Orginal Article

Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014

  • LIU Xianfeng , 1, 2 ,
  • ZHU Xiufang 1, 2 ,
  • *PAN Yaozhong , 1, 2 ,
  • LI Shuangshuang 1, 3 ,
  • MA Yuqi 2 ,
  • Nie Juan 4
  • 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
  • 2. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China
  • 3. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
  • 4. National Disaster Reduction Center of China, Beijing 100124, China

Author: Liu Xianfeng (1986-), PhD Candidate, specialized in resource and environmental remote sensing and disaster remote sensing. E-mail:

*Corresponding author: Pan Yaozhong (1965-), PhD and Professor, specialized in statistics and disaster remote sensing research. E-mail:

Received date: 2015-08-03

  Accepted date: 2015-08-30

  Online published: 2016-01-25

Supported by

Major Project of High-resolution Earth Observation System

Beijing Natural Science Foundation, No.8144052


Journal of Geographical Sciences, All Rights Reserved


Using the Moderate Resolution Imaging Spectroradiometer-normalized difference vegetation index (NDVI) dataset, we investigated the patterns of spatiotemporal variation in vegetation coverage and its associated driving forces in the Qinling-Daba (Qinba) Mountains in 2000-2014. The Sen and Mann-Kendall models and partial correlation analysis were used to analyze the data, followed by calculation of the Hurst index to analyze future trends in vegetation coverage. The results of the study showed that (1) NDVI of the study area exhibited a significant increase in 2000-2014 (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) Spatially, vegetation cover showed a “high in the middle and a low in the surroundings” pattern. High values of vegetation coverage were mainly found in the Qinba Mountains of Shaanxi Province. (3) The area with improved vegetation coverage was larger than the degraded area, being 81.32% and 18.68%, respectively, during the study period. Piecewise analysis revealed that 71.61% of the total study area showed a decreasing trend in vegetation coverage in 2010-2014. (4) Reverse characteristics of vegetation coverage change were stronger than the same characteristics on the Qinba Mountains. About 46.89% of the entire study area is predicted to decrease in the future, while 34.44% of the total area will follow a continuously increasing trend. (5) The change of vegetation coverage was mainly attributed to the deficit in precipitation. Moreover, vegetation coverage during La Nina years was higher than that during El Nino years. (6) Human activities can induce ambiguous effects on vegetation coverage: both positive effects (through implementation of ecological restoration projects) and negative effects (through urbanization) were observed.

Cite this article

LIU Xianfeng , ZHU Xiufang , *PAN Yaozhong , LI Shuangshuang , MA Yuqi , Nie Juan . Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014[J]. Journal of Geographical Sciences, 2016 , 26(1) : 45 -58 . DOI: 10.1007/s11442-016-1253-8

1 Introduction

The Qinling-Daba (Qinba) Mountains are an important geoecological boundary in central China and the water source for the middle route of South-to-North Water Diversion Project and the largest continuous poverty areas in China. Hence it is of great significance for the region to sustainable development of both economy and ecological environment. As many problems have emerged in the global population, resources, and environment since the 20th century, sustainable development of human beings faces great challenges (Fu, 2014), and the terrestrial ecosystem has been greatly disturbed by climate change and human activities. As an ecologically vulnerable area that is extremely sensitive to climate change, the ecosystem of the Qinba Mountains may be changed or even damaged under rapid climate change (Walther et al., 2002; Parmesan, 2006). However, little is known about the ecosystem of the area because of limited and fragmented data, and an overall survey and evaluation of the main geographical features are required to improve our understanding of the effect of climate change on typical terrestrial ecosystems(Presentation of academicians Sun Honglie, Zheng Du, and Sun Jiulin at a seminar conference on the environment and development of the Qinba Mountains and rivers and their impacts on arid and semi-arid regions.). As the main factor of a terrestrial ecosystem, vegetation is significantly affected by climate change, and in turn, it influences the atmospheric environment to a great extent through feedback of biochemical processes. Moreover, vegetation coverage is widely recognized as a priority indicator in monitoring the ecological environment and evaluating ecological risk and vulnerability (Liu et al., 2013). Thus, it is important to analyze spatiotemporal variation in vegetation coverage on the Qinba Mountains in order to understand the influence of climate and human factors on its ecosystem.
With increasing concern over global climate change, there is much interest in the relationship between the terrestrial ecosystem and global climate change (Walker et al., 1997; Shi et al., 2014); the response of vegetation to climate change is being studied by scholars worldwide (Nemani et al., 2003; Ma et al., 2012). According to previous studies, climate change can be divided into trend, fluctuation, and extreme events (Stocker et al., 2013; Shi et al., 2014). Many scholars have ascribed the variation in vegetation coverage to the former two aspects of climate change (Zhang et al., 2013; Ding et al., 2007), while there are fewer studies on the influence of extreme climate events on vegetation growth. Recently, scholars worldwide have evaluated the response of vegetation to extreme climate conditions in key ecological zones, such as Amazon and Congo rainforests, and have drawn conclusions (Hilker et al., 2014; Lewis et al., 2011; Zhou et al., 2014). However, in China, there are few studies in this area, especially studies of regions extremely vulnerable to climate change, like the Qinba Mountains. According to IPCCAR5, atmospheric temperature has globally increased by 0.72°C (0.49-0.89°C) on average in the past 60 years (1951-2012) (Stocker et al., 2013), while it has increased by 1.38°C in China between 1960 and 2009. This suggests that the speed of temperature increase is higher in China than globally and in the Northern Hemisphere (Compiling Committee, 2011); the greatest increase was observed in the northern part of China, resulting in a growth trend of the frequency and intensity of extreme events (Liu et al., 2014; Reichstein et al., 2013). This increased the risk and vulnerability of terrestrial ecosystems and endangered ecological security. In addition, other studies have suggested that the correlation between vegetation growth and atmospheric temperature is weakening and that drought is probably the reason (Piao et al., 2014). Thus, extreme climate events influence not only vegetation but also the relationship between vegetation and climate features. Since there is a significant difference in the structure of geographical features and their changing patterns in different regions, focusing on a regional study is more beneficial for understanding the evolution of terrestrial ecosystems (Fu, 2014). Thus, it is necessary to re-evaluate vegetation coverage and response to the climate because recent climate change has caused vegetation to be exceptionally sensitive to extreme weather conditions.
Considering that the Qinba Mountains are a key ecological zone, studying its geographical features by now still weak. Moreover, since extreme climate events are occurring more frequently because of recent great climate fluctuations, it is urgent to study the relation between extreme climate and vegetation coverage change for the smooth implementation of the projects for synthetic risks prevention and disaster reduction and control. Meanwhile, the initiation of the “New-Round Grain for Green Project Plan”, acquiring a complete understanding the background feature of vegetation and its response to climate change has become very important. Therefore, we analyzed the current trend, fluctuation, and predicted future trend of vegetation coverage on the Qinba Mountains by using the normalized difference vegetation index (NDVI) data and trend analysis, residual analysis, and the Hurst exponent method. Through analyzing the changing trend in vegetation coverage, we can find the variation characteristics of the regional environment, facilitating the synergetic development of regional ecology and social economy.

2 Data and methods

2.1 Study area

The Qinba Mountains, located in central China between 102°54′-112°40′E and 30°50′- 34°59′N (Figure 1), has a total area of 222,300 km2 and across Shaanxi, Gansu, Sichuan, Hubei, Henan, and Chongqing (Bai, 2014). They are divided into three units on the basis of topographical features: Qinling Mountains, Hanjiang River valley, and Daba Mountains. Since Qinling Mountains and Daba Mountains both help in blocking cold air from the north and warm air from the south-west and the region is controlled by Mongolian cold high in winter and by both west-extended Pacific subtropical high and Sichuan Basin warm low in summer, the climate is cold and dry in winter and wet with summer drought in summer across the study area (Liu, 1983). The vegetation is highly diversified because of a climate transition of the subtropical zone and warm temperate zone, with the north and south biotas converging together. In Qinling Mountains, warm temperate deciduous broad-leaved forests are dominantly found, and to the south of the region, a mixed broad-leaved deciduous forests and evergreen broad-leaved forests are found (Ren et al., 2003).
Figure 1 Study area and distribution of meteorological stations in the Qinba Mountains

2.2 Data source

Remote sensors were used to monitor vegetation coverage, mainly on the basis of spectral reflection information and the contrast between strong absorption in the visible spectrum and high reflection in the near-infrared spectrum (Jiang et al., 2008). Hundreds of vegetation indices have been proposed on the basis of these characters, and the normalized difference vegetation index (NDVI) is most widely used because it effectively responds to vegetation fluctuation (Barati et al., 2011). Considering that the study area is a mountain region, we selected the MOD13Q1NDVI dataset obtained from EOS/MODIS product of NASA to analyze vegetation coverage change in the study area because of the high spatial resolution of 250 m × 250 m and time resolution of 16 days. The time span of NDVI used in our study was from 2000 to 2014. The dataset was guaranteed to be of high quality and dealt with water, clouds, and heavy aerosol, and it is widely used in studies on regional vegetation coverage. First, data form and projection of the raw MODIS-NDVI data were transformed using MODIS Reprojection Tools. Then, we acquired monthly NDVI data by using the max value composite to eliminate the effect of outliers (Holben, 1986). Finally, we calculated maximum annual NDVI, which reflects the true situation of vegetation coverage in a region, to detect spatiotemporal variation in NDVI.
The meteorological datasets (18 meteorological stations in the Qinba Mountains in 2000-2014), including average temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, and precipitation, were obtained from the China Meteorological Data Sharing Service System ( Southern oscillation index (SOI) data for the same period were obtained from 74 circulation index datasets published by the national climate center on climate change and prediction research associated with the China Bureau of Meteorology.

2.3 Methods

2.3.1 Trend analysis
The trend of NDVI in the study area was calculated using Sen trend analysis (Sen, 1968), and the significance of the trend was further examined using the Mann-Kendall statistical test (Kendall, 1948). The advantage of Sen trend analysis is that the method does not require a specific sample distribution and is free from the interference of outliers. Therefore, this method is robust and resistant to errors due to measurements and outlier data. The formula is as follows:
where β represents the trend of NDVI; i and j denote the time series, and xi and xj denote the NDVI value at time i or j, respectively. β > 0 suggests that the vegetation coverage increases, and β< 0 suggests otherwise.
2.3.2 Stability analysis
Coefficient of variation (CV) can be used to describe the relative fluctuation in geographical data (Xu, 2002). Thus, we adopted CV to reflect the stability of NDVI change. The formula for CV is as follows:
where Cv is the CV; i is time series; xi is the NDVI value at time i, and denotes the average of NDVI during the study period. A higher Cv value denotes the existence of a large fluctuation in the time series and vice versa.
We further analyzed the fluctuation in NDVI by using residual analysis, in order to obtain more robust results. Specifically, (1) calculate the residual values of NDVI according to the regression model, (2) use the absolute value of the residual to generate a residual time series, and (3) perform regression analysis of the residual time series (the value of the trend is approximated to zero, which suggests less fluctuations in NDVI and vice versa).
2.3.3 Future trend analysis
The Hurst exponent, proposed by the British hydrologist Hurst, is estimated using R/S analysis and is an effective way of measuring the long-time dependence of a time series. The main principles are as follows (John et al., 2008).
1. Given a time series {ξ(t)} t=1, 2, …, n, divide the time series into τ subseries ξ(t).
2. Define the mean sequence of the time series:
τ=1, 2, (3)
3. Calculate cumulative deviation:
1≤t≤τ (4)
4. Calculate range sequence:
τ=1, 2, … (5)
5. Calculate standard deviation sequence:
τ=1, 2, … (6)
If R/SτH, the time series shows the Hurst phenomenon, and the H value is called the Hurst exponent, which can be obtained by least squares fitting in the double logarithmic coordinate system. According to Hurst (1951), the value of the Hurst exponent expands from 0 to 1, and it can be divided into three groups: (1) H > 0.5 refers to the persistence of the series, which indicates the same trend in the time series in the future, with a greater value for more persistence. (2) H = 0.5 implies that the time series was random without persistence, which indicates that changes in the time series in the future would be unrelated to those in the study period. (3) H < 0.5 refers to anti-persistence of the time series, which indicates an anti-trend in the time series in the future, with smaller values for more anti-persistence sustainability.

3 Results

3.1 Temporal variations of vegetation coverage

Spatial averaged NDVI showed a significant increasing trend in the Qinba Mountains between 2000 and 2014, with a linear tendency of 2.8%/10a (p < 0.001). Piecewise analysis showed that the variation trend of NDVI reversed in 2010. Vegetation coverage was continuously growing at the rate of 4.32%/10a before 2010 (p < 0.001), and it declined sharply at 6.59%/10a (p < 0.05) afterwards; in 2014, it decreased to the greatest extent (Figure 2). This phenomenon suggested that despite ecological restoration projects, like the large-scale Grain for Green project, in the area, vegetation coverage did not grow stably after 2010, but showed a growing trend at first and a decreasing trend afterwards; this may be attributed to the large- scale drought observed recently.
Figure 2 Temporal variation in vegetation coverage in the Qinba Mountains during 2000-2014

3.2 Spatial variations of vegetation coverage

3.2.1 General characteristics of NDVI
Generally, vegetation coverage is high in the central part of the region and low at the edges. High values of NDVI were mainly distributed in the Qinling and Daba Mountains of Shaanxi Province because these regions are covered by well-grown broad-leaved forests, coniferous forests, and shrubs. Low values of NDVI were distributed in Lixian, Xihe, and Wudu of Longnan; Wushan and Gangu of Tianshui; Zhouqu of south Gansu; and some parts of the Hanjiang river valley, mostly because these areas are mainly croplands with low NDVI. However, vegetation coverage in these areas is rapidly recovering because of the Grain for Green Project. According to the NDVI frequency plot, vegetation coverage is generally high in the Qinba Mountains, and areas where NDVI is greater than 0.7 account for 92.48% of the whole region (Figure 3a). With increasing altitude, the vegetation coverage on the Qinba Mountains is gradually growing. Vegetation coverage between 500 and 3600 m is stable, and the highest at 1200 m. However, it decreases rapidly after 3600 m, probably because only grasslands and shrubs are found at a low density at this height (Figure 3b).
Figure 3 Spatial distribution (a) and altitude-dependent (b) of NDVI on the Qinba Mountains
3.2.2 Trend of NDVI change
To analyze the variation trend in vegetation coverage in the Qinba Mountain region, we calculated the trend of NDVI between 2000 and 2014 and the result was classified into 4 levels after the M-K test: extremely significant variation (p < 0.01), significant variation (p < 0.05), weakly significant variation (p < 0.1), and no significant variation. Results show that, generally, vegetation coverage in the region increased. The area with improved vegetation coverage was larger than the degraded area, being 81.32% and 18.68%, respectively, during the study period. As for the former part, 46.86% shows no obvious variation, while extremely significant increased and significant increased areas account for 7.18% and 16.14%, respectively, of the entire study area (Table 1). Spatially, areas with significant vegetation coverage growth were mainly located in the northwestern part of the region because of the implementation of the Grain for Green Project (Figure 4). Further analysis of the variation trend between 2010 and 2014 showed that NDVI values decreased in 71.61% of the whole Qinba Mountain region, of which significant variation and extremely significant variation accounted for 6.38% and 3.45%, respectively, being mainly distributed in eastern Longnan, southwestern Baoji, all counties in Hanzhong, and southwestern Ankang.
Figure 4 Trend and significance of NDVI on the Qinba Mountains in 2000-2014
Table 1 Area and proportion of different vegetation change types on the Qinba Mountains
Type 2000-2014 2010-2014
Percentage/% Cumulative percentage/% Pixel number Percentage
Cumulative percentage/%
Decrease 795821 18.68 18.68 2384471 55.96 55.96
Weakly significantly decrease - - - 248378 5.83 61.78
Significantly increase - - - 271746 6.38 68.16
Extremely significantly decrease - - - 147003 3.45 71.61
Increase 1996785 46.86 65.53 1099103 25.79 97.40
Weakly significantly increase 474747 11.14 76.67 46298 1.09 98.49
Significantly increase 687832 16.14 92.82 43585 1.02 99.51
Extremely significantly increase 306127 7.18 100.00 20727 0.49 100.00
3.2.3 Stability of NDVI change
The coefficient of variation for 2000-2014 was between 0.004 and 0.84, suggesting significant spatial differences in the stability of vegetation coverage in the study area. Specifically,areas with a low value were located mainly on the Qinling and Daba Mountains of Shaanxi Province, where broad-leaved forests and coniferous forests account for most of the vegetation and the variation is relatively stable. Areas with a high value were mainly located in the northwestern and eastern parts, where the land cover is arable land. Because of ecological restoration projects like Grain for Green, vegetation coverage in these areas is recovering at an unstable pace (Figure 5a). Furthermore, a residual analysis trend close to 0 also confirmed the low value of the variation coefficient (Figure 5b). Together, residual analysis trend and variation coefficient results were used in cross-validation for testing the credibility of this study.
Figure 5 Trend and significance of NDVI on the Qinba Mountains in 2000-2014

3.3 Future trend of NDVI

The above mentioned analysis mainly focused on the spatiotemporal variation of NDVI on the Qinba Mountains for 15 years; however, the trend of NDVI in the future is still unclear. Therefore, we calculated the future trend of NDVI on the basis of the Hurst exponent (Figure 6a). Results show that the average Hurst exponent is 0.4857 (0.0996-0.9837). Pixels where the Hurst exponent is less than 0.5 account for 57.12%, which indicates that the reverse characteristics of vegetation coverage change were stronger than the same characteristics on the Qinba Mountains. Statistical analysis showed that 46.89% of the entire study area is predicted to decrease from increase and 8.44% is predicted to degrade continuously in future, while 34.44% of the total area will follow a continuously increasing trend. Meanwhile, areas where the Hurst exponent between 0.4 and 0.6 account for 68.15% and areas where the Hurst exponent is less than 0.4 and 0.6 account for 19.36% and 12.49%, respectively, indicating that a relatively small area exhibited the strong reverse and same characteristics. It should be noted that the Hurst exponent varies slowly with an increase in altitude. While the value is mostly less than 0.5 under 4500 m, it increases significantly afterwards; this suggests that areas less than 4500 m mainly showed reverse characteristics (Figure 6b).
Figure 6 Future trend of NDVI and its difference with altitude on the Qinba Mountains

4 Attribution analysis of vegetation change

4.1 Response of vegetation coverage to climate factors

Global climate change has altered the regional pattern of temperature and precipitation, and therefore influenced vegetation growth. Statistical analysis showed that both temperature and precipitation showed a weakly increasing trend between 2000 and 2013 at the speed of 0.15°C/10a (p = 0.56) and 32.06 mm/10a (p = 0.66), respectively. Precipitation at Lueyang and Hanzhong in Shaanxi (stations 6 and 7) and Guangyuan in Sichuan (Station 12) has increased significantly, while precipitation showed a significantly decreasing trend at Fengjie in Chongqing (Station 18), which is consistent with a sharp increase in temperature and intensifies the magnitude of water deficit in this region (Figure 7).
Figure 7 Trend of temperature and precipitation on the Qinba Mountains in 2000-2013
To ensure the accuracy of the data and enhance the reliability of the research results, we adopted site-by-site partial correlation analysis to explore the response of vegetation to climate factors. Details of this method are as follows: (1) first, the NDVI values were extracted within a boundary of 3 km × 3 km according to the meteorological sites. (2) The partial correlation coefficient between NDVI and temperature and precipitation was calculated using detrend NDVI values, temperature, and precipitation time series data in 2000-2013. Results showed that the correlation of NDVI and temperature is -0.012 (-0.448 to 0.228), while the correlation of NDVI and precipitation is 0.612 (-0.110 to 0.695), through the significant level of 0.05; and stations 1 and 10 are both significantly correlated. Since the correlation of NDVI and precipitation is higher than that of NDVI and temperature, suggesting that vegetation growth in the study area is influenced to a greater extent by precipitation than by temperature.
Additionally, we further analyzed the variation in precipitation in the region. Considering that the vegetation coverage has shown a continuously decreasing trend since 2010, we focused on the changes of precipitation and vegetation after 2010. According to Figure 8a, there was a significant decline in precipitation in the central-eastern part, and only a limited area in the west showed growth due to rainfall. Moreover, regional average consecutive dry days (CDD) showed a significant increasing trend (1.15 d/a) on the Qinba Mountains in 2000-2013, and the highest increase occurred between 2010 and 2013 (3.38 d/a). Stations of 3, 5, 7, 10, 13, and 17 showed a notable increase in CDD, indicating an increase in drought in this area. Comparatively, vegetation growth showed a declining trend over a large extent at the same time of precipitation decrease, and thus water deficit may be responsible for the decrease in vegetation (Figure 8b). A previous study has reported that drought limits vegetation growth, lessens productivity, and eventually reduces the amount of fixed carbon in forests (Piao et al., 2014). Another study also documented that water use efficiency will be enhanced under drought conditions (Peñuelas et al., 2011). However, the water use efficiency in our study showed no obvious increase but a stable fluctuation in recent 15 years.
Figure 8 Precipitation deficit (a) and changes in the rate of NDVI (b) in 2010-2014
Climate fluctuation at a global scale results in extreme climate events and has a heavy impact on the terrestrial ecosystem. The results showed that the correlation between ENSO events and NDVI was 0.49 (p < 0.05) on the Qinba Mountains, suggesting that ENSO event has a significant influence on vegetation growth (Figure 9a). This finding is also consistent with the result of Hilker et al. (2014). To elucidate the relationship between vegetation growth and ENSO, we performed a composite analysis of NDVI and ENSO. First, we selected years with extreme El Nino (2002, 2006, and 2009) and La Nina (2007 and 2010), according to the statistics of El Nino and La Nina published by the National Climate Center, and then calculated the average NDVI in El Nino and La Nina years, respectively. As a result, years with extreme La Nina showed higher vegetation coverage than years with extreme El Nino (Figure 9b). According to the quantitative statistical results, in years with extreme El Nino, only 28.37% NDVI values showed a positive anomaly based on the average NDVI during the study period, while those that showed a negative anomaly were 71.63%. On the contrary, in years with extreme La Nina, NDVI values that showed a positive anomaly were 80.48%, while those that showed a negative anomaly were 19.52%; this further confirmed that ENSO has a strong impact on vegetation growth. In order to justify the above phenomenon, we further analyzed precipitation in the region. The results show that in the years with extreme La Nina, higher precipitation was observed, which is beneficial for vegetation growth, while lower precipitation in the years with extreme El Nino restrains the growth of vegetation, especially in summer (Babst et al., 2013). In summary, climate fluctuation at a global scale has a huge influence on regional vegetation growth, and vegetation growth will be further threatened if there are more extreme events like drought in the future.
Figure 9 Correlations between NDVI and SOI

4.2 Anthropogenic influences on vegetation dynamics

In addition to the change in climate and atmospheric composition, human activity is also responsible for the change in vegetation growth, and it is divided into positive activities, such as ecological restoration projects like Grain for Green, and negative activities, such as expansion of cities and forest damage. Therefore, we performed the analysis on the basis of two aspects. First, we collected data for annual forestation area in 96 counties from the Statistic Yearbook of China Forestry (Figure 10a). According to the statistics, between 2002 and 2004, the highest afforestation and NDVI growth were observed, ecological projects caused a gradual increase in vegetation coverage; this proves that these projects are effective in improving the regional ecological environment. It should be noted that, between 2010 and 2014, vegetation coverage declined despite the restoration of the ecological environment, and extreme climate events like drought were most probably the reason. Second, on the basis of the exemplification of Hanzhong, we believe that city expansion will damage vegetation and reduce vegetation coverage. We calculated the rate of change in NDVI between 2010 and 2014 and then created 10 buffer circles with the center at the city and a radius of 2 km in order to perform quantitative analysis of city expansion on vegetation coverage. Furthermore, we evaluated the rate of change in every buffer circle and found out that vegetation coverage in circles around cities has decreased; this proves that the expansion of cities is responsible for the decline in vegetation coverage. It should be noted that although the expansion area is limited, the damage of the expansion on vegetation is greater than the occupation of the land alone.
Figure 10 Influence of human activities on vegetation coverage

5 Conclusions and discussion

5.1 Conclusions

By using the MODIS-NDVI data and methods like trend analysis, Hurst exponent, and partial correlation analysis, we analyzed the variation in and factors that influenced vegetation coverage in the Qinba Mountain region. Our conclusions were as follows:
(1) Between 2000 and 2014, NDVI of the study area revealed a significant increase at an average speed of 2.8%/10a. However, a stable increase was detected before 2010 at the speed of 4.32%/10a, followed by a sharp decline at -6.59%/10a.
(2) Vegetation coverage pattern was higher in the central part and lower at the edges. Higher values were observed in the Qinling and Daba Mountains in Shaanxi Province, and vegetation coverage increases with the increase in altitude until 3600 m. NDVI declined rapidly afterwards.
(3) Vegetation coverage in the study area increased in general. The area with improved vegetation coverage was larger than the degraded area, being 81.32% and 18.68%, respectively during the study period. Piecewise analysis revealed that 71.61% of the total study area showed a decreasing trend in vegetation coverage in 2010-2014.
(4) The reverse characteristics of vegetation coverage change were stronger than the same characteristics on the Qinba Mountains. About 46.89% of the entire study area is predicted to decrease in the future, while 34.44% of the total area will show a continuously increasing trend. The Hurst exponent showed little variation with the increase in altitude, and the value was generally less than 0.5 under 4500 m.
(5) The change in vegetation coverage was mainly attributed to the deficit in precipitation. Meanwhile, ENSO has a large influence on vegetation growth, and composite analysis showed that vegetation coverage during the La Nina years was greater than that during the El Nino years.
(6) Human activities can induce ambiguous effects on vegetation coverage: both positive effects (through implementation of the ecological restoration project) and negative effects (through urbanization) were observed.

5.2 Discussion

Currently, scholars worldwide have focused on the influence of extreme climate events on vegetation (Hilker et al., 2014; Zhou et al., 2014; Zhao et al., 2015). Since drought occurs over a large area and lasts for a long time, it is considered the most fatal threat to vegetation growth. In this study, we analyzed the effect of extreme events on vegetation growth in typical years with extreme climate conditions, and we discussed the relationship between ENSO and vegetation change. However, considering that vegetation responds differently to drought in different seasons, drought is not always the primary factor. Since the climate is relatively cool in winter and spring, temperature may be the main limiting factor for vegetation growth. As for summer and autumn, which are relatively hot, precipitation may be more important. Therefore, how vegetation responds to extreme climate in different seasons requires further study. Separation of the contribution of climate change and human activities on vegetation coverage change is still an issue. New creative methods and theories are required.
In this study, we only focused on the influence of climate on vegetation, and the feedback of vegetation to the regional climate, which is also an important research area about the relationship between vegetation and climate, was not included. In addition, the sensibility of vegetation to climate change under extreme climate conditions requires further analysis. Moreover, we only used MODIS-NDVI data, which is of the highest resolution, and we did not use multi-source NDVI datasets to verify the results. It should be noted that the results of vegetation coverage variation may not be the same because of the difference in resolution and quality of different NDVI datasets. Thus, further analysis of the consistency and uncertainty of variation evaluation of different NDVI datasets is required in order to obtain more robust results.

The authors have declared that no competing interests exist.

Babst Flurin, Poulter Benjamin, Trouet Valerieet al., 2013. Site- and species-specific responses of forest growth to climate across the European continent.Global Ecology and Biogeography, 22(6): 706-717.Aim To evaluate the climate sensitivity of model-based forest productivity estimates using a continental-scale tree-ring network. Location Europe and North Africa (3070 degrees N, 10 degrees W40 degrees E). Methods We compiled close to 1000 annually resolved records of radial tree growth for all major European tree species and quantified changes in growth as a function of historical climatic variation. Sites were grouped using a neural network clustering technique to isolate spatiotemporal and species-specific climate response patterns. The resulting empirical climate sensitivities were compared with the sensitivities of net primary production (NPP) estimates derived from the ORCHIDEE-FM and LPJ-wsl dynamic global vegetation models (DGVMs). Results We found coherent biogeographic patterns in climate response that depend upon (1) phylogenetic controls and (2) ambient environmental conditions delineated by latitudinal/elevational location. Temperature controls dominate forest productivity in high-elevation and high-latitude areas whereas moisture sensitive sites are widespread at low elevation in central and southern Europe. DGVM simulations broadly reproduce the empirical patterns, but show less temperature sensitivity in the boreal zone and stronger precipitation sensitivity towards the mid-latitudes. Main conclusions Large-scale forest productivity is driven by monthly to seasonal climate controls, but our results emphasize species-specific growth patterns under comparable environmental conditions. Furthermore, we demonstrate that carry-over effects from the previous growing season can significantly influence tree growth, particularly in areas with harsh climatic conditions an element not considered in most current-state DGVMs. Modeldata discrepancies suggest that the simulated climate sensitivity of NPP will need refinement before carbon-cycle climate feedbacks can be accurately quantified.


Bai Hongying, 2014. The Response of Vegetation to Environmental Change in Qinba Mountains. Beijing: Science Press. (in Chinese)

Barati Susan, Rayegani Behzad, Saati Mehdiet al., 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas.The Egyptian Journal of Remote Sensing and Space Science, 14(1): 49-56.Quantitative estimation of canopy biophysical variables are very important in different studies such as meteorology, agriculture and ecology, so knowledge of the spatial and temporal distribution of these variables would be highly beneficial. Meanwhile, remote sensing is known as an important source of information to estimate fractional vegetation cover in large areas. Today spectral indices have been very popular in the remote sensing of vegetation features. But often reflections of soil and rocks are much more than reflections of sparse vegetation in these areas, that makes separation of plant signals difficult. So in this study measured fractional vegetation cover of a desert area were evaluated with 20 vegetation indices in five different categories as the most appropriate category, or indicator for desert vegetation to be identified. The five categories were including: (1) conventional ratio and differential indices such as NDVI; (2) indices corrected and derived from the traditional indicators such as NDVIc and GNDVI; (3) soil reflectance adjusted indices such as SAVI; (4) triangle indices based on three discreet bands in their equation (Green, Red and NIR) like TVI; and (5) non-conventional ratio and differential indices such as CI. According to the results of this research, DVI index with 0.668 the coefficient of determination ( R 2 ) showed the best fractional vegetation cover estimation. But according to the sparse vegetation in desert areas and the results of this research it seems none of these indicators alone can accurately estimate the percentage of vegetation cover, however, to do a proper estimation it is possible to enter data of these indices in a multivariate regression model. Using this method enabled us to increase the coefficient of determination of fractional vegetation cover estimation model up to 0.797.


Compiling Committee of National Assessment Report on Climate Change, 2011. Second National Assessment Report on Climate Change. Beijing: Science Press. (in Chinese)

Ding Mingjun, Zhang Yili, Liu Linshanet al., 2007. The relationship between NDVI and precipitation on the Tibetan Plateau.Journal of Geographical Sciences, 17(3): 259-268.<a name="Abs1"></a>The temporal and spatial changes of NDVI on the Tibetan Plateau, as well as the relationship between NDVI and precipitation, were discussed in this paper, by using 8-km resolution multi-temporal NOAA AVHRR-NDVI data from 1982 to 1999. Monthly maximum NDVI and monthly rainfall were used to analyze the seasonal changes, and annual maximum NDVI, annual effective precipitation and growing season precipitation (from April to August) were used to discuss the interannual changes. The dynamic change of NDVI and the correlation coefficients between NDVI and rainfall were computed for each pixel. The results are as follows: (1) The NDVI reached the peak in growing season (from July to September) on the Tibetan Plateau. In the northern and western parts of the plateau, the growing season was very short (about two or three months); but in the southern, vegetation grew almost all the year round. The correlation of monthly maximum NDVI and monthly rainfall varied in different areas. It was weak in the western, northern and southern parts, but strong in the central and eastern parts. (2) The spatial distribution of NDVI interannual dynamic change was different too. The increase areas were mainly distributed in southern Tibet montane shrub-steppe zone, western part of western Sichuan-eastern Tibet montane coniferous forest zone, western part of northern slopes of Kunlun montane desert zone and southeastern part of southern slopes of Himalaya montane evergreen broad-leaved forest zone; the decrease areas were mainly distributed in the Qaidam montane desert zone, the western and northern parts of eastern Qinghai-Qilian montane steppe zone, southern Qinghai high cold meadow steppe zone and Ngari montane desert-steppe and desert zone. The spatial distribution of correlation coefficient between annual effective rainfall and annual maximum NDVI was similar to the growing season rainfall and annual maximum NDVI, and there was good relationship between NDVI and rainfall in the meadow and grassland with medium vegetation cover, and the effect of rainfall on vegetation was small in the forest and desert area.


Fu Bojie, 2014. The integrated studies of geography: Coupling of patterns and processes.Acta Geographica Sinica, 69(8): 1052-1059. (in Chinese)Geography is a subject which perceptibly reveals integration and regionalism. The integration means that the diversiform subjects in which geography is involved, and that the regionalism of geography is reflected by the regional differentiation. Through the comprehensive study of the interrelationships among the constituent elements of earth system and the relationship between natural and human systems, it helps us understand the variations of the past, present and future of earth system, and grasp the essence of these changes. Pattern helps us to understand the external features of the world and the process is conducive to the understanding of the internal biophysical mechanism of the world. On the basis of field observations and long- term comprehensive surveys, coupling of patterns and processes at different spatiotemporal scales is an effective way to understand and solve the problems in the field of geography. By analysis of the case studies in the Loess Plateau, the methods of coupling the patterns and processes in the integrated research of geography are discussed and explored.


Hilker Thomas, Lyapustin Alexeii, Tucker Comptonjet al., 2014. Vegetation dynamics and rainfall sensitivity of the Amazon.Proceedings of the National Academy of Sciences, 111(45): 16041-16046.We show that the vegetation canopy of the Amazon rainforest is highly sensitive to changes in precipitation patterns and that reduction in rainfall since 2000 has diminished vegetation greenness across large parts of Amazonia. Large-scale directional declines in vegetation greenness may indicate decreases in carbon uptake and substantial changes in the energy balance of the Amazon. We use improved estimates of surface reflectance from satellite data to show a close link between reductions in annual precipitation, El Ni o southern oscillation events, and photosynthetic activity across tropical and subtropical Amazonia. We report that, since the year 2000, precipitation has declined across 69% of the tropical evergreen forest (5.4 million km) and across 80% of the subtropical grasslands (3.3 million km). These reductions, which coincided with a decline in terrestrial water storage, account for about 55% of a satellite-observed widespread decline in the normalized difference vegetation index (NDVI). During El Ni o events, NDVI was reduced about 16.6% across an area of up to 1.6 million kmcompared with average conditions. Several global circulation models suggest that a rise in equatorial sea surface temperature and related displacement of the intertropical convergence zone could lead to considerable drying of tropical forests in the 21st century. Our results provide evidence that persistent drying could degrade Amazonian forest canopies, which would have cascading effects on global carbon and climate dynamics.


Holben Brentn, 1986. Characteristics of maximum-value composite images from temporal AVHRR data.International Journal of Remote Sensing, 7(11): 1417-1434.Not Available


Jiang Zhangyan, Huete Alfredor, Didan Kamelet al., 2008. Development of a two-band enhanced vegetation index without a blue band.Remote Sensing of Environment, 112(10): 3833-3845.The enhanced vegetation index (EVI) was developed as a standard satellite vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a blue band, in addition to the red and near-infrared bands, making it difficult to generate long-term EVI time series as the normalized difference vegetation index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-band EVI (EVI2), without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted vegetation index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a blue band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied. 2008 Elsevier Inc.


John Ranjeet, Chen Jiquan, Lu Nanet al., 2008. Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia.Remote Sensing of Environment, 112(5): 2018-2032.Changes in species composition and diversity are the inevitable consequences of climate change, as well as land use and land cover change. Predicting species richness at regional spatial scales using remotely sensed biophysical variables has emerged as a viable mechanism for monitoring species distribution. In this study, we evaluate the utility of MODIS-based productivity (GPP and EVI) and surface water content (NDSVI and LSWI) in predicting species richness in the semi-arid region of Inner Mongolia, China. We found that these metrics correlated well with plant species richness and could be used in biome- and life form-specific models. The relationships were evaluated on the basis of county-level data recorded from the Flora of Inner Mongolia , stratified by administrative (i.e., counties), biome boundaries (desert, grassland, and forest), and grouped by life forms (trees, grasses, bulbs, annuals and shrubs). The predictor variables included: the annual, mean, maximum, seasonal midpoint (EVI mid ), standard deviation of MODIS-derived GPP, EVI, LSWI and NDSVI. The regional pattern of species richness correlated with GPP SD ( R 2 =0.27), which was also the best predictor for bulbs, perennial herbs and shrubs ( R 2 =0.36, 0.29 and 0.40, respectively). The predictive power of models improved when counties with >50% of cropland were excluded from the analysis, where the seasonal dynamics of productivity and species richness deviate patterns in natural systems. When stratified by biome, GPP SD remained the best predictor of species richness in grasslands ( R 2 =0.30), whereas the most variability was explained by NDSVI max in forests ( R 2 =0.26), and LSWI avg in deserts ( R 2 =0.61). The results demonstrated that biophysical estimates of productivity and water content can be used to predict plant species richness at the regional and biome levels.


Kendall Mauricegeorge, 1948. Rank Correlation Methods. London: Charles Griffin & Company Limited.

Lewis Simonl, Brando Paulom, Phillips Oliverlet al., 2011. The 2010 Amazon drought.Science, 331(6017): 554.

Liu Xianfeng, Ren Zhiyuan, Lin Zhihuiet al., 2013. The spatial-temporal changes of vegetation coverage in the Three-River Headwater Region in recent 12 years.Acta Geographica Sinica, 68(7): 897-908. (in Chinese)The Three-River Headwater Region is the source areas of the Yangtze River, the Yellow River, and the Lancang River. The region is not only of key importance to the ecological security of China. Due to climate change and human activities, ecological degradation occurred in this region. Therefore, &quot;The nature reserve of Three-River Source Regions&quot; was established, and &quot;The project of ecological protection and construction for the Three-River Headwater Nature Reserve&quot; was implemented by Chinese government. This study, based on MODIS-NDVI and climate data, aims to analyze the spatial-temporal changes of vegetation coverage and its driving factors in the Three-River Headwater Region between 2000 and 2011 from three dimensions. Linear regression, Hurst index and partial correlation analysis were employed. The results showed that: (1) In the past 12 years (2000-2011), the NDVI of the study area increased, with a linear tendency being 1.2%/10a, of which the Yangtze and the Yellow river source regions presented an increasing trend, while the Lancang River source region showed a decreasing trend. (2) Vegetation coverage presented an obvious spatial difference in the Three-River Headwater Region, and NDVI frequency was featured by a bimodal structure. (3) The vegetation coverage improved area was larger than the degraded area, being 64.06% and 35.94% respectively in the 12 years, and presented an increase pattern in the north and a decrease one in the south. (4) The reverse characteristic of vegetation coverage change is significant. In future, the degradation trends will be mainly found in the Yangtze River Basin and north of the Yellow River, while the improving trend areas are mainly distributed in the Lancang River Basin. (5) The response of vegetation coverage to precipitation and potential evapotranspiration has time lag, while the temperature does not have. (6) The increased vegetation coverage is mainly attributed to the warm-wet climate change and the implementation of the ecological protection project.

Liu Xianfeng, Zhu Xiufang, Pan Yaozhonget al., 2014. The spatial-temporal changes of cold surge in Inner Mongolia during recent 53 years.Acta Geographica Sinica, 69(7): 1013-1024. (in Chinese)Using the daily minimum temperature data of 121 meteorological stations in Inner Mongolia and its surrounding areas, this paper analyzed the spatiotemporal variation of cold surge and its possible influencing factors in Inner Mongolia during 1960-2012, based on piecewise regression model, Sen+Mann-Kendall model, and correlation analysis. The results show that, (1) The occurrence frequency of single-station cold surge presented a decreasing trend in Inner Mongolia during recent 53 years, with a linear tendency of-0.5 times/10a (-2.4-1.2 times/10a), of which a significant decreasing trend was detected before 1991, being-1.1 times/10a (-3.3-2.5 times/10a), while an increasing trend of 0.45 times/10a (-4.4-4.2 times/ 10a) was found after 1991. On the seasonal scale, the trend of spring cold surge was consistent with that of the annual value, and the most obvious change of cold surge also occurred in spring. The frequency of monthly cold surge showed a bimodal structure, and November witnessed the highest incidence of cold surge. (2) Spatially, the high incidence of cold surge is mainly observed in the northern and central parts of Inner Mongolia, and higher in the northern than the central part. The inter-decadal characteristic also detected that high frequency and low frequency regions presented a decreasing trend and an increasing trend, respectively, during 1960-1990, while high frequency regions expanded after the 1990s, regions with high frequency of cold surge were mainly distributed in Tol Gol, Xiao' ergou, and Xi Ujimqin Banner. (3) On annual scale, the cold surge was dominated by AO, NAO, CA, APVII, and CQ, while the difference in driving forces among seasons was detected. Winter cold surge was significantly correlated with AO, NAO, SHI, CA, TPI, APVII, CW, and IZ, indicating that cold surge in winter was caused multifactor. Autumn cold surge was mainly affected by CA and IM, while spring cold surge was significantly correlated with CA and APVII.


Liu Yinhan, 1983. Research on land category.Geography Central, (1): 47-56. (in Chinese)

Ma Zhihai, Peng Changhui, Zhu Qiuanet al., 2012. Regional drought-induced reduction in the biomass carbon sink of Canada's boreal forests.Proceedings of the National Academy of Sciences, 109(7): 2423-2427.

Nemani Ramakrishnar, Keeling Charlesd, Hashimoto Hirofumiet al., 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999.Science, 300(5625): 1560-1563.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.


Parmesan Camille, 2006. Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution, and Systematics, 637-669.

Peñuelas Josep, Canadell Josepg, Ogaya Romà, 2011. Increased water-use efficiency during the 20th century did not translate into enhanced tree growth.Global Ecology and Biogeography, 20(4): 597-608.Aim;The goals of this study are: (1) to determine whether increasing atmospheric CO(2) concentrations and changing climate increased intrinsic water use efficiency (iWUE, as detected by changes in Delta 13C) over the last four decades; and if it did increase iWUE, whether it led to increased tree growth (as measured by tree-ring growth); (2) to assess whether CO(2) responses are biome dependent due to different environmental conditions, including availability of nutrients and water; and (3) to discuss how the findings of this study can better inform assumptions of CO(2) fertilization and climate change effects in biospheric and climate models.<br/>Location;A global range of sites covering all major forest biome types.<br/>Methods;The analysis encompassed 47 study sites including boreal, wet temperate, mediterranean, semi-arid and tropical biomes for which measurements of tree ring Delta 13C and growth are available over multiple decades.<br/>Results;The iWUE inferred from the Delta 13C analyses of comparable mature trees increased 20.5% over the last 40 years with no significant differences between biomes. This increase in iWUE did not translate into a significant overall increase in tree growth. Half of the sites showed a positive trend in growth while the other half had a negative or no trend. There were no significant trends within biomes or among biomes.<br/>Main conclusions;These results show that despite an increase in atmospheric CO(2) concentrations of over 50 p.p.m. and a 20.5% increase in iWUE during the last 40 years, tree growth has not increased as expected, suggesting that other factors have overridden the potential growth benefits of a CO(2)-rich world in many sites. Such factors could include climate change (particularly drought), nutrient limitation and/or physiological long-term acclimation to elevated CO(2). Hence, the rate of biomass carbon sequestration in tropical, arid, mediterranean, wet temperate and boreal ecosystems may not increase with increasing atmospheric CO(2) concentrations as is often implied by biospheric models and short-term elevated CO(2) experiments.


Piao Shilong, Nan Huijuan, Huntingford Chriset al., 2014. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity.Nature Communications, 5.ABSTRACT Satellite-derived Normalized Difference Vegetation Index (NDVI), a proxy of vegetation productivity, is known to be correlated with temperature in northern ecosystems. This relationship, however, may change over time following alternations in other environmental factors. Here we show that above 30掳N, the strength of the relationship between the interannual variability of growing season NDVI and temperature (partial correlation coefficient RNDVI-GT) declined substantially between 1982 and 2011. This decrease in RNDVI-GT is mainly observed in temperate and arctic ecosystems, and is also partly reproduced by process-based ecosystem model results. In the temperate ecosystem, the decrease in RNDVI-GT coincides with an increase in drought. In the arctic ecosystem, it may be related to a nonlinear response of photosynthesis to temperature, increase of hot extreme days and shrub expansion over grass-dominated tundra. Our results caution the use of results from interannual time scales to constrain the decadal response of plants to ongoing warming.


Reichstein Markus, Bahn Michael, Ciais Philippeet al., 2013. Climate extremes and the carbon cycle.Nature, 500(7462): 287-295.The terrestrial biosphere is a key component of the global carbon cycle and its carbon balance is strongly influenced by climate. Continuing environmental changes are thought to increase global terrestrial carbon uptake. But evidence is mounting that climate extremes such as droughts or storms can lead to a decrease in regional ecosystem carbon stocks and therefore have the potential to negate an expected increase in terrestrial carbon uptake. Here we explore the mechanisms and impacts of climate extremes on the terrestrial carbon cycle, and propose a pathway to improve our understanding of present and future impacts of climate extremes on the terrestrial carbon budget.


Ren Zhiyuan, Li Jing, 2003. The valuation of ecological services from the vegetation ecosystems in the Qinling-Daba Mountains.Acta Geographica Sinica, 58(4): 503-511. (in Chinese)lt;p>Based on the characteristics of eco-environment in the Qinling-Daba Mountains of Shaanxi Province, according to differences of the vegetation types and coverage in the Qinling-Daba Mountains, combined with the advance of new research, this paper explores the theory and methods for the valuation of the vegetations ecosystem services, establishes the data basis of vegetation ecosystem, GIS and vegetation eco-account. By determining vegetation productions and eco-adjusted mass, taking advantage of the theory and methods on ecology-economics, the paper studies the value of the vegetation ecosystem services. The results show that: (1) the value of land-vegetation's primary productivity is 199.6 billion yuan/a; (2) the value of vegetation's soil and fertilization conservation is 22.64 billion yuan/a; (3) the value of vegetation water conservation is 22.66 billion yuan/a; (4) the value of fixing CO2 and releasing O2 is 352.24 billion yuan/a and 374.19 billion yuan/a respectively; and (5) the total value of ecosystem services is 968.33 billion yuan/a. The rate of contribution to the temperate deciduous broad-leaved forest is the highest, accounting for 29.35% of the total value.</p>


Sen Pranabkumar, 1968. Estimates of the regression coefficient based on Kendall's tau.Journal of the American Statistical Association, 63(324): 1379-1389.ABSTRACT


Shi Peijun, Kong Feng, Fang Jiayi, 2014. Spatio-temporal patterns of China decadal storm rainfall.Scientia Geographica Sinica, 34(11): 1281-1290. (in Chinese)lt;p>Extreme precipitation events in recent years have become an important factor affecting the global and regional environmental risk, which has become the focus of attention of academia. It is the hot topic whether China's extreme precipitation changed significantly or not on the overall. Previous studies have not addressed the China's decadal storm rainfall, so there is no global understanding to this problem. To address this problem, we conducted the in-depth study. In this research, daily precipitation datasets of 659 meteorological stations come from China Meteorological Administration in 1951-2010. According to the precipitation intensity grading standards promulgated by the China Meteorological Administration, we calculated the decadal storm rainfall amounts, storm rainfall days and storm rainfall intensity in 1951-1960, 1961-1970, 1971-1980, 1981-1990, 1991-2000, 2001-2010. The results showed that: in the time dimension, China's decadal storm rainfall amount and storm rainfall days increased significantly. At the same time, the storm rainfall intensity also showed a lightly increasing trend. In the space dimension, China's decadal storm rainfall amounts and storm rainfall days showed an gradient increase trend, which showed gradual expansion from southeast coastal areas to the Central and Southwest China. The regions are about located in the east of the line from Mohe County in Heilongjiang Province to Tengchong County in Yunnan Province. However, China's decadal storm rainfall intensity is far less apparent than storm rainfall amount and storm rainfall days. The meteorological stations number of China's decadal storm rainfall amounts also changed significantly. The number of meteorological station with decadal storm rainfall amount less than 5 000 mm was 647 in 1951-1960, and reduced to 333 in 2001-2010. At the same time, the number of meteorological station with decadal storm rainfall amount more than 8 000 mm was 2 in 1951-1960, and increased to 286 in 2001-2010. The number of meteorological station with decadal storm rainfall days less than 30 d was 547 in 1951-1960, and reduced to 233 in 2001-2010. At the same time, the number of meteorological station with decadal storm rainfall days more than 120 d was 0 in 1951-1960, and increased to 265 in 2001-2010. The number of meteorological station with decadal storm rainfall intensity less than 75 mm/d was 476in 1951-1960, and reduced to 466 in 2001-2010. At the same time, number the meteorological station with decadal storm rainfall intensity 75-100 mm/d was 174 in 1951-1960, and increased to 191 in 2001-2010. Spatio-temporal patterns of China's decadal storm rainfall in 1951-2010 may be the result of global warming in the large-scale background topography and urbanization joint affect throughout China. To what extent natural factors and human factors played a great role to the storm rainfall, it is yet to be further in-depth study.</p>

Shi Peijun, Sun Shao, Wang Minget al., 2014. Climate change regionalization in China (1961-2010).Science China: Earth Sciences, 44(10): 2294-2306. (in Chinese)Since climatic condition is the important foundation for human subsistence and development and the key factor in sustainable development of economy and society, climate change has been a global issue attracting great attentions of politicians, scientists, governments, and the public alike throughout the world. Existing climate regionalization in China aims to characterize the regional differences in climate based on years of the mean value of different climate indexes. However, with the accelerating climate change nowadays, existing climate regionalization cannot represent the regional difference of climate change, nor can it reflect the disasters and environmental risks incurred from climate changes. This paper utilizes the tendency value and fluctuation value of temperature and precipitation from 1961 to 2010 to identify the climate change quantitatively, and completes the climate change regionalization in China (1961-2010) with county administrative regionalization as the unit in combination with China's terrain feature. Level-I regionalization divides China's climate change (1961-2010) into five tendency zones based on the tendency of temperature and precipitation, which are respectively Northeast China-North China warm-dry trend zone, East China-Central China wet-warm trend zone, Southwest China-South China dry-warm trend zone, Southeast Tibet-Southwest China wet-warm trend zone, and Northwest China-Qinghai-Tibet Plateau warm-wet trend zone; level-II regionalization refers to fourteen fluctuation regions based on level-I regionalization according to the fluctuation of temperature and precipitation.


Stocker T F, Qin D, Plattner G Ket al., 2013. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

Walker B, Steffen W, 1997. IGBP Science No. 1: A Synthesis of GCTE and Related Research. Stockholm: IGBP: 1-24.

Walther Gian-reto, Post Eric, Convey Peteret al., 2002. Ecological responses to recent climate change.Nature, 416(6879): 389-395.Nature is the international weekly journal of science: a magazine style journal that publishes full-length research papers in all disciplines of science, as well as News and Views, reviews, news, features, commentaries, web focuses and more, covering all branches of science and how science impacts upon all aspects of society and life.


Xu Jianhua, 2002. Mathematical Method in Modern Geography. 2nd ed. Beijing: Higher Education Press. (in Chinese)

Zhang Xuezhen, Dai Junhu, Ge Quansheng, 2013. Variation in vegetation greenness in spring across eastern China during 1982-2006.Journal of Geographical Sciences, 23(1): 45-56.Abstract<br/><p class="a-plus-plus">Vegetation greenness is a key indicator of terrestrial vegetation activity. To understand the variation in vegetation activity in spring across eastern China (EC), we analysed the variation in the Normalised Difference Vegetation Index (NDVI) from April to May during 1982–2006. The regional mean NDVI across EC increased at the rate of 0.02/10yr (<em class="a-plus-plus">r</em><sup class="a-plus-plus">2</sup>=0.28; <em class="a-plus-plus">p</em>=0.024) prior to 1998; the increase ceased, and the NDVI dropped to a low level thereafter. However, the processes of variation in the NDVI were different from one region to another. In the North China Plain, a cultivated area, the NDVI increased (0.03/10yr; <em class="a-plus-plus">r</em><sup class="a-plus-plus">2</sup>=0.52; <em class="a-plus-plus">p</em>&lt;0.001) from 1982 to 2006. In contrast, the NDVI decreased (−0.02/10yr; <em class="a-plus-plus">r</em><sup class="a-plus-plus">2</sup>=0.24; <em class="a-plus-plus">p</em>=0.014) consecutively from 1982 to 2006 in the Yangtze River and Pearl River deltas, two regions of rapid urbanisation. In the eastern region of the Inner Mongolian Plateau and the lower reaches of the Yangtze River in East China, the NDVI increased prior to 1998 and decreased thereafter. In the Hulun Buir area and the southern part of the Yangtze River Basin, the NDVI increased prior to 1998 and remained static thereafter. The NDVI in the grasslands and croplands in the semi-humid and semi-arid areas showed a significant positive correlation with precipitation, while the NDVI in the woodlands in the humid to semi-humid areas showed a significant positive correlation with temperature. As much as 60% of the variation in the NDVI was explained by either precipitation or temperature.</p><br/>


Zhao Zhiping, Wu Xiaopu, Li Guoet al., 2015. Drought in southwestern China and its impact on the net primary productivity of vegetation from 2009-2011.Acta Ecologica Sinica, 35(2): 1-16. (in Chinese)Droughts are weather patterns involving prolonged reductions in precipitation that are distinct from normal weather cycles. They can be accompanied by extreme heat. There are three types of drought that affect vegetation: weather,soil,and physiology. In recent years,global climate change has significantly increased the frequency of drought and other extreme weather in China. Severe drought interferes with agricultural production and has caused a sharp decline in the net primary productivity of vegetation. It has decreased the total volume of rivers,dried up lakes,and degraded local environments. Southwestern China has suffered from a long-term drought that began in the autumn of 2009. Precipitation is half of what it was years ago. In this study,meteorological station data was used to analyze the process and magnitude of this drought from 2009—2011. Then a light-use-efficiency-based model for calculating net primary productivity called Glo PEM was used to determine the impact of drought on the net primary productivity of vegetation during this time. The study area included Guizhou,Yunnan,and Sichuan Provinces,the Guangxi Zhuang Autonomous Region,and Chongqing City. The results showed that the drought was severe in Yunnan,Guizhou,northwestern Guangxi,and in southern Sichuan.And the precipitation and moisture index of 2009—2011 were obviously lower than the average of 1980—2011 in southwestern China. In 2009,the precipitation and moisture index declined sharply. In 2010,the precipitation and moisture index returned to near-normal levels. In 2011,the precipitation and moisture index fell to the lowest point in thepast 32 years. For light-use-efficiency-based model,the total radiation is an important parameter. In this study,variations in simulated total radiation were closely correlated with observation results( R2= 0. 84,P 0. 01). This drought may have reduced the net primary productivity of vegetation,decreasing plant' s ability to create a carbon sink. In the study area,the average of net primary productivity from 2009—2011 was 12. 55 g C m- 2a- 1lower than the average value from2001—2011. It was 0. 017 Pg C / a in total. This reduced China' s total carbon sink by 7. 91%. In 2010 alone,this loss reduced China's total carbon sink by 22. 33% for that year. Variations in simulated net primary productivity were closely correlated with observation results( R2= 0. 64,P 0. 01),which indicated that simulation of net primary productivity from Glo PEM model was reliable and the parameters of Glo PEM model were suitable in southwestern China. From 2001—2011,variations in net primary productivity were closely correlated with evapotranspiration( R2= 0. 44,P 0. 05) in southwestern China. From 2009—2011,variations in net primary productivity and evapotranspiration were synchronized,but variations in precipitation and moisture index were not synchronous with those in net primary productivity or evapotranspiration. Statistical analysis of areas covered and affected by drought from 2009—2011 confirmed this. Variations of soil moisture levels were closely correlated with net primary productivity( R2= 0. 25,P 0. 01). This phenomenon might have a relationship with the water conservation function of the ecosystem,which causes a delayed correlation between soil moisture levels and precipitation.

Zhou Liming, Tian Yuhong, Myneni Rangabet al., 2014. Widespread decline of Congo rainforest greenness in the past decade.Nature, 509(7498): 86-90.Tropical forests are global epicentres of biodiversity and important modulators of climate change, and are mainly constrained by rainfall patterns. The severe short-term droughts that occurred recently in Amazonia have drawn attention to the vulnerability of tropical forests to climatic disturbances. The central African rainforests, the second-largest on Earth, have experienced a long-term drying trend whose impacts on vegetation dynamics remain mostly unknown because in situ observations are very limited. The Congolese forest, with its drier conditions and higher percentage of semi-evergreen trees, may be more tolerant to short-term rainfall reduction than are wetter tropical forests, but for a long-term drought there may be critical thresholds of water availability below which higher-biomass, closed-canopy forests transition to more open, lower-biomass forests. Here we present observational evidence for a widespread decline in forest greenness over the past decade based on analyses of satellite data (optical, thermal, microwave and gravity) from several independent sensors over the Congo basin. This decline in vegetation greenness, particularly in the northern Congolese forest, is generally consistent with decreases in rainfall, terrestrial water storage, water content in aboveground woody and leaf biomass, and the canopy backscatter anomaly caused by changes in structure and moisture in upper forest layers. It is also consistent with increases in photosynthetically active radiation and land surface temperature. These multiple lines of evidence indicate that this large-scale vegetation browning, or loss of photosynthetic capacity, may be partially attributable to the long-term drying trend. Our results suggest that a continued gradual decline of photosynthetic capacity and moisture content driven by the persistent drying trend could alter the composition and structure of the Congolese forest to favour the spread of drought-tolerant species.