Vegetation dynamics and responses to climate change and human activities in Central Asia

Vegetation dynamics and responses to climate change and human activities in Central Asia

Science of the Total Environment 599–600 (2017) 967–980 Contents lists available at ScienceDirect Science of the Total Environment journal homepage:...

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Science of the Total Environment 599–600 (2017) 967–980

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Vegetation dynamics and responses to climate change and human activities in Central Asia Liangliang Jiang a,b, Guli·Jiapaer a,⁎, Anming Bao a, Hao Guo a,b, Felix Ndayisaba a,b a b

State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China University of Chinese Academy of Sciences, Beijing 100049, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• The contributions of climate change and human activities were determined for different vegetation types. • Drought is the main factor affecting vegetation degradation in the Kyzylkum Desert. • Increased irrigation was an important cause of the vegetation degradation around the Large Aral Sea in study period. • The effects of human activities on vegetation changed from negative to positive in eastern Central Asia.

a r t i c l e

i n f o

Article history: Received 6 February 2017 Received in revised form 29 April 2017 Accepted 1 May 2017 Available online xxxx Editor: D. Barcelo Keywords: Vegetation dynamics NDVI Climatic change Human activities Hurst exponent

a b s t r a c t Knowledge of the current changes and dynamics of different types of vegetation in relation to climatic changes and anthropogenic activities is critical for developing adaptation strategies to address the challenges posed by climate change and human activities for ecosystems. Based on a regression analysis and the Hurst exponent index method, this research investigated the spatial and temporal characteristics and relationships between vegetation greenness and climatic factors in Central Asia using the Normalized Difference Vegetation Index (NDVI) and gridded high-resolution station (land) data for the period 1984–2013. Further analysis distinguished between the effects of climatic change and those of human activities on vegetation dynamics by means of a residual analysis trend method. The results show that vegetation pixels significantly decreased for shrubs and sparse vegetation compared with those for the other vegetation types and that the degradation of sparse vegetation was more serious in the Karakum and Kyzylkum Deserts, the Ustyurt Plateau and the wetland delta of the Large Aral Sea than in other regions. The Hurst exponent results indicated that forests are more sustainable than grasslands, shrubs and sparse vegetation. Precipitation is the main factor affecting vegetation growth in the Kazakhskiy Melkosopochnik. Moreover, temperature is a controlling factor that influences the seasonal variation of vegetation greenness in the mountains and the Aral Sea basin. Drought is the main factor affecting vegetation degradation as a result of both increased temperature and decreased precipitation in the Kyzylkum Desert and the northern Ustyurt Plateau. The residual analysis highlighted that sparse vegetation and the degradation of some shrubs in the southern part of the Karakum Desert, the southern Ustyurt Plateau and the wetland delta of the Large Aral Sea were mainly triggered by human activities: the excessive exploitation of water resources in the upstream areas of the Amu Darya basin and oil and natural gas extraction in the southern part of the Karakum Desert and the southern Ustyurt Plateau. The results also indicated that after the collapse of the Soviet Union,

⁎ Corresponding author. E-mail addresses: [email protected] (Guli·Jiapaer), [email protected] (A. Bao), [email protected] (H. Guo).

http://dx.doi.org/10.1016/j.scitotenv.2017.05.012 0048-9697/© 2017 Elsevier B.V. All rights reserved.

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abandoned pastures gave rise to increased vegetation in eastern Kazakhstan, Kyrgyzstan and Tajikistan, and abandoned croplands reverted to grasslands in northern Kazakhstan, leading to a decrease in cropland greenness. Shrubs and sparse vegetation were extremely sensitive to short-term climatic variations, and our results demonstrated that these vegetation types were the most seriously degraded by human activities. Therefore, regional governments should strive to restore vegetation to sustain this fragile arid ecological environment. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Global temperatures in the past century have increased by approximately 0.74 °C, and the intensity of precipitation events is expected to increase, especially in wet regions. Decreases in mean precipitation in most mid-latitude and arid areas contribute to drying during the summer, indicating a great risk of drought in these areas (IPCC and WGI, 2007; Meehl et al., 2007). According to forecasts, dryland regions will become warmer and drier, and wet regions will become warmer and moister (Guli·Jiapaer et al., 2015; Lioubimtseva et al., 2005; Ndayisaba et al., 2016; Yu et al., 2003). Moreover, Central Asia is located in a mid-continental region and contains one of the world's largest arid areas. Precipitation has exhibited a slight decreasing trend in this region with spatially heterogeneous changes (Li et al., 2015; Mannig et al., 2013; Sorg et al., 2012). The temperature in Central Asia has been rising rapidly, particularly since the mid-1990s, and it is presently higher than at any other period in recorded history (Davi et al., 2015; Hu et al., 2014; Li et al., 2015). In this region, vegetation ecosystems typically lack biodiversity and stability and have been susceptible to high climatic variability in recent decades (Bohovic, 2016; de Beurs et al., 2009; Han et al., 2016; Zhou et al., 2015), especially in three temperate deserts: the Muyunkum Desert, Karakum Desert and Kyzylkum Desert (Fig. 1) (C. Zhang et al., 2016). Vegetation in drylands is ecologically beneficial, not only sustaining domestic and wild animals but also providing important ecological functions, such as the prevention of soil desertification (Lioubimtseva, 2014; Tao et al., 2017). Thus, the responses of dryland vegetation to climate change are emerging as a research focus worldwide.

In addition to climate change, human activities are a key factor affecting vegetation growth. After the disintegration of the Soviet Union in 1991, politics and the economic development model in Central Asia were changed to varying degrees, resulting in large-scale rural–urban migrations, broad-scale private ownership, and an economic development model that transformed from a planned economy to a free market economy. The management model of agriculture and animal husbandry also switched from state-owned and collective farming to private and free market farming. Indeed, changes in the method and scale of human activities inevitably impacted vegetation growth (Hostert et al., 2011). There are three important aspects of these changes that may control vegetation greenness in Central Asia: (1) extensive derelict lands restored to other vegetation types; (2) the expansion of irrigated agriculture resulting in vegetation degradation, such as the environmental disaster of the Aral Sea Basin; and (3) the collapse of animal husbandry in Central Asia (Klein et al., 2012; Lioubimtseva and Henebry, 2009; Xi and Sokolik, 2016). Thus, in the broader context of sustainable development, a more profound understanding of vegetation dynamics following the disintegration of the Soviet Union is vital for planning strategies to adapt to concurrent climatic fluctuations. However, despite the great social and economic changes that have occurred, the different mechanisms that drive vegetation changes in response to climate change have received little consideration in Central Asia. Generally, vegetation changes triggered by climatic factors and human activities, separately or together, influence vegetation greenness on regional and global scales (Breshears et al., 2005; King et al., 2015; Nezlin et al., 2005; Sun et al., 2015; Wang et al., 2015; Y. Zhang et al., 2016). Thus, understanding vegetation dynamics and its response to

Fig. 1. Topographical map of the nations of Central Asia.

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climatic changes and anthropogenic activities is important. Previous studies have used climate datasets and Normalized Difference Vegetation Index (NDVI) time series to analyse the vegetation responses to climate change in Central Asia (de Beurs et al., 2009; Gessner et al., 2013; Li et al., 2015; Lioubimtseva et al., 2005; Mohammat et al., 2013; Propastin, 2008; Wahren et al., 2005; Yin et al., 2016; Zhou et al., 2015). Studies have shown significant decreasing trends in vegetation growth in many parts of Central Asia (Lioubimtseva, 2014; Mohammat et al., 2013; Xu et al., 2016), such as in the Aral Sea basin. However, the most important driving factors for vegetation growth in different areas remain obscure and cannot be precisely discussed. Although great efforts have been made to elucidate the diverse responses of vegetation changes to climatic fluctuations in Central Asia, limited effort has targeted differentiating between the influences of climatic factors and human activities on vegetation growth in this region. Neglecting the influences of human activities on vegetation can result in mistakes during the evaluation of land conditions. Moreover, previous studies were limited to assessing vegetation changes and responses to climate change and did not take the peculiarities of different vegetation types into account (de Beurs et al., 2015; Evans and Geerken, 2004; Lioubimtseva, 2014; Propastin et al., 2008). Climatic fluctuations have different influences on different vegetation types (Wang et al., 2015; Xu et al., 2016). Therefore, it is important to consider different vegetation types in this research. Furthermore, because the phenology of vegetation is influenced by seasonal variation in both temperature and precipitation, the relationships between vegetation and both seasonal precipitation and temperature are significant and vary among different vegetation types (Sun et al., 2015). However, most previous studies only investigated the relationships between vegetation and yearly climatic factors and did not actually account for seasonal climate changes. Therefore, knowledge of the current changes and dynamics of different vegetation types in relation to seasonal climatic changes and anthropogenic activities is needed to develop adaptation strategies to address the challenges posed by climatic changes and human activities on vegetative ecosystems in Central Asia, especially near the Aral Sea and Central Asian deserts. This study focused on NDVI dynamics and responses to climatic factors and human activities in different vegetation types. The NDVI was chosen as a valid indictor for monitoring vegetation growth (Fensholt and Proud, 2012; Tian et al., 2015). The aims of this study are as follows: (1) to explore the vegetation dynamics and the sustainability of change trends for different vegetation types, (2) to analyse the relationships between vegetation and local climatic factors, and (3) to distinguish the relative importance of the impacts of climate change and human activities on vegetation. Hopefully, knowledge of the variation characteristics and patterns of the vegetation will promote the protection of the ecological environment in this region and maintain ecological construction efforts under the impacts of climate change and human activities. 2. Materials and methods 2.1. Study area Central Asia (46°29′–87°19′E, 35°07′–55°26′N) is located in the mid-continental area of the Asian mainland, stretching from Russia in the north to Siberia in the south and from the Caspian Sea in the west to China in the east (Fig. 1). The study area includes five states: Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. The elevation gradually decreases from the mountainous ranges of the Altai, Tien Shan and Pamir Mountains in eastern Kazakhstan and Uzbekistan, across Kyrgyzstan and Tajikistan to the coast of the Caspian Sea in western Kazakhstan and Turkmenistan (de Beurs et al., 2015). The region has a temperate continental climate, with spatial variability in precipitation and temperature that follows a gradient from the mountains to the plains and from north to south. As indicated by a long-term climate record, with the exception of the mountainous and hilly areas, which have

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an annual average temperature below 0 °C, the annual average temperature varies from 2 °C in northern Kazakhstan to above 18 °C in southern Uzbekistan and Turkmenistan (Mohammat et al., 2013). The mountainous and hilly areas have an annual average precipitation between 600 and 800 mm, whereas the annual average precipitation elsewhere ranges from less than 100 mm in southern Uzbekistan and northern Turkmenistan to approximately 400 mm in northern Kazakhstan (Klein et al., 2012). Thus, the study area gradually transitions from a semi-arid zone in the north to an arid zone in the south, with the driest regions found in the Karakum Desert of northern Turkmenistan and the Kyzylkum Desert of southern Uzbekistan. The wettest regions are located in the Altai, Tien Shan and Pamir Mountains of Kazakhstan, Tajikistan and Kyrgyzstan. Central Asia is characterized by complicated arid and semi-arid natural environments that comprise a delicate ecological zone. Major vegetation types include forests, grasslands, crops, shrubs and sparse vegetation, which account for approximately 1.5%, 39.34%, 18.98%, 22.27% and 17.31% of the vegetated areas, respectively. Most of the plains areas in southern Kazakhstan, Turkmenistan and Uzbekistan are dominated by shrubs and sparse vegetation, including the Muyunkun, Karakum and Kyzylkum Deserts, respectively. The mountainous region in Turkmenistan and Kyrgyzstan is characterized by rich grasslands and forests because of high precipitation and vertical zonality. The northern region is dominated by rain-fed agriculture, whereas the Amu Darya and Syr Darya basins in the southern region are widely known for their broad-scale irrigated agriculture. Agropastoralism is another prevalent vegetation cover type in both the mountains and hills, especially in eastern Kazakhstan (de Beurs et al., 2015). In recent decades, the study area has experienced rapid warming at a rate that is approximately twice that of the global average temperature (Hu et al., 2014). The spatial variation of changes in precipitation is obvious (Guo et al., 2015; Lioubimtseva and Henebry, 2009). In addition to the effects of temporal and spatial variations of climate on vegetation growth, the disintegration of the Soviet Union in 1991 led to changes in the region's socioeconomic conditions, which further affected vegetation growth (Hostert et al., 2011). Therefore, the effects of climate change and human activities on the vegetation dynamics in Central Asia have attracted the attention of scientists, governments and the public. 2.2. Data source The datasets used in the paper comprise NDVI data, gridded temperature and precipitation data, vegetation type data and elevation data. The NDVI has been utilized to monitor vegetation changes and desertification at continental and global scales and is calculated based on the near-infrared and red bands of remote sensing data. In this research, the NDVI dataset was acquired from the Global Inventory Monitoring and Modelling Studies (GIMMS) group, which originated from the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) Land dataset at a spatial resolution of 1/12° and a temporal resolution of 15 days for the period from 1984 to 2013 (http://glcf.umd.edu). This dataset is corrected for inconsistencies resulting from sensor degradation, orbital drift and increased atmospheric aerosol content (Huang et al., 1998; Pinzón et al., 2005; Vermote and Kaufman, 1995; Vermote et al., 1994). Because of adjustments to the temporal resolution of the temperature and precipitation data, the NDVI dataset was temporally assembled to monthly values using the mean value composition. After data processing, a time-series with a temporal resolution of 1 month covering the period from 1984 to 2013 was produced. We obtained monthly global gridded high-resolution station (land) data for temperature and precipitation during 1984–2014 at 0.5° resolution (https://www.esrl.noaa.gov/). Cort Willmott & Kenji Matsuura of the University of Delaware combined data from a large number of stations worldwide, including data from the Global Historical Climate

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Network Version 2 (GHCN2) and, more extensively, the archive of Legates & Willmott (Willmott and Matsuura, 2001). The result is a monthly climatology dataset of temperature and precipitation that uses spatial interpolation to create global gridded data at a spatial resolution of 0.5°. Based on further analysis of NDVI data, the spatial resolution of the climatic datasets was resampled to 1/12° to match that of the NDVI time-series. The other datasets employed in this research included digital elevation data at a spatial resolution of 90 m obtained from the National Aeronautics and Space Administration (NASA) Shuttle Radar Topographic Mission. These data were used to distinguish mountains from plains (http://www2.jpl.nasa.gov/srtm/). The data of different vegetation types (Fig. 2) were provided by the Chinese Academy of Sciences through the Database of Global Change Parameters. 2.3. Methods 2.3.1. Linear regression analysis In this research, based on a linear regression analysis method, the spatial and temporal fluctuations of the seasonal accumulative precipitation, seasonal mean temperature and average annual NDVI in the growing seasons were analysed. The time series of each pixel was calculated to obtain the slope coefficients of the trend line. Specifically, the slope coefficients of the trend line were computed using Eq. (1) (Chatfield, 2016):

and the seasonal mean temperature (independent variables) using Pearson correlation. The significance of the correlation coefficients was judged at the 95% level and indicated a closer relationship between NDVI and climatic factors. 2.3.2. Hurst exponent The Hurst exponent was pioneered by a British hydrologist in 1951 (Hurst and E., 1951) and was improved by Mandelbrot and Wallis in 1969 (Mandelbrot and Wallis, 1969). Currently, the Hurst exponent index is widely applied in climatology and vegetation studies and is used to assess the durability of changes in time series data over long periods (Guli·Jiapaer et al., 2015; Hou et al., 2012; Jiang et al., 2015; Ndayisaba et al., 2016). The main equations are as follows: (1) Divide the long time series {NDVI (τ)} (τ = 1, 2, …, n) into τ sub series X (t), and for each series, t = 1, …, τ. (2) Define the long-term memory of the time series of the mean NDVI,

NDVIðτÞ ¼

1 τ ∑ NDVIðτÞ τ t

τ ¼ 1; 2; …; n

ð2Þ

(3) Calculate the accumulated deviation from each mean NDVI, i¼1

i¼1

i¼1

n∑n X i Y i −∑n X i ∑n Y i Slope ¼  2 i¼1 i¼1 n∑n X i 2 − ∑n X i

ð1Þ t

X ðt;τÞ ¼ ∑ NDVIðt Þ −NDVIðτÞ where Xi and Yi are the values of the independent variable and the dependent variable in the ith year, respectively, and n is the accumulative number of years during the study period. Generally speaking, if Slope N 0, the variation of the dependent variable exhibits an upward trend, whereas if Slope b 0, the variation of the dependent variable exhibits a downward trend. Correlation coefficients were calculated between the average annual NDVI (dependent variable) and the seasonal accumulative precipitation



t¼1

1≤t ≤τ

ð3Þ

(4) Define the range sequence of R,

RðτÞ ¼ max X ðt;τÞ − min X ðt;τÞ 1≤t ≤τ

1≤t ≤τ

Fig. 2. Spatial distribution of different vegetation types in Central Asia.

τ ¼ 1; 2; …; n

ð4Þ

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(5) Define the standard deviation sequence of S,

SðτÞ

 τ 1=2  1 ¼ ∑ NDVIðt Þ −NDVI 2ðτÞ τ t¼1

τ ¼ 1; 2; …; n

is an inconsistency series, indicating that the past trends are most likely opposite the future trends.

ð5Þ

(6) Calculate the Hurst exponent index,

Rð τ Þ ¼ ðcτÞ SðτÞ

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ð6Þ

(7) The H value (the Hurst exponent) is acquired by fitting the following formula,

2.3.3. Residuals analysis The residual analysis approach can separate NDVI changes caused by human activities from those resulting from climatic variations (Evans and Geerken, 2004). As described by Herrmann and Wessels (Herrmann et al., 2005; Wessels et al., 2012), the NDVI residuals were calculated for each pixel. The best relationship between the mean NDVI and climatic factors was acquired by using a multiple correlation regression. Then, the predicted NDVI could be computed using this relationship. The NDVI residuals were obtained as the differences between the predicted and observed NDVI values. The residuals were also analysed to detect trends over time. When the change trend of the NDVI residuals was insignificant, changes in NDVI were explained by climatic trends. In contrast, when the change trend of the NDVI residuals was significant, changes in NDVI were not explained by climatic trends and may have been caused by human activities. 3. Results

logðR=SÞn ¼ a þ H  logðnÞ

ð7Þ

3.1. The characteristics of the vegetation dynamics trend from 1984 to 2013

As in previous studies (Hurst and E., 1951; Mandelbrot and Wallis, 1969), a Hurst exponent value varying from 0 to 1 can be categorized into three behaviours: When the Hurst exponent value is close to 0.5, then the NDVI time series is a random series, indicating that the future trend is independent of that in the research period. When the Hurst exponent value exceeds 0.5, the time series is a consistency series, indicating that the future trend will be consistent with that in the study period. Finally, when the Hurst exponent value is lower than 0.5, the time series

Based on a linear regression analysis method, the changes of the mean NDVI (Fig. 3a, b) in the growing season indicated a slight upward trend of vegetation greenness in the eastern part of Central Asia, with annual change rates of up to 0.006/a. A slight downward trend of vegetation greenness was detected in the western part of Central Asia, with annual change rates as low as −0.004/a. Turkmenistan and Uzbekistan have experienced significant vegetation degradation, and thus, vegetation changes in Central Asia exhibit obvious regional characteristics.

Fig. 3. Change trends of the mean NDVI in the growing season from 1984 to 2013. (a) annual change trends in NDVI; (b) significance (p-value) of the change trends in NDVI; (c) the overall trend classified into five types: significant improvement, slight improvement, stable or non-vegetated, slight degradation, and significant degradation; and (d) statistical results of the percentage changes in NDVI for different vegetation types. CA: Central Asia; CP: crop; GL: grassland; BF: broadleaved forest; CF: coniferous forest; MF: mixed forest; SB: shrub; SV: sparse vegetation. The legend of (d) is the same as that of (c).

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Table 1 Division of the degrees of variation of the NDVI change trend. SNDVI

Z

NDVI trend

≥ 0.001 0.0001–0.001 −0.0001-0.0001 −0.001−0.0001 b −0.001

≥1.96 −1.96–1.96 −1.96–1.96 −1.96-1.96 ≤ − 1.96

Significant improvement Slight improvement Stable or non-vegetated Slight degradation Significant degradation

The variation in NDVI trends can be effectively characterized by combining the results of the Mann–Kendall test and the Theil–Sen median trend analysis. Based on a confidence level of 0.05, the results of the Mann–Kendall test are categorized as insignificant changes (−1.96 b Z b 1.96) or significant changes (Z ≥ 1.96 or Z ≤ −1.96). The results were classified into five classes (Table 1). Figs. 3b and 4 demonstrate that the NDVI trend patterns were spatially heterogeneous from 1984 to 2013 and that the overall trend of vegetation in Central Asia was gradually increasing; however, the decreasing trend in certain areas cannot be ignored. Most of vegetated areas (59.6%) showed a slightly significant increasing trend in vegetation, and these areas accounted for 33.05% of the pixels and exhibited a slightly significant degreasing trend; the number of pixels identified as stable and non-vegetated was low (7.34%). Moreover, vegetation degradation in Uzbekistan and Turkmenistan was more serious than in other countries of the region. Over the last 30 years, the trends varied for different types of vegetation, and higher proportions of vegetation increases were found for forests, grasslands and crops than for shrubs and sparse vegetation (Fig. 3d). Broadleaved forests accounted for 72.88% and 19.49% of the pixels with significant and slight increases, respectively, followed by mixed forests and coniferous forests, especially in the Altai Mountains. For crops, 47.69% and 25.42% of pixels showed significant and slight increases, respectively, and the significant and slight improvement areas of grasslands were almost the same as those for crops. However, the area of significantly degraded crops was 8.36%, higher than that of forests and grasslands. In contrast, more regions of degradation were identified for shrubs and sparse vegetation. Shrubs accounted for 11.54% and 34.86% of the pixels with significant and slight decreases, respectively, whereas sparse vegetation accounted for 17.44% and 39.33% of the pixels showing significant and slight decreases, respectively. 3.2. Consistency of trends in vegetation dynamics Fig. 4a presents the forecasted future vegetation growth patterns based on the superimposed Hurst exponent. The areas with Hurst exponent values exceeding 0.5 accounted for 87.21% of the vegetated areas, indicating consistency. Few regions had Hurst exponent values lower

than 0.5, which indicate inconsistency; these regions accounted for 11.51% of the vegetated areas and were scattered in northwest Kazakhstan. Hurst exponent values near 0.5 were found for only 1.28% of the area, primarily near water bodies, such as Balkhash Lake and the Aral Sea. Most of the vegetated areas in Central Asia appeared to be consistent. To determine the consistency in the dynamic trend of the mean NDVI, the results of the overall trend analysis and the Hurst exponent were superimposed to double the information on the fluctuations in consistency and change trends (Table 2 and Fig. 4b). The results shown in Fig. 4b and Table 2 indicate that the area with consistent change trends accounts for 81.17% of the total vegetated area, with degradation accounting for 30.15% and improvement accounting for 51.02%. The region of consistent improvement was concentrated in the eastern part of Central Asia. The region of consistent degradation is mainly distributed in the Karakum and Kyzylkum Deserts and to the south of the Aral Sea. The inconsistent area only accounts for 10.09%. Therefore, 10.09% of the study area may be positively steadied, reversed or exhibit random fluctuations. Most of the inconsistent regions are scattered in northwest Kazakhstan; non-vegetated or stable areas account for 19.23% and are located in the no-vegetation and water regions. In general, most of the vegetation in Central Asia is in a state of consistent change. Based on the proportions of the different vegetation areas, the consistency results of the trends for different vegetation types were compared (Fig. 5). The proportion of consistent improvement was highest in forests, followed by grasslands. For shrubs and sparse vegetation, 47.26% and 58.7% of the total area, respectively, exhibited consistent degradation. The results revealed that the vegetation growth in mountainous areas is consistently improving at a higher rate than in plains areas. The proportion of stable variation decreased among the different vegetation types in the following order: broadleaf forest, mixed forest, coniferous forest, sparse vegetation, crop, shrub and grassland. Additionally, trends in areas of grasslands and shrubs were more likely to be inconsistent. 3.3. Climatic variability trends from 1984 to 2013 The spatial distribution of the seasonal climatic change trends from 1984 to 2013 is shown in Fig. 6. Precipitation showed different spatial trends in every season, with the greatest increase observed in the Kazakhskiy Melkosopochnik in different seasons. In winter, precipitation increased in the Altay and western Tienshan Mountains in Central Asia, whereas a significant decrease occurred in the Turan Lowland. The change rates ranged from − 2.52 mm/a to 2.16 mm/a. In spring, the precipitation slightly decreased in the Turan Lowland and Pamir Mountains but increased in the western Ustyurt Plateau, northern Kazakhstan and the southern Karakum Desert. In summer, most Central

Fig. 4. Spatial distribution of the Hurst exponent (a) and consistency (b) of the mean NDVI in the growing season in Central Asia from 1984 to 2013. In (b), the Hurst exponent and overall trend results of the mean NDVI are superimposed.

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Table 2 Statistical analysis results of trends and the Hurst index. SNDVI

Z

H

Variation types

Area percentage (%)

≥ 0.001 0.0001–0.001 ≥ 0.0001 -0.0001-0.0001 ≤ −0.0001 −0.001–0.0001 b −0.001

≥ 1.96 −1.96–1.96 −1.96–1.96 −1.96–1.96 −1.96–1.96 −1.96–1.96 b−1.96

N 0.5 N 0.5 b 0.5 – b 0.5 N 0.5 N 0.5

Consistent and significant improvement Consistent and slight improvement Inconsistent and changed from degradation to improvement Stable or non-vegetated area Inconsistent and changed from improvement to degradation Consistent and slight degradation Consistent and significant degradation

28.6 22.42 7.48 8.74 2.61 20.98 9.17

Asian regions presented a decreasing or constant trend, except for a significant decreasing trend in the western Ustyurt Plateau, which manifested an opposite trend in spring. The precipitation presented a slight constant decreasing trend in the Karakum and Kyzylkum Deserts. In autumn and summer, the change trends were similar, except in the southern Karakum Desert, where the change trend was initially constant and subsequently began to increase. The temperatures in spring and autumn followed a significantly increasing trend from 1984 to 2013, with change rates of 0.19 °C/a and 0.15 °C/a, respectively. The temperature in winter presented the opposite trend, decreasing in the Kazakhskiy Melkosopochnik and exhibiting the highest change rate of −0.16 °C/a. In summer, the temperature significantly decreased in the western Ustyurt Plateau region and the Pamir Mountains, and a slight decrease was observed in the Kazakhskiy Melkosopochnik. In other regions, the temperature increased to varying degrees. Overall, the study area has experienced a rapid and accelerated warming trend in the last 30 years. Congruent with previous studies, the findings of this study confirmed the occurrence of low winter temperatures in Central Asia (Cohen et al., 2014). Although precipitation generally decreased over the past three decades, opposite trends were identified in the Kazakhskiy Melkosopochnik, consistent with the results of previous studies (Lioubimtseva, 2014; Lioubimtseva and Henebry, 2009). 3.4. Correlation between NDVI dynamics and climatic variations The correlation coefficients (R) between the mean NDVI in the growing season and seasonal precipitation and temperature were calculated to determine the relationship between vegetation dynamics and seasonal climatic variables. The mean NDVI showed different responses to changes in seasonal precipitation and temperature, as presented in Figs. 7 and 8.

According to these results, a definite positive correlation exists between the mean NDVI and seasonal precipitation in most of the vegetated areas, and the correlation coefficients are characterized by seasonal and spatial variations in the study area. On a seasonal scale, the areas in spring accounted for 90.7% of the pixels with positive correlation coefficients, exceeding the values in winter and summer. In autumn, the correlation coefficients were negative in most of the vegetated areas, especially in the Kazakhskiy Melkosopochnik. Spatially, the positive correlation between NDVI and precipitation in the deserts was significantly higher than those in other regions of Central Asia. In contrast, mountains presented negative correlations between NDVI and precipitation, especially the Altai and Pamir Mountains. Fig. 8 illustrates the negative correlations observed between the mean NDVI and seasonal temperature in summer over most of the vegetated areas. The correlation coefficients in the other three seasons exhibited obvious spatial differences. In winter, most of the vegetated areas showed positive correlations, whereas eastern Kazakhstan presented negative correlations. In spring and autumn, the correlation coefficients were significantly positive in the eastern part of Central Asia and significantly negative in the western part of Central Asia. Specifically, strong negative correlations were observed in spring in the Karakum and Kyzylkum Deserts. In addition, the correlation coefficients in spring, summer and autumn were positive in the Altai and Tienshan Mountains. The correlation coefficients between the area-averaged mean NDVI and seasonal climatic variables were calculated for different vegetation types (Table 3). For broadleaved forests, coniferous forests and mixed forests, which were mainly distributed in the mountains and hills, the mean NDVI was negatively correlated with precipitation in the autumn, with correlation coefficients of − 0.442, −0.431 and − 0.427, respectively. These correlation coefficients were more significant (p b 0.05) than the positive correlation coefficients found in other seasons.

Fig. 5. Statistical results of the NDVI change trends for different vegetation types from 1984 to 2013: CP: crop; GL: grassland; BF: broadleaved forest; CF: coniferous forest; MF: mixed forest; SB: shrub; SV: sparse vegetation.

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Fig. 6. Spatial distributions of seasonal precipitation and temperature changes from 1984 to 2013.

Coniferous forests showed a positive correlation with precipitation in the other three seasons (p b 0.05), whereas broadleaved forests did not exhibit significant correlations. Because mixed forests consist of broadleaved and coniferous trees, the correlations for mixed forests were complicated. For grasslands, crops, shrubs and sparse vegetation, which were mainly distributed in plain regions, the NDVI of shrubs and sparse vegetation showed tenuous positive correlations with precipitation in winter, spring and summer, with correlation coefficients N0.5 (p b 0.01). The correlation between the NDVI and precipitation in spring and summer was positive for grasslands and crops, and the correlation coefficients were more significant (p b 0.01) than those found in the other seasons. The relationship between the mean NDVI and seasonal temperature showed significant differences between vegetation types. In the mountains, the correlations between the mean NDVI and

temperature for broadleaf forests and mixed forests were positive in the spring and autumn (p b 0.05) but not significant in the other seasons. In contrast, temperature was not significantly correlated with the mean NDVI of coniferous forests. In the plains, the mean NDVI of shrubs and sparse vegetation exhibited a negative correlation with temperature in spring and summer. Additionally, a negative correlation for grasslands and crops was observed between the mean NDVI and temperature in summer. The above analyses show that precipitation is the main factor affecting the growth of coniferous forests, shrubs, sparse vegetation, grasslands and crops, whereas temperature is the main factor affecting the growth of broadleaf forests and mixed forests. Thus, climatic factors have different influences on different vegetation types, and as the vegetation coverage decreases, the extent of the influence increases.

Fig. 7. Spatial distribution of the correlation coefficients between the mean NDVI and seasonal precipitation.

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Fig. 8. Spatial distributions of the correlation coefficients between the mean NDVI and seasonal temperature.

3.5. Residual analysis The linear regression analysis based on the NDVI residuals (Fig. 9a) showed a slight increasing trend of the NDVI residuals in eastern Kazakhstan, with annual change rates up to 0.007/a, and a slight decreasing trend of the NDVI residuals in the southwest part of Central Asia, with annual change rates as low as − 0.005/a. These results indicate that overall trends of the NDVI residuals have obvious regional characteristics. Significant changes (p-value) in NDVI residuals and the overall trend of the mean NDVI were superimposed (Fig. 9b). The result revealed that light-green positive hot spots with significance in NDVI residuals and areas with increasing vegetation trends are spatially aggregated and are mainly located in eastern Kazakhstan, Kyrgyzstan and Tajikistan. Areas with light-red shading showing the significance of NDVI residuals and the decreasing trend of vegetation are scattered in the southern part of the Karakum Desert, the southern Ustyurt Plateau and the wetland delta of the Aral Sea. This variation in vegetation could not be explained by changes in precipitation and temperature and was, thus, mainly induced by human activities. The dark-red or green shading showing a lack of significant change in NDVI residuals is related to changes in precipitation and temperature. Degradation was identified in the northern part of the Karakum and Kyzylkum Deserts, and improvement was identified in the Kazakhskiy Melkosopochnik. Thus, climate factors were the principal drivers of vegetation changes in Central Asia. Table 4 shows the proportions of the area corresponding to the superimposition of significant changes (p-value) in NDVI residuals and overall trend results of the mean NDVI for different vegetation types. Of the areas with increasing trends of vegetation and a significant change in NDVI residuals, grasslands and crops accounted for 10.54% and 5.75%, respectively. Thus, many areas of grasslands and crops had NDVI growth rates that were faster than what would be expected from climate changes alone. Shrubs and sparse vegetation accounted for 1.97% and 2.62% of the vegetated areas with decreasing trends of

Table 3 Correlation coefficients between the mean NDVI and climatic variables for different vegetation types. Vegetation cover type

Season

Precipitation

Temperature

R

p

R

p

Broadleaved forest

Winter Spring Summer Autumn

0.158 0.037 0.190 −0.442

0.395 0.843 0.307 0.013⁎

−0.061 0.490 0.001 0.363

0.744 0.005⁎⁎ 0.997 0.045⁎

Coniferous forest

Winter Spring Summer Autumn

0.366 0.365 0.357 −0.431

0.043⁎ 0.043⁎ 0.049⁎ 0.015⁎

0.190 0.349 −0.017 0.226

0.306 0.054 0.929 0.222

Mixed forest

Winter Spring Summer Autumn

0.238 0.205 0.369 −0.427

0.196 0.269 0.041⁎ 0.016⁎

0.255 0.392 0.072 0.479

0.165 0.029⁎ 0.699 0.006⁎⁎

Shrub

Winter Spring Summer Autumn

0.522 0.707 0.511 −0.232

0.003⁎⁎ 0.000⁎⁎ 0.003⁎⁎ 0.209

0.337 −0.206 −0.370 0.170

0.063 0.265 0.040⁎ 0.360

Grassland

Winter Spring Summer Autumn

0.312 0.570 0.503 −0.220

0.087 0.001⁎⁎ 0.004⁎⁎ 0.235

0.447 0.265 −0.360 0.078

0.012⁎ 0.149 0.046⁎ 0.676

Sparse vegetation

Winter Spring Summer Autumn

0.624 0.726 0.557 −0.093

0.000⁎⁎ 0.000⁎⁎ 0.001⁎⁎ 0.620

0.323 −0.357 −0.352 0.060

0.076 0.048⁎ 0.052 0.749

Crop

Winter Spring Summer Autumn

0.162 0.456 0.572 0.162

0.385 0.010⁎⁎ 0.001⁎⁎ 0.385

0.310 0.197 −0.343 0.188

0.090 0.289 0.059 0.310

⁎ Correlation significant at the 0.05 level (2-tailed). ⁎⁎ Correlation significant at the 0.01 level (2-tailed).

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Fig. 9. Overall trends of the NDVI residuals in a regression of the growing season mean NDVI with precipitation and temperature (a) and the superimposition of significant changes (pvalue) in NDVI residuals and overall trend results of the mean NDVI (b). The dark-green and dark-red shading indicate that the NDVI variation could be explained by climatic trends, whereas light-green and light-red shading indicate that the NDVI variation could not be explained by climatic trends (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

vegetation and significant changes in NDVI residuals. Thus, more pixels showed degradation that cannot be explained by climatic trends for shrubs and sparse vegetation than for the other vegetation types. The significance test of the NDVI residual trend also suggested that the change trends of grasslands, sparse vegetation and shrubs were close to what would be expected based on climatic factors. The following statistics also identified the area with no significant change in NDVI residuals, of which 28.66% was grasslands, 16.98% was shrubs and 12.59% was sparse vegetation. These results suggest that the change trends of these three types vegetation may be explained by climatic variables. Generally, the overall trends of vegetation types were more closely correlated with climate change. The areas of shrubs and sparse vegetation showed more degradation and the areas of grasslands and crops more improvement that cannot be explained by climatic variables and may be the result of human activities. 4. Discussion 4.1. Vegetation dynamics and their relationships with climatic variables Central Asia has experienced significant climate change under the influence of global warming. This study confirmed previous reports that precipitation has decreased and temperature has increased in most regions during recent decades (de Beurs et al., 2015; Lioubimtseva, 2014; Lioubimtseva et al., 2005; Xu et al., 2016), possibly related to protracted La Niña episodes (Syed et al., 2006). However, a low wintertime temperature in Central Asia and increased precipitation in the Kazakhskiy Melkosopochnik were noted, as reported in other studies (Cohen et al., 2014; Lioubimtseva, 2014; Lioubimtseva and Henebry, 2009). Vegetation dynamics strongly corresponded to climate change: A significantly increasing trend in vegetation growth was

Table 4 The areal proportions corresponding to the superimposition of significant changes (p-value) in NDVI residual and overall trend results of the mean NDVI. Vegetation cover type

Decreasing trend of vegetation

Increasing trend of vegetation

Significant

Insignificant

Significant

Insignificant

Broadleaved forest Coniferous forest Mixed forest Grassland Shrub Sparse vegetation Crop

0.01 0.01 0.01 0.71 1.97 2.62 0.36

0.02 0.04 0.05 7.73 8.35 7.19 3.96

0.36 0.12 0.22 10.54 3.30 2.07 5.75

0.16 0.17 0.34 20.93 8.62 5.40 8.98

The percentages of the vegetated areas of Central Asia that showed significant or insignificant overall trends of NDVI residuals were evaluated at the 0.05 level.

observed in the eastern part of Central Asia, whereas a significantly decreasing trend was found in the western part of Central Asia. Vegetation degradation was more serious in the Karakum and Kyzylkum Deserts, the Ustyurt Plateau and the wetland delta of the Amu Darya than in the other regions. According to the Hurst exponent analysis, most of the observed vegetation changes are consistent, and forests are more consistent than grasslands, shrubs and sparse vegetation. Indeed, forest soils are capable of holding large amounts of water that could be released over a longer time, avoiding being immediately affected by climate. In contrast, the soil of shrubs and sparse vegetation cannot hold water for long durations; thus, the vegetation is immediately affected by climate (Propastin, 2008). Vegetation changes varied regionally in Central Asia and were driven by different factors. In the Kyzylkum Desert and the northern part of the Ustyurt Plateau, drought is the main factor affecting vegetation degradation due to both temperature and precipitation changes. Central Asia has experienced decreasing precipitation in the west, where a significantly decreasing trend in vegetation growth was found. We also found that the mean NDVI values of shrubs, grasslands, and sparse vegetation exhibited relatively high positive correlations with seasonal precipitation in these regions. This finding is primarily attributable to the fact that natural vegetation is widely distributed and is reliant on precipitation in the absence of irrigation. Temperature is often used as an indirect parameter to reflect the available energy for vegetation growth, which is indispensable. Previous studies reported that an increase in temperature could result in a significant increase in vegetation greenness in Central Asia (Zhou et al., 2015). However, elevated temperatures typically increase the evaporation of surface water, which can strongly limit vegetation growth, especially the growth of shrubs and sparse vegetation. The mean NDVI values of shrubs and sparse vegetation presented negative relationships with temperature in the spring and summer, and the mean NDVI of grasslands was negatively correlated with temperature in the summer. Therefore, any long-term increase in the summer temperature would inhibit the photosynthesis of different vegetation types, resulting in decreased vegetation greenness during summer months. Accordingly, drought in this region has led to the loss of more than 50 g−2 biomass (C. Zhang et al., 2016). Similarly, de Beurs found that this area experienced a significant NDVI decline and indicated that drought was the main driver of vegetation degradation in this region (de Beurs et al., 2009). In the Kazakhskiy Melkosopochnik, the northern Karakum Desert, and northern Kazakhstan, precipitation is the dominant factor affecting vegetation growth. Grassland is widely distributed in the Kazakhskiy Melkosopochnik, and the mean NDVI of grasslands exhibited a relatively high positive correlation with seasonal precipitation, i.e., grassland greenness increased significantly with the increased precipitation. Moreover, many shrub areas reverted to grasslands, and the boundary

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of the grassland ecosystem has expanded southward (Chen et al., 2015). Similarly, vegetation greenness increased slightly with the increased precipitation in the northern part of Karakum Desert. Biogenic soil crusts (BSC), with mosses, liverworts, and lichens, have been observed in the northern part of the Karakum Desert, and the biomass production of BSC is reported to be mainly controlled by precipitation (Orlovsky et al., 2004). Orlovsky et al. reported that BSC in the northern Karakum desert increased from 1984 to 2000 and that the biological productivity of BSC increased by 92 kg/ha over this period. In contrast, rain-fed crops account for 10.12% of the total croplands in Central Asia and are mainly distributed in northern Kazakhstan. The greenness of rain-fed crops decreased significantly with the decreased precipitation as these crops rely on precipitation for survival (de Beurs et al., 2015). Thus, precipitation in the spring and summer had a relatively strong effect on rain-fed crops. This relationship can explain the stronger positive correlation of crop mean NDVI with precipitation in the spring and summer than with that in the winter and autumn. Therefore, although precipitation changes in these three regions resulted in significant change trends in vegetation greenness, the causes of the vegetation changes differed among the different land cover types. In the Altay and western Tienshan Mountains and the Aral Sea Basin, phenological changes affect the seasonal variation of vegetation greenness. In these areas, dense vegetation such as grasslands and irrigated crops exhibited rapidly increasing NDVI values in the spring. Because of the increased temperatures, their growing seasons were advanced to earlier in the year. Similarly, Roman Bohovic et al. found that the start of the season in grasslands and crops advanced by 11.7 and 2.1 days, respectively, during 1982–2011 in Central Asia (Bohovic, 2016). In contrast, autumn precedes the decrease in the NDVI signal in Central Asia, and increased temperatures can delay leaf fall in the autumn (Bohovic, 2016; Kariyeva and Van Leeuwen, 2011). This trend can explain why the mean NDVI exhibited a stronger positive correlation with temperature than with precipitation in the autumn. Among forests, most broadleaved forests are broadleaved deciduous forests, which are distributed in the mountains. Increased temperature could promote the growth of leaves in broadleaved deciduous forest in the spring and delay leaf fall in the autumn (Pignatti and Pignatti, 2014), consistent with Bohovic et al.’s study of vegetation phenology in Central Asia. Roman Bohovic et al. reported that in broadleaved forests, the start of the season was advanced by 4.5 days, and the end of the season was delayed by 10.8 days (Bohovic, 2016). Furthermore, broadleaved forests were more strongly correlated with temperature than coniferous forests in the spring and autumn. In contrast, the NDVI of coniferous forests showed a stronger correlation with precipitation than did broadleaf forests, likely because coniferous forests do not rely on groundwater but utilize precipitation through their shallow root systems. Therefore, increased precipitation could benefit coniferous forests. In most of Central Asia, positive correlations exist between the mean NDVI and both precipitation and temperature in the winter. Snow cover in the winter is an important water resource for vegetation growth in the growing season. It exerts beneficial effects on vegetation growth by reducing the destruction caused by winter winds and temperature extremes and by increasing the winter soil temperatures and spring runoff, particularly for sparse vegetation and shrubs (Wahren et al., 2005). 4.2. Correlation of vegetation and human activities Since variations in vegetation are driven by both climatic fluctuations and anthropogenic activities, the contribution of each factor must be considered to accurately determine its relative importance for changes in vegetation. The residual analysis revealed that some shrubs and sparse vegetation degraded in the southern part of the Karakum Desert, the southern Ustyurt Plateau and the wetland delta of the Aral Sea and that some grasslands improved in eastern Kazakhstan, Kyrgyzstan and Tajikistan that could not be explained by climate changes and

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were, thus, mainly induced by human activities (Fig. 9b). These activities were oil and natural gas extraction in the southern part of the Karakum Desert and Ustyurt Plateau, agricultural abandonment in Kazakhstan, cultivation of cropland in Uzbekistan and pasture abandonment in eastern Kazakhstan, Kyrgyzstan and Tajikistan. We used Google Earth software to explore the degradation in the Karakum Desert and Ustyurt Plateau and found that many oil and gas processing plants and chemical industries were distributed in these regions (Fig. 10). The lightened areas in Fig. 10 are the locations of gas processing and chemical industries. The Karakum Desert is one of the largest deserts in the world and contains large reserves of oil, gas, and mineral deposits, which cover approximately 80% of its territory. This desert is vital for the economic development of Turkmenistan. Kazakhstan was the largest oil-producing country among the five Central Asian countries before the collapse of the Soviet Union, and it contains more than 2% of the world's known reserves of oil, mostly in the Ustyurt Plateau (Karnieli et al., 2008). Oil and gas processing and chemical industries are heavily developed in these areas, and the large number of associated vehicles and heavy-duty equipment can damage surface vegetation, especially in the southern parts. Such damage might explain the degradation observed for shrubs and sparse vegetation (Fig. 9b). After the disintegration of the Soviet Union in 1991, politics and the economic development model in Central Asia changed. Annual variation in cropland area was significant in Kazakhstan and Turkmenistan (Fig. 11a). Unsecure land tenure and reduced subsidies for farming caused millions of hectares of agricultural abandonment in Kazakhstan (Kraemer et al., 2015; Zhou et al., 2015), especially in northern Kazakhstan (Fig. 9a). This present study determined that 0.36% of crop degradation was related to human activities, likely cropland abandonment. Nearly 7.70 million hectares in cultivated land were abandoned from 1991 to 2002 (Fig. 11a). Accordingly, many cropland areas reverted to grassland, explaining the large decrease in cropland greenness in Kazakhstan. However, many grasslands were converted into croplands along the northern and southern edges of Turkmenistan (Fig. 9a) (Chen et al., 2015). The area of cultivated land in Turkmenistan has increased from 1.27 to 1.94 million hectares during the last 30 years, resulting in increased vegetation greenness in this region. Similarly, a weak correlation was found between climate and vegetation greenness in some areas of eastern Uzbekistan. This correlation can be explained by the expansion of irrigation agriculture (de Beurs et al., 2015; Saiko and Zonn, 2000). A significantly increasing trend of cropland area was found in Uzbekistan before the disintegration of the Soviet Union (Fig. 11a) (Chen et al., 2015). Therefore, we can conclude that the upward trend of crop greenness in some areas of eastern Uzbekistan was mainly induced by human activities. During the Soviet period, animal husbandry was heavily subsidized and intensified, and livestock numbers greatly increased, causing the degradation of large areas of pasture (Hostert et al., 2011; Mirzabaev et al., 2015). However, with the collapse of the Soviet Union and political independence, the high government subsidy for animal husbandry disappeared (Karnieli et al., 2008), and animal husbandry became much less profitable. Recorded decreases in livestock inventories ranged between 32% and 64% in Kazakhstan, Kyrgyzstan and Tajikistan (Fig. 11b) as these states heavily withdrew from livestock production, and pastures changed in these regions. Remote pastures were abandoned in eastern Kazakhstan, Kyrgyzstan and Tajikistan, and the vegetation has recovered (Hauck et al., 2016; Robinson, 2016). Many pastures reverted to high covered grassland. Rehabilitation of the former pasture land resulted in increased vegetation greenness in these regions. In contrast, In Uzbekistan and Turkmenistan, the livestock inventories increased (Fig. 11b), which might have increased the degradation of shrubs and sparse vegetation in deserts. The shrinking of the Aral Sea is one of the most severe ecological disasters of the 20th century. The Aral Sea basin flows through most parts of Central Asia. The water resources in the Aral Sea basin are known as transnational and mainly originate from the Amu Darya and Syr Darya

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Fig. 10. The location of oil and gas processing plant and chemical industries. The yellow rectangles bound specific regions where vegetation has been degraded due to extraction of gas and oil. The lightened areas in the images are areas of gas processing and chemical industries. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Rivers. The Amu Darya flows primarily through three Central Asian countries: Tajikistan, Turkmenistan, and Uzbekistan, whereas the Syr Darya mainly flows through two Central Asian countries: Kyrgyzstan and Kazakhstan (Fig. 1). Farmland irrigation, particularly in Uzbekistan and Kazakhstan, was a key factor affecting the recession of the Aral Sea.

The excessive exploitation of the water resources in the upstream areas of rivers led to a significant reduction in river runoff, resulting in the shrinking of the Aral Sea. As a result of the consistent recession, the Aral Sea was divided into the Small and Large Aral Seas in 1990. The Small and Large Aral Seas are located in Kazakhstan and Uzbekistan

Fig. 11. Annual variation of crop area (a) and livestock number (b) in the five countries of Central Asia. Rectangular purple shadows highlight the changes that occurred after the collapse of the Soviet Union. The data of crop area were acquired from World Bank Open Data (http://data.worldbank.org/). The data of livestock number were obtained from FAO. All of the livestock were transformed into sheep units using the “animal unit equivalent” (http://www.chinaforage.com/standard/zaixuliang.htm). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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and are supplied with water from the Syr Darya and Amu Darya (Fig. 1), respectively. However, the trends of water level change differed between the two seas after 1990. As presented in Table 5, the average river inflow of the Small Aral Sea increased by 1.6 m3, and the water level was raised by 0.56 m. In contrast, the average river inflow of the Large Aral Sea decreased by 0.8 m3, and the water level decreased by 6.34 m. Subsequently, some shrub and sparse vegetation areas were significantly degraded by human activities in the wetland delta lands of the Large Aral Sea (Fig. 9b). Saiko also reported that degradation and changes in vegetation cover in the wetland delta lands of the Large Aral Sea caused this area to become a low-productivity desert (Saiko and Zonn, 2000). In contrast, in the wetland delta lands of the Small Aral Sea, some shrub and sparse vegetation areas were slightly improved by human activities, as shown in previous studies (Micklin et al., 2014). These changes reflect changes in national land use and government policy in the Aral Sea basin. Millions of hectares of agricultural land in Kazakhstan were abandoned after the collapse of the Soviet Union (Fig. 11a), reducing agricultural water withdrawals. Moreover, the government of Kazakhstan carried out an ecological restoration program of the Small Aral Sea in 1993, maintaining the water level and restoring the surrounding wetlands. However, in Turkmenistan, the area of cultivated land has increased. To meet the demands of irrigation of cultivated land, water resources were diverted from the Amu Darya into the Karakum canal. This diversion was an important cause of water loss from the Large Aral Sea (Micklin et al., 2014).

Kazakhstan. Temperature is a controlling factor affecting the seasonal variation of vegetation greenness. Increased temperature can promote the growth of leaves in the spring and delay leaf fall in the Altay and western Tienshan Mountains and the Aral Sea Basin. However, increased temperature in the summer typically resulted in increased evaporation, which strongly limited vegetation growth, especially for shrubs and sparse vegetation in the Kyzylkum Desert and the northern Ustyurt Plateau. Drought is the main factor affecting vegetation degradation in the Kyzylkum Desert and the northern Ustyurt Plateau due to the combined effects of increased temperature and decreased precipitation. The residual analysis revealed that shrubs and sparse vegetation in the southern part of the Karakum Desert, the southern Ustyurt Plateau and the wetland delta of the Large Aral Sea underwent degradation caused by human activities. Oil and gas extraction in the southern part of the Karakum Desert and Ustyurt Plateau triggered the degradation of shrubs and sparse vegetation in these regions. The average river inflow from the Amu Darya to the Large Aral Sea was decreased due to the increase in cropland in Turkmenistan. This decrease in inflow was the dominant factor driving vegetation degradation in the wetland delta of the Large Aral Sea in recent decades. In contrast, the abandonment of pastures after the breakdown of the Soviet Union caused vegetation improvement in eastern Kazakhstan, Kyrgyzstan and Tajikistan. Many abandoned cropland areas reverted to grassland in northern Kazakhstan, leading to a decrease in cropland greenness. This research revealed the vegetation dynamics and responses to seasonal climate change and human activities in different vegetation types of Central Asia. Complex processes underlie the variation in vegetation and involve both climatic fluctuations and anthropogenic activities. We determined the relative importance of these climatic and anthropogenic factors for changes in vegetation. Because of the limited spatial and temporal resolution of the AVHHR NDVI product and the climatic gridded data, this study focused only on NDVI change trends and responses to climate change and human activities and did not consider their effects on different growth stages of different vegetation types or on assessing vegetation responses to climate extremes. Future research that integrates these factors will be critical to gain a deeper understanding of the driving forces of vegetation activity in Central Asia.

5. Conclusions

Author Contributions

This research analysed the temporal and spatial changes in NDVI over Central Asia from 1984 to 2013 and distinguished between the influences of climate factors and human activities on the spatial distribution and dynamics of vegetation based on the AVHHR NDVI dataset and gridded climate data. The results showed that Central Asia experienced decreased precipitation and increased temperature over most regions. However, a low wintertime temperature in Central Asia and increased precipitation in the Kazakhskiy Melkosopochnik were noted. A significant increasing trend in vegetation growth was identified in the eastern part of Central Asia, whereas a significantly degreasing trend was found in the western part of Central Asia. This study determined that more pixels with vegetation degradation were located in areas of shrubs and sparse vegetation than in those corresponding to forests, grasslands and crops. The degradation of shrubs and sparse vegetation in the Karakum and Kyzylkum Deserts, the Ustyurt Plateau and the wetland delta of the Aral Sea was more serious than that in the other regions. According to the Hurst exponent analysis, most of the observed vegetation changes are consistent, and the changes in forests are more consistent than those in grasslands, shrubs and sparse vegetation. Vegetation changes varied regionally in Central Asia and were driven by different factors. The mean NDVI shows a stronger relationship with precipitation than with temperature, confirming that precipitation is the dominant factor affecting vegetation dynamics in the Kazakhskiy Melkosopochnik, the northern Karakum Desert, and northern

Guli·Jiapaer designed the research. Liangliang Jiang processed the data, analysed the results and wrote the manuscript. Anming Bao, Hao Guo and Felix Ndayisaba provided analysis tools and technical assistance. All authors contributed to the final version of the manuscript by proofreading and providing constructive ideas.

Table 5 Average river inflow and water levels for the Aral Sea from 1981 to 2010. Period

Water body

Average river inflow (km3)

Water level (m)

1981–1990 1991–2000

Aral Sea Small Aral Large Aral Small Aral Large Aral

5.1 5.2 8.6 6.8 4.4

40.03 40.56 36.13 41.12 29.79

2001–2010

Average river inflow is the annual average flow of Amu Darya and Syr Darya Rivers to the Aral Sea for the indicated period. The data were obtained from two hydrological stations. The hydrological station of Aralskoye morye is near the Small Aral Sea, and the hydrological station of Aktumsyk is near the Large Aral Sea.

Conflicts of Interest The authors declare no conflict of interest. Acknowledgments We would like to express sincere gratitude to the United States Geological Survey and the NASA team for the provision of data. We acknowledge the Physical Sciences Division for sharing UDel_AirT_Precip data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/. We also thank the Database of Global Change Parameters, Chinese Academy of Sciences, for providing data on different vegetation types (http:// globalchange.nsdc.cn). This research has been funded by the Special Institute Main Service Program of the Chinese Academy of Sciences (Grant No. TSS-2015-014-FW-1-1), the National Natural Science Foundation of China (No. 41171295) and the Key Laboratory Program of Xinjiang (No. 2015KL003).

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References de Beurs, K.M., Wright, C.K., Henebry, G.M., 2009. Dual scale trend analysis for evaluating climatic and anthropogenic effects on the vegetated land surface in Russia and Kazakhstan. Environ. Res. Lett. 4, 045012. de Beurs, K.M., Henebry, G.M., Owsley, B.C., Sokolik, I., 2015. Using multiple remote sensing perspectives to identify and attribute land surface dynamics in Central Asia 2001– 2013. Remote Sens. Environ. 170, 48–61. Bohovic, R., 2016. The Spatial and Temporal Dynamics of Remotely-sensed Vegetation Phenology in Central Asia in the 1982–2011 Period. Euro. J. Rem. Sens. 279. Breshears, D.D., Cobb, N.S., Rich, P.M., Price, K.P., Allen, C.D., Balice, R.G., et al., 2005. Regional vegetation die-off in response to global-change-type drought. Proc. Natl. Acad. Sci. U. S. A. 102, 15144–15148. Chatfield, C., 2016. The analysis of time series: an introduction. CRC press. Chen, X., Luo, G., Wu, S., Wang, W., Fang, H., Chen, Q., 2015. Land Use/Cover Change in Arid Land of Central Asia. Science Press. Cohen, J., Screen, J.A., Furtado, J.C., Barlow, M., Whittleston, D., Coumou, D., et al., 2014. Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci. 7, 627–637. Davi, N.K., D'Arrigo, R., Jacoby, G.C., Cook, E.R., Anchukaitis, K.J., Nachin, B., et al., 2015. A long-term context (931–2005 C.E.) for rapid warming over Central Asia. Quat. Sci. Rev. 121, 89–97. Evans, J., Geerken, R., 2004. Discrimination between climate and human-induced dryland degradation. J. Arid Environ. 57, 535–554. Fensholt, R., Proud, S.R., 2012. Evaluation of Earth observation based global long term vegetation trends — comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ. 119, 131–147. Gessner, U., Naeimi, V., Klein, I., Kuenzer, C., Klein, D., Dech, S., 2013. The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Glob. Planet. Chang. 110, 74–87. Guli·Jiapaer, Liang, S., Yi, Q., Liu, J., 2015. Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator. Ecol. Indic. 58, 64–76. Guo, H., Chen, S., Bao, A., Hu, J., Gebregiorgis, A., Xue, X., et al., 2015. Inter-comparison of high-resolution satellite precipitation products over Central Asia. Remote Sens. 7, 7181–7211. Han, Q., Luo, G., Li, C., Shakir, A., Wu, M., Saidov, A., 2016. Simulated grazing effects on carbon emission in Central Asia. Agric. For. Meteorol. 216, 203–214. Hauck, M., Artykbaeva, G.T., Zozulya, T.N., Dulamsuren, C., 2016. Pastoral livestock husbandry and rural livelihoods in the forest-steppe of east Kazakhstan. J. Arid Environ. 133, 102–111. Herrmann, S.M., Anyamba, A., Tucker, C.J., 2005. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Glob. Environ. Chang. 15, 394–404. Hostert, P., Kuemmerle, T., Prishchepov, A., Sieber, A., Lambin, E.F., Radeloff, V.C., 2011. Rapid land use change after socio-economic disturbances: the collapse of the Soviet Union versus Chernobyl. Environ. Res. Lett. 6, 045201. Hou, X., Wu, T., Yu, L., Qian, S., 2012. Characteristics of multi-temporal scale variation of vegetation coverage in the Circum Bohai Bay Region, 1999–2009. Acta Ecol. Sin. 32, 297–304. Hu, Z., Zhang, C., Hu, Q., Tian, H., 2014. Temperature changes in Central Asia from 1979 to 2011 based on multiple datasets. J. Clim. 27, 1143–1167. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., et al., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences. 454, pp. 903–995. Hurst, E., H., 1951. Long-term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 116, 770–808. IPCC, WGI, 2007. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., et al. (Eds.), Climate Change 2007: the Physical Science Basis. Contribution ofWorking Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, p. 996. Jiang, W., Yuan, L., Wang, W., Cao, R., Zhang, Y., Shen, W., 2015. Spatio-temporal analysis of vegetation variation in the Yellow River basin. Ecol. Indic. 51, 117–126. Kariyeva, J., Van Leeuwen, W., 2011. Environmental drivers of NDVI-based vegetation phenology in Central Asia. Remote Sens. 3, 203–246. Karnieli, A., Gilad, U., Ponzet, M., Svoray, T., Mirzadinov, R., Fedorina, O., 2008. Assessing land-cover change and degradation in the central Asian deserts using satellite image processing and geostatistical methods. J. Arid Environ. 72, 2093–2105. King, D.A., Bachelet, D.M., Symstad, A.J., Ferschweiler, K., Hobbins, M., 2015. Estimation of potential evapotranspiration from extraterrestrial radiation, air temperature and humidity to assess future climate change effects on the vegetation of the Northern Great Plains, USA. Ecol. Model. 297, 86–97. Klein, I., Gessner, U., Kuenzer, C., 2012. Regional land cover mapping and change detection in Central Asia using MODIS time-series. Appl. Geogr. 35, 219–234. Kraemer, R., Prishchepov, A.V., Müller, D., Kuemmerle, T., Radeloff, V.C., Dara, A., et al., 2015. Long-term agricultural land-cover change and potential for cropland expansion in the former virgin lands area of Kazakhstan. Environ. Res. Lett. 10, 054012. Li, Z., Chen, Y., Li, W., Deng, H., Fang, G., 2015. Potential impacts of climate change on vegetation dynamics in Central Asia. J. Geophys. Res.-Atmos. 120, 2045–2057. Lioubimtseva, E., 2014. A multi-scale assessment of human vulnerability to climate change in the Aral Sea basin. Environ. Earth Sci. 73, 719–729. Lioubimtseva, E., Henebry, G.M., 2009. Climate and environmental change in arid Central Asia: impacts, vulnerability, and adaptations. J. Arid Environ. 73, 963–977. Lioubimtseva, E., Cole, R., Adams, J.M., Kapustin, G., 2005. Impacts of climate and landcover changes in arid lands of Central Asia. J. Arid Environ. 62, 285–308. Mandelbrot, B., Wallis, J.R., 1969. Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resour. Res. 5, 967–988.

Mannig, B., Müller, M., Starke, E., Merkenschlager, C., Mao, W., Zhi, X., et al., 2013. Dynamical downscaling of climate change in Central Asia. Glob. Planet. Chang. 110, 26–39. Meehl, G.A., Stocker, T.F., Collins, W.D., Friedlingstein, P., Gaye, A.T., Gregory, J.M., et al., 2007. Global climate projections. Climate Change 3495, 747–845. Micklin, P.P., Aladin, N.V., Plotnikov, I., 2014. The Aral Sea: The devastation and partial rehabilitation of a great lake. Springer. Mirzabaev, A., Ahmed, M., Werner, J., Pender, J., Louhaichi, M., 2015. Rangelands of Central Asia: challenges and opportunities. J. Arid. Land 8, 93–108. Mohammat, A., Wang, X., Xu, X., Peng, L., Yang, Y., Zhang, X., et al., 2013. Drought and spring cooling induced recent decrease in vegetation growth in inner Asia. Agric. For. Meteorol. 178-179, 21–30. Ndayisaba, F., Guo, H., Bao, A., Guo, H., Karamage, F., Kayiranga, A., 2016. Understanding the spatial temporal vegetation dynamics in Rwanda. Remote Sens. 8, 129. Nezlin, N.P., Kostianoy, A.G., Li, B.-L., 2005. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. J. Arid Environ. 62, 677–700. Orlovsky, L., Dourikov, M., Babaev, A., 2004. Temporal dynamics and productivity of biogenic soil crusts in the central Karakum desert, Turkmenistan. J. Arid Environ. 56, 579–601. Pignatti, E., Pignatti, S., 2014. Forests of broadleaved trees and shrubs at low elevations. Plant life of the Dolomites. Spring 71–119. Pinzón, J.E., Brown, M.E., Tucker, C.J., 2005. Emd correction of orbital drift artifacts in satellite data stream. Propastin, P.A., 2008. Inter-annual changes in vegetation activities and their relationship to temperature and precipitation in Central Asia from 1982 to 2003. J. Environ. Inform. 12, 75–87. Propastin, P.A., Kappas, M., Muratova, N.R., 2008. A remote sensing based monitoring system for discrimination between climate and human-induced vegetation change in Central Asia. Manag. Environ. Qual. 19, 579–596 An International Journal. Robinson, S., 2016. Land Degradation in Central Asia: Evidence. Perception and Policy. Springer, Berlin Heidelberg. Saiko, T.A., Zonn, I.S., 2000. Irrigation expansion and dynamics of desertification in the Circum-Aral region of Central Asia. Appl. Geogr. 20, 349–367. Sorg, A., Bolch, T., Stoffel, M., Solomina, O., Beniston, M., 2012. Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Nat. Clim. Chang. 2, 725–731. Sun, W., Song, X., Mu, X., Gao, P., Wang, F., Zhao, G., 2015. Spatiotemporal vegetation cover variations associated with climate change and ecological restoration in the Loess Plateau. Agric. For. Meteorol. 209–210, 87–99. Syed, F.S., Giorgi, F., Pal, J.S., King, M.P., 2006. Effect of remote forcings on the winter precipitation of central southwest Asia part 1: observations. Theor. Appl. Climatol. 86, 147–160. Tao, Y., Wu, G.-L., Zhang, Y.-M., 2017. Dune-scale distribution pattern of herbaceous plants and their relationship with environmental factors in a saline–alkali desert in Central Asia. Sci. Total Environ. 576, 473–480. Tian, F., Fensholt, R., Verbesselt, J., Grogan, K., Horion, S., Wang, Y., 2015. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sens. Environ. 163, 326–340. Vermote, E., Kaufman, Y.J., 1995. Absolute calibration of avhrr visible and near-jnfrared channels using ocean and cloud views. Int. J. Remote Sens. 16, 2317–2340. Vermote, E., Saleous, N.E., Kaufman, Y.J., Dutton, E., 1994. Data pre-processing: stratospheric aerosol perturbing effect on the remote sensing of vegetation: correction method for the composite NDVI after the Pinatubo eruption. Remote Sens. Rev. 15, 7–21. Wahren, C.H.A., Walker, M.D., Bret-Harte, M.S., 2005. Vegetation responses in Alaskan arctic tundra after 8 years of a summer warming and winter snow manipulation experiment. Glob. Chang. Biol. 11, 537–552. Wang, J., Wang, K., Zhang, M., Zhang, C., 2015. Impacts of climate change and human activities on vegetation cover in hilly southern China. Ecol. Eng. 81, 451–461. Wessels, K.J., van den Bergh, F., Scholes, R.J., 2012. Limits to detectability of land degradation by trend analysis of vegetation index data. Remote Sens. Environ. 125, 10–22. Willmott, C., Matsuura, K., 2001. Terrestrial air temperature and precipitation: monthly and annual climatologies (version 3.02). Center for Climatic Research. Department of Geography, University of Delaware. Xi, X., Sokolik, I.N., 2016. Quantifying the anthropogenic dust emission from agricultural land use and desiccation of the Aral Sea in Central Asia. J. Geophys. Res. Atmos. 121, 270–281. Xu, H.-j., Wang, X.-p., X-x, Zhang, 2016. Decreased vegetation growth in response to summer drought in Central Asia from 2000 to 2012. Int. J. Appl. Earth Obs. Geoinf. 52, 390–402. Yin, G., Hu, Z., Chen, X., Tiyip, T., 2016. Vegetation dynamics and its response to climate change in Central Asia. J. Arid. Land 8, 375–388. Yu, F., Price, K.P., Ellis, J., Shi, P., 2003. Response of seasonal vegetation development to climatic variations in eastern central Asia. Remote Sens. Environ. 87, 42–54. Zhang, C., Lu, D., Chen, X., Zhang, Y., Maisupova, B., Tao, Y., 2016a. The spatiotemporal patterns of vegetation coverage and biomass of the temperate deserts in Central Asia and their relationships with climate controls. Remote Sens. Environ. 175, 271–281. Zhang, Y., Zhang, C., Wang, Z., Chen, Y., Gang, C., An, R., et al., 2016b. Vegetation dynamics and its driving forces from climate change and human activities in the three-river source region, China from 1982 to 2012. Sci. Total Environ. 563–564, 210–220. Zhou, Y., Zhang, L., Fensholt, R., Wang, K., Vitkovskaya, I., Tian, F., 2015. Climate contributions to vegetation variations in central Asian Drylands: pre- and post-USSR collapse. Remote Sens. 7, 2449–2470.