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Global Environmental Change 16 (2006) 340–348
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NDVI-based increase in growth of temperate grasslands
and its responses to climate changes in China
Shilong Piaoa, Anwar Mohammata, Jingyun Fanga,, Qiang Caia,b, Jianmeng Fenga
a
Department of Ecology, College of Environmental Sciences, Center for Ecological Research and Education,
and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
b
Department of Geography, University of Iowa, IA 52245, USA
Received 11 October 2004; received in revised form 7 February 2006; accepted 27 February 2006
Abstract
This study analyzes the temporal change of Normalized Difference Vegetation Index (NDVI) for temperate grasslands in China and its
correlation with climatic variables over the period of 1982–1999. Average NDVI of the study area increased at rates of 0.5% yr1 for the
growing season (April–October), 0.61% yr1 for spring (April and May), 0.49% yr1 for summer (June–August), and 0.6% yr1 for
autumn (September and October) over the study period. The humped-shape pattern between coefficient of correlation (R) of the growing
season NDVI to precipitation and growing season precipitation documents various responses of grassland growth to changing
precipitation, while the decreased R values of NDVI to temperature with increase of temperature implies that increased temperature
declines sensitivity of plant growth to changing temperature. The results also suggest that the NDVI trends induced by climate changes
varied between different vegetation types and seasons.
r 2006 Elsevier Ltd. All rights reserved.
Keywords: China; Climate change; Normalized Difference Vegetation Index (NDVI); Interannual change in NDVI; Spatial pattern of NDVI; Temperate
grassland
1. Introduction
China’s temperate grassland is the third largest grassland
area in the world (Lee et al., 2002). It not only supports the
world’s largest population of sheep and goats, but also
plays an important role in the regional climate and
reducing soil loss by wind or water erosion (Chen and
Wang, 2000; Xie et al., 2001). Recent studies (Shi et al.,
2003; Qin, 2002) have shown that the climate of this region
has experienced dramatic change in the past several
decades. This change likely directly affects grassland
growth, as primary production of temperate grasslands in
China is highly sensitive to interannual variation in
climate, especially to the change in precipitation (Xiao
et al., 1995; Chen and Wang, 2000). Therefore, studies on
the impact of climate change on productivity of temperate
grasslands in China are of significance to both the
Corresponding author. Tel: +86 10 6276 5578; fax: +86 10 6275 6560.
E-mail address: [email protected] (J. Fang).
0959-3780/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.gloenvcha.2006.02.002
understanding of the grassland ecosystem carbon cycle
and the sustainable use of grassland resources in China.
The relatively simple structure of grassland ecosystems
makes them ideal subjects for research using remote
sensing imageries to study vegetation dynamics. Normalized Density Vegetation Index (NDVI), which is derived
from infrared channel and near-infrared channel remote
sensing data, is a good indicator of photosynthesis
(vegetation activity). With a broad spatial coverage and
high temporal resolution, the advanced very high-resolution radiometer (AVHRR) of the National Oceanic and
Atmospheric Administration (NOAA) NDVI datasets
provides unique opportunities for monitoring terrestrial
vegetation conditions at regional and global scales (Yang
et al., 1997), and has widely been used in research areas of
NPP (Potter et al., 1993; Paruelo et al., 1997), vegetation
coverage (Tucker et al., 1991; Myneni et al., 1997; Zhou
et al., 2001; Los et al., 2001; Piao et al., 2003), biomass
(Myneni et al., 2001; Dong et al., 2003), and phenology
(Reed et al., 1994; Moulin et al., 1997).
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S. Piao et al. / Global Environmental Change 16 (2006) 340–348
Climate change is one of the main drivers of the
interannual variation in vegetation activity (Zhou et al.,
2001; Schimel et al., 2001). Investigations of the correlation
between NDVI and climate factors aid in finding key
factors that control changes in the terrestrial ecosystem
carbon cycle and shed light on the mechanisms controlling
the response of terrestrial carbon storage to climate
variability (Braswell et al., 1997; Potter and Brooks,
1998). Since the 1980s, a number of analyses have explored
the relationships between NDVI and climate factors in
different geographic areas and ecosystems. However, the
mechanisms of the response of the vegetation to climate
change are far from clear (Wang et al., 2003). Most of these
studies have related NDVI with climate factors during the
growing season or examined their spatial changes (Schultz
and Halpert, 1995; Yang et al., 1997; Potter and Brooks,
1998; Suzuki et al., 2000). Few studies have documented
the relationships between change in NDVI and climate
variables over time (season), and described their spatial
patterns (but see Jobbagy et al., 2002; Zhou et al., 2003;
Wang et al., 2003; Piao et al., 2003, 2004).
Previous studies have suggested that vegetation activity
in most region of China increased over the past two
decades (Piao et al., 2003, 2004; Fang et al., 2004), but the
linkage between climate change and vegetation growth of
temperate grasslands has not been adequately quantified,
because these studies focused mainly on vegetation
responses to climate change at national scale. Furthermore,
no effort has been made to investigate the effects of climate
change on the relationships between vegetation growth and
climate variables, although this is essential for quantifying
and understanding future interactions between climate and
terrestrial ecosystems. The main purpose of this study is to
explore the interannual variation in growth of China’s
temperate grasslands from 1982 to 1999, and to investigate
the relationships between NDVI and climate variables
using concurrent satellite and climate data sets. More
specifically, we are to address the following questions: (1)
how has the productivity of temperate grasslands analyzed
by NDVI changed over the past two decades in China? (2)
how does this change correlate with climate change? and
(3) how does the relationship between NDVI and climate
vary temporally?
In order to avoid spurious NDVI trends due to winter
snow, our study focuses on growing season NDVI (Zhou et
al., 2001; Piao et al., 2004). Here we define growing season
as April–October, and further divide the growing season
into three seasons: spring (April and May), summer
(June–August), and autumn (September and October).
2. Data and methods
2.1. Study area
Temperate grasslands in China are mainly distributed
north of 35 1N, with an area of 164 104 km2, comprising
nearly 17% of China’s territory. A strong east-to-west
precipitation gradient results in a decrease in annual
precipitation from more than 600 mm in northeastern
China to less than 200 mm in northwestern China. With
this large precipitation range, China’s temperate grassland
is divided into three major types, temperate meadow,
temperate steppe, and temperate desert steppe (Fig. 1).
Temperate meadows mainly occur in northeastern China,
with annual average precipitation ranging from 300 to
600 mm and annual mean temperature from 2 to 5 1C.
Temperate steppe spreads from approximately 1081 to
1201 E in longitude and 401 to 501 N in latitude. The annual
precipitation in this area is approximately 200 to 400 mm,
mainly falling in July and August, while annual average
temperature ranges from 0 to 8 1C. Temperate desert steppe
extends to the west of 108 1E in longitude, where annual
precipitation is only 0–200 mm and annual mean temperature is between 5 and 10 1C.
N
45 70
341
80 90 100 110 120 130140
40
35
30
25
20
15
Temperate steppe
Temperate desert
Temperate meadow
Fig. 1. Spatial distribution of temperate grasslands in China (based on the Commission for Integrated Survey of Natural Resources, 1996).
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S. Piao et al. / Global Environmental Change 16 (2006) 340–348
2.2. Data sets
0.30
3. Results and discussion
3.1. Trends and interannual variations in mean growing
season NDVI
Fig. 2 illustrates the variations in mean growing season
NDVI, growing season precipitation, growing season
(a)
y=0.0014x - 2.586
R=0.73, P=0.001
(b)
y=0.7899x - 1310.3
R=0.14, P=0.60
0.28
0.26
0.24
350
250
16
Precipitation (mm)
400
300
Temperature (°C)
NDVI data at a spatial resolution of 8 8 km and 15day interval were acquired from the Global Inventory
Monitoring and Modeling Studies (GIMMS) group
derived from the NOAA/AVHRR Land data set for the
period January 1982 to December 1999. The dataset is
known for its high quality, having been calibrated to
eliminate noise from volcano eruptions, solar angle and
sensor errors, and has been widely used in studies on
vegetation dynamics at regional and global scales (Myneni
et al., 2001; Zhou et al., 2001; Slayback et al., 2003). The
maximum NDVI value for each pixel during a given month
was assigned as the monthly value in an effort to further
reduce residual atmospheric and bidirectional effects.
NDVI images of spring (April and May), summer
(June–August), fall (September and October), and growing
season (April–October) were generated separately through
computed averages of respective months. Furthermore, in
order to eliminate the impact of bare and sparsely
vegetated regions, only grid cells with annual mean NDVI
greater than 0.1 during the 18 year period were used in this
study.
Monthly climate data used in this study include
temperature and precipitation data, produced by our
research group, for the years 1982–1999 (Piao et al.,
2003). These datasets were generated from 680 welldistributed climate stations across China by using kriging
interpolation (Piao et al., 2003). In order to explore the
relationships between vegetation growth and moisture
condition, Thornthwaite’s moisture index (Im), an integrated bioclimatic index that involves precipitation and
temperature parameter (Thornthwaite, 1948; Fang and
Yoda, 1990; Potter et al., 1993), was calculated in this
study.
The information on distribution of temperate grasslands
was obtained from grassland map of China with a scale of
1:4,000,000 (Commission for Integrated Survey of Natural
Resources, Chinese Academy of Sciences, 1996), and
vegetation map of China with a scale of 1:1,000,000
(Editorial Board of Vegetation Map of China, 2001). Based
on these maps, there are 17 grassland types in China. To
easily document different responses of grassland growth to
climate change, the grasslands in the temperature region in
China (north of 35 1N) were further grouped into three
types: temperate desert steppe (temperate steppe-desert and
temperate desert), temperate steppe (temperate meadowsteppe, temperate steppe and temperate desert-steppe), and
temperate meadow (lowland meadow) (Fig. 1).
NDVI
2
200
(c)
15
y=0.0521x - 89.071
R=0.59, P=0.009
14
13
-20
(d)
y=0.0256x - 80.877
R=0.04, P=0.883
-25
-30
Im
342
-35
1982 1984 1986 1988 1990 1992 1994 1996 1998
Year
-40
Fig. 2. Interannual changes in (a) area-weighted growing season normalized difference vegetation index (NDVI), (b) growing season precipitation
(mm), (c) growing season mean temperature (1C), and (d) Thornthwaite’s
moisture index (Im) over the period 1982–1999 for temperate grasslands in
China.
temperature and Im in the study area during 1982–1999.
Mean growing season NDVI increased significantly
(R ¼ 0.73, P ¼ 0.001) from 0.25 in 1982–0.28 in 1999, with
an annual increase of 0.0014 (Fig. 2(a)). While growing
season precipitation and Im showed only a weak increase
(R ¼ 0.14, P ¼ 0.60 and R ¼ 0.04, P ¼ 0.883, respectively), the patterns of rainfall fluctuation corresponded
well to that of NDVI (Fig. 2(b and d)). In 1984, 1988, 1990
and 1998, growing season precipitation and Im was
relatively high, coinciding with peaks of growing season
NDVI. Similarly, the growing season NDVI minima of
1986, 1989, and 1995 correspond with the minima of the
growing season precipitation and Im over these years.
Growing season mean temperature dramatically increased
over the 18 years, with an average increase of 0.05 1C per
year (R ¼ 0.59, P ¼ 0.009) (Fig. 2(c)).
NDVI trends for different grassland types allow us to
better understand patterns of NDVI change. With an
increase of mean growing season NDVI of the study
area, mean growing season NDVI for all these three
grassland types clearly increased (Po0.05) from 1982 to
1999. Temperate meadow exhibited the most significant
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S. Piao et al. / Global Environmental Change 16 (2006) 340–348
increasing trend, with an average annual increment of
0.0016 (R ¼ 0.82, Po0.001) (Fig. 3(a)), and temperate
desert steppe the largest annual increase rate, at 0.71%
(Fig. 3(c)).
For temperate meadow, interannual variation in growth
season NDVI corresponded closely to that of temperature
(Fig. 3(a)). Peak temperature values were reached in
1985, 1988, 1990, 1994, and 1998, which corresponded to
higher NDVI values in the same years (except for in 1998).
Lower NDVI values in 1983, 1986, 1989, 1992, and 1995
Temperate steppe
0.35
0.33
P=-0.1808x + 712.28
R=0.02, P=0.928
460
410
310
15
T=0.0467x -79.02
R=0.58, P=0.012
14
13
Im=-0.1157x + 212.44
R=-0.12, P=0.634
0
NDVI=0.0015x - 2.7815
R=0.66, P=0.003
0.28
0.26
P=0.9467x -1589.9
R=0.13, P=0.615
410
360
310
260
T=0.052x -89.294
R=0.584, P=0.011
15
210
14
Im=0.0311x -89.02
R=0.04, P=0.884
13
-10
-10
-20
Im
Im
Temperature (°C)
360
0.30
-30
-20
(a)
1982 1984 1986 1988 1990 1992 1994 1996 1998
Year
Precipitation (mm)
NDVI
0.37
0.32
NDVI=0.0016x -2.8215
R=0.82,P<0.001
Precipitation (mm)
NDVI
Temperate meadow
accordingly corresponded with lower temperatures. For
temperate steppe and temperate desert steppe, NDVI,
precipitation and Im show similar pattern of interannual
variability (Fig. 3(b and c)). For example, precipitation, Im
and NDVI of temperate steppe peaked in 1988, 1990, 1996,
and 1998, while were relatively low in 1986, 1989, 1991, and
1997.
Such an increase in NDVI derived primarily from
climate changes suggests that the vegetation productivity
of temperate grasslands, to some extent, may overcome
Temperature (°C)
0.39
343
-30
(b)
1982 1984 1986 1988 1990 1992 1994 1996 1998
Year
-40
0.17
Temperate desert steppe
NDVI=0.0011x - 1.9894
R=0.62, P=0.006
0.15
0.13
P=1.2116x -2288.5
R=0.31, P=0.207
170
140
Temperature (˚C)
110
17
T=0.0564x -96.395
R=0.53, P=0.024
80
Im=0.1231x -290.84
R=0.28, P=0.255
-40
Precipitation (mm)
NDVI
0.19
16
15
14
Im
-45
-50
(c)
1982 1984 1986 1988 1990 1992 1994 1996 1998
Year
-55
Fig. 3. Interannual changes in area-weighted growing season NDVI, growing season precipitation (mm), growing season mean temperature (1C) and
Thornthwaite’s moisture index (Im) for (a) temperature meadow, (b) temperature steppe, and (c) temperature desert steppe during 1982–1999.
ARTICLE IN PRESS
negative effects of overgrazing in the study period. This
also can be evidenced by ground-based observations.
Several studies have revealed that desertification in some
parts of north China has been reversed since the 1980s
(Runnström, 2000; Zhong and Qu, 2003; Fang et al., 2004).
The frequency of sand storms in north China has decreased
by about ten days in the 1990s compared to the 1980s
(Qian et al., 2002). The reduction in sandstorms and
possible, localized reversal of desertification may be due to
the increase of vegetation growth in temperate desert
steppe and temperate steppe in north China, which are
deemed as main source areas for sandstorms (Zou and
Zhai, 2004).
3.2. Correlation between mean growing season NDVI and
climate variables
The coefficients of correlation between growing season
NDVI and climatic variables differed in the different
grassland types. For temperate meadow, growing season
NDVI correlated strongly with temperature (R ¼ 0.76,
Po0.001), and weakly with precipitation (R ¼ 0.07,
P ¼ 0.79) and Im (R ¼ 0.05, P ¼ 0.832). In contrast, that
of temperate desert steppe more closely related with
precipitation (R ¼ 0.56, P ¼ 0.016) and Im (R ¼ 0.59,
P ¼ 0.010) than with temperature (R ¼ 0.19, P ¼ 0.441).
Growing season NDVI of temperate steppe significantly
correlated with precipitation (R ¼ 0.63, P ¼ 0.005), Im
(R ¼ 0.53, P ¼ 0.023), and temperature (R ¼ 0.56,
P ¼ 0.015). The different growth environments contribute
to these differences. Temperate meadow mainly appears
under conditions of abundant precipitation and low
temperature, in which temperature probably is the limiting
factor for its vegetation growth. Temperate desert steppe is
dominated under a dry climate, and thus likely limited by
precipitation. The differences found in the relationships
between NDVI and climates for the different grassland
types can be interpreted phytophysiologically (Nemani
et al., 2003), and are congruent with the results of several
other studies (Braswell et al., 1997; Wang et al., 2003; Zhou
et al., 2003).
To further analyze the relationship between NDVI and
climatic variables, we calculated Pearson correlation
coefficients between NDVI and the two climatic variables
(temperature and precipitation) for each grid cell. The
correlation between NDVI and precipitation was strengthened with increasing precipitation at growing season
precipitation below 200 mm, a precipitation threshold of
grassland biome proposed by Tucker et al. (1991). On the
other hand, the NDVI-precipitation relationship weakened
when growing season precipitation fell below or exceeded
200 mm (Fig. 4(a)). This implies that vegetation growth is
most sensitive to precipitation when the annual precipitation is about 200 mm. In extremely arid areas, strong
evaporation processes ensure that slight increases in
precipitation have only limited effect on vegetation growth.
As precipitation increases, the relationship between NDVI
Coefficients of correlation
between NDVI and precipitation
S. Piao et al. / Global Environmental Change 16 (2006) 340–348
1
0.5
0
-0.5
Y= -7E-06x2+0.031x+0.12
R2=0.34, P<0.001
-1
0
100
200
300
(b)
400
500
600
Growing season precipitation (mm)
(a)
Coefficients of correlation
between NDVI and temperature
344
1
0.5
0
-0.5
Y= -0.03x+0.517
R2=0.08, P<0.001
-1
6
9
12
15
18
21
24
Growing season temperature (˚C)
Fig. 4. Change in (a) correlation between growing season NDVI and
precipitation with growing season precipitation, and (b) correlation
between growing season NDVI and temperature with growing season
mean temperature.
and precipitation becomes closer, up to approximately
200 mm. After that value, greater precipitation increasingly
fails to raise NDVI, perhaps indicating the limits of the
water-use capacity of temperate grasslands. Alternatively,
this may arise from the lower temperature and sunshine
levels as precipitation increases.
In contrast, the temperature-NDVI relationship exhibited a strictly linear relationship. The Pearson correlation
coefficient between NDVI and temperature decreased with
increasing temperature (R ¼ 0.37, Po0.001), indicating
that the positive effect of temperature on the growth of
grassland decreased as temperature rose (Fig. 4(b)). This
implies that the sensitivity of vegetation in high-cold
regions to temperature may decline under global warming
conditions. The negative correlation between NDVI and
temperature found in areas with high temperature may be a
result of increased temperature which accelerates water
evaporation, causes water scarcity, and therefore restricts
vegetation growth (Jobbagy et al., 2002).
3.3. Trends in seasonal NDVI
To recognize the changes in seasonal NDVI, we
calculated the annual increase and increasing rates of
seasonal NDVI by the study area and by vegetation type,
as well as the corresponding coefficients of correlation
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S. Piao et al. / Global Environmental Change 16 (2006) 340–348
345
Table 1
Mean increase of seasonal NDVI (*0.01) (S), mean increase rate of seasonal NDVI (% yr1) (B), and coefficients of correlation between seasonal NDVI
and year (R) for different grassland types during 1982–1999 (R ¼ 0.47 and R ¼ 0.59 correspond statistically to 5% and 1% significance levels, respectively)
Vegetation type
Spring
Summer
Autumn
S
B
R
S
B
R
S
B
R
Temperature meadow
Temperature steppe
Temperature desert steppe
0.19
0.10
0.07
0.81
0.53
0.55
0.55
0.57
0.52
0.13
0.19
0.12
0.29
0.53
0.72
0.56
0.55
0.56
0.16
0.16
0.12
0.52
0.59
0.80
0.57
0.53
0.63
Total
0.11
0.61
0.61
0.16
0.49
0.60
0.14
0.60
0.62
between NDVI and time in the period of 1982–1999
(Table 1).
Rising trends were noticeable in the average NDVI of
the study area for all the three seasons, implying that all
these seasons contributed to the increase in growing season
NDVI. Among the three seasons, summer had the largest
total increase with an annual increase of 0.0016 yr1. The
increasing amount of spring NDVI was minimal at
0.0011 yr1, but exhibited the fastest increasing rate.
Annual increase rate for spring NDVI was about
0.61% yr1. The average annual increase and rate of
autumn NDVI for the study area were 0.0014 yr1 and
0.6% yr1, respectively.
The average seasonal NDVI increased for all the three
seasons and for all three grassland types. Temperate
meadow tended to have the largest NDVI increase amount
(0.0019 yr1) and rate of increase (0.81% yr1) in spring.
Summer showed relatively low total NDVI increase
amount (0.0013 yr1), with a rate of increase less than half
that of spring.
Although there is a clear increase in spring NDVI for
both temperate steppe and temperate desert steppe, the
magnitude of increase was less than that of the other two
seasons. For temperate steppe, summer was the season
with the largest annual mean increase (0.0019 yr1), and
the rate of NDVI increase peaked in autumn, at
0.59% yr1. For temperate desert steppe, annual average
increases amount of NDVI in summer and autumn were
almost equal (0.0012 yr1), but the rate of increase in
autumn exceeded that of both spring and summer. This
suggests the growing season of temperate desert steppe has
been significantly prolonged.
3.4. Correlation between climates and NDVI in different
seasons
Table 2 summarizes coefficients of correlation between
seasonal NDVI and corresponding climatic variables
(temperature and precipitation) of the whole study area
and each vegetation type. Considering the lag in response
of vegetation growth to climate change (Braswell et al.,
1997; Los et al. 2001; Wang et al., 2003), we also computed
correlation coefficients between seasonal NDVI and
climatic variables in the preceding season.
For the whole study area, spring NDVI correlated
positively not only with spring temperature (R ¼ 0.66,
P ¼ 0.003), but also with non-growing season precipitation
(R ¼ 0.54, P ¼ 0.021). The increase of precipitation during
the non-growing season may improve the efficiency of soil
moisture and benefit to the growth of temperate grassland
in spring. Summer NDVI positively correlated with
summer precipitation (R ¼ 0.49, P ¼ 0.04), but no obvious
lag effect of spring climate on summer NDVI was
observed. Autumn NDVI has significant positive correlation with autumn temperature (R ¼ 0.48, P ¼ 0.046) and
with summer precipitation (R ¼ 0.52, P ¼ 0.026), but
correlated only weakly with autumn precipitation
(R ¼ 0.06, P ¼ 0.826).
For temperate meadow, the correlation between NDVI
and temperature was stronger than that between NDVI
and precipitation in each of the three seasons, suggesting
that temperature may be the key factor for growth of
temperate meadow.
Spring NDVI of temperate steppe correlated in a
significant, positive fashion with both spring temperature
and precipitation, suggesting that increases in either
spring temperature or precipitation boost vegetation
growth. In addition, temperature and precipitation increases in the non-growing season may also have some
positive effects on vegetation growth in spring (R ¼ 0.45,
P ¼ 0.06; and R ¼ 0.31, P ¼ 0.21). Summer NDVI correlated strongly with summer precipitation (R ¼ 0.53,
P ¼ 0.025), but had no significant correlation with summer
temperature (R ¼ 0.28, P ¼ 0.27). Autumn NDVI was
mainly coupled with summer precipitation and fall
temperature (R ¼ 0.64, P ¼ 0.004; R ¼ 0.54, P ¼ 0.02,
respectively).
Seasonal NDVI of temperate desert steppe did not show
a significant correlation with temperature in any season.
However, the correlation between NDVI and precipitation
was notable and appeared to exhibit strong lag effects. For
example, spring NDVI positively correlated with spring
precipitation (R ¼ 0.34, P ¼ 0.16), but the relationship
with precipitation in non-growing season was stronger
(R ¼ 0.88, Po0.001), implying that precipitation in nongrowing season is probably critical to spring vegetation
growth in temperate desert steppe. Similarly, increased
summer precipitation enhanced plant growth in these
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S. Piao et al. / Global Environmental Change 16 (2006) 340–348
346
Table 2
Correlation coefficients between seasonal mean NDVI and precipitation and temperature for different grassland types (R ¼ 0.47 and 0.59 correspond
statistically to 5% and 1% significance levels, respectively)
Vegetation type
Spring
Summer
Autumn
Ra
Ra1
Te
Te1
Ra
Ra1
Te
Te1
Ra
Ra1
Te
Te1
Temperature meadow
Temperature steppe
Temperature desert steppe
0.13
0.58
0.34
0.30
0.31
0.89
0.78
0.70
0.26
0.36
0.45
0.23
0.15
0.53
0.57
0.01
0.31
0.65
0.64
0.28
0.34
0.04
0.16
0.03
0.00
0.07
0.09
0.06
0.64
0.80
0.24
0.54
0.11
0.36
0.12
0.13
Total
0.42
0.54
0.66
0.38
0.49
0.32
0.42
0.13
0.06
0.52
0.48
0.26
Ra: correlation coefficient between seasonal NDVI and precipitation in the same season; Ra1: correlation coefficient between seasonal NDVI and
precipitation in the previous season; Te: correlation coefficient between seasonal NDVI and temperature in the same season; Te1: correlation coefficient
between seasonal NDVI and temperature in the previous season.
regions in summer and autumn (R ¼ 0.57, P ¼ 0.014;
R ¼ 0.80, Po0.001, respectively).
Such variations in the correlation between NDVI and
climates with seasons have been observed in a few recent
studies. For instance, Wang et al. (2003) documented that
early and later seasons of vegetation growth were positively
correlated with temperature, while NDVI in the middle
growing season was negatively related with temperature in
the Great Plains region of North America.
Our study also found temporal lags in vegetation
response to climate change, which has been widely observed
in other regions (Braswell et al., 1997; Potter and Brooks,
1998; Los et al., 2001; Piao et al., 2003; Wang et al., 2003).
However, the range of these lags is likely to vary spatially
and temporally. Braswell et al. (1997) reported that NDVI
at the global scale had a 2-year lag response to temperature.
At the biome scale, Piao et al. (2003) concluded that the lag
time of vegetation response to temperature was only 3
months in China. Wang et al. (2003) observed a 2- to 4week temporal lag in NDVI to the change of precipitation
in the Great Plains area in America. Our present study
documented that spring and autumn NDVI all positively
correlated with the precipitation of the preceding season
(both the non-growing season and summer) for temperate
steppe and temperate desert steppe, suggesting an approximate three-month temporal lag.
4. Conclusions
Correlation analyses between NDVI and climate variable is a powerful tool for probing ecosystem function
response to global climate change (Potter and Brooks,
1998). The present study combined datasets of NDVI from
1982 to 1999 and climate parameters to analyze year-toyear variations in temperate grassland productivity and its
relationship with climate in China. The results demonstrate
that the vegetation productivity of China’s temperate
grassland rose in the late 1990s compared to the early
1980s, and changes in climate likely function as major
controllers for interannual variations in vegetation productivity. The effects of changes in climate on vegetation
growth varied among different vegetation types, seasons
and climates. Specifically, the coefficient of correlation
between NDVI and the precipitation in growing seasons
peaked at 200 mm per growing season, perhaps serving as
an indicator of temperate grassland sensitivity. Additionally, a 3-month lag in the response of China’s temperate
grasslands to climate change was identified. Our results
suggest that more detailed information is necessary when
using satellite data to quantify relationships between
variations in vegetation activity and climate at a large scale.
While some general goals have been achieved in this
study, there are a few points that are still unclear and
should be addressed. First, despite a significant increase in
the vegetation growth and biomass production of China’s
temperate grassland over the last two decades, we still do
not know how the net carbon productivity changes because
recent global warming also accelerates carbon loss through
soil heterotrophic respiration (Raich et al., 2002). Further
studies are needed to characterize the role of China’s
temperate grassland ecosystems in the regional carbon
balance. Second, as well as climate variables, atmospheric
CO2 fertilization effect (Schimel et al., 2001), increased
nutrient deposition (Holland et al., 1997), and human
activity such as grazing management, land abandonment
due to migration into urban areas (Runnström, 2000) may
also partly account for the observed NDVI changes.
Elevated atmospheric CO2 concentration will lead to
increase in water use efficiency through decrease in
stomatal conductance, and thereby promote vegetation
growth particularly in water stressed ecosystems (Graham
and Nobel, 1996). Quantifying the relative roles of these
myriad factors on vegetation growth is an extraordinary
challenge for developing strategies for the sustainable use
of grassland resources in China. Finally, there is a large
uncertainty in the prediction of future trends in vegetation
productivity. We do not know yet how long will such
greening trend of China’s temperate grasslands be persistent, which depends in part on future climate changes.
Acknowledgments
The authors wish to thank L.M. Zhou for providing the
GIMMS NDVI data, F.Y. Wei for collecting the climate
ARTICLE IN PRESS
S. Piao et al. / Global Environmental Change 16 (2006) 340–348
data, and Dan Flynn and anonymous reviewers for their
constructive comments on the manuscript. This research
was supported by the National Natural Science Foundation of China (90211016, and 40021101), State Key Basic
Research and Development Plan (G2000046801), and
Peking University.
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Further reading
Fang, J.Y., Piao, S.L., Tang, Z.Y., Peng, C.H., Ji, W., 2001. Interannual
variability in net primary production and precipitation. Science 293,
1723a.