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Journal of Geographical Sciences >

2018 , Vol. 28 >Issue 5: 595 - 610

DOI: https://doi.org/10.1007/s11442-018-1493-x

Research Articles

Analyzing vegetation dynamic trend on the Mongolian Plateau based on the Hurst exponent and influencing factors from 1982-2013

  • TONG Siqin , 1, 2, 3 ,
  • ZHANG Jiquan , 1, 2 ,
  • BAO Yuhai 3, 4 ,
  • LAI Quan 1, 3, 4 ,
  • LIAN Xiao 5 ,
  • LI Na 1 ,
  • BAO Yongbin 1
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  • 1. School of Environment, Northeast Normal University, Changchun 130024, China
  • 2. Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China
  • 3. College of Geography, Inner Mongolia Normal University, Hohhot 010022, China
  • 4. Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Inner Mongolia Normal University, Hohhot 010022, China
  • 5. Graduate School of Life and Environmental Sciences, Tsukuba University, Ibaraki 305, Japan

Author: Tong Siqin (1991-), PhD, specialized in vegetation dynamic change, long-term climate change, remote sensing and GIS. E-mail:

*Corresponding author: Zhang Jiquan (1965-), Professor, E-mail:

Received date: 2017-04-01

  Accepted date: 2017-09-28

  Online published: 2018-03-30

Supported by

National Key Technology R&D Program of China, No.2013BAK05B01, No.2013BAK05B02

National Natural Science Foundation of China, No.41571491, No.61631011

The Program of Introducing Talents of Discipline to Universities, No.B16011

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Journal of Geographical Sciences, All Rights Reserved
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Abstract

This study analyzed the spatial and temporal variations in the Normalized Difference Vegetation Index (NDVI) on the Mongolian Plateau from 1982-2013 using Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data and explored the effects of climate factors and human activities on vegetation. The results indicate that NDVI has slight upward trend in the Mongolian Plateau over the last 32 years. The area in which NDVI increased was much larger than that in which it decreased. Increased NDVI was primarily distributed in the southern part of the plateau, especially in the agro-pastoral ecotone of Inner Mongolia. Improvement in the vegetative cover is predicted for a larger area compared to that in which degradation is predicted based on Hurst exponent analysis. The NDVI-indicated vegetation growth in the Mongolian Plateau is a combined result of climate variations and human activities. Specifically, the precipitation has been the dominant factor and the recent human effort in protecting the ecological environments has left readily detectable imprints in the NDVI data series.

Key words： remote sensing; GIMMS NDVI3g; vegetation dynamic trend; Hurst exponent; residual trend analysis; Mongolian Plateau

Cite this article

TONG Siqin , ZHANG Jiquan , BAO Yuhai , LAI Quan , LIAN Xiao , LI Na , BAO Yongbin . Analyzing vegetation dynamic trend on the Mongolian Plateau based on the Hurst exponent and influencing factors from 1982-2013[J]. Journal of Geographical Sciences, 2018 , 28(5) : 595 -610 . DOI: 10.1007/s11442-018-1493-x

1 Introduction

The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) indicated that there has been a consistent increase in global temperatures for the past decade (Zhao et al., 2007). Global warming is expected to bring about a series of serious environmental and social problems, including droughts, water shortages, and species extinction. These problems are most apparent in sensitive areas, especially arid zones such as the Sahel of Africa or the ecotones of China, North America, and Australia, all of which face the challenges of desertification, food insecurity, and changing land ecology (Hulme et al., 2001; Zhao and Qiu, 2001; Seager et al., 2007; Whetton et al., 1993). Furthermore, the vulnerability and instability of arid and semi-arid regions makes them particularly sensitive to climate change through desertification, soil erosion, and other regional degradation problems (Li et al., 2006).
Climate change in arid and semi-arid regions is therefore an important aspect of global climate change research. Changes in these regions have unique characteristics besides their consistency with global climate change. For example, the Mongolian Plateau is an area sensitive to global warming (Wang et al., 2008): temperature increases on the Mongolian Plateau have been faster than the global or regional warming rates. As this plateau is in an arid and semi-arid region, the primary biome is grassland and the eco-system is fragile. Temperatures in the Mongolian Plateau have increased significantly over the past 40 years (Li et al., 2006), while decreased rainfall has aggravated drought conditions (Yatagai and Yasunari, 1995; Li and Liu, 2012). Desertification of the region has become an issue due to the simultaneous influence of climate change and human activity (Zhuo, 2007). The former impacts the vegetation environment, thus influencing vegetation growth. As a link between the soil, atmospheric, and water systems, vegetation cover can thus be used as an indicator for global climate change (Lambin and Strahler, 1994; Liu et al., 2006). Therefore, the study of vegetation changes and influencing factors on the Mongolian Plateau is important for planning sustainable development and understanding global climate change.
Remote sensing technology is an important method for studying a wide range of regional and global ecosystems. The Normalized Difference Vegetation Index (NDVI), a remote sensing product, provides a convenient method for displaying vegetation distribution and changes. Recently, changes in local NDVI and its relationship with climate change have become a focus of global climate change research (Ichii et al., 2012; Park and Sohn, 2010; Song and Ma, 2011; Piao et al., 2006). The Global Inventory Modeling and Mapping Studies (GIMMS) NDVI dataset is widely used to monitor changes in vegetation cover at regional and global scales because of its high temporal resolution, long time-series, and high data quality (Ichii et al., 2002; Song and Ma, 2011; Piao et al., 2006; Nemani et al., 2003; Jong et al., 2011).
Environmental problems on the Mongolian Plateau have become increasingly serious in recent years; however, previous studies on vegetation cover in this region have mainly focused on the response of vegetation to climate change (Zhang et al., 2009; Zhao et al., 2015; Miao et al., 2015; John et al., 2013). However, vegetation cover is affected by both climate change and human activity; the rapid expansion and intensity of human activities has already produced significant impacts on vegetation. Previous research has considered climate change almost exclusively, mostly ignoring the effects of human activity on land use and vegetation cover changes, has limited our understanding of changes in vegetation cover. Therefore, in this study we used 32 years of NDVI data (1982-2013) from the Advanced Very High Resolution Radiometer (AVHRR) in combination with Sen’s slope, Pearson correlation analysis, and residual trend analysis to determine changes in the vegetation cover on the Mongolian Plateau and to identify the effects of climate and human activities on vegetation changes. We also conducted an R/S analysis using the Hurst exponent to predict future trends in vegetation dynamics on the plateau. This study will assist decision makers in formulating relevant laws and policies and improve the overall understanding of regional and global climate and environmental changes.

2 Data and methods

2.1 Study area

The Mongolian Plateau is located in the interior of the Eurasian continent, spanning Mongolia, southern Russia, and northern China (Wei et al., 2009). In this study, we chose the central region of the Mongolian Plateau (referred to hereafter simply as the Mongolian Plateau) as the study area, covering all of Mongolia and Inner Mongolia, from 37°22′ to 53°20′N and 87°43′ to 126°04′E, with a total area of 275×104 km2. The terrain is generally mountainous in the northwest, while the Gobi desert covers the southwestern section. The central and eastern parts of the plateau are relatively flat and covered in hilly grassland. The elevation decreases gradually across the plateau from west to east, with an average elevation of about 1580 m (Figure 1a). Due to the gradual changing climate, vegetation types in the region shift from forest to steppe to desert from east to west (Figure 1b). Precipitation in the northern plateau is brought by polar air masses originating in the Arctic Ocean, while that in the southern plateau is brought by tropical air masses originating in the Pacific Ocean. Rainfall ranges from 300 mm in the north to 100 mm in the south (Figure 1c). At the same time, temperatures are lower in the north than in the south (Figure 1d).
Figure 1 Geographic characteristics of the Mongolian Plateau: Elevation (a), vegetation types (b), annual mean precipitation (c), and temperature (d)

2.2 Data sources

The GIMMS NDVI3g data have a spatial resolution of 8 km and a temporal resolution of 15 days. Monthly NDVI data were obtained from 1982-2013 using the maximum value composite (MVC) method, which eliminates the effects of solar elevation angle, cloud cover, and atmospheric interference (Holben, 1986). Since vegetation in most parts of the Mongolian Plateau shows minimal growth in winter (or is covered by snow), the growing season (April to October) was selected for this study. In the Gobi desert area, observations are affected by the ground surface, making them unreliable for describing the actual vegetation cover. Therefore, in areas with low vegetation cover or desert biomes, the annual or monthly average NDVI value serves as a threshold to exclude non-vegetation factors. An average NDVI value of 0.05 was used as the threshold in earlier studies (Myneni et al., 1997) but an annual mean of 0.1 has been used as the threshold value in more recent studies (Zhou et al., 2001; Zhou et al., 2003). For this study, any areas with NDVI values less than 0.1 were defined as “non-vegetation”; these areas were largely distributed in the desert area of the western plateau (Figure 2).
Monthly mean temperatures and monthly precipitation data were provided by the Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems. Observations were collected at 170 meteorological stations (129 in Inner Mongolia and 60 in Mongolia, Figure 1a) across the plateau. Using the station locations, the observations were then interpolated monthly at the same resolution as the NDVI to obtain spatio-temporal distributions of temperature and precipitation.

2.3 Methods

2.3.1 Sen’s slope
The Sen’s slope estimator was used to analyze dynamic trends in NDVI. The median value of the slope series is used as the basis for determining trend, which can help reduce the influence of missing or abnormal data (Sen, 1968; Fensholt et al., 2012). The calculation of slope was as follows:
where xi and xj are the sequence values at times i and j, respectively, and 1≤i<j≤n, n being the sequence length, i.e., the number of years in the study period. An upward trend is indicated with slope > 0 and a downward trend is indicated with slope < 0. The significance of the trend was assessed using the Mann-Kendall non-parametric statistical test.
2.3.2 Mann-Kendall test
The Mann-Kendall statistical test (MK) is a non-distribution test (non-parametric statistical test) (Kendall, 1975; Tošić, 2004) in which the data do not need to be in a particular order and are not affected by outliers. For the sequence Xi=(x1,x2,…,xn), the size relation of xi and xj in allomorph (xi, xj, j>i) is first determined and set as S. The test hypotheses are H0 (the data in the sequences are randomly arranged, i.e. there is no significant trend) and H1 (the sequence has a monotonic tendency upward or downward). The test statistic S was calculated using Equations 2 and 3:
When n≥10, the statistic S approximates the standard normal distribution and the Z-value (Equation 4) can be used to test the trend:
where , n is the number of data points in the sequence, m is the number of repeated datasets in the sequence, and ti is the number of repeated data values in the i th group. At the given significance level, α=0.05, the threshold of the normal distribution is Z1-a/2. When |Z|≤Z1-a/2, the null hypothesis can be accepted (the trend is insignificant), and when |Z|>Z1-a/2, the null hypothesis is rejected and the trend is significant, Z1-a/2=Z0.975=1.96.
2.3.3 Hurst exponent and R/S analysis
R/S analysis was first proposed by Hurst (1951) in the analysis of hydrological data for the Nile, and is often used to analyze long-term time-series correlations. The principle of R/S is briefly described as follows (Granero et al., 2008):
A time series x(t) is defined as:
The cumulative deviation is calculated as:
The extreme deviation sequence is calculated as:
The standard deviation sequence is calculated as:
Taking the ratio of R(τ) and S(τ), we arrive at:
R/S=R(τ)/S(τ) (9)
And assume that:
The Hurst phenomenon exists in the time series, such that H is called the Hurst exponent with a range from 0 to 1. When H=0.5, there is no change in the data. When H>0.5, the process has a continuous characteristic and the future trend is consistent with the past. When H<0.5, the future trend is expected to be reversed from the past trend. We also used the Pearson correlation (Chang et al., 2014) and residual trend analysis (Wessels et al., 2007) to study the relationships of vegetation with climate factors and human activities, respectively.

3 Results

3.1 Spatio-temporal variations in NDVI on the Mongolian Plateau from 1982-2013

3.1.1 Temporal variations in NDVI
The spatial pattern of annual average NDVI during the study period (Figure 2) shows that the vegetation cover is generally low to the west and high to the east, which is consistent with the desert-grassland-forest distribution shown in Figure 1. The greatest vegetation cover in Mongolia is located in the north and east, while the southwest is relatively desolate. In Inner Mongolia, the greatest vegetation cover is mainly distributed in the north, while the west is a desert area with poor vegetation cover.
Figure 2 Annual mean NDVI across the Mongolian Plateau from 1982-2013
Figure 3 Annual average NDVI values for the Mongolian Plateau (MP), Inner Mongolia (IM), and Mongolia (MGL) from 1982-2003
Figure 3 shows the annual NDVI trends for Inner Mongolia, Mongolia, and the entire Mongolian Plateau during the study period. The NDVI trends in Inner Mongolia and Mongolia are consistent with those of the entire Mongolian Plateau over the past 32 years, showing a slight upward trend with a rate of 0.0003/yr. This may be related to a general increase in global temperature, especially at the middle and high latitudes (Parry et al., 2007). At high latitudes and altitudes (where there is less heat) the rising temperatures will likely extend the growing season, accelerate the growth rate, increase photosynthesis and water use efficiency, and thus directly enhance vegetation growth (Fang, 2000). During the study period, the inter-annual variability of NDVI in the Mongolian Plateau ranged from 0.3023 (2007) to 0.3387 (2012), with an average value of 0.3179. For Inner Mongolia, the range was 0.3445 (1983) to 0.3776 (2012), with an average value of 0.3565, and for the entire plateau the range was 0.2712 (2007) to 0.3159 (1994), with an average value of 0.2904. The multi-year NDVI average for Inner Mongolia was higher than that for all of Mongolia. The change rates for coniferous forest and cropland were the largest, at 0.0006/yr and 0.0007/yr, respectively. Broadleaf forest and desert both had small change rates, at 0.0002/yr and 0.0001/yr, respectively (Table 1).
Table 1 Annual average NDVI, slope, and Hurst value for each vegetation type in the Mongolian Plateau from 1982-2013
Vegetation types NDVI range NDVI average Sen’s slope/yr Mean for H
Broadleaf forest 0.59-0.66 0.63 0.0002 0.419
Coniferous forest 0.52-0.58 0.56 0.0006 0.412
Meadow 0.51-0.56 0.54 0.0004 0.426
Shrub 0.43-0.48 0.45 0.0005 0.447
Cropland 0.37-0.43 0.39 0.0007 0.440
Steppe 0.32-0.39 0.35 0.0004 0.404
Sandy land 0.28-0.35 0.31 0.0005 0.437
Alpine grassland 0.27-0.31 0.28 0.0005 0.418
Desert 0.13-0.16 0.14 0.0001 0.389
3.1.2 Spatial variations in NDVI
Spatial vegetation changes are significantly differentiated through the study area (Figure 4). Increasing trends in NDVI accounted for 67.79% (about 144.84×104 km2) of the total Mongolian Plateau area, with 24.54% (about 52.45×104 km2) showing a significant increase, mainly in the Hetao Irrigation District, Ordos, southern Chifeng, northern Hulunbuir and Khuvsugul, the western edge of Bayan-ulgii, Bulgan, Darkhan-uul, Selenge, Dornot, Sukhbaatar, Sumber, and Khentii in Mongolia. The areas in which NDVI increased significantly were concentrated in the agro-pastoral ecotone, such as the Hetao Irrigation District, Hunshandake, and Horqin Sandy Land in Inner Mongolia, and in middle and low mountainous areas in Mongolia. Another 32.21% (about 68.84×104 km2) of the area showed a decreasing trend in NDVI, of which 5.90% (about 12.61×104 km2) decreased significantly, mainly in Arkhangai, Uvurkhangai, Dundgovi, and Dornogovi in central Mongolia, the transitional zone from steppe to desert areas. A few such pixels were also located at the junction of Xilingol and Chifeng and in eastern Hulunbuir in Inner Mongolia. In general, the NDVI in Inner Mongolia increased significantly, while the NDVI decreased significantly over a larger area of Mongolia. Overall, the NDVI increased over a larger area than that in which it decreased.
Figure 4 Spatial distribution of NDVI trends on the Mongolian Plateau from1982-2013

3.2 Analysis of the factors influencing vegetation growth on the Mongolian Plateau

3.2.1 Effects of climatic factors on vegetation
Precipitation and temperature are the main meteorological factors that affect vegetation growth in arid and semi-arid regions. The correlation between NDVI and precipitation or temperature on the Mongolian Plateau is shown in Figures 5 and 6, respectively. NDVI is positively correlated with precipitation over a much larger area than temperature, at 73.19% (~156.42×104 km2) and 35.25% (~75.35×104 km2), respectively. This indicates that precipitation is the dominant factor affecting vegetation growth on the Mongolian Plateau and that the inter-annual variability of NDVI is more sensitive to precipitation than to temperature. The area with significant positive correlations between NDVI and precipitation accounted for 24.07% of the total area and was mainly distributed in the grassland areas of Ordos, Xilingol, Tongliao, and Hulunbuir in Inner Mongolia and Dornot, Dundgovi, Dornogovi, and Arkhangai in Mongolia (Figure 5). The regions showing positive correlation between NDVI and temperature were concentrated in high-latitude areas of the Mongolian Plateau, such as the mountainous region of Mongolia (including Khuvsugul, Bulgan, Darkhan-uul, and Selenge), and the Greater Khingan Range of Inner Mongolia (Figure 6). In addition, NDVI in these regions was negatively correlated with precipitation, indicating that temperature, not precipitation, was the main factor affecting vegetation growth in these high latitude and alpine forest areas.
Figure 5 Spatial correlation between NDVI and precipitation from 1982-2013
Figure 6 Spatial correlation between NDVI and temperature from 1982-2013
3.2.2 Effects of human activities on vegetation
Climate change is an important factor influencing vegetation cover on the Mongolian Plateau, but human activity is also an important driving factor that cannot be neglected. We carried out a residual trend analysis of NDVI to identify and quantify the impact of human activities. First, a linear regression model between NDVI and climatic factors (precipitation and temperature) was constructed, in which climatic factors were the explanatory variables. Using the regression model, existing climate data could be used to predict an annual NDVI value in each grid cell. The predicted NDVI value was then subtracted from the remotely sensed NDVI value to arrive at residual NDVI values from 1982-2013 (Li et al., 2012). Figure 7 shows the spatial distribution of these results. The significantly increased NDVI residuals are mainly concentrated in the Ordos, southern Ulanqab, Chifeng, Tongliao, northwestern Xilingol, and northern Hulunbuir areas of Inner Mongolia and the western Bayan-ulgii, Dornot, western Sukhbaatar, and eastern Dornogovi areas of Mongolia.
The overall significant increase in NDVI residuals indicates that vegetation growth in these areas cannot be explained by climatic factors only. For example, in the Yellow River Basin of Inner Mongolia, which flows through the Ordos and Ulanqab areas, the NDVI change reflects the impact of human activities, which has been shown to depend strongly on irrigation such that there is no dependence on precipitation. Other measures, including the extensive use of chemical fertilizers and pesticides and the construction of irrigation facilities, also influence vegetation cover in the basin (Xin et al., 2008), indicating that human activities play a key role in the increasing vegetation cover.
The NDVI residuals were significantly reduced in the southern part of the Alxa Right Banner, Bayannur, Horqin Sandy Land, Arongqi, Morindavaa, and Oroqen of Hulunbuir areas in Inner Mongolia (Figure 7), indicating that vegetation growth in these areas is lagging behind the growth predicted by climate. One hypothesis for this trend is that human activities are leading to land degradation, resulting in a reduction in NDVI. For example, deterioration of ecosystems, serious land desertification, and arable land reduction since the 1950s has forced a large number of farmers to relocate to the southern part of Alxa Right Banner, adjacent to Minqin County in Gansu Province. They began collecting herbs and wild plants from the sandy soil, so that the ecological environment has become more fragile, with surface vegetation suffering serious damage. Another example can be found in the degradation of Horqin Sandy Land, where the development and subsequent abandonment of large areas of wasteland has led not only to reduction in grassland, but also to increased soil erosion. Overgrazing also causes land degradation in this area (Jiang et al., 2004).
In other regions, the role of human activities in increasing or decreasing NDVI still requires further validation. However, based on analysis of the Yellow River Basin, Alxa Right Banner, and Horqin Sandy Land, we assume that if the NDVI residual trend is positive, human activities must have played a large role in increasing NDVI, while a negative slope indicates that human activities had a destructive effect. The area of significantly increasing residual trend occupied 26.14% (about 39.35×104 km2) of the total study area (9.50% in Mongolia, 16.64% in Inner Mongolia), while the area of significantly decreasing residual occupied only 3.77% (about 7.94×104 km2), indicating that human activity had a net positive effect on NDVI. Moreover, the population of Mongolia is smaller, one-tenth that of Inner Mongolia. Additionally, the state is currently following an official ecological policy, so the intensity of human activity in Mongolia is weaker than that in Inner Mongolia.
Figure 7 Spatial distribution of residual NDVI trends on the Mongolian Plateau from 1982-2013

3.3 Future vegetation dynamic trends based on the Hurst exponent

Figure 8 shows the spatial distribution of the Hurst exponents for the NDVI time-series. Using H = 0.5 as the threshold value, the predicted trend of vegetation change can be divided into three categories: H = 0.5 indicates that there will not be significant changes in future NDVI, H > 0.5 indicates that future NDVI trends will remain consistent with current trends, and H < 0.5 indicates that future NDVI trends will reverse from current trends. NDVI trends expected to remain consistent with the current state accounting for 12.05% (about 25.75×104 km2) of the total area of the Mongolian Plateau, while 87.95% (about 187.97×104 km2) of the area is predicted to see a reverse in the current NDVI trends. The Hurst exponent value was lower around the border between the two countries and relatively high in other regions. The region with H > 0.5 was mainly concentrated in Ordos and the southern part of Chifeng in Inner Mongolia (Figure 8). The Hurst exponent is closely related to vegetation cover, with high H values for lush vegetation and low H values for sparsely vegetated land.
Figure 8 Spatial distribution of the Hurst exponent for the annual average NDVI time series on the Mongolian Plateau from 1982-2013. Values over 0.5 suggest a continuation in the past trend, while values below 0.5 suggest a reversal in the past trend
Table 2 Parameters for predicted future vegetation change trends
-20 < Zc < -1.96 1.96 < Zc < 20
H<0.5 Improvement Degradation
H>0.5 Consistent degradation Consistent improvement
The MK test quantitatively identifies trends in NDVI over a given time period, while the R/S analysis qualitatively identifies whether future trends were predicted to continue in the same direction or switch to the opposite direction. However, neither approach indicates whether an increasing or decreasing trend can be expected in the future. By combining results from the two analyses, reasonable predictions of future trends in vegetation changes can be made (Table 2 and Figure 9). We set 0.05 as the significance level, and used 20 > |Zc| > 1.96 as the reference range for significant increases or decreases. A value of H = 0.5 was used as a criterion to judge whether the change in NDVI would continue in same direction. “Improvement” indicates that NDVI has been decreasing, but will increase in the future. “Degradation” indicates that NDVI has been increasing, but will decrease in the future. “Consistent improvement” or “Consistent degradation” indicates that the NDVI changes will continue in the same direction as before.
Figure 9 Predicted vegetation changing trends on the Mongolian Plateau
The results suggest that there will be more improved area than degraded area in the future; the degradation area accounted for 5.46% (about 11.65×104 km2) and the improvement area 6.60% (about 14.10×104 km2) of the total plateau (Figure 9). The degradation area accounted for 4.44% of the study area, mainly in south-central Xilingol of Inner Mongolia and central Mongolia. The improvement area accounted for 3.14% of the study area, but was scattered throughout the region. The areas of consistent improvement accounted for 3.46% of the study area. Most of these areas implemented large-scale desertification control and ecological management projects during the study period, as was the case in Horqin Sandy Land and Ordos (Ma et al., 2015; Li et al., 2006). In general, the vegetation of the Mongolian Plateau is not expected to change significantly in the future, as significant values were found for only about 13% of the area. The improvement area is larger than the degradation area, and the greatest vegetation improvement occurred in Ordos and Tongliao in Inner Mongolia. The reasons for these observed changes involved both natural and human factors, as discussed below.

4 Discussion

From the above analysis, we can conclude that precipitation is the most important factor affecting vegetation growth on the Mongolian Plateau. However, precipitation has substantially decreased and temperatures have significantly increased over the past 32 years, causing significant aridity. In this region, the precipitation rate gradually increases from south to north. Precipitation has decreased in most regions (excepting the desert regions of the southwestern plateau), with the precipitation reduction in Mongolia being larger than that in Inner Mongolia (Figure 10a). Conversely, temperatures increased across the entire plateau, with the largest increases occurring in the central plateau (Figure 10b). In general, the climate of the Mongolian Plateau has become drier (Figure 10c). The degree of drought in Mongolia is higher than that in Inner Mongolia, and human activities are more apparent in Inner Mongolia, which may be the cause of the better vegetation growth in Inner Mongolia than in Mongolia.
On the other hand, agriculture and animal husbandry is the main economic base of the Mongolian Plateau, with 26.2% of the gross national product for Mongolia coming from agriculture and animal husbandry, of which 80% comes from nomadic animal husbandry (Zhen et al., 2008). Animal husbandry in Inner Mongolia has shifted from nomadic to settled villages. At the beginning of the 21st century, the government implemented ecological protection and vegetation restoration projects, including Three-North Shelterbelt, Grain for Green, Beijing and Tianjin Sandstorm Source Control, and Establishment of a Nature Reserve, to protect and improve the ecological environment, and ensure sustainable development of animal husbandry. These programs have achieved many beneficial results.
In Ordos and Tongliao of Inner Mongolia, the number of livestock has increased annually since 2000 (Figure 11). Excessive grazing of livestock reduces grass height, which decreases NDVI and leads to different degrees of surface exposure (Guo, 2007). However, the cumulative afforested areas have consistently increased since the implementation of ecological restoration projects with the result that vegetation in these two regions has increased significantly. This explanation supports the use of residual trend analysis for studying the impact of human activity on NDVI, and this conclusion was consistent with the result of Li et al. (2014). In addition, the Forestry Law, which was enacted in 1945, has reduced forest disease and fire disasters and increased the vegetation cover in the study area (Zhou et al., 2012). Measures such as fence-blocked grazing, prohibition of deforestation, popularization of anti-wind erosion devices, and improved cultivation techniques have also played a key role in the increased NDVI (Liu et al., 2005).
Figure 10 Dynamic trends of precipitation (a), temperature (b), and Standardized Precipitation Evapotranspiration Index (SPEI) (c) on the Mongolian Plateau from1982-2013
Figure 11 Statistics for livestock numbers and cumulative afforested area in Ordos (a) and Tongliao (b), Inner Mongolia, from 2000-2013
We used the Hurst exponent to predict the future vegetation trend on the Mongolian Plateau. This provides a new approach to the study of vegetation and has been widely used to analyze the consistency of future vegetation trends in recent years with effective results (Peng et al., 2012; Liu et al., 2015; Liu et al., 2016). Compared with previous studies on vegetation dynamics, it considers whether the future trend in vegetation is consistent with current state. However, the proposed R/S analysis with the Hurst exponent could not determine how long the anticipated vegetation trend would continue in the future. Therefore, it is important for future research to focus on extending the time span for which future vegetation dynamics can be forecasted.
The Mongolian Plateau is an important ecological boundary for China, playing a significant role in the country’s ecological environment. As an important part of the East Asian ecosystem it plays important role in the global carbon cycle (Lu et al., 2009). Because of its special geographical location, it is important to study vegetation changes in this region in the context of global climate change. As vegetation dynamics in the plateau result from climate change and human activities, our results demonstrate that these methods can provide a theoretical basis for the development of rational measures to protect the vegetation environment in the future.

5 Conclusions

(1) From 1982-2013, NDVI showed an upward tendency with a rate of 0.0003/yr in the Mongolian Plateau. Vegetation growth in Inner Mongolia was better than that in Mongolia, with an annual average value of 0.0066.
(2) The area of increasing NDVI on the Mongolian Plateau was much larger than that of decreasing NDVI, at 67.79% (~144.84×104 km2) and 32.21% (~68.84×104 km2) of the total plateau area, respectively. The areas of significant increase and decrease accounted for 24.54% and 5.90% of the total area, respectively. Areas with a significant increase in NDVI were located mainly in Ordos and Tongliao in the agro-pastoral ecotone of Inner Mongolia, along the southern part of the plateau. Areas with significant decreasing trends were distributed in central Mongolia and parts of Chifeng, southeast Xilingol, and eastern Hulunbuir in Inner Mongolia.
(3) From the Hurst exponent analysis, we concluded that the predicted vegetation growth would remain consistent with previous trends in 12.05% (about 25.75×104 km2) of the total area, while the other 87.95% (about 187.97×104 km2) was predicted to reverse trends. The Hurst exponent was closely related to the vegetation cover, with high values in lush vegetation and low values in sparse vegetation. The predicted vegetation trends for the Mongolian Plateau were not significant in most of the area, with only about 13% of the vegetation showing significant trends. The predicted improvement area was larger than the predicted degradation area.
(4) The area with positive correlations between NDVI and precipitation (73.19%, ~156.42×104 km2) was much larger than that with positive correlations between NDVI and temperature (35.25%, ~75.35×104 km2), indicating that precipitation is the dominant climate factor for vegetation growth in the plateau. The significantly increased area of NDVI residuals occupied 26.14% (~39.35×104 km2) of the total area of the Mongolian Plateau, while the significantly decreased area was only 3.77% (~7.94×104 km2), indicating that the human influence on vegetation cover was positive for most of the Mongolian Plateau. The intensity of human activity in Mongolia was weaker than that in Inner Mongolia.

The authors have declared that no competing interests exist.

References

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[1]
Chang Z, Gong H, Zhang J , et al. 2014. Correlation analysis on interferometric coherence degree and probability of residue occurrence in interferogram.Sensors Journal IEEE, 14(7): 2369-2375.The interferometric coherence degree is a very important indicator of the quality of interferometric phase values in interferogram; while the existence of residues is a stubborn problem for phase unwrapping. Both of them are the vital factors influencing the performance of phase unwrapping algorithms in interferometric synthetic aperture radar (SAR) processing. In general, residues tend to be located in the regions with low coherence degree, which is just a qualitative description for the relationship between interferometric coherence degree and probability of residue occurrence. In order to further reveal the quantitative relationship between them, we actually used the residue density in the interferogram as an approximate substitute for the probability of residue occurrence, and then calculated the Pearson correlation coefficients between the ensemble coherence average and the residue density in the corresponding interferogram with several kinds of data from different SAR sensors, including ERS1/2, JERS-1, and Envisat-1 ASAR. The calculation results show a very strong inverse correlation between them, the Pearson correlation coefficients range from -0.702 to -0.963. In other words, the interferometric coherence degree not only can indicate the quality of interferometric phase, but also to a great extent reflect or even predict the density of residues in the interferogram using the linear regression analysis.

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[2]
Fang J Y, 2000. Forest biomass carbon pool of middle and high latitudes in the north hemisphere is probably much smaller than present estimates.Acta Phytoecologica Sinica, 24(5): 635-638. (in Chinese)

[3]
Fensholt R, Langanke T, Rasmussen K , et al. 2012. Greenness in semi-arid areas across the globe 1981-2007: An earth observing satellite based analysis of trends and drivers.Remote Sensing of Environment, 121: 144-158.78 Trends in dryland vegetation greenness (NDVI) based on AVHRR data are analyzed. 78 Climatic constraints to plant growth are anlysed to study causes of NDVI changes. 78 Global drylands on average experience an increase in NDVI from 1981 to 2007. 78 Trends have regional specific explanations and generalizations are not supported.

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[4]
Granero M A S, Segovia J E T, Pérez J G, 2008. Some comments on Hurst exponent and the long memory processes on capital markets.Physica A Statistical Mechanics and Its Applications, 387(22): 5543-5551.The analysis of long memory processes in capital markets has been one of the topics in finance, since the existence of the market memory could implicate the rejection of an efficient market hypothesis. The study of these processes in finance is realized through Hurst exponent and the most classical method applied is R/S analysis. In this paper we will discuss the efficiency of this methodology as well as some of its more important modifications to detect the long memory. We also propose the application of a classical geometrical method with short modifications and we compare both approaches.

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[5]
Guo X J, 2007. Relationship between grazing intensity with vegetation structure and grassland ecological environment. Qinghai Prataculture, 16(2): 17-20. (in Chinese)The article reviewed the development about grazing on effect of plant composition and plant biomass,different grazing method and grazing density on livestock and grassland,establishing and utilization of artificial grassland in alpine meadow,it should be bear appropriate grazing presure in order to protecting plant diversity and gaining higher grassland biomass.

[6]
Holben B N, 1986. Characteristics of maximum-value composite images from temporal AVHRR data.International Journal of Remote Sensing, 7(11): 435-445.

[7]
Hulme M, Doherty R, Ngara Tet al., 2001. African climate change: 1900-2100.Climate Research, 17(2): 145-168.

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[8]
Hurst H E, 1951. Long term storage capacity of reservoirs.Transactions of the American Society of Civil Engineers, 116(12): 776-808.Search all the public and authenticated articles in CiteULike. Include unauthenticated resultstoo (may include "spam") Enter a search phrase. You can also specify a CiteULike article id(123456),. a DOI (doi:10.1234/12345678). or a PubMed ID (pmid:12345678).

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[9]
Ichii K, Kawabata A, Yamaguchi Y, 2002. Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982-1990.International Journal of Remote Sensing, 23(18): 3873-3878.

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[10]
Jiang D M, Liu Z M, Kou Z W , et al. 2004. Ecological environment and its sustainable management of Horqin steppe: A report on the survey of Horqin sandy land.Chinese Journal of Ecology, 23(5): 179-185. (in Chinese)A joint survey on ecologial environmemt of Horqin steppe was carried out by both Shenyang Institute of Applied Ecology,Chinese Academy of Sciences and China Central Television in August,2003.The investigation showed that grassland is degrading seriously.Land reclamation in a large scale is one of main reason of grassland degradation and decrease of grassland area.According to the investigation,a major issue is the remarkable declination of water resources,including declination of river water, lake water and falling of the ground-water table.Investigation showed that the desertification area in Horqin steppe is decreasing.The established artificial vegetation is increasingly reforced and its effect is remarkable. At present,however,area covered with the sand dunes out of control is still very large,and to control this area is considered as a very hard work.

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[11]
John R, Chen J, Ouyang Z , et al. 2013. Vegetation response to extreme climate events on the Mongolian Plateau from 2000-2010. Environmental Research Letters, 8, 035033 (12pp).Climate change has led to more frequent extreme winters (aka, dzud) and summer droughts on the Mongolian Plateau during the last decade. Among these events, the 2000–2002 combined summer drought–dzud and 2010 dzud were the most severe on vegetation. We examined the vegetation response to these extremes through the past decade across the Mongolian Plateau as compared to decadal means. We first assessed the severity and extent of drought using the Tropical Rainfall Measuring Mission (TRMM) precipitation data and the Palmer drought severity index (PDSI). We then examined the effects of drought by mapping anomalies in vegetation indices (EVI, EVI2) and land surface temperature derived from MODIS and AVHRR for the period of 2000–2010. We found that the standardized anomalies of vegetation indices exhibited positively skewed frequency distributions in dry years, which were more common for the desert biome than for grasslands. For the desert biome, the dry years (2000–2001, 2005 and 2009) were characterized by negative anomalies with peak values between 611.5 and 610.5 and were statistically different (P < 0.001) from relatively wet years (2003, 2004 and 2007). Conversely, the frequency distributions of the dry years were not statistically different (p < 0.001) from those of the relatively wet years for the grassland biome, showing that they were less responsive to drought and more resilient than the desert biome. We found that the desert biome is more vulnerable to drought than the grassland biome. Spatially averaged EVI was strongly correlated with the proportion of land area affected by drought (PDSI <61 1) in Inner Mongolia (IM) and Outer Mongolia (OM), showing that droughts substantially reduced vegetation activity. The correlation was stronger for the desert biome (R= 65 and 60, p < 0.05) than for the IM grassland biome (R= 53, p < 0.05). Our results showed significant differences in the responses to extreme climatic events (summer drought and dzud) between the desert and grassland biomes on the Plateau. (letter)

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[12]
Jong R D, Bruin S D, Wit A D , et al. 2011. Analysis of monotonic greening and browning trends from global NDVI time-series.Remote Sensing of Environment, 115(2): 692-702.Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.

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[13]
Kendall M, 1975. Rank Correlation Methods. London: Charles Griffin.

[14]
Lambin E F, Strahler A H, 1994. Indicators of land-cover change for change-vector analysis in multitemporal space at coarse spatial scales.International Journal of Remote Sensing, 15(10): 2099-2119.Change-vector analysis in multi-temporal space is a powerful tool to analyse the nature and magnitude of land-cover change. The change vector compares the difference in the time-trajectory of a biophysical indicator for successive time periods. This change detection method is applied to three remotely-sensed indicators of land-surface conditions090000vegetation index, surface temperature and spatial structure090000in order to improve the capability to detect and categorize subtle forms of land-cover change. It is tested in a region of West Africa, using multi-temporal Local Area Coverage imagery obtained by the Advanced Very-High Resolution Radiometer on NOAA-9 and NOAA-II orbiting platforms. The three indicators show a low degree of redundancy and detect different land-cover change processes, which operate at different time scales. Change vector analysis is being developed for application to the land-cover change product to be produced using NASA''s Moderate-Resolution Imaging Spectroradiometer instrument, scheduled for flight in 1998 and 2000on EOS-AM and -PM platforms.

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[15]
Li A, Wu J G, Huang J H, 2012. Distinguishing between human-induced and climate-driver vegetation changes: A critical application of RESTREND in Inner Mongolia. Landscape Ecology, 27: 969-982.Changes in the spatiotemporal pattern of vegetation alter the structure and function of landscapes, consequently affecting biodiversity and ecological processes. Distinguishing human-induced vegetation changes from those driven by environmental variations is critically important for ecological understanding and management of landscapes. The main objectives of this study were to detect human-induced vegetation changes and evaluate the impacts of land use policies in the Xilingol grassland region of Inner Mongolia, using the NDVI-based residual trend (RESTREND) method. Our results show that human activity (livestock grazing) was the primary driver for the observed vegetation changes during the period of 1981–2006. Specifically, vegetation became increasingly degraded from the early 1980s when the land use policy—the Household Production Responsibility System—led to soaring stocking rates for about two decades. Since 2000, new institutional arrangements for grassland restoration and conservation helped curb and even reverse the increasing trend in stocking rates, resulting in large-scale vegetation improvements in the region. These results suggest that most of the degraded grasslands in the Xilingol region can recover through ecologically sound land use policies or institutional arrangements that keep stocking rates under control. Our study has also demonstrated that the RESTREND method is a useful tool to help identify human-induced vegetation changes in arid and semiarid landscapes where plant cover and production are highly coupled with precipitation. To effectively use the method, however, one needs to carefully deal with the problems of heterogeneity and scale in space and time, both of which may lead to erroneous results and misleading interpretations.

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[16]
Li A M, Han Z W, Xu J , et al. 2006. Transformation dynamics of desertification in Horqin sandy land at the beginning of the 21st century.Acta Geographica Sinica, 61(9): 976-984. (in Chinese)Visual interpretation remarks were established by taking TM images in 2000 and 2005 as information source.Then desertification dynamic in Horqin Sandy Land in recent five years is monitored with the help of images and data processing function of GIS.The results showed that desertified land changed from 22423.1 km2 in 2000 to 22422.4 km2 in 2005,only at a reducing rate of 0.14 km2路a-1.This indicated that the constant aggravation of the desertification trend in the study area has been basically controlled and tends to be in a relatively stable state.The degree of transformation dynamics of desertification is different for different types of land,desertification degree has been mitigated evidently for the primary desertified land,and the difference is 958.9 km2 between reversal area and deteriorative area.At the same time,there are 113.3 km2 of non-desertified land desertification at a rate of 22.7 km2 a-1.Therefore,great attention should be paid to this phenomenon.

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[17]
Li J Y, Chen P Q, Ma Z Get al., 2006. Regional research: A main approach to understanding the global environmental change.Advances in Earth Science, 21(5): 441-450. (in Chinese)Regional research is a powerful approach to understanding the Earth System,which is necessary for deepening global change research.Some integrated regional cases study from Southern Africa,South Asia,East Asia,Southeast Asia,etc.and some great regional projects such as LBA,AMMA,MAIRS and ProMed indicate that the outcomes of regional change research are the great contribution to a better understanding of the Earch System as a whole,and the integrated experiment,modelling and analysis are the powerful tools for enhancing understanding of the Earth System.Some key scientific issues in regional environmental change should be focused on:(i) the response and adaptation of integrated regional key process to global change,such as land surface process,land-ocean interactions in the coastal zone and regional climate change;(ii) regional marginal phenomena,threshold and catastrophe;(iii) regional orderly adaptation of human activity to global change.It is necessary for regional studies to make regional programs related to global change,Earth observation at the regional scale,new integrated method,regional experiment and scaling,Earth modeling and simulation,etc.

[18]
Li W Y, Qian Z A, An M H , et al. 2006. Temporal and spatial feature analyses of winter and summer surface air temperature over CMASA, part (Ⅱ): July.Plateau Meteorology, 25(4): 624-632. (in Chinese)To analyse the temporal and spatial change features of the winter surface air temperature over China- Mongolia Arid- and Semiarid- Areas(CMASA) as a whole,the EOF and REOF analyses have been conducted utilizing the observed air temperature data from the selected even-distributed 104 stations in the area in January of 1961-1997,and their multi-year mean temperature field and standard variance one have been analysed as well.The main results are as follows:(1) Latitude,topography and Siberia cold high are three main factors affecting the winter temperature distribution in CMASA.(2) The standard variancesof winter temperature in most of the area arelarger than 2 .(3) There are three temperature anomaly distribution modes: With the same anomaly in whole area,with opposite anomalies in the south and north or in the east and west according to the EOF analyses.(4) According to the REOF analyses of six subregions of the area,like one on the northeast side of the Plateau,South Xinjiang one,Qinghai Plateau one,North Xinjiang one,Mid- and East- Mongolia one and West-Mongolia one,were divided.(5) The winter temperature hasbeen warmed considerably by more than 2 since 1961,particularly near the south border and after 1977.(6) There existed 4 or 8 year periodic changes for the winter temperature in CMASA.

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[19]
Li X G, Liu H M, Wang L Xet al., 2014. Vegetation cover change and its relationship between climate and human activities in Ordos plateau.Chinese Journal of Agrometeorology, 35(4): 470-476. (in Chinese)The vegetation cover change and its relationship between climate and human activities in Ordos plateau was studied based on the MODIS NDVI datasets,average monthly temperature and precipitation from meteorological stations in Ordos from 2001 to 2013,by using of the method of regression,correlation,residual analysis and the technique of GIS space.The results showed that there was significant spatial difference of Ordos vegetation,in which 30.36% of the vegetation cover grown significantly,which mainly located in the east and northwest of Ordos;0.57% of the vegetation cover decreased significantly,which mainly located in the northern border of Ordos.There was significant spatial difference relationship between vegetation NDVI and temperature,and precipitation,in which the most typical one was the relationship between vegetation NDVI in desert-steppe region and precipitation.By using the method of residual analysis to quantify the situation of human activities in recent years,it was found that the significant change of vegetationNDVI was mainly influenced by artificial factors under the background of no significant change of temperature and precipitation.The results could provide scientific decision basis for further reasonable organization of human activitiesin Ordos region,based on the accurate interpretation of process and cause of the vegetation change in Ordos region.

[20]
Li X Z, Liu X D, 2012. A modeling study on drought trend in the Sino-Mongolian arid and semiarid regions in the 21st century.Arid Zone Research, 29(2): 262-272. (in Chinese)Using a regional climate model(RegCM3) nested in one-way mode within the Community Climate System Model(CCSM3),some high-resolution numerical experiments were conducted under the SRES A2,A1B and B1 scenarios for the 21st century,and the drought characteristics and their possible trends in the Sino-Mongolian arid and semiarid regions were analyzed.The results show that the precipitation and surface temperature have an obvious increasing trend under all scenarios.There is no clear drying trend in the 21st century based on the analyzed results of precipitation.Considering from the Palmer Drought Severity Index(PDSI),however,the signals of precipitation and temperature reveal that the drought area may be expanded continuously.The proportions of extremely severe,severe and moderate drought areas are increased,which are all significant at significance level of 95%.The extremely severe drought area was expanded by higher than 3% under all the scenarios in the late-21st century compared with that in the mid-21st century.Compared with mid-21st century and modern time,the drought areas will be enlarged by 8% and 11.51% under A2,12.48% and 18.34% under A1B,and 9.73% and 10.82% under B1 in the late-21st century,respectively.The change of both precipitation and temperature must be taken into account in predicting climate change in arid and semiarid regions so as to more accurately reflect the characteristics of drought and drying trend.

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[21]
Liu X, Pan Y, Zhu X , et al. 2015. Spatiotemporal variation of vegetation coverage in Qinling-Daba Mountains in relation to environmental factors.Acta Geographica Sinica, 70(5): 705-716. (in Chinese)The Qinling- Daba(Qinba) Mountains, a key ecological zone of terrestrial ecosystem, has experienced a significant change of vegetation coverage in recent years, which is characterized by rapid climate change. Using MODIS- NDVI dataset, the current study investigated the patterns of spatiotemporal variation of vegetation coverage in the Qinba Mountains during the period 2000- 2014. In addition, possible environmental factors affecting this variation were identified. Sen + Mann- Kendall model and partial correlation analysis were used to analyze the data, followed by the calculation of Hurst index in order to analyze future trends of vegetation coverage. The results of the study showed that(1) the Normalized Difference Vegetation Index(NDVI) of the study area revealed a significant increase during2000- 2014(linear tendency 2.8%/10a). During this period, a stable increase was detected before 2010(linear tendency 4.32%/10a), followed by a sharp decline after 2010(linear tendency- 6.59%/10a).(2) In terms of spatiotemporal variation, vegetation cover showed a "high in the middle and low in surroundings" pattern. High values of vegetation coverage were mainly found in the Qinba Mountains of Shaanxi Province, while low values of vegetation coverage were mainly observed in Longnan, Tianshui, and Gannan prefectures occupied by arable land.(3) The area covered with vegetation was larger than the degraded area, accounting for 81.32% and 18.68% of the total area, respectively. Piecewise analysis revealed that 71.61%of the total study area showed a decreasing trend in vegetation coverage during 2010-2014, of which, the extremely significant and significant decrease accounted for 6.38% and 3.45%,respectively.(4) The reverse characteristics of vegetation coverage change were stronger than the same characteristic in the Qinba Mountains. Some 46.89% of the entire study area is predicted to decrease in future, while 34.44% of the total area will follow a continuous increasing trend.(5) The change of vegetation coverage was mainly attributed to the deficit of precipitation. At the same time, vegetation coverage during La Nina years was larger than that during El Nino years. Statistical analysis showed that positive and negative anomaly pixels accounted for 28.37% and 71.63%, respectively during El Nino years and 80.48% and 19.52%,respectively during La Nina years.(6) Human activities can induce ambiguous effects on vegetation coverage: both positive effect(through the implementation of the ecological restoration project) and negative effect(through urbanization) were observed.

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[22]
Liu Y, Li C Z, Liu Z Het al., 2016. Assessment of spatio-temporal variations in vegetation cover in Xinjiang from 1982 to 2013 based on GIMMS-NDVI.Acta Ecologica Sinica, 36(19): 1-11. (in Chinese)Fraction of Absorbed Photosynthetically Active Radiation(FPAR) is an important biophysical factor for monitoring vegetation growth,as well as a critical parameter in the terrestrial ecosystem modeling and a key indicator for studying global climate change.Remote sensing technology has been proved to be an effective tool in estimating FPAR at regional and global scales,because satellite data can provide a spatially and periodic,comprehensive view of vegetation growing status.Many methods have been developed in estimating FPAR with remote sensing,which can be generally grouped into two categories.The first category of approaches are the empirical statistics models based on the relationships between vegetation indices,derived from reflectance at canopy level,and FPAR.These models are easy to use with high efficiency and much more suitable for detecting within-field spatial variability,yet they may lead to inaccurate results when applied over another place or broad scale with different land cover types.Another category of approaches for FPAR retrieval are to invert canopy reflectance models based on the BRDF(Bi-Directional Reflectance Distribution Functions) models such as the radiative transfer model and geometrical optics model,which describe the transfer and interaction of radiation inside the canopy based on physical mechanism between FPAR and vegetation canopy reflectance.These models have strong applicability and are taken as the algorithm bases among most widely used FPAR products.However,the inversion process is ill-posed due to the complexity of these physical models;the parameters and prior knowledge required by these models are hard to acquire over large areas.At the same time,other methods such as the method based on the concept of effective FPAR,which is FPAR absorbed by chlorophyll,and the method based on the airbome lidar data which is useful to characterize spatial variability of canopy structure,bring significant improvement to the two categories of methods.Due to the complexity of FPAR itself and its influencing factors,as well as the quality of remote sensing data,plenty of uncertainties existed in satellite based FPAR estimation.For statistical model,most vegetation indices are easily affected by soil background,saturation problem,atmospheric condition,and so on.These factors bring much uncertainty in the relationship between FPAR and vegetation indices.For physical models,problems including top-of-atmosphere radiance uncertainties and errors in land cover mapping are hard or even impossible to avoid.In order to deal with these uncertainties and meet the requirements of further research for terrestrial ecological process,future research focuses on FRAR retrieval based on satellite will be: further research on theoretical mechanism of FPAR estimation,seeking to minimize noise effects on vegetation indices for more accurate estimation of FPAR,improvement of the inversion methods for physically-bases models,acquisition and accumulation of prior knowledge in FPAR estimation based on systematic observation network,construction of long-term FPAR dataset based on multi-source remote sensing data,and algorithm for deriving FPAR with both high spatial and high temporal resolutions.

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[23]
Liu Y L, Pan Z H, Fan J L , et al. 2005. Spatial and temporal analyses on vegetation cover dynamics in north piedmont of Yinshan Mountain.Resources Science, 27(4): 168-174. (in Chinese)North piedmont of Yinshan Mountain is a typical ecological fragile zone and an important eco-shelter in north China. It is therefore important to study impact of global change in this area in order to monitor and analyze dynamics of vegetation cover. Based on annual SPOT4/VEGETATION images of the area from 1998 to 2003, we did time series analysis on dynamics of annual maximum NDVI and annual mean NDVI, and drew the following conclusions: 1)Annual maximum NDVI of the whole area is related to annual mean NDVI, which indicates that the former could represent the best vegetation condition in a year, and the average condition as well; 2)The annual maximum NDVI of the area decreased continuously from year 1999 to 2001, while increased continuously in the next two years, and remained equivalent in 2003 and 1998; 3)Ranking of annual maximum NDVI in a year is croplandwoodlandgrassland, while the ranking of change during 1998 2003 is: woodlandgrasslandcropland. In addition, annual maximum NDVI of cropland and woodland in 2003 has been improved in comparison with those of 1998, while grassland has degenerated; 4)Compared with the year of 1998, annual maximum NDVI of the whole area in year 2001 has been degraded seriously, except for the areas in middle Damao Banner,east Duolun County and southeast Shangdu County, while it has been improved in 2003 in comparison with that of the year 2001, except for northwest Damao Banner and sporadic spots in east Duolun County. In general, annual maximum NDVI in 2003 has been degraded in the northwest part of the area, compared with that of the year 1998, and degeneration and improvement coexisted in other places.

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[24]
Liu Z, Notaro M, Kutzbach J , et al. 2006. Assessing global vegetation-climate feedbacks from observations.Journal of Climate, 19(5): 787-814.

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[25]
Lu Y, Zhuang Q, Zhou G , et al. 2009. Possible decline of the carbon sink in the Mongolian Plateau during the 21st century.Environmental Research Letters, 4(4): 940-941.The Mongolian Plateau is dominated by grassland ecosystems. It frequently experiences drought and is underlain by permafrost in the north. Its complex responses of plant carbon uptake and soil carbon release to climate change are considered to have affected the global carbon cycle during the 21st century. Here we combine spatially explicit information on vegetation, soils, topography and climate with a process-based biogeochemistry model to assess the carbon responses for the 20th and 21st centuries. We estimate the region acted as a C sink of 31 Tg C yrin the 1990s, but that this sink will likely decline in both magnitude and extent under future climate conditions. This change is due to the relatively larger enhancement of soil organic matter decomposition, which releases carbon to the atmosphere, than the corresponding enhancement of plant C uptake, by rising temperatures and atmospheric COconcentrations. Future plant C uptake rates are expected to become more limited due to drier soils caused by increasing evapotranspiration rates. Complex soil thermal and moisture dynamics result in large interannual and spatial variability as a consequence of the different rates of change of air temperature and precipitation in this region.

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[26]
Ma W Y, He L, Zhao C Y, 2015. Desertification dynamics in Alxa League over the period of 2000-2012. Journal of Lanzhou University, 51(1): 55-60, 71. (in Chinese)Using MODISQ1-NDVI data over the period 2000-2012 based on vegetation coverage indicator and Sen's estimator,we found:the desertification problem in Abca League in recent years was pretty grim,which severe desertification land took the large portion,followed by moderate desertification land,again mild desertification and non-desertification land.The desertification problem in Ejina Banner in Alxa League was the most serious,followed by Abca Left Banner,Alxa Right Banner.The proportion of serve desertification in Abca League had gone down in recent years,and the moderate desertification was on the contrary.And they were continually in transition with the other in 2000-2012.The proportion of mild desertification increased slightly,and non-desertification was basically unchanged.In general,the desertification problem in Abca League was slightly getting better.The hot spots of land degradation shared 20.4%of Abca League.There was the desertification land most in Ejina Banner and Alxa Left Banner,followed by Abca Right Banner.

[27]
Miao L J, Liu Q, Fraser R , et al. 2015. Shifts in vegetation growth in response to multiple factors on the Mongolian Plateau from 1982 to 2011. Physics and Chemistry of the Earth Parts A/B/C, 87-88, 50-59.The Mongolian Plateau (MP) steppe is one of the largest steppe environments in the world. To monitor the terrestrial vegetation dynamics on the MP and to ascertain what the driving forces, this study examined the vegetation dynamics in Republic of Mongolia (M) and the Inner Mongolia Autonomous Region (IM) of China from the period 1982 to 2011, based on the satellite-derived GIMMS NDVI3g (Normalized Difference Vegetation Index) data across three biomes (desert, grassland and forest). The results are as followed: (1) Vegetation coverage in IM was generally greater than that in M. Before 2002, time series of NDVI over the MP increased at an average rate of 0.05%02yr 611 . Additionally, after 2002, the NDVI increased at a rate of 0.21%02yr 611 . From 1982 to 2011, the area of IM and M with positive anomalies in the NDVI increased at a separate rate of 1.82%02yr 611 and 1.76%02yr 611 , respectively. (2) At the biome scale, the inter-annual forest NDVI variation in IM and desert NDVI for the entire MP had a significant increasing trend (0.06%02yr 611 and 0.04%02yr 611 , respectively). (3) Climate forcing was a dominant controlling factor affecting the vegetation, and the anthropogenic behavior exhibited no significant value in the whole region. However, overgrazing was the most important reason for the regional degradation, particularly in IM. (4) In the future, the forest biome will go to recovery, whereas both the grassland and desert biomes are predicted to degrade continuously.

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[28]
Myneni R B, Keeling C D, Tucker C J , et al. 1997. Increased plant growth in the northern high latitudes from 1981 to 1991.Nature, 386(6626): 698-702.

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[29]
Nemani R R., Keeling C D, Hashimoto Het al., 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999.Science, 300(5625): 1560-3.Recent climatic changes have enhanced plant growth in northern mid-latitudes and high latitudes. However, a comprehensive analysis of the impact of global climatic changes on vegetation productivity has not before been expressed in the context of variable limiting factors to plant growth. We present a global investigation of vegetation responses to climatic changes by analyzing 18 years (1982 to 1999) of both climatic data and satellite observations of vegetation activity. Our results indicate that global changes in climate have eased several critical climatic constraints to plant growth, such that net primary production increased 6% (3.4 petagrams of carbon over 18 years) globally. The largest increase was in tropical ecosystems. Amazon rain forests accounted for 42% of the global increase in net primary production, owing mainly to decreased cloud cover and the resulting increase in solar radiation.

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[30]
Park H S, Sohn B J, 2010. Recent trends in changes of vegetation over East Asia coupled with temperature and rainfall variations.Journal of Geophysical Research Atmospheres, 115(D14): 1307-1314.1] In this study, we investigated whether long-term normalized difference vegetation index (NDVI) data show climate change signals after the mid-1990s which are inferred from other studies on changing trends in precipitation and dust frequencies. In doing so, mean NDVI data for the growing seasons (April090009October) from 1982 to 2006 were used for examining the spatiotemporal variations in the vegetation over East Asia, in conjunction with precipitation and temperature data. Results indicate that there was a prominent change in the trend of NDVI around the mid-1990s: a pronounced positive trend over most of the East Asian domain before the mid-1990s (19820900091996) and a reverse (or weakened) trend after the mid-1990s (19970900092006). The reverse trend is evident over the higher-latitude regions north of 5000°N and the eastern Mongolian border area. The EOF and SVD analysis suggest that the dominant warming trend until the mid-1990s led to the increased NDVI over the high-latitude regions. However, after the mid-1990s, the reverse NDVI trend found primarily in the east of Lake Baikal and the arid and semiarid regions south of 5000°N seems to be closely linked to local precipitation changes occurred abruptly in the mid-1990s. However, precipitation influences on the reverse NDVI changes are not clear over the high-latitude regions north of 5000°N.

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[31]
Parry M L, Canziani O F, Palutikof J P , et al. 2007. IPCC Fourth Assessment Report: Climate Change, 1340-1356.This report is based on the assessment carried out by the three working groups of the IPCC, and provides an integrated view of climate change as the final part of the IPCC's Fourth Assessment Report

[32]
Peng J, Liu Z, Liu Y , et al. 2012. Trend analysis of vegetation dynamics in Qinghai-Tibet Plateau using Hurst exponent.Ecological Indicators, 14(1): 28-39.As one of the most sensitive areas responding to global environmental change, especially global climate change, Qinghai–Tibet Plateau has been recognized as a hotspot for coupled studies on global terrestrial ecosystem change and global climate change. As an important component of terrestrial ecosystems, vegetation dynamic has become one of the key issues in global environmental change, and numerous case studies have been conducted on vegetation dynamic trend in different study periods. However, few are focused on the quantitative analysis of the consistency of vegetation dynamic trends after the study periods. In the study, taking Qinghai–Tibet Plateau as a case, vegetation dynamic trend during 1982–2003 were analyzed, with the application of the method of linear regression analysis. The results showed that, vegetation dynamics in Qinghai–Tibet Plateau experienced a significant increasing as a whole, with nearly 50% forest degradation in the study period. And among the 7 kinds of vegetation types, the change of forest was the most fluctuant with desert the least one. Furthermore, the consistency of vegetation dynamic trends after the study period, was quantified using Hurst Exponent and the method of R/S analysis. The results showed high consistency of future vegetation dynamic trends for the whole plateau, and inconsistent areas were mainly meadow and steppe distributed in the middle or east of the plateau. It was also convinced that, vegetation dynamic trends in the study area were significantly influenced by topography, especially the elevation.

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[33]
Piao S L, Mohammat A, Fang J Yet al., 2006. NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China.Global Environmental Change, 16(4): 340-348.This study analyzes the temporal change of Normalized Difference Vegetation Index (NDVI) for temperate grasslands in China and its correlation with climatic variables over the period of 1982–1999. Average NDVI of the study area increased at rates of 0.5%02yr 611 for the growing season (April–October), 0.61%02yr 611 for spring (April and May), 0.49%02yr 611 for summer (June–August), and 0.6%02yr 611 for autumn (September and October) over the study period. The humped-shape pattern between coefficient of correlation ( R ) of the growing season NDVI to precipitation and growing season precipitation documents various responses of grassland growth to changing precipitation, while the decreased R values of NDVI to temperature with increase of temperature implies that increased temperature declines sensitivity of plant growth to changing temperature. The results also suggest that the NDVI trends induced by climate changes varied between different vegetation types and seasons.

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[34]
Seager R, Ting M, Held I , et al. 2007. Model projections of an imminent transition to a more arid climate in southwestern North America. Science, 316(5828): 1181-1184.How anthropogenic climate change will affect hydroclimate in the arid regions of southwestern North America has implications for the allocation of water resources and the course of regional development. Here we show that there is a broad consensus among climate models that this region will dry in the 21st century and that the transition to a more arid climate should already be under way. If these models are correct, the levels of aridity of the recent multiyear drought or the Dust Bowl and the 1950s droughts will become the new climatology of the American Southwest within a time frame of years to decades.

DOI PMID

[35]
Sen P K, 1968. Estimates of the regression coefficient based on Kendall’s tau.Journal of the American Statistical Association, 63(324): 1379-1389.

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[36]
Song Y, Ma M G, 2011. A statistical analysis of the relationship between climatic factors and the normalized difference vegetation index in China.International Journal of Remote Sensing, 45(14): 374-82.Climate change has a large impact on vegetation dynamics. A series of statistical analyses were employed to demonstrate the relationship between Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data with an 865×658 km resolution and meteorological data, during the period 1982–2005. Rainfall has a great impact on vegetation with varying time lags. The sensitivity of NDVI to the threshold of accumulated temperature varies regionally. To identify a ‘best factor’ for each meteorological station simple and partial correlation analyses were carried out. Multiple correlation analysis was used to validate the association between the two climatic factors and monthly maximum NDVI (MNDVI). This study led to the conclusion that good correlations between MNDVI and two climatic factors are prevalent in China. It also indicated that the ‘best factors’ for some regions identified by partial correlation analysis are better than those selected by simple correlation analysis. The partial correlation coefficients of MNDVI and each climate factor were calculated to describe the singular influence of each meteorological variable. The results indicated that the impact of other variables on vegetation should be considered in the ‘best factor’ selection for one climatic variable. Temperature has a significant positive influence on vegetation growth in China. Precipitation is the most important climatic factor that closely correlates with MNDVI, particularly in arid and semi-arid environments. However, in some wet regions, precipitation is not a limiting factor on vegetation growth. A trend analysis was carried out to study climate change and its impacts on vegetation. The annual accumulated temperature had an increasing trend in China during 1982–2005. Temperature increases had different influences on vegetation dynamics in different parts of China. The results coincided with those of the multiple and partial correlation analysis.

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[37]
Tošić I, 2004. Spatial and temporal variability of winter and summer precipitation over Serbia and Montenegro.Theoretical and Applied Climatology, 77(1): 47-56.The main characteristics of the spatial and temporal variability of winter and summer precipitation observed at 30 stations in Serbia and Montenegro were analysed for the period 1951-2000. The rainfall series were examined spatially by means of Empirical Orthogonal Functions (EOF) and temporally by means of the Mann-Kendall test and spectral analysis. The Alexandersson test was used to detect the inhomogeneity of the data set.The EOF analysis gave three winter and summer dominant modes of variations, which explained 89.7% and 70.4% of the variance, respectively. The time series associated with the first pattern showed a decreasing trend in winter precipitation. The spectral analysis showed a 16-year oscillation for the dominant winter pattern, around a 3-year oscillation for the dominant summer pattern, and a quasi-cycle of 2.5 years for the winter third pattern.

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[38]
Wang L, Zhen L, Liu X L , et al. 2008. Comparative studies on climate changes and influencing factors in central Mongolian Plateau region.Geographical Research, 27(1): 171-180. (in Chinese)Mongolian Plateau Region is facing many problems in environment and sustainable development including land degradation,soil erosion,water pollution,solid and hazardous waste disposal,land use conflicts and desertification,which have led to social consequences such as urban unemployment and poverty.Those issues are very serious in central part of the region where population density is relatively high and intensive economic activities have caused several problems such as climate change.The central Mongolian Plateau Region covers seven provinces(municipalities) of Mongolia including Selenge,Darhan-Uul,Ulaanbaatar,Govisumber,Tov,Dundgovi and Dornogovi and four sub-provinces of Inner Mongolian Autonomous Region of China like Huhhot,Baotou,XilinGol,and Ulan Qab.The study aims at comparative analysis of climate changes,impacts on major economic activities and affecting factors in Mongolia and Inner Mongolia using temperature and precipitation data extending from 1940 to 2004 from six stations of Mongolia and 1951~2004 from six stations of Inner Mongolia of China.The results show that in comparison with temperature data in the 1960s,the average temperature has risen by 1.35 in the 1990s in Inner Mongolia of China,while it is 1.13 in Mongolia.In the period of 2000-2004,the average temperature increase was 1.89 in Inner Mongolia and 1.37 in Mongolia in comparison with the 1960s,showing a faster temperature increase in Inner Mongolia than that in Mongolia.Mutation test of the changing trend of temperature indicates that temperature mutation usually occurs in the areas with high latitude followed by those of low latitude,and big cities followed by small and medium-sized towns.However,significant change of precipitation was not observed,but periodic changes instead.For instance,duration of precipitation in Inner Mongolia is 2.8 years,while it is 4 and 8 years in Mongolia,and those results reach 95% level of correction test.The climate change has brought about significant impacts on agricultural production,livestock raising and environment,which have been considered as important components of sustainable development of the region in a long run. The results are significant for understanding interaction between climate change,impacts and driving factors,and identifying most important areas for policy intervention,and finally for sustainable use and management of fragile natural resources.

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[39]
Wei Y J, Zhen L, Ochirbat B , et al. 2009. Empirical study on consumption of ecosystem services and its spatial differences over Mongolian Plateau.Resources Science, 31(10): 1677-1684. (in Chinese)As a hotspot in field of ecological economics since the 1990s,ecosystem service and its value have been widely studied and evaluated.However,processes and characteristics of the supply,consumption and valuation on ecosystem services were still not well known.There is no systemic theory that can rationally explain and analyze behaviors of producers and consumers of ecosystem services.The presented study on human consumption of natural ecosystem services would shed light on the interactions between natural system and human system for sustainable management of ecosystems.Population growth with associated increasing interventions and the consumption of natural ecosystem services have caused significant changes in ecosystems.The Mongolian Plateau is one of the most frangible dry land ecosystems in the world which are likely to be affected by global climate change.Based on primary survey data and corresponding statistics,from the perspective of ecosystem service consumers,the food and fuelwood consumptions of ecosystem services in typical pastoral areas of Mongolia were empirically investigated.The spatial differences in the consumptions with their impacts were subsequently discussed.The results were compared with the consumption of ecosystem services in the pastoral areas of Inner Mongolia,P.R.China,indicating that Mongolians in the study area mainly consume wheat,a variety of meat and dairy products as food and consumes firewood and animal dung as important sources.Inner Mongolians in the study site consume little horse meat and more per capita consumption of vegetables and straw than do the Mongolians.Socio-economic factors,the availability or accessibility of ecosystem services as well as consumer behaviors,such as consumer preferences,jointly affect the human consumption patterns and willingness of ecosystem services,and affect the spatial differences in the ecosystem of consumer services.A series of natural and social effects caused by land use change have significant impacts on eco-environmental and socio-economic developments in the Mongolian Plateau.In addition,we explored the consumption differences in ecosystem services over the Mongolian Plateau under different degrees of human interactions with ecosystems.Under different conditions of economic systems and socio-economic development levels,local people are faced with serious problems about land use change and ecosystem degradation in both Mongolia and Inner Mongolia.The study would provide a scientific basis for ecological-environmental-economic sustainable development in the Mongolian Plateau.

[40]
Wessels K J, Prince S D, Malherbe J , et al. 2007. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa.Journal of Arid Environments, 68(2): 271-297.Advanced Very High Resolution Radiometer (AVHRR), Normalized Difference Vegetation Index data (NDVI, 102km 2 , 1985–2003) and modeled net primary production (NPP, 802km 2 , 1981–2000) data were used to estimate vegetation production in South Africa (SA). The linear relationships of Log e Rainfall with NPP and ΣNDVI were calculated for every pixel. Vegetation production generally had a strong relationship with rainfall over most of SA. Therefore, human-induced land degradation can only be detected if its impacts on vegetation production can be distinguished from the effects of rainfall. Two methods were tested (i) Rain-Use Efficiency (RUE=NPP/Rainfall or ΣNDVI/Rainfall) and (ii) Residual Trends (RESTREND), i.e. negative trends in the differences between the observed ΣNDVI and the ΣNDVI predicted by the rainfall. Degraded areas mapped by the National Land Cover in north-eastern SA had reduced RUE; however, annual RUE had a very strong negative correlation with rainfall and varied greatly between years. Therefore, RUE was not a reliable indicator of degradation. The RESTREND method showed promising results at a national scale and in the Limpopo Province, where negative trends were often associated with degraded areas in communal lands. Both positive and negative residual trends can, however, result from natural ecological processes, e.g. the carryover effects of rainfall in previous years. Thus, the RESTREND method can only identify potential problem areas at a regional scale, while the cause of negative trends has to be determined by local investigations.

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[41]
Whetton P H, Fowler A M, Haylock M R , et al. 1993. Implications of climate change due to the enhanced greenhouse effect on floods and droughts in Australia. Climatic Change, 25(3): 289-317.Potential impacts of climate change on heavy rainfall events and flooding in the Australian region are explored using the results of a general circulation model (GCM) run in an equilibrium enhanced greenhouse experiment. In the doubled CO 2 simulation, the model simulates an increase in the frequency of high-rainfall events and a decrease in the frequency of low-rainfall events. This result applies over most of Australia, is statistically more significant than simulated changes in total rainfall, and is supported by theoretical considerations. We show that this result implies decreased return periods for heavy rainfall events. The further implication is that flooding could increase, although we discuss here the many difficulties associated with assessing in quantitative terms the significance of the modelling results for the real world.The second part of the paper assesses the implications of climate change for drought occurrence in Australia. This is undertaken using an off-line soil water balance model driven by observed time series of rainfall and potential evaporation to determine the sensitivity of the soil water regime to changes in rainfall and temperature, and hence potential evaporation. Potential impacts are assessed at nine sites, representing a range of climate regimes and possible climate futures, by linking this sensitivity analysis with scenarios of regional climate change, derived from analysis of enhanced greenhouse experiment results from five GCMs. Results indicate that significant drying may be limited to the south of Australia. However, because the direction of change in terms of the soil water regime is uncertain at all sites and for all seasons, there is no basis for statements about how drought potential may change.

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[42]
Xin Z B, Xu J X, Zheng W, 2008. Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981-2006): Impacts of climate changes and human activities.Science China Earth Sciences, 51(1): 67-78. (in Chinese)Spatiotemporal variations of Chinese Loess Plateau vegetation cover during 1981–2006 have been investigated using GIMMS and SPOT VGT NDVI data and the cause of vegetation cover changes has been analyzed, considering the climate changes and human activities. Vegetation cover changes on the Loess Plateau have experienced four stages as follows: (1) vegetation cover showed a continued increasing phase during 1981–1989; (2) vegetation cover changes came into a relative steady phase with small fluctuations during 1990–1998; (3) vegetation cover declined rapidly during 1999–2001; and (4) vegetation cover increased rapidly during 2002–2006. The vegetation cover changes of the Loess Plateau show a notable spatial difference. The vegetation cover has obviously increased in the Inner Mongolia and Ningxia plain along the Yellow River and the ecological rehabilitated region of Ordos Plateau, however the vegetation cover evidently decreased in the hilly and gully areas of Loess Plateau, Liupan Mountains region and the northern hillside of Qinling Mountains. The response of NDVI to climate changes varied with different vegetation types. NDVI of sandy land vegetation, grassland and cultivated land show a significant increasing trend, but forest shows a decreasing trend. The results obtained in this study show that the spatiotemporal variations of vegetation cover are the outcome of climate changes and human activities. Temperature is a control factor of the seasonal change of vegetation growth. The increased temperature makes soil drier and unfavors vegetation growth in summer, but it favors vegetation growth in spring and autumn because of a longer growing period. There is a significant correlation between vegetation cover and precipitation and thus, the change in precipitation is an important factor for vegetation variation. The improved agricultural production has resulted in an increase of NDVI in the farmland, and the implementation of large-scale vegetation construction has led to some beneficial effect in ecology.

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[43]
Yatagai A, Yasunari T, 1995. Interannual variations of summer precipitation in the arid/semi-arid regions in China and Mongolia: Their regionality and relation to the Asian summer monsoon.Journal of the Meteorological Society of Japan, 73(5): 909-923.ABSTRACT In this study, interannual variations of summertime precipitation over arid and semi-arid regions in China and Mongolia are investigated. To clarify the regionality of the interannual variability in summer precipitation, an analysis technique of rotated empirical orthogonal functions is applied for a recent 40-year (1951-1990) period of summer precipitation. As a result of the REOF, five regions have been determined: I) Taklimakan Desert, II) Loess Plateau, III) North China to central and the southeastern part of Mongolia, IV) the north of Tianshan Mountains, and V) the northern part of Mongolia. Summertime precipitation over Region III) shows a significant decreasing trend after 1955. Next, to examine how the variations in precipitation in these regions are influenced by the Asian (Indian) summer monsoon activity in the mid-latitudes, correlations with all-India monthly and seasonal rainfall (IMR) are investigated. Further, the change of atmospheric circulation patterns with the interannual variation of summer precipitation of Regions I and II are also examined. The results are summarized as follows: The interannual variation of summer precipitation of Region I (Taklimakan Desert) is mainly related to the windward mid-latitude circulation and eastward (westward) shift of the Tibetan High in a wet (dry) year. This region shows a clear negative correlation with IMR in June and July, and the relationships are caused by a rather local circulation change with IMR variation over Central Asia. In Region II (Loess Plateau, the middle reaches of Yellow River), interannual variation of summer precipitation shows a positive correlation with IMR through the summer monsoon season. It shows a clear 2-3 year periodic oscillation, and seems to be closely related to the atmosphere/ocean interaction in the equatorial Pacific.

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[44]
Zhang X Y, Hu Y F, Zhuang D Fet al., 2009. NDVI spatial pattern and its differentiation on the Mongolian Plateau.Journal of Geographical Sciences, 19(4): 403-415.GIMMS NDVI database and geo-statistics were used to depict the spatial distribution and temporal stability of NDVI on the Mongolian Plateau. The results demonstrated that: (1) Regions of interest with high NDVI indices were distributed primarily in forested mountainous regions of the east and the north, areas with low NDVI indices were primarily distributed in the Gobi desert regions of the west and the southwest, and areas with moderate NDVI values were mainly distributed in a middle steppe strap from northwest to southeast. (2) The maximum NDVI values maintained for the past 22 years showed little variation. The average NDVI variance coefficient for the 22-year period was 15.2%. (3) NDVI distribution and vegetation cover showed spatial autocorrelations on a global scale. NDVI patterns from the vegetation cover also demonstrated anisotropy; a higher positive spatial correlation was indicated in a NW-SE direction, which suggested that vegetation cover in a NW-SE direction maintained increased integrity, and vegetation assemblage was mainly distributed in the same specific direction. (4) The NDVI spatial distribution was mainly controlled by structural factors, 88.7% of the total spatial variation was influenced by structural and 11.3% by random factors. And the global autocorrelation distance was 1178 km, and the average vegetation patch length (NW-SE) to width (NE-SW) ratio was approximately 2.4:1.0.

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[45]
Zhao X, Hu H F, Shen H H , et al. 2015. Satellite-indicated long-term vegetation changes and their drivers on the Mongolian Plateau.Landscape Ecology, 30(9): 1599-1611.The Mongolian Plateau, comprising the nation of Mongolia and the Inner Mongolia Autonomous Region of China, has been influenced by significant climatic changes and intensive human activities. Previous satellite-based analyses have suggested an increasing tendency in the vegetation cover over recent decades. However, several ground-based observations have indicated a decline in vegetation production. This study aimed to explore long-term changes in vegetation greenness and land surface phenology in relation to changes in temperature and precipitation on the Plateau between 1982 and 2011 using the normalized difference vegetation index (NDVI). Across the Plateau, a significantly positive trend in the growing season (Mayeptember) NDVI was observed from 1982 to 1998, but since that time, the NDVI has not shown a persistent increase, thus causing an insignificant trend over the entire study period. For the steppe vegetation (a major vegetation type on the Plateau), the NDVI increased significantly in spring but decreased in summer. Precipitation was the dominant factor related to changes in steppe vegetation. Warming in spring contributed to earlier vegetation green-up only in meadow steppe vegetation, implying that water deficiency in typical and desert steppe vegetation may eliminate the effect of warming. Our results also suggest a combined effect of climatic and non-climatic factors and highlight the need to examine the role of regional human activities in the control of vegetation dynamics.

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[46]
Zhao Y X, Qiu, G W, 2001. Study of climate change impact on northern farming-pastoral region.Meteorological Monthly, 27(5): 3-7. (in Chinese)From the view point of sustainable development,the relationship between climate change and sustainable development in northern farming pastoral region is discussed.The emphasis is put on the impact study of climate change on agriculture and animal husbandry in the farming pastoral region.It points out that climate change may cause the farming pastoral area′s transfer to southeast,the certain unfavorable impacts on agriculture and the certain unfavorable and favorable impacts on animal husbandry.

[47]
Zhao Z C, Wang S W, Luo Y, 2007. Assessments and projections of temperature rising since the establishment of IPCC.Advances in Climate Change Research, 3(3): 83-84. (in Chinese)

[48]
Zhen L, Liu J Y, Liu X L , et al. 2008. Structural change of agriculture-livestock system and affecting factors in Mongolian Plateau.Journal of Arid Land Resources and Environment, 22(1): 144-151. (in Chinese)In recent years,the structure of agriculture-livestock system in Mongolian plateau has changed significantly.In order to explore the change and its reasons,researchers from China,Mongolia and Japan have began to work together to deal with the common problems.The present situation about agriculture and livestock,and the structural change of agriculture-livestock system in different time scales in Mongolia were illustrated.Statistical and temporal-geographical comparison analysis was done by using statistical data and information from field investigation.The conclusions demonstrated that the plantation area ratio was low in Mongolia;productivity of main crops was extremely low;Plantation area began expanding from 1950s and reached a higher level in 1980s,and then decreased consistently;grain ield increased before the middle of 1980s,then decreased steadily;The change of grain per capita was associated with the change of total yield;livestock was Mongolia's fundamental industry,but because of its extensive management model animal products' production was far from reaching a high level.Livestock's number,especially sheep,was increasing since the beginning of the 20th century.However,rapid population increase leaded to fluctuant decrease of livestock per capita.It was considered that climate change,technical input in agriculture,livestock management and economic and social policies were the main factors influencing Mongolian agriculture and livestock development.Among these factors,climate disasters and open market economy affected Mongolian agriculture and livestock mostly.The problems that Mongolia and Inner Mongolia faced was comparied.

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[49]
Zhou L M, Kaufmann R K, TianY , et al. 2003. Relation between interannual variations in satellite measures of northern forest greenness and climate between 1982 and 1999. Journal of Geophysical Research Atmospheres, 108(D1): ACL 3-16.1] This paper analyzes the relation between satellite-based measures of vegetation greenness and climate by land cover type at a regional scale (200° 0103 200° grid boxes) between 1982 and 1999. We use the normalized difference vegetation index (NDVI) from the Global Inventory Monitoring and Modeling Studies (GIMMS) data set to quantify climate-induced changes in terrestrial vegetation. Climatic conditions are represented with monthly data for land surface air temperature and precipitation. The relation between NDVI and the climate variables is represented using a quadratic specification, which is consistent with the notion of a physiological optimum. The effects of spatial heterogeneity and unobserved variables are estimated with specifications and statistical techniques that allow coefficients to vary among grid boxes. Using this methodology, we are able to estimate statistically meaningful relations between NDVI and climate during spring, summer, and autumn for forests between 4000°N and 7000°N in North America and Eurasia. Of the variables examined, changes in temperature account for the largest fraction of the change in NDVI between the early 1980s and the late 1990s. Changes in stratospheric aerosol optical depth and precipitation have a smaller effect, while artifacts associated with variations in solar zenith angle are negligible. These results indicate that temperature changes between the early 1980s and the late 1990s are responsible for much of the observed increase in satellite measures of northern forest greenness.

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[50]
Zhou L M, Tucker C J, Kaufmann R K , et al. 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999.Journal of Geophysical Research Atmospheres, 106(D17): 20069-20084.

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[51]
Zhou X Y, Shi H D, Wang X R , et al. 2012. Study on the temporal and spatial dynamic changes of land use and driving forces analysis of Mongolia Plateau in recent 30 years.Acta Agriculture Zhejiangensis, 24(6): 1102-1110. (in Chinese)With the aid of the 3S technology,the land use change data during 1970s and 2005 in Mongolian Plateau were obtained.The spatial and temporal characteristics of the Mongolian Plateau land use change in the past 30 years was revealed by analyzing the changing process.The results showed that the total area of grassland was significantly reduced,a large area of bare land increased,urban and rural construction land expanded significantly,the area of cultivated land,forest land and water increased in Mongolian Plateau in recent 30 years.Grassland degradation and desertification became the significant spatial and temporal characteristics of land-use change in Mongolian Plateau,and the situation of Inner Mongolia was more serious than Mongolia.The forestry ecological engineering in Inner Mongolia played a positive role in protecting the ecological environment.Population,policy,economic and climate change were the main driving forces of the land use dynamic change in Mongolian Plateau.

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[52]
Zhuo Y, 2007. The ration remote sensing method study of desertification of Mongolian Plateau based on MODIS data [D]. Huhhot: Inner Mongolia Normal University, 1-51. (in Chinese)

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