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)

1699

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)

1700

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)

1701

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)

1702

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)

1703

CHANGES IN NDVI, TEMPERATURE AND PRECIPITATION IN INNER MONGOLIA

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)

1704

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). References Bonan GB, Levis S, Sitch S, Vertenstein M, Oleson KW. 2003. A dynamic global vegetation model for use with climate models: concepts and description of simulated vegetation dynamics. Global Change Biology 9: 1543–1566, DOI: 10.1046/j.1365–2486.2003.00681.x. Braswell BH, Schimel DS, Linder E, Moore B. 1997. The response of global terrestrial ecosystems to interannual temperature variability. Science 278: 870–873, DOI: 10.1126/science.278.5339.870. Camberlin P, Martiny N, Philippon N, Richard Y. 2007. Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sensing Environment 106: 199–216, DOI: 10.1016/j.rse.2006.08.009. Chen XQ, Wang H. 2009. Spatial and temporal variations of vegetation belts and vegetation cover degrees in Inner Mongolia from 1982 to 2003. Acta Geographica Sinica 64(1): 84–94, DOI: 0375–5444 (2009)64:1 < 84:12NNMG>2.0.TX;2–5 (in Chinese). Crucifix M, Betts RA, Cox PM. 2005. Vegetation and climate variability: a GCM modelling study. Climate Dynamics 24: 457–467, DOI: 10.1007/s00382-004-0504-z. Cui L L, Shi J. 2010. Temporal and spatial response of vegetation NDVI to temperature and precipitation in eastern China. Journal of Geographical Sciences 20(2): 163–176, DOI: 10.1007/s11442-0100163-4. Ding XH, Chen YT. 2008. The characteristics of precipitation and temperature in Inner Mongolia for the last 50 years. Meteorology Journal of Inner Mongolia 2: 17–19 (in Chinese). Editorial Board of Vegetation Map of China. 2001. Vegetation Atlas of China. Science Press: Beijing. Fang JY, Piao SL, He JS, Ma WH. 2004. Increasing terrestrial vegetation activity in China, 1982–1999. Science in China (Series C) 47: 229–240, DOI: 10.1360/03yc0068. Fung T, Siu W. 2000. Environmental quality and its changes, an analysis using NDVI. International Journal of Remote Sensing 21(5): 1011–1024, DOI: 10.1080/014311600210407. Gao T, Xiao S J, Wu L. 2009. Temporal-spatial characteristics of precipitation and temperature in Inner Mongolia for the last 47 Years (1961–2007). Meteorology Journal of Inner Mongolia 1: 3–7 (in Chinese). Gitelson AA. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology 161: 165–173, DOI: 10.1078/0176-1617-01176. Guo ZX, Wang ZM, Song KS, Zhang B, Li F, Liu DW. 2007. Correlation analysis of forest vegetation NDVI and water/thermal factors in the Northeast China from 1982 to 2003. Chinese Journal of Ecology 26(12): 1930–1936 (in Chinese). Guo N, Zhu YJ, Wang JM, Deng CP. 2008. The relationship between NDVI and climate elements for 22 years in different vegetation areas of Northeast China. Chinese Journal of Plant Ecology 32(2): 319–327 (in Chinese). Ichii K, Kawabrata A, Yamaguchi Y. 2002. Global correlation analysis for NDVI and climate variables and NDVI trends: 1988–1990. International journal of Remote Sensing 23: 3873–3878, DOI: 10.1080/01431160110119416. Ji L, Peters AJ. 2004. A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Copyright  2012 Royal Meteorological Society

1705

Great Plains. International journal of Remote Sensing 25: 297–311, DOI: 10.1080/0143116031000102548. Jobbagy EG, Sala OE, Paruelo JM. 2002. Patterns and controls of primary production in the Patagonian steppe: a remote sensing approach. Ecology 83: 307–319, DOI: 10.2307/2680015. Li X, Li XB, Chen YH, Ying G. 2007. Temporal responses of vegetation to climate variables in temperate steppe of northern China. Acta Phytoecologica Sinica 31(6): 1054–1062, DOI: 1005-264X (2007)31 : 62.0.TX;2–5 (in Chinese). Li XH, Shi QD, Chang SL, Qi JG. 2009. Response of NDVI to climate variability in arid area of northwestern China from 1981 to 2001. Journal of Desert Research 29(5): 989–994, DOI: 1000-694X (2009)29 : 52.0.TX;2-X (in Chinese). Liu CL, Fan RH, Wu JJ, Yan F. 2009. Temporal lag of grassland vegetation growth response to precipitation in Xilinguolemeng. Arid Land Geography 32(4): 512–517, DOI: 1000–6060 (2009)32 : 42.0.TX;2-X (in Chinese). Los SO, Collatz GJ, Bounoua L, Sellers PJ, Tucker CJ. 2001Global interannual variations in sea surface temperature and land surface vegetation, air temperature, and precipitation.. Journal of Climate 14: 1535–1549, DOI: 10.1175/1520-0442 (2001)0142.0.CO;2. Luo L, Wang ZM, Song KS, Zhang B, Liu DW, Ren CY, Zhang SM. 2009. Research on the correlation between NDVI and climatic factors of different vegetations in the northeast China. Xibei Zhiwu Xuebao 29(4): 800–808. Meng QH, Fei M, Han S, Ying YH. 2011a. Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region. International Journal of Applied Earth Observation and Geoinformation 13: 24–33, DOI: 10.1016/j.jag.2010.06.003. Meng M, Ni J, Zong MJ. 2011b. Impacts of changes in climate variability on regional vegetation in China: NDVI-based analysis from 1982 to 2000. Ecological Research 26: 421–428, DOI: 10.1007/s11284-011-0801-z. Nemani R R, Keeling CD, Hashimoto H, Jolly WM, Piper SC, Tucker CJ, Myneni RB, Running SW. 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300(5625): 1560–1563, DOI: 10.1126/science.1082750. Ni J, Harrison S P, Prentice IC, Kutzbach JE, Sitch S. 2006. Impact of climate variability on present and Holocene vegetation: a model-based study. Ecoogical Modeling 191(3–4): 469–486, DOI: 10.1016/j.ecolmodel.2005.05.019. Piao SL, Fang JY, Ji W, Guo H, Ke JH, Tao S. 2004. Variation in a satellite-based vegetation index in relation to climate in China. Journal of Vegetation Science 15: 219–226, DOI: 10.1658/11009233(2004)015[0219:VIASVI]2.0.CO;2. Piao SL, Mohammat A, Fang JY, Cai Q, Feng JM. 2006. NDVIbased increase in growth of temperate grasslands and its responses to climate changes in China. Global Environmental Change 16: 340–348, DOI: 10.1016/j.gloenvcha.2006.02.002. Piao SL, Ciais P, Friedlingstein P, Peylin P, Reichstein M, Luyssaert S, Margolis H, Fang JY, Barr A, Chen AP, Grelle A, Hollinger DY, Laurila T, Lindroth A, Richardson AD, Vesala T. 2008. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451: 49–53, DOI: 10.1038/nature06444. Potter CS, Brooks V. 1998. Global analysis of empirical relations between annual climate and seasonality of NDVI. International Journal of Remote Sensing 15: 2921–2948, DOI: 10.1080/014311698214352. Qiu H, Cao M. 2011. Spatial and temporal variations in vegetation cover in China based on SPOT vegetation data. Resources Science DOI: CNKI:SUN:ZRZY.0.2011-02-024. Qu CP, Guan DX, Wang AZ, Jin CJ, Wu JB, Wang JJ, Ni P, Yuan FH. 2009. Relationships between Horqin meadow NDVI and meteorological factors. Chinese Journal of Applied Ecology 20(1): 58–64, DOI: 1001–9332(2009)20:1 < 58:KEQCDC>2.0.TX;2-G (in Chinese). Rasmusen MS. 1998. Developing simple, operational consistent NDVI-Vegetation models by applying environmental and climatic information: Part II. Crop yield assessment. International Journal of Remote Sensing 19: 119–139, DOI: 10.1080/014311698216459. Roerink GJ, Menenti M, Soepboer W, Su Z. 2003. Assessment of climate impact on vegetation dynamics by using remote sensing. Physics and Chemistry of the Earth 28: 103–109, DOI: 10.1016/S1474-7065 (03) 00011-1. Rouse JW Jr, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the great plains with ERTS. Presented at Int. J. Climatol. 33: 1696–1706 (2013)

1706

X. W. CHUAI et al.

Third Earth Resources Technology Satellite-1 Symposium, NASA, Washington, DC, 309–317. Schimel DS, House JI, Hibbard KA, Bousquet P, Ciais P, Peylin P, Braswell BH, Apps MJ, Baker D, Bondeau A, Canadell J, Churkina G, Cramer W, Denning AS, Field CB, Friedlingstein P, Goodale C, Heimann M, Houghton RA, Melillo JM, Moore B, Murdiyarso D, Noble I, Pacala SW, Prentice IC, Raupach MR, Rayner P J, Scholes RJ, Steffen WL, Wirth C. 2001. Recent patterns and mechanism of carbon exchange by terrestrial ecosystems. Nature 414: 169–172, DOI: 10.1038/35102500. Schnur MT, Hongjie X, Wang X. 2010. Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA. Ecological Informatics 5: 400–409, DOI: 10.1016/j.ecoinf.2010.05.001. Schultz PA, Halpert MS. 1995Global analysis of the relationships among a vegetation index, precipitation, and land surface temperature.. International Journal of Remote Sensing 16: 2755–2777. Sivakumar MVK, Das HP, Brunini O. 2005. Impacts of present and future climate variability and change on agriculture and forestry in the arid and semi-arid tropics. Climatic Change 70: 31–72, DOI: 10.1007/s10584-005-5937-9. Sun YL, Guo P, Yan XD, Zhao TB. 2010. Dynamics of vegetation cover and its relationship with climate change and human activities in Inner Mongolia. Journal of Natural Resources 25(3): 407–414, DOI: 1000–3037 (2010)25:3 < 407:NMGZBF>2.0.TX;2-J (in Chinese). Suzuki R, Masuda K, Dye DG. 2007. Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVI of northern Asia. Remote Sensing Environment 106: 387–398, DOI: 10.1016/j.rse.2006.10.016. Wang J, Rich PM, Price KP. 2003. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International Journal of Remote Sensing 11: 2345–2364, DOI: 10.1080/01431160210154812. Wardlow BD, Egbert SL. 2008. Large-area crop mapping using timeseries MODIS 250m NDVI data: an assessment for the U.S. Central Great Plains. Remote Sensing of Environment 112(3): 1096–1116, DOI: 10.1016/j.rse.2007.07.019. Weiss JL, Gutzler DS, Coonrod JEA, Dahm CN. 2004. Seasonal and inter-annual relationships between vegetation and climate in central New Mexico, USA. Journal of Arid Environments 57(4): 507–534, DOI: 10.1016/S0140-1963 (03) 00113 − 7. Wen G, Fu CB. 2000. The large scale features of the seasonal phenological response to the monsoon climate in East China:

Copyright  2012 Royal Meteorological Society

multi-year average results. Chinese Journal of Atmospheric Sciences 24(5): 676–682 (in Chinese). Xia L, Liu YM, Ke CQ. 2008. Study on the dynamic changes of vegetation in the Loess Plateau based on data from SPOT4. Remote Sensing Technology and Application 23(1): 67–71, DOI: 10040323(2008)23:1 < 67:JYSSJD>2.0.TX;2–3 (in Chinese). Xu X, Li XB, Liang HW, Huang LM. 2010. Change in vegetation coverage and its relationships with climatic factors in temperate steppe, Inner Mongolia. Acta Ecologica Sinica 30(14): 3733–3743, DOI: 1000-0933(2010)30:14 < 3733:NMGWDC>2.0.TX;2-C (in Chinese). Yan XY, Dong WJ, He Y. 2008. Analysis of the suitability from different remote sensing data on the monitoring of vegetation change in Ruoergai marsh. Remote Sensing Technology and Application 23(3): 300–304, DOI: 1004-0323(2008)23:3 < 300:BTCGQS>2.0.TX; 2–5 (in Chinese). Yin H, Li ZG, Wang YL, Cai F. 2011. Assessment of desertification using time series analysis of hyper-temporal vegetation indicator in Inner Mongolia. Acta Geographica Sinica 66(5): 653–661, DOI: 0375–5444(2011)66:05 < 653:JYSJXL>2.0.TX;2-N (in Chinese). Zhang XX, Ge QS, Zheng JY. 2005. Impacts and lags of global warming on vegetation in Beijing for the last 50 years based on remotely sensed data and phonological information. Chinese Journal of Ecology 24(2): 123–130 (in Chinese). Zhang GL, Xu XL, Zhou CP, Zhang HB, Ouyang H. 2011a. Responses of grassland vegetation to climatic variations on different temporal scales in Hulun Buir Grassland in the past 30 years. Journal of Geographical Sciences 21(4): 634–650, DOI: 10.1007/s11442-0110869-y. Zhang YD, Zhang XH, Liu SR. 2011b.. Correlation analysis on normalized difference vegetation index (NDVI) of different vegetations and climatic factors in Southwest China. Chinese Journal of Applied Ecology 22(2): 323–330 (in Chinese). Zhao YX, Wang CY, Wang S L, Tibig L V, 2005. Impacts of present and future climate variability on agriculture and forestry in the humid and sub-humid tropics. Climatic Change 70: 73–116, DOI: 10.1007/s10584-005-005-5938-8. Zhou LM, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB. 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research-Atmospheres 106(D17): 20069–20083, DOI: 10.1029/2000JD000115.

Int. J. Climatol. 33: 1696–1706 (2013)