipitation and temperature data from November to

March) can explain over 50% of the interannual variations

in the onset dates in the typical desert.

Spring precipitation is negatively correlated with onset,

indicating an early trend of onset for increased spring

precipitation, or late trend for decreased spring precipitation.

Similar results were also reported for the warm grasslands of

southern U.S. Great Plains where grasslands remains dor-

mant until the arrival of raining season in late spring (Kemp,

1983). The timing of onset of green-up in the IMAR typical

steppe is therefore largely controlled by the intensity and

timing of spring precipitation. Since most of the meteoro-

logical stations in the typical steppe of eastern IMAR

showed no significant increase or decrease in precipitation

patterns in May and June from 1982 to 1990, this may

partially explain the non-significant changes in the onset

date of this grassland in eastern IMAR as shown in Fig. 4b.

4.3.3. Desert steppe

Two significant monthly climate variables entered into

the seasonal model: March temperature and May precipita-

tion. March temperature and May precipitation explained

about 42% and 32% of the variance in the observed onset

dates, respectively (Table 1). The entire monthly preseason

climate (precipitation and temperature data from November

to June) can explain about 87% of the interannual variation

in the onset dates in the typical desert.

The relative high correlation between March temperature

and the onset date suggests that warming in winter or early

spring may lead to a late onset of growing season in this

region. Regression between onset dates and the mean winter

Fig. 7. Mean winter temperature (November–March) for three meteoro-

logical stations in the desert steppe, which experienced significant late trend

of onset from 1982 to 1990.

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–54 51

and early spring temperatures (averaged from previous

November through March) was then used to examine

whether changes in winter temperature had caused the

variability in onset date. A significant positive relationship

was also found between the onset dates and the mean

temperature from November to March (P < 0.10, n = 24).

Fig. 7 shows the mean winter temperature (November–

March) and May precipitation for three meteorological

station sites in the desert steppe. The early onset of 1985

seems to be associated with the relatively wet May that year.

The winter mean temperatures for all three stations exhibit a

clear warming winter pattern beginning in 1985, while May

precipitation was highly variable during this period (precip-

itation CV in May = 1.33, 1.00, and 0.84 for stations 15, 17,

and 18, respectively). We therefore believe that it is warm-

ing in winter and early spring, especially in March that

caused the later onset in desert steppe during the study

period (Fig. 4b).

5. Discussion

Our observation of vegetation green-up pattern at the

relatively wet area of taiga forest and part of the meadow

steppes confirmed the advance in the beginning of growing

season at high latitudes (>50jN) over the study period

between 1982 and 1991 (Myneni et al., 1997, 1998). The

change in green-up pattern in these areas thus suggests that

temperature-limited, but not water-limited ecosystems in

early spring, could experience an advance in the green-up

timing due to the warming temperature.

The variable responses of the typical steppe to the

climate change may be seen as the results of the character-

istics of specific sites, or the varying seasonal water budgets

over space and time occurring in this vegetation type. For

example, our analysis of the changing onset patterns of the

comparable vegetation communities along the southwest–

northeast trending boundary of Mongolia and IMAR that

extend across the political (Fig. 4a and b) border show some

major inconsistencies in green-up onset trends within veg-

etation types. Typical steppe has a trend of advanced and/or

slightly delayed green-up on the Mongolia side and a broad

mix of delayed green-up site, and sites with no change on

the IMAR side. These different responses of seasonal

vegetation to the climatic variations appear to be associated

with the different levels of grazing intensity and land use

management across the international boundary of Mongolia

and IMAR, where stocking rates are much higher on IMAR

side of the border (Sneath, 1998). Recent research shows

that current policies in northern China promote privatization

and fragmentation of rangeland in IMAR (Sneath, 1998).

Overgrazing, or as a combined result of overgrazing and

climate stress, in association with the small scale and pattern

of livestock grazing has led to land degradation in the

typical and meadow steppes of IMAR (Christensen, 2001;

Ellis et al., 2002). It appears that the degraded land surface

may delay the onset dates of green-up by influencing local

atmospheric conditions through several possible land–at-

mosphere feedback processes. If human land use does

influence the course of climate-induced vegetation change,

then this cross-boundary situation provides an extraordinary

opportunity to begin to dissect the intertwined effects of

changes in atmospheric conditions and changes in land use

on steppe ecosystems.

Our climate and onset date analysis in the desert steppe

show that where the vegetation is water-limited, onset

dates of green-up are delayed by the warming trend. The

positive regression coefficients between the winter temper-

ature and onset dates also supports the finding from the

model simulations by Mabutt (1989) and Greco, Moss,

Viner, and Jenne (1994) that, in the desert areas, increases

in temperature of 0.5–2.0 jC raise evapotranspiration by

0.2–2.0 mm/day, unless accompanied by increased rain-

fall. Analysis of the meteorological records shows that

over last 60 years spring rainfall in the Mongolian steppes

has declined by 17%, mostly in May (Natsagdorj, 2000;

Baatarbileg et al., 2001). We assembled the available long-

term winter climate records (1961–1995 for winter tem-

perature and 1961–1990 for May rainfall) from three

meteorological stations in the desert steppe of IMAR

(Fig. 8). These stations showed a strong warming trend

in the winter (November through March) over the period

of 1961–1995, which is consistent with the report of

winter and early spring warming in the high latitudes

(Chapman & Walsh, 1993). The mean annual May rainfall

from 1961–1990 decreased slightly with substantial year-

to-year variation. This long-term climate change further

confirms the effect of spring drought on the later onset

dates of green-up observed in the desert steppes in the

Mongolian Plateau.

The result of the onset-climate relation in the Mongolian

steppes exhibits the complex effects of temperature on

vegetation growth. Increases in spring temperature may

stimulate earlier vegetation photosynthesis activity, while

at the same time increase the water stress in arid and semi-

Fig. 8. Summed winter (November through March) temperature at three meteorological stations in the desert steppe. The dashed lines are for annual values;

solid lines are for the annual values smoothed with 5-year running and fitted with regression line.

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–5452

arid ecosystems. In most regions of the Mongolia steppe, the

vast majority (85–90%) of annual precipitation comes in a

relatively short summer rainy season, June, July, and August

(Dagvadorg, Batima, & Natsagdorj, 2001). About 10% of

the annual precipitation falls as winter snow. This winter

snow may be very important in the seasonal water budget

because snow appears to stay on the ground until relatively

late winter, providing soil moisture through the spring

drought (Ellis et al., 2001). As a result, warming in winter

and early spring stimulates earlier onset dates of green-up in

wet areas while delays the occurrence of green-up in dry

areas. Our analysis of this study supports the hypothesis that

water budget limitations in arid and semiarid region is

determining the patterns of vegetation responses to atmo-

spheric warming (Neilson, 1995).

6. Conclusions

The findings of this study show a linkage between onset

date of green-up predicted using AVHRR NDVI GAC 15-

day maximum composites and climatic factors. Our findings

also show that the onset dates of green-up vary considerably

within and across the steppes of the Inner Mongolian study

area. Unlike the findings of Myneni et al. (1997) that show

an earlier date of onset at the global scale (>50jN), ourfindings show that the response of vegetation to climate

change varies among different land cover types in central

Asia. While some of the taiga forest (including the meadow

steppe) north to the Mongolian steppe (>50jN) experiencesan earlier onset of green-up during the study period, a

significantly later onset is observed in the desert steppes

banding the eastern and northern edges of the Gobi Desert

(40–50jN). Results from most of the typical steppe region,

which falls between the meadow steppe and the desert

steppe, are varied. Some typical steppe sites show a weak

trend of delayed onset of green-up, other sites show no

change and a few have an earlier green-up. For much of the

Gobi Desert, onset of green-up was not detectable by NDVI

analysis in most years.

For the typical steppe, spring precipitation, especially in

May and June, is the most important factor influencing the

onset date of green-up, while for the desert steppe, the

timing of green-up is more influenced by temperature,

especially the temperature in late winter and early spring.

In water-limited regions, changes in temperature can signif-

icantly alter seasonal water budgets that influence vegeta-

tion phenological patterns and photosynthetic activity. Our

analysis of the regional green-up patterns along the interna-

tional border suggests that the differential impact of heavy

grazing in IMAR vs. relatively light grazing in Mongolia

may modify the effects of atmospheric warming. We believe

that the significantly delayed onset in the desert steppe is

mainly caused by warming-induced drought stress over the

study period. Future studies will concentrate on extending

our remote sensing and meteorological data sets to the

present time period to determine whether patterns observed

during this study continue to persist.

Acknowledgements

This study was supported by the United States National

Science Foundation, Models and Methods for Integrated

F. Yu et al. / Remote Sensing of Environment 87 (2003) 42–54 53

Assessment Program. We wish to express the appreciation

to Dr. Li Zhenghai, Mr. Pai Hao, Dr. Wang Yanrong, and

Mr. Zhou Wuguan for their assistance with this project. We

are grateful to Dr. Hanqin Tian for the comments on the

manuscript, Dr. Compton J. Tucker for providing the

AVHRR GAC data, and Mr. Chenyang Xiao for help with

the statistical analysis.

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