INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 33: 1696–1706 (2013)
Published online 4 September 2012 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.3543
NDVI, temperature and precipitation changes and their
relationships with different vegetation types during
1998–2007 in Inner Mongolia, China
X. W. Chuai,a X. J. Huang,a,b * W. J. Wanga and G. Baoc,d
a
School of Geographic and Oceanic Science, Nanjing University, Nanjing, China
Land Development and Consolidation Technology Engineering Center of Jiangsu Province, Nanjing, China
Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information System, Inner Mongolia Normal University, Hohhot, China
d International Institute for Earth System Science, Nanjing University, Nanjing, China
b
c
ABSTRACT: Based on vegetation maps of Inner Mongolia, SPOT-VEGETATION normalized difference vegetation index
(NDVI) data, and temperature and precipitation data from 118 meteorological stations, this study analysed changes in
NDVI, temperature and precipitation, and performed correlation analyses of NDVI, temperature and precipitation for eight
different vegetation types during the growing seasons (April–October) of the period 1998–2007 in Inner Mongolia, China.
We also investigated seasonal correlations and lag-time effects, and our results indicated that for different vegetation
types, NDVI changes during 1998–2007 showed great variation. NDVI correlated quite differently with temperature and
precipitation, with obvious seasonal differences. Lag-time effects also varied among vegetation types and seasons. On the
whole, Inner Mongolia is becoming warmer, and drier for most regions, and ecological pressure in Inner Mongolia is
increasing, and our focus on such issues is therefore important. Copyright  2012 Royal Meteorological Society
KEY WORDS
normalized difference vegetation index (NDVI); temperature; precipitation; lag-time effect; correlation analysis;
different vegetations; Inner Mongolia
Received 8 February 2012; Revised 25 April 2012; Accepted 17 June 2012
1.
Introduction
Vegetation is the Earth’s natural linkage of soil, atmosphere and moisture. It displays obvious seasonal and
annual changes (Cui and Shi, 2010; Zhang et al., 2011)
and acts as a sensitive indicator of global climate changes
(Schimel et al., 2001; Weiss et al., 2004). Vegetation
responds to climate changes in both explicit and subtle
ways. Studying these changes has become a global interdisciplinary effort for researchers who seek to understand
what is happening and to find the most efficient means
of doing so (Meng et al., 2011a, 2011b).
The normalized difference vegetation index (NDVI)
was proposed by Rouse et al. (1974) based on differences in pigment absorption features in the red and
near-infrared regions of the electromagnetic spectrum
(Equation (1)). The values of NDVI range from −1.0 to
1.0, increasing positive NDVI values indicate increasing
amounts of green vegetation. NDVI values near zero and
decreasing negative values indicate nonvegetated features
such as barren surfaces (rock and soil) and water, snow,
ice and clouds (Schnur et al., 2010). Since it has many
advantages such as the simplicity of the algorithm, the
capacity to broadly distinguish vegetated areas from other
* Correspondence to: X. J. Huang, School of Geographic and Oceanic
Science, Nanjing University, Nanjing 210093, China.
E-mail: [email protected]
Copyright  2012 Royal Meteorological Society
surface types, more sensitive to detect green vegetation
than using a single band (Zhang et al., 2005), it can be
used to monitor local or global vegetation changes, which
can indicate environmental changes brought by natural
factor such as climate changes (Qiu and Cao, 2011) and
anthropogenic activities such as urban expansion process
(Fung and Siu, 2000), to assess crop production (Wardlow
and Egbert, 2008) and net primary productivity (NPP)
of vegetation (Piao et al., 2006, 2008), and it was also
mostly used to indicate climate changes by establishing
relationship between climatic factors and NDVI (Nemani
et al., 2003; Roerink et al., 2003) and so on:
NDVI =
NIR − RED
NIR + RED
(1)
where RED and NIR stand for the spectral reflectance
measurements acquired in the visible (red) and nearinfrared regions, respectively.
Climatic factors, land use changes, the fertilization
effect of CO2 and so on could make different impacts
on vegetation; among them, temperature and precipitation are the main indicators used to describe climate
conditions, and they can affect vegetation growth in an
obvious manner (Fang et al., 2004; Ji and Peters, 2004).
NDVI, temperature and precipitation data have been used
to study the effects of climate change on vegetation for a
long time by many scholars. Since the 1980s, NDVI has
CHANGES IN NDVI, TEMPERATURE AND PRECIPITATION IN INNER MONGOLIA
been widely used for studying climatic effects on changes
in vegetation productivity (Ichii et al., 2002; Nemani
et al., 2003; Roerink et al., 2003; Meng et al., 2011b;
Zhang et al., 2011). However, the results of these studies
varied because of the complexity of vegetation characteristics and regions (Bonan et al., 2003; Crucifix et al.,
2005; Ni et al., 2006; Meng et al., 2011b). Most studies
considered the different types of vegetation as a whole,
and thus did not consider their differential responses to
climate change. Schultz and Halpert (1995) conducted
a global analysis of the relationships between NDVI,
precipitation and land surface temperature and found
no significant correlations among them. However, other
studies have found significant relationships between these
variables. For example, Guo et al. (2008) reported that
NDVI variations were significantly correlated with both
temperature and precipitation. Rasmusen (1998) found
a positive correlation between NDVI and precipitation.
Ichii et al. (2002) reported a strong positive correlation
between NDVI and temperature in high-latitude districts
of the northern hemisphere in both spring and autumn.
In recent years, a few studies have analysed the relationships between NDVI, temperature and precipitation
for different vegetation types at a regional scale. Luo
et al. (2009) reported that there were strong correlations
between NDVI, precipitation and temperature for different vegetation types in northeast China, and that the effect
of temperature on NDVI was more obvious than that of
precipitation. In that study, increased precipitation even
inhibited the growth of marsh and scrubs in summer.
Zhang et al. (2011) reported that a positive correlation
between NDVI and temperature was most obvious for
scrubs and coniferous forests, that the effect of precipitation on NDVI was not as significant as the effect of
temperature and that bush NDVI correlated more strongly
with precipitation than the NDVI of other vegetation.
Regional responses to global warming show wide variation. In China, the strength of the increasing temperature
trend decreases from the south to the north (Gao et al.,
2009). Inner Mongolia is located in the north of China,
the green vegetation types is rich and is covered with
China’s largest area of grassland, and NDVI can well be
used to describe their growth in spatial and the changes
in temporal. What is more, most of the area has an
arid to semi-arid climate, and the ecological environment in this district is fragile and sensitive to global
climate change (Chen and Wang, 2009). Some scholars
have studied the relationship between NDVI and climate
factors in Inner Mongolia (Liu et al., 2009; Qu et al.,
2009; Sun et al., 2010; Xu et al., 2010), but most have
only focused on grassland or have not considered different vegetation types. Few studies have analysed the
relationships between NDVI, temperature and precipitation in recent years for different vegetation types in
Inner Mongolia. Furthermore, different remote-sensing
observations provide different results in terms of vegetation responses to climate change (Sivakumar et al., 2005;
Zhao et al., 2005; Camberlin et al., 2007). Compared
Copyright  2012 Royal Meteorological Society
1697
with the NOAA/AVHRR sensor, the SPOT-4 VEGETATION sensor has many advantages, such as being more
sensitive to chlorophyll absorption in its red band, the
elimination of strong water vapour absorption in its nearinfrared band and higher spatial resolution. Therefore,
using the SPOT-4 VEGETATION NDVI will increase
analytical precision (Xia et al., 2008; Yan et al., 2008;
Cui and Shi, 2010).
Based on SPOT-4 VEGETATION NDVI data, a vegetation map of Inner Mongolia, and temperature and
precipitation data, this study analysed changes in NDVI,
temperature and precipitation and investigated the correlations between NDVI, temperature and precipitation
for different vegetation types during the growing seasons
of the period 1998–2007 in Inner Mongolia. To avoid
spurious NDVI trends caused by winter snow, this study
focuses only on growing season NDVI (Zhou et al., 2001;
Piao et al., 2004). The growing season was defined as
April–October and was further divided into three seasons: April–May (spring), June–August (summer) and
September–October (autumn) (Piao et al., 2006; Guo
et al., 2007).The objectives of this study were (1) to analyse NDVI, precipitation and temperature changes during
the growing seasons for different types of vegetation in
Inner Mongolia over the past 10 years; (2) to compare
correlations between NDVI, temperature and precipitation for different vegetation types in different growing
seasons; and (3) to explain the reasons behind the different relationships identified.
2.
2.1.
Materials and methods
Study area
Inner Mongolia is located in northwest inland China with
an area of 118.3 × 104 km2 , and accounts for 12.3%
of China’s full land area. It lies between latitudes 37°
24 N–53° 20 N and longitudes 77° 10 E–126° 29 E. Most
of the area is located on the flat Mongolian Plateau. The
climate in Inner Mongolia varies from the west arid and
semi-arid climates, to the east wet, half humid monsoon
climate. The mean annual precipitation ranges from 35
to 530 mm, and the mean temperature is between −5 ° C
and 10 ° C (Chen and Wang, 2009). The main vegetation types include cultivated vegetation, marshes, shrubs,
steppes, meadows, desert vegetation, coniferous forests
and broadleaf forests. There is an obvious zonal vegetation distribution affected by precipitation and temperature
(Figure 1). Because of the effects of climate change and
human disturbance, the ecological environment in this
district is fragile and sensitive to further global climate
changes.
2.2.
Data sources
Data used in this study were as follows: (1) NDVI
data from 1998 to 2007 used in this study were from
SPOT VGT-DN; in March of 1998, SPOT-4 satellite was
launched and its vegetation sensor began to receive global
vegetation observation data in April, 1998, with 10 day
Int. J. Climatol. 33: 1696–1706 (2013)
1698
X. W. CHUAI et al.
Figure 1. Vegetation map and the locations of meteorological stations in Inner Mongolia, China.
composite (maximum-value) temporal resolution, 1 km
×1 km spatial resolution and stretched values ranging
from 0 to 255, available at http://www.geodata.cn, which
was provided by the Data Sharing Infrastructure of Earth
System Science, where the atmospheric, radiometric and
geometric corrections had been made. (2) Monthly mean
temperature and monthly precipitation data were obtained
from all of the meteorological stations distributed in
Inner Mongolia with a total of 118, which was provided
by the Inner Mongolia Weather Bureau and Climatic
Data Center and National Meteorological Information
Center. (3) Information on the distribution of different
vegetation types was obtained from the vegetation map
of Inner Mongolia with a scale of 1 : 1 000 000 (Editorial
Board of Vegetation Map of China, 2001), provided by
the Data Sharing Infrastructure of Earth System Science
(http://www.geodata.cn).
2.3. Methods
2.3.1. Annual and seasonal NDVI, temperature and
precipitation
True NDVI was restored with the formula NDVI =
DN × 0.004 − 0.1 (Cui and Shi, 2010) in ArcGIS10.0.
Each month includes three 10 day NDVI grid images.
The maximum values of the three images were calculated
as monthly NDVI. NDVI images for spring (April–May),
summer (June–August) and fall (September–October)
from 1998 to 2007 were generated separately through
computed averages of respective monthly NDVI values.
In this way, a seasonal NDVI series with 1 km ×1 km
spatial resolution was obtained. The annual growing
season (April–October) NDVI was defined as the average
of NDVI values from April to October.
For the monthly mean temperature and precipitation
data from meteorological stations, averages were also
calculated to obtain seasonal and annual mean temperature and precipitation data, respectively, for each station
Copyright  2012 Royal Meteorological Society
from 1998 to 2007 (April–October). Kriging methods
were employed using ArcGIS10.0, and then seasonal and
annual precipitation and temperature grid maps were produced that cover the whole area of Inner Mongolia.
According to the vegetation map, we extracted NDVI,
temperature and precipitation values for each vegetation
type during the growing seasons of the period 1998 to
2007. The average values of NDVI, temperature and
precipitation associated with a particular vegetation type
were calculated from the averages of all grid cells
belonging to the same vegetation type.
2.3.2. Response of vegetation NDVI to temperature and
precipitation
Pearson’s correlations between seasonal NDVI–
temperature and NDVI–precipitation were analysed using
SPSS (version 11.5), and two-tailed P -values were
used to determine significance. Considering the lagged
response of NDVI to temperature and precipitation
(Braswell et al., 1997; Wen and Fu, 2000; Cui and Shi,
2010), the NDVI–temperature and NDVI–precipitation
correlation analyses were also carried out between each
seasonal NDVI and the previous season’s temperature
and precipitation (for each growing season). Since the
SPOT-4 VEGETATION sensor just began to receive
global vegetation observation data in April, 1998, the
time series seems to be a little shorter, which may be a
limitation that may influence the accuracy of our analysis
at some extent.
3. Results and discussion
3.1. Annual changes in growing season NDVI,
precipitation and temperature for different vegetation
types
Figure 2 illustrates the changes in growing season
NDVI, precipitation and temperature during 1998–2007
Int. J. Climatol. 33: 1696–1706 (2013)
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0.54
50
30
15.2
P=0.1089X-203.54
R=0.90 P=0.001
14.4
14.0
1998
2000
2002
Year
2004
2006
75
Temperatrue(°c)
NDVI
30
T=0.0936X-173.69
R=0.797 P=0.0058
14.0
13.5
1998
2000
30
T=0.0689X-127.69 R=0.444
10.8
10.2
9.6
1998
2000
2002
2004
0.30
0.28
0.26
0.24
13.5
NDVI
T=0.0692X+126.03
R=0.642 P=0.045
12.4
2002
Year
2004
2006
NDVI
Coniferous forests
R=0.658 P=0.037
Temperature(°c)
P=-0.6654X+1377.59
R=-0.19 P=0.6
T=0.0542X-98.94 R=0.357 P=0.312
1998
2000
2002
2004
70
60
50
40
30
2000
2002
2004
2006
2006
Year
Desert vegetation
0.08
NDVI=0.0455X+0.4149
R=0.114 P=0.753
0.07
R=-0.312 P=0.381
18.0
T=0.096X-175.08
17.6
R=0.661 P=0.037
17.2
16.8
16.4
1998
2000
2002
Year
0.48
Broadleaf forests
0.47
0.46
0.45
0.44
2004
16
14
12
10
2006
NDVI=0.002X-3.59
R=0.5 P=0.142
75
P= -1.997X+4046.99
R= -0.497 P=0.144
60
45
Temperature(°c)
NDVI=0.0045X-8.3693
Precipitation(mm)
NDVI
Temperature(°c)
NDVI
30
10.4
10.0
9.6
9.2
8.8
40
13.0
P=-0.2606X+534.01
Temperature(°c)
50
Precipitation(mm)
60
40
0.58
0.56
0.54
0.52
0.50
50
Year
P=-0.9698X+1652.64
R=-0.384 P=0.274
2000
Steppes
T=0.1017X-190.17
R=0.79 P=0.007
1998
NDVI=-0.02X+0.5753
R=-0.029 P=0.938
12.0
1998
2006
20
14.0
0.09
12.8
2004
30
2006
Meadows
13.2
2002
NDVI=0.0024X-4.9856
R=-0.35 P=0.319
Year
0.37
0.36
0.35
0.34
0.33
P=0.198
P=-1.3955X+2826.6
R=-0.56 P=0.096
Temperature(°c)
NDVI
40
Precipitation(mm)
50
P=-0.8581X+1754.3
R=-0.347 P=0.3263
14.5
60
Year
NDVI=-0.0912X-4.0433
R=-0.09 P=0.807
0.28
R=-0.413 P=0.235
45
Shrubs
0.30
R=0.591 P=0.072
P=-1.595X+3242.18
0.34
0.32
NDVI=0.0034X-6.283
0.48
Precipitation(mm)
14.8
0.50
13.5
30
T=0.063X-112.55
Precipitation(mm)
Temperature(°c)
40
Marshes
0.52
Precipitation(mm)
P=-0.6924X+1426.7
R=-0.26 P=0.474
60
Temperature(°c)
NDVI=0.0022X-4.0433
R=0.31 P=0.383
NDVI
Cultivated vegetation
R=0.493 P=0.147
Precipitation(mm)
0.36
0.34
0.32
0.30
0.28
Precipitation(mm)
NDVI
CHANGES IN NDVI, TEMPERATURE AND PRECIPITATION IN INNER MONGOLIA
13.0
12.5
12.0
1998
2000
2002
2004
2006
Year
Figure 2. Annual changes in growing season NDVI, growing season precipitation (mm) and growing season temperature (° C) for eight different
vegetation types during 1998–2007 in Inner Mongolia, China.
in Inner Mongolia for cultivated vegetation, marshes,
shrubs, steppes, meadows, deserts, coniferous forests and
broadleaf forests.
The mean growing season NDVI did not change significantly. There was a moderate increase in marshes,
coniferous forests, broadleaf forests and cultivated vegetation (r = 0.59, P = 0.072; r = 0.658, P = 0.037; r =
Copyright  2012 Royal Meteorological Society
0.5, P = 0.142; r = 0.31, P = 0.383; respectively), and
a weak increase in desert vegetation (r = 0.114, P =
0.753). The mean growing season NDVI of shrubs and
meadows both decreased weakly (r = −0.09, P = 0.807
and r = −0.029, P = 0.938; respectively), whereas the
NDVI of steppes decrease moderately (r = −0.35, P =
0.319). All mean growing season precipitation conditions
Int. J. Climatol. 33: 1696–1706 (2013)
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X. W. CHUAI et al.
during 1998–2007 decreased nonsignificantly with P >
0.05: cultivated vegetation (r = −0.26, P = 0.474),
marshes (r = −0.413, P = 0.235), shrubs (r = −0.347,
P = 0.326), steppes (r = −0.56, P = 0.096), meadows
(r = −0.384, P = 0.274), deserts (r = −0.312, P =
0.381), coniferous forests (r = −0.19, P = 0.6) and
broadleaf forests (r = −0.497, P = 0.142). All the mean
growing season temperature conditions during 1998–
2007 increased significantly for cultivated vegetation,
shrubs, steppes, meadows and desert vegetation, whose
all P -values can meet 0.05 significant test, but the
increase trend is not significant for marshes (r =
0.444, P = 0.198), coniferous forests (r = 0.357, P =
0.312) and broadleaf forests (r = 0.493, P = 0.147),
respectively. The weak decreasing precipitation and significant increasing temperature trends are consistent
with the findings of Ding and Chen (2008) and Gao
et al. (2009), who analysed temperature and precipitation
changes during the last 47 and 50 years, respectively, in
Inner Mongolia.
Figure 2 shows that the NDVI fluctuations corresponded well with those of precipitation for cultivated
vegetation, shrubs, steppes, meadows and desert vegetation. For cultivated vegetation, growing season precipitation was relatively high in 1998 and 2003, and
low in 1999, 2000 and 2001, and coincided with peaks
and troughs in growing season NDVI. For shrubs, the
growing season precipitation maximum in 1998 and
2003 and minimum in 2000, 2001 and 2007 corresponded with maximum and minimum NDVI, respectively. For steppes, peak NDVI and precipitation values were reached in 1998 and 2003. For meadows, all
low NDVI and precipitation values appeared in 2000,
2001 and 2007, and peak values appeared in 1998. For
desert vegetation, peak NDVI and precipitation values
both appeared in 1998, 2003 and 2007 and low values
appeared in 2001. The NDVI and precipitation patterns
for marshes and coniferous forests were reversed, as indicated in Figure 2. For marshes, maximum NDVI values
occurred in 1999, 2006 and 2007, but the values of precipitation in these years were much lower than those in
other years. Minimum values occurred in 1998 and 2003,
but values of precipitation in those 2 years reached their
maximum. Similarly, for coniferous forests, low NDVI
values corresponded with high precipitation values in
1998 and 2008, and high NDVI values corresponded with
low precipitation values in 1999, 2002, 2005, 2006 and
2007. Fluctuations in temperature corresponded well with
NDVI for marshes and coniferous forests except in 1998.
For marshes, high NDVI and temperature values occurred
in 2000, 2002, 2004, 2005 and 2007 and low values
occurred in 2003 and 2006. For coniferous forests, high
NDVI and temperature values occurred in 2000, 2001,
2002, 2004, 2005 and 2007 and low values occurred in
2003. For broadleaf forests, fluctuations in precipitation
and temperature did not obviously correspond with fluctuations in NDVI.
Copyright  2012 Royal Meteorological Society
3.2. Annual correlations between growing season
NDVI, precipitation and temperature
It can be seen from the above analysis that both precipitation and temperature can affect NDVI, and the effect
of precipitation seems more significant than that of temperature. The effects of precipitation and temperature on
NDVI varied for different vegetation types; therefore,
we conducted separate correlation analyses for each vegetation type to compare the differences between them
(Table I).
Table I shows that the correlation coefficients for growing season NDVI and precipitation were all much higher
than those for NDVI and temperature. For cultivated
vegetation, shrubs and steppes, growing season NDVI
correlated strongly with precipitation (r = 0.638, P =
0.047; r = 0.722, P = 0.018; r = 0.706, P = 0.022,
respectively). The correlations were moderate for meadows and desert vegetation (r = 0.554, P = 0.097; r =
0.51, P = 0.132, respectively). However, for marshes
and coniferous forests, there were strong negative correlation coefficients for growing season NDVI and precipitation (r = −0.791, P = 0.006 and r = −0.728, P =
0.017, respectively). For broadleaf forests, the correlation between growing season NDVI and precipitation
was not significant (r = −0.303, P = 0.395). The correlations between growing season NDVI and temperature were weak for all vegetation types when compared
with precipitation. As indicated in Table I, the correlation coefficients were positive for cultivated vegetation,
marshes, shrubs, desert vegetation and coniferous forests,
but negative for others.
The different correlations can be explained by their
different temporal and spatial growth environments, and
differences in their degree of human disturbance. Shrubs,
steppes, meadows and desert vegetation are mainly
located in dry climate districts where precipitation is
the limiting factor for vegetation growth. Higher temperatures accelerate the evaporation process, which leads
Table I. Annual correlation coefficients (r) and two-tailed
significance test values (P ) between growing season NDVI,
precipitation and temperature for different vegetation types
during 1998–2007 in Inner Mongolia, China.
Vegetation type
Cultivated vegetation
Marshes
Shrubs
Steppes
Meadows
Desert vegetation
Coniferous forests
Broadleaf forests
NDVI and
temperature
NDVI and
precipitation
r
P
r
P
0.390
0.154
0.001
−0.245
−0.041
−0.003
0.250
0.154
0.265
0.672
0.998
0.495
0.911
0.992
0.487
0.671
0.638∗
−0.791∗∗
0.722∗
0.706∗
0.554
0.51
−0.728∗
−0.303
0.047
0.006
0.018
0.022
0.097
0.132
0.017
0.395
Significant at ∗ P = 0.05 and
∗∗
P = 0.01 levels.
Int. J. Climatol. 33: 1696–1706 (2013)
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CHANGES IN NDVI, TEMPERATURE AND PRECIPITATION IN INNER MONGOLIA
to water scarcity and prohibits vegetation growth (Jobbagy et al., 2002; Chen and Wang, 2009; Li et al., 2009;
Xu et al., 2010). There was a significant increase in
temperature and a weak decrease in precipitation during 1998–2007, as discussed above, which led to weak
decreases in NDVI for shrubs, steppes and meadows.
The weak increase in NDVI for desert vegetation may
be attributed to the effects of the Three-North Shelterbelt
Construction and the Returning Farmland to Forest and
Grassland project, which protected the growth environment (Yin et al., 2011). However, for marshes, coniferous forests and broadleaf forests, which are located in
northeast Inner Mongolia under conditions of abundant
precipitation but low temperatures, temperature increases
are beneficial for vegetation growth (Piao et al., 2006).
Marshes grow in rich water environments, and two much
water will prohibit growth (Luo et al., 2009; Zhang
et al., 2011). Precipitation will also decrease temperatures, which is not good for vegetation growth in high,
cold northeast Inner Mongolia. Therefore, the moderate
increase in temperature and weak decrease in precipitation led to a moderate NDVI increase for marshes,
coniferous forests and broadleaf forests. However, there
are also studies that show positive correlations between
precipitation and NDVI for coniferous forests, and negative correlations between NDVI and temperature for
broadleaf forests (Guo et al., 2007; Luo et al., 2009);
these discrepant results may be caused by different study
areas with different climate environments and degrees
of human disturbance. Cultivated vegetation is mainly
located in the east of Inner Mongolia where there is better water and heat conditions, but because the growth of
cultivated vegetation requires more water and heat compared with other vegetation types, adequate precipitation
and temperature is crucial, and especially the demand
for precipitation, which is consistent with the findings of
Luo et al. (2009) who conducted a study of cultivated
vegetation in northeast China.
3.3. Seasonal correlations between NDVI,
precipitation and temperature
The effect of climate factors on NDVI may differ according to growth phase (Piao et al., 2006). Therefore, to
recognize seasonal differences, we performed correlation
analyses between NDVI, precipitation and temperature in
spring (April–May), summer (June–August) and autumn
(September–October) for different vegetation types. In
addition, because some studies have shown an obvious
lag-time effect (Luo et al., 2009; Cui and Shi, 2010; Xu
et al., 2010), we performed correlation analyses between
summer NDVI and spring precipitation and temperature,
and between autumn NDVI and summer precipitation and
temperature.
Table II shows that in spring, NDVI correlated positively with temperature for all vegetation types. NDVI
also correlated positively with precipitation for all vegetation types with the exceptions of marshes and coniferous
forests. These findings indicate that in spring, the warmer
Copyright  2012 Royal Meteorological Society
environment and increased precipitation in Inner Mongolia are critical for the growth of most of the vegetation
types. For marshes and coniferous forests, higher temperatures and less precipitation are beneficial for vegetation
growth in spring, because they are located under conditions of abundant precipitation, and more precipitation
can decrease temperatures, as discussed above. For cultivated vegetation, desert vegetation and broadleaf forests,
the effects of temperature and precipitation on NVDI
seem equivalent. For shrubs, steppes and meadows, the
positive effects of precipitation (r = 0.561, P = 0.091;
r = 0.561, P = 0.092; r = 0.555, P = 0.096, respectively) on NDVI were more obvious than the effects
of temperature (r = 0.241, P = 503; r = 0.365, P =
0.300; r = 0.398, P = 0.254, respectively).
Table III shows the results of correlation analyses
between NDVI, temperature and precipitation in summer,
and between summer NDVI, and temperature and precipitation in spring for different vegetation types. It shows
that for cultivated vegetation, summer NDVI correlated
positively with summer precipitation, but negatively with
temperature. This can be explained by the fact that under
the background of global warming, the heat conditions
are sufficient for the growth of most cultivated vegetation planted in Inner Mongolia, high temperatures lead
to evaporation, which prohibits growth. NDVI correlated positively and more strongly with precipitation for
shrubs, steppes and desert vegetation in summer than
that in spring (r = 0.682, P = 0.030; r = 0.638, P =
0.047; r = 0.81, P = 0.004, respectively), which indicates that precipitation in summer is not abundant and
is still the limiting factor for the growth of these vegetation types. However, unlike in spring, NDVI had a strong
negative correlation with temperature (r = −0.728, P =
0.017; r = −0.692, P = 0.027; r = −0.711, P = 0.021,
respectively). This can be explained by the fact that
the much warmer environment in summer provided
enough heat for vegetation growth and that increased
temperatures accelerate water evaporation and restrict
vegetation growth (Jobbagy et al., 2002; Piao et al.,
2006). For meadows, NDVI correlated weakly with temperature (r = −0.15, P = 0.679) and precipitation (r =
Table II. Correlation coefficients (r) and two-tailed significance
test values (P ) for NDVI, precipitation and temperature, for
different vegetation types in spring in Inner Mongolia, China.
Spring
Vegetation type
Cultivated vegetation
Marshes
Shrubs
Steppes
Meadows
Desert vegetation
Coniferous forests
Broadleaf forests
NDVI and
temperature
NDVI and
precipitation
r
P
r
P
0.426
0.485
0.241
0.365
0.398
0.527
0.595
0.436
0.219
0.155
0.503
0.300
0.254
0.118
0.07
0.208
0.431
−0.329
0.561
0.561
0.555
0.547
−0.543
0.422
0.214
0.353
0.091
0.092
0.096
0.098
0.105
0.225
Int. J. Climatol. 33: 1696–1706 (2013)
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X. W. CHUAI et al.
0.002, P = 0.995), which indicates that the effect of temperature on NDVI was minimal and that the summergrowth demand for precipitation is low for meadows (Luo
et al., 2009). Compared with desert vegetation, meadows
may have a much higher precipitation demand, but meadows in Inner Mongolia are located under conditions with
much more precipitation than desert vegetation, so there
is a strong and positive correlation coefficient between
NDVI and precipitation for desert vegetation but not for
meadows in summer. For marshes, broadleaf forests and
coniferous forests, as in spring, NDVI also correlated
positively with temperature and negatively with precipitation in summer. This can be explained by the fact that
precipitation for the three vegetation types is abundant
in summer, and increases in precipitation will decrease
temperatures, which is not good for vegetation growth.
What is more, according to our calculation, NDVI of
the three vegetation types are higher than that of others
(Figure 2); they have high biomass in Inner Mongolia,
and in the mid-growing season of summer, NDVI values
will reach maximum compared with those in other seasons (Sun et al., 2010; Xu et al., 2010), which is close to
saturation, and the apparent saturation of NDVI in high
biomass region during the mid-growing season could also
have contributed to this negative relationship (Gitelson,
2004).
Table IV shows the correlations between NDVI, temperature and precipitation in autumn, and the lag-time
effect between autumn NDVI, and temperature and precipitation in summer for different vegetation types. It
shows that in autumn, the effect of precipitation on
NDVI weakened compared with that in spring and
summer for cultivated vegetation, steppes and desert
vegetation, and for shrubs, the direction of the effect
changed from positive to negative. This indicated that
the demand for precipitation by these vegetation types
decreased, and that precipitation does not limit vegetation growth in autumn. However, for meadows, precipitation correlated with NDVI more strongly in autumn
(r = 0.389, P = 0.267) than in summer (r = 0.002, P =
0.995). Piao et al. (2006) found that the correlation coefficient between NDVI and precipitation reached its
maximum at about 200 mm annual precipitation for temperate grassland (including meadows) during the growing season. Too little or too much precipitation both
failed to raise NDVI, so the low demand for precipitation and the decrease of precipitation in autumn led
to the correlation coefficient in autumn being much
higher than that in summer. For marshes and coniferous
forests, the correlation coefficient for NDVI and precipitation was still negative, but for broadleaf forests, the
negative effect of precipitation in summer on autumn
NDVI decreased and became weakly positive (r =
0.012, P = 0.973) because of the decreased amount of
precipitation in autumn. For shrubs, steppes and meadows, the effects of temperature on NDVI were positive (r = 0.455, P = 0.187; r = 0.458, P = 0.183; r =
0.585, P = 0.076, respectively), as in spring, which indicated that temperature can boost vegetation growth. This
may be because temperatures decreased in autumn, and
evaporation was weak, and not sufficient enough to cause
water scarcity. For broadleaf forests, the correlation was
stronger (r = 0.431, P = 0.213) in autumn than in summer (r = 0.239, P = 0.353). The correlation coefficients
for the other vegetation types all decreased and did not
seem as obvious as those found in spring and summer.
Table III shows the lag-time effect of spring temperature on summer NDVI was not significant (P > 0.05)
for any vegetation types except cultivated vegetation
(r = 0.696, P = 0.025), with the r-values all lower than
those for the effects of summer NDVI and summer temperature. But there was a significant lag-time effect of
spring precipitation on summer NDVI, especially for
Table III. Correlation coefficients and two-tailed significance test values between NDVI, precipitation and temperature for different
vegetation types in summer.
Summer
Vegetation type
Cultivated vegetation
Marshes
Shrubs
Steppes
Meadows
Desert vegetation
Coniferous forests
Broadleaf forests
NDVI and
temperature
NDVI and
precipitation
NDVI and previous
season temperature
NDVI and previous
season precipitation
r
P
r
P
r−1
P−1
r−1
P−1
−0.681∗
0.609
−0.728∗
−0.692∗
−0.15
−0.711∗
0.598
0.329
0.03
0.062
0.017
0.027
0.679
0.021
0.068
0.353
0.332
−0.71∗
0.682∗
0.638∗
0.002
0.81∗∗
−0.571
−0.654∗
0.348
0.021
0.03
0.047
0.995
0.004
0.085
0.04
0.696∗
0.261
0.47
0.252
−0.085
0.453
0.307
0.502
0.025
0.446
0.171
0.482
0.816
0.188
0.389
0.117
0.522
−0.551
0.628
0.71∗
0.415
0.554
−0.527
0.747∗
0.122
0.099
0.052
0.021
0.233
0.096
0.118
0.015
Significant at
∗ P = 0.05 and
∗∗ P = 0.01 levels.
r: correlation coefficient between NDVI and temperature or precipitation in the same season; r−1 : correlation coefficient between NDVI and
temperature or precipitation in the previous season; P : two-tailed significance test values between NDVI and temperature or precipitation in the
same season; P−1 : two-tailed significance test values between NDVI and temperature or precipitation in the previous season.
Copyright  2012 Royal Meteorological Society
Int. J. Climatol. 33: 1696–1706 (2013)
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Table IV. Correlation coefficients and two-tailed significance test values between NDVI, precipitation and temperature for different
vegetation types in autumn.
Autumn
Vegetation type
Cultivated vegetation
Marshes
Shrubs
Steppes
Meadows
Desert vegetation
Coniferous forests
Broadleaf forests
NDVI and
temperature
NDVI and
precipitation
NDVI and previous
season temperature
NDVI and previous
season precipitation
r
P
r
P
r−1
P−1
r−1
P−1
0.175
0.108
0.455
0.458
0.585
−0.292
0.052
0.431
0.629
0.767
0.187
0.183
0.076
0.414
0.886
0.213
0.336
−0.286
−0.116
0.446
0.389
0.411
−0.596
0.012
0.343
0.422
0.749
0.196
0.267
0.238
0.069
0.973
0.427
0.061
−0.523
−0.721∗
−0.548
−0.757∗
0.208
0.096
0.218
0.866
0.121
0.019
0.101
0.011
0.564
0.792
0.231
−0.302
0.615
0.78∗∗
0.533
0.566
−0.398
−0.156
0.52
0.396
0.059
0.008
0.113
0.088
0.255
0.667
Significant at
∗ P = 0.05 and
∗∗ P = 0.01 levels.
r: correlation coefficient between NDVI and temperature or precipitation in the same season; r−1 : correlation coefficient between NDVI and
temperature or precipitation in the previous season; P : two-tailed significance test values between NDVI and temperature or precipitation in the
same season; P−1 : two-tailed significance test values between NDVI and temperature or precipitation in the previous season.
cultivated vegetation, steppes and meadows, which had
higher r-values for this relationship than for that of summer NDVI and summer precipitation. Unlike summer
precipitation, spring precipitation correlated positively
and strongly with summer NDVI for broadleaf forests
(r = 0.747, P = 0.015), which indicated that spring is
the limiting growing phase for broadleaf forests and
that vegetation growth in spring can greatly affect
growth conditions in summer. Table IV shows that
there also exists lag-time effect of summer temperature on autumn NDVI, and that the effect was significant for steppes and desert vegetation (r = −0.721, P =
0.019; r = −0.757, P = 0.011, respectively), moderate
for shrubs, meadows and cultivated vegetation (r =
−0.523, P = 0.121; r = −0.548, P = 0.101; r = 0.427,
P = 0.218; respectively), and weak for marshes, coniferous forests and broadleaf forests, which was not obvious (r = 0.061, P = 0.866; r = 0.208, P = 0.564; r =
0.096, P = 0.792; respectively). The lag-time effect
of summer precipitation on autumn NDVI was even
more obvious than the effect of autumn precipitation, with higher r-values for most of the vegetation
types. And it was significant for steppes (r = 0.78, P =
0.008), moderate for shrubs, meadows and desert vegetation (r = 0.615, P = 0.059; r = 0.533, P = 0.113; r =
0.566, P = 0.088; respectively), but the effect is weak
for cultivated vegetation (r = 0.231, P = 0.52), marshes
(r = −0.302, P = 0.396), coniferous forests (r =
−0.398, P = 0.255) and broadleaf forests, which was
not obvious (r = −0.156, P = 0.667). Temporal lags in
vegetation response to climate change, which has been
widely observed in other regions (Braswell et al., 1997;
Los et al., 2001; Wang et al., 2003; Piao et al., 2006).
However, the range of these lags is likely to vary spatially and temporally, the 2–3 months lag time of NDVI
response to temperature and precipitation can be tested
in many studies (Piao et al., 2006; Li et al., 2007), and
Copyright  2012 Royal Meteorological Society
results of their studies are very similar to those of our
study.
As discussed above, the correlations between NDVI,
temperature and precipitation differed according to vegetation type and season. For cultivated vegetation, temperature and precipitation both had positive effects on
NDVI in spring and autumn, and the effects of temperature and precipitation were equivalent in spring. The
effect of temperature was negative and more obvious
than precipitation in summer, but in autumn the effect
of temperature was not obvious as that of precipitation.
The previous season’s temperature and precipitation both
had lag-time effects on NDVI in summer and autumn,
and especially obvious for the effect of spring temperature on summer NDVI, significant at P < 0.05 level. For
marshes and coniferous forests, temperature had positive
and precipitation negative effects on NDVI during the
whole growing season. The positive effect of temperature
was moderate in spring, more stronger in summer than
spring, but weak in autumn. The negative effect of precipitation was also the most obvious in summer, and similar
in spring and autumn. The lag-time effect was the most
obvious for spring precipitation and summer NDVI but
still meet significant test at P < 0.05 level. For shrubs,
steppes and meadows, temperature had a moderate positive effect on NDVI in spring and autumn, but negative
effect in summer. Precipitation had a positive effect on
NDVI during the whole growing season for steppes, and
it was significant at P < 0.05 level in summer. The effect
of precipitation on NDVI decreased and became weakly
negative in autumn for shrubs. Precipitation had moderate positive effect on NDVI in spring, but the effect
was weak in summer and autumn for meadows. There
exists lag-time effect of the previous season’s temperature and precipitation on NDVI both in summer and
in autumn, and the effect was more obvious in autumn
than that in summer and more obvious for precipitation
Int. J. Climatol. 33: 1696–1706 (2013)
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X. W. CHUAI et al.
than temperature in general. For desert vegetation, precipitation is critical to boost vegetation growth, especially
in summer. Increased temperatures in spring are beneficial for vegetation growth, but in summer and autumn,
it will prohibit vegetation growth, especially in summer.
A lag-time effect of the previous season’s precipitation
and temperature on NDVI was moderate both in summer
and in autumn, and in autumn the lag-time effect was
even stronger than the effects brought by autumn temperature and precipitation, especially for the significant
negative effect of summer temperature on autumn NDVI
(r = −0.757, P = 0.011). For broadleaf forests, temperature had a positive effect on NDVI during the whole
growing season, although this was not obvious. The effect
of precipitation was positive in spring and autumn but
negative in summer. A lag-time effect of the previous
season’s precipitation and temperature on NDVI was only
obvious in summer, and its effect was even stronger than
that of spring temperature and precipitation.
4.
Conclusions
Correlation analyses between NDVI and climate variables are powerful tools for probing ecosystem function
responses to global climate change (Potter and Brooks,
1998; Piao et al., 2006).This study analysed NDVI, temperature and precipitation changes and investigated the
correlations between NDVI, temperature and precipitation for eight different vegetation types during the growing seasons (April–October) of the period 1998–2007
in Inner Mongolia, China. We also analysed seasonal
correlations and lag-time effects. Our main findings are
summarized below.
During the growing seasons of 1998–2007, in the
east wet, half humid monsoon climate district, average NDVI increased moderately for marshes, coniferous forests, broadleaf forests and cultivated vegetation.
However in the west arid and semi-arid climates district, average NDVI of shrubs and meadows decreased
weakly, decreased moderately for steppes, but increased
weakly for desert vegetation. The average precipitation
conditions of all vegetation types decreased slightly,
but the average temperature conditions of all vegetation
types increased and the increase is significant for shrubs,
steppes, meadows and desert vegetation in arid and semiarid climates district.
Correlation coefficients between growing season NDVI
and precipitation were high during the whole growing
season, and the NDVI of shrubs, steppes and cultivated
vegetation was more sensitive to precipitation than temperature. The effect of precipitation on NDVI was negative for marshes, coniferous forests and broadleaf forests,
but an effect was not obvious for broadleaf forests. The
effect of temperature on NDVI was not as obvious as that
of precipitation for all vegetation types during the whole
growing season. This finding is quite different from those
studies from the 1990s (Piao et al., 2006), which implies
that the sensitivity of vegetation in high, cold regions to
Copyright  2012 Royal Meteorological Society
temperature may decline under global warming conditions (Jobbagy et al., 2002).
The effects of precipitation and temperature on NDVI
varied among different vegetation types and seasons.
In spring, both temperature and precipitation correlated
with NDVI moderately, but precipitation correlated with
NDVI negatively for marshes and coniferous forests.
In summer, NDVI correlated with temperature negatively and precipitation positively for cultivated vegetation, shrubs, steppes, meadows and desert vegetation.
However, NDVI correlated positively with temperature
and negatively with precipitation for marshes, coniferous forests and broadleaf forests. In autumn, temperature
had positive effects on NDVI for all vegetation types
except deserts, and precipitation correlated with NDVI
positively for all vegetation types with the exceptions of
marshes, shrubs and coniferous forests. The differences
in the observed relationships can be explained by the fact
that the demand for heat and water varied among different vegetation types and seasons. Precipitation that is too
much or too little, and temperatures that are too high or
too low all prohibit vegetation growth. The responses to
climate change were more sensitive for shrubs, steppes,
meadows and desert vegetation.
Lag-time effects of the previous season’s precipitation
on NDVI were moderate or significant in summer for all
vegetation types. In autumn, they were only obvious for
shrubs, steppes, meadows and desert vegetation, and the
effects were negative for marshes and coniferous forests
both in summer and autumn. There were no significant
lag-time effects of spring temperature on summer NDVI
for any vegetation types except cultivated vegetation.
There exists moderate or significant lag-time effects of
the previous season’s temperature on NDVI, which were
obvious in autumn for most of the vegetation types,
with the exceptions of marshes, coniferous forests and
broadleaf forests.
The interaction effect between NDVI and climate factors is obvious. Since precipitation decreased and temperature increased more rapidly compared with the early
1990s (Piao et al., 2006; Gao et al., 2009), climate
changes during 1998–2007 in Inner Mongolia seems
more obvious in recent years. What is more, some studies
showed NDVI correlated positively with evapotranspiration in growing seasons of north Asia (Suzuki et al.,
2007). The moderate decrease NDVI for shrubs, steppes
and meadows will decrease evapotranspiration into the
atmosphere, which will make the arid and semi-arid
climates district drier. For marshes, coniferous forests,
broadleaf forests and cultivated vegetation covered district, the moderate NDVI increase will accelerate the
evaporation process and may make local atmosphere wetter. For desert vegetation, although its NDVI is weakly
increased, due to its low vegetation cover density and
biomass, the effect to evapotranspiration is negligible.
On the whole, Inner Mongolia is becoming warmer, the
west arid and semi-arid climates district may become
drier, air of the east wet, half humid monsoon climate
district may become more moist, but since most of the
Int. J. Climatol. 33: 1696–1706 (2013)
CHANGES IN NDVI, TEMPERATURE AND PRECIPITATION IN INNER MONGOLIA
area in Inner Mongolia is covered with arid and semiarid climates, the ecological pressure in Inner Mongolia
is increasing.
Acknowledgements
This research was supported by the National Social Science Foundation of China, No.10ZD&M030; the Nonprofit Industry Financial Program of Ministry of Land and
Resources of China, No.200811033; a Project Funded by
the Priority Academic Program Development of Jiangsu
Higher Education Institutions (geography of Nanjing University); and The National Natural Science Foundation of
China (No.40801063, 40971104, 41101160).
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