Research Articles

Spatial and temporal variation of vegetation phenology and its response to climate changes in Qaidam Basin from 2000 to 2015

  • FU Yang ,
  • CHEN Hui , * ,
  • NIU Huihui ,
  • ZHANG Siqi ,
  • YANG Yi
  • College of Resources and Environment Sciences, Hebei Normal University, Hebei Key Laboratory of Environmental Change and Ecological Construction, Shijiazhuang 050024, China

Author: Fu Yang (1987-), Master, specialized in global change and environmental change. E-mail:

*Corresponding author: Chen Hui (1972-), Professor, specialized in ecology and integrated physical geography. E-mail:

Received date: 2017-05-31

  Accepted date: 2017-09-28

  Online published: 2018-03-30

Supported by

National Natural Science Foundation of China, No.40971118

Physical Geography Key Disciplines Construction Subjects of Hebei Province


Journal of Geographical Sciences, All Rights Reserved


Based on TIMESAT 3.2 platform, MODIS NDVI data (2000-2015) of Qaidam Basin are fitted, and three main phenological parameters are extracted with the method of dynamic threshold, including the start of growth season (SGS), the end of growth season (EGS) and the length of growth season (LGS). The spatial and temporal variation of vegetation phenology and its response to climate changes are analyzed respectively. The conclusions are as follows: (1) SGS is mainly delayed as a whole. Areas delayed are more than the advanced in EGS, and EGS is a little delayed as a whole. LGS is generally shortened. (2) With the altitude rising, SGS is delayed, EGS is advanced, and LGS is shortened and phenophase appears a big variation below 3000 m and above 5000 m. (3) From 2000 to 2015, the temperature appears a slight increase along with a big fluctuation, and the precipitation increases evidently. (4) Response of phenophase to precipitation is not obvious in the low elevation humid regions, where SGS arrives early and EGS delays; while, in the upper part of the mountain regions, SGS delays and EGS advances with temperature rising, SGS arrives early and EGS delays with precipitation increasing.

Cite this article

FU Yang , CHEN Hui , NIU Huihui , ZHANG Siqi , YANG Yi . Spatial and temporal variation of vegetation phenology and its response to climate changes in Qaidam Basin from 2000 to 2015[J]. Journal of Geographical Sciences, 2018 , 28(4) : 400 -414 . DOI: 10.1007/s11442-018-1480-2

1 Introduction

Phenology is a subject to study various seasonal phenomena in biological populations and the relationship between phenology and environmental periodic changes (such as climate, hydrology and soil, etc.) (Lieth et al., 1974; Zhu et al., 1999). As a research object of phenology, phenological phenomena, especially the interannual variations of start of growth season (SGS), end of growth season (EGS) and length of growth season (LGS), reflect the changes of climate, hydrology and soil for a period of time. So many scholars figuratively speak that phenology is the most sensitive comprehensive indicator of seasonal and interannual variation of climate and environment. Phenological changes directly affect biological production and have a critical role in ecosystem productivity and carbon cycling. There are many characteristics in the use of remote sensing means to monitor phenology, such as multi-phases, covering a wide, continuous space, long-time series, being able to quantitatively reflect the seasonal growth and development of plants and its interannual changes and so on (Reed et al., 1994; Mouslin et al., 1997; Schwartz, 1998; Chen et al., 2005). As more and more experts and scholars use remote sensing technology on vegetation phenology change research in depth, it has gradually developed into an independent discipline and has become a frontier for global climate change and terrestrial ecosystem dynamics research (Chen et al., 2009).
Good hydrothermal conditions provide reliable protection for plant growth and development. Chmielewski et al. (2001) found that SGS occurred 7 d in advance at every 1°C rise in spring temperature in Europe. Through the study of more than 20 species of vegetation phenology in Germany, Menzel et al. (2003) found that when the temperature rose by 1°C, the flowering and leaf expansion of spring plants occurred 2.5-6.7 d in advance. Zheng et al. (2002) analyzed the effects of climate warming on the phenological changes of vegetation in China in recent 40 years by collecting 26 observation sites, and found that when China’s spring temperatures rose 0.5°C, SGS had an average of 2 d in advance in the 1980s. Ma et al. (2012) studied the phenology of vegetation in China by means of ground observation, remote sensing monitoring and model simulation. The results showed that China’s temperature rises every 1°C, the spring phenophase would be ahead of 4.93 d in the past 30 years. In addition to the temperature, the impact of precipitation on vegetation phenology should not be ignored. When drought occurs, even the photothermal conditions are sufficient but cannot meet the needs of plant growth, the moisture has become a major factor affecting the growth and development of vegetation. In the study of phenology-climatic relationships in four areas of the Qinghai Plateau, Li et al. (2010) believed that annual precipitation and stage precipitation had some influence on the critical period of vegetation phenology growth, especially SGS postponement may be caused by accelerated evaporation of water at elevated temperatures, which emphasized the importance of moisture. In the study of phenology-climatic relationships in the Qinghai-Tibet Plateau, Ding et al. (2012) found that SGS was negatively correlated with precipitation. Song et al. (2012) studied the relations between phenological changes of four typical vegetation types and climate in the Tibetan Plateau. The results showed that the precipitation in the previous year had a great influence on the SGS of alpine shrub meadow, and its influence degree was higher than that in spring temperature.
Qinghai-Tibet Plateau is the most sensitive area of response to the global climate change, its hydrothermal condition is at the limit of biological growth and the ecosystem is extremely fragile (Yao et al., 2006). As the highest elevation basin in the world, Qaidam Basin is located in the northeastern part of the Qinghai-Tibet Plateau. It is a transitional zone between the Qinghai-Tibet Plateau and the inland arid and semi-arid regions. Desert, oasis and alpine grassland are distributed inside it, forming a strong contrast, so that Qaidam Basin has become a unique geographical unit. In the past 30 years, the trend of increasing temperature and precipitation in Qaidam Basin has been stronger than that in the hinterland of the Qinghai-Tibet Plateau (Li et al., 2010; Han et al., 2001). Due to its significant ecosystem vulnerability and interannual volatility of climatic factors, Qaidam Basin is an ideal area for studying vegetation phenology and its response to climate change.
At present, there are few studies on phenology related to Qaidam Basin, but many researches on the Qinghai-Tibet Plateau. The area of Qaidam Basin is about 1/10 of the total Qinghai-Tibet Plateau, and it is difficult to express the details of the phenological changes in Qaidam Basin under such a scale difference. The study of remote sensing phenology of the Qinghai-Tibet Plateau, some are regional differences study based on pixel scale, rarely explore its climate-driven mechanism (Ding et al., 2012). Involving the relationship between phenology and climate, the study often only selected some typical sample sites, can not capture the changes in the regional space (Li et al., 2010). With the deepening of research and the progress of related technologies, it is necessary to improve precision to the pixel scale and to carry out the study on dynamic relationship between phenology and climate in Qaidam Basin.

2 General situation of the study area

Qaidam Basin is located in the northeastern part of the Qinghai-Tibet Plateau, and is an approximately triangular, closed plateau mountain tectonic basin (Figure 1). The northeastern, northwestern and southern edges of the basin are surrounded by Qilian Mountains, Altun Mountains and Kunlun Mountains respectively. The inner elevation of the basin is between 2652 m and 3350 m. If the outer mountain range are covered, it can reach 6066 m. From the edge of the basin to the center, the geomorphic types are mountain, gobi, fixed and semi-fixed sand dune and wind erosion hill, alluvial-proluvial plain, and lacustrine plain (Lu et al., 2015). There are lakes in the basin in decentralized distribution, dotted with multi-center circular zonary terrains. The Qaidam Basin is located in the transitional zone between the Qinghai-Tibet Plateau and the arid area of northwest China, which is less in precipitation and high in evaporation, a typical continental climate. The arid and alpine natural conditions make Qaidam Basin both salty desert landscapes and the attributes of the Qinghai-Tibet Plateau (Zhang et al., 1988). The vegetation is sparse in the basin center, where there are mainly salt lakes and deserts, accounting for 53.68% of the whole basin area. The dense vegetation area mainly lies in inland rivers, lakes and alluvial and diluvium deposits originated from the former formed by the melted ice and snow from the surrounding mountains (Ma et al., 2008). There is a certain amount of alpine meadow in the upper part of the mountain and gentle slope. Farmland is mainly distributed in alluvial and diluvium fan of the basin center.
Figure 1 Location of Qaidam Basin and vegetation distribution

3 Data and methods

3.1 Data sources and pretreatment

According to the characteristics of the arid and semi-arid climate and vegetation growth in Qaidam Basin, this study was given dual requirements in terms of spatial and temporal accuracy of the data source. In the end, we selected the MODIS NDVI dataset provided by the CAS data center (, with a time range of 2000-2015, spatial resolution of 500 m, temporal resolution of 5d, and a total of 1152 periods.
Meteorological data is derived from the National Meteorological Science Data Sharing Service Platform ( Daily temperature and precipitation data from 2000 to 2015 were collected from 18 meteorological stations in Qaidam Basin and its surroundings. The daily temperature and precipitation were treated respectively as the 5 d mean or 5 d sum. With separate interpolation, the climate raster images were obtained with the same spatial and temporal resolution as the phenological data. The specific approach was as follows: Temperature interpolation method: first, the temperature data were corrected to the elevation of 0 m, and then were interpolated with the Kriging method. Finally, the DEM data of Qaidam Basin were used for altitude correction. Method of precipitation interpolation: Kriging method was used to interpolate directly.

3.2 Reconstruction of time series and extraction of phenological parameters

Three parameters of vegetation, the start of growth season (SGS), the end of growth season (EGS) and the length of growth season (LGS) were used to reflect the phenological changes of vegetation. SGS, which is based on remote sensing technology, refers to the date when most plants in the community begin to expand leaves and grow normally. After this date, vegetation enters the rapid growth stage. EGS refers to the date in which the majority of plants in the community can not grow normally and the leaves begin to turn yellow, after which the vegetation enters the stage of death or dormancy. LGS equals EGS minus SGS, which is different from the traditional growth season concept “the number of days a plant can grow in a year”. The results of remote sensing estimation are based on the phenological status of vegetation in a community or region. The results have a certain difference from those of traditional phenology, but the correlation is very high (Zhang et al., 2003).
Figure 2 Comparison of three time series method for reconstructing a high-quality NDVI time series data
This study is based on the development and improvement of the TIMESAT 3.2 platform by Jönsson and Eklundh (2015), which provides three time series reconstruction methods: Asymmetric Gaussians function fitting method (A-G) (Jönsson et al., 2002), Double Logistic function fitting method (D-L) (Fisher et al., 2006) and Savizky-Glolay filter algorithm (S-G) (Savitzky et al., 1964; Chen et al., 2004). The above algorithms are compared by extracting several typical vegetation types in Qaidam Basin. The results show that S-G algorithm is best fit for the details of the original NDVI curve. But at the same time, a lot of noise will be retained. The determination of phenological thresholds is subject to strong interferences. In view of this feature of S-G algorithm, the advantages may be apparent when the forest time series with higher vegetation indices are fitted. A-G algorithm has a high degree of coincidence when fitting the growth season curve and the envelope line, but in some cases, it is easy for the curve to produce mutations affected by the NDVI noise. It is difficult to capture the real growth of vegetation, so it is not suitable for the fitting of vegetation index in arid and semiarid areas. A-G algorithm can further reduce the larger noise in MODIS NDVI time series of Qaidam Basin and more smoothly reflect the growth of vegetation. Combined with previous studies and experiments in this study area, it is decided that A-G algorithm is used as a fitting method for the NDVI time series of Qaidam Basin.
For threshold settings, in a multi-regional study, Jönsson (2002) suggested setting the SGS and EGS thresholds to 20% of the NDVI amplitude in a year. Song et al. (2012) and Ma et al. (2016) studied the phenology of Northern Tibet and Qinghai-Tibet Plateau, respectively, using 0.1 for SGS threshold and 0.2 for EGS. Ma et al. (2014) believed that the threshold of 0.2 has a stronger consistency with the ground data after he compared 0.15, 0.2 and 0.25 to study the vegetation in Central Asia and Xinjiang arid area. Combined with the ground observation data of Qaidam Basin, and on the basis of a large number of experiments, it is found out that when the threshold was 0.2, the phenology was closer to local ground observation phenology. So, the threshold of 0.2 for SGS and LGS was determined in this study. The LGS will be obtained through the operations between SGS and EGS.

3.3 Statistical analysis method

(1) Trend analysis (Xu et al., 2013). In order to obtain the spatial and temporal characteristics of both vegetation phenology and climatic factors in Qaidam Basin, respectively, the trend analysis method was used to fit the interannual variation of both phenology and climatic factors from 2000 to 2015 in this study. And the statistical test method was used to test the changing trend.
(2) Partial correlation analysis (Zhang et al., 2011). The influences both of temperature and precipitation on phenological changes were investigated with the partial correlation analysis of pixels. First, the linear correlation coefficient between the phenology, temperature and precipitation was calculated (Yin et al., 2016). And then the partial correlation coefficient was calculated. Finally, the T-test was used to test the significance of the partial correlation coefficient.

4 Result analysis

4.1 The spatial and temporal pattern of plant phenology in Qaidam Basin

The spatial characteristics of multi-year average plant phenology in Qaidam Basin are calculated by raster processing, shown in Figure 3. Except deserts and bare rocks from the basin center to the northwest, multi-year SGS in the basin is between 130th and 170th d (Figure 3a). From the mid-east to the west, as well as to the edge of north and south, SGS is delayed gradually and the mean is 156th d as a whole. In a tiny part of the area, approximately accounted for 4% of the vegetation cover, SGS comes as early as 140th d, with its distribution in the small intermontane basin in the northeast of Qaidam Basin and the core area of the Oasis in the leading edge of alluvial and diluvial fan (Dulan-Nuomuhong-Golmud-Urt Moron) from the east to the center. The area with the value ranging from 140th d to 160th d is concentrated in the eastern low mountainous area and basin edge interval, which accounts for 56% of vegetation area. For the northern and southern edge with a high elevation in the mountainous area of Qaidam Basin and the central and southern edge in alluvial-proluvial fan apart from the core area of the Oasis, SGS is delayed over 160th d. In the aspect of interannual variation, the SGS of the vegetation in Qaidam Basin is delayed by 0.36 d/16 a as a whole. SGS in 67.9% of the vegetation area is delayed. The area where SGS advances significantly (P<0.01 and 0.01≤P<0.05) accounts for 5.6%, and 1.2% of the area is delayed over 2 d, with its main distribution in the high-elevation region of Qilian Mountains and Kunlun Mountains locating in the northern and southern edges of the basin. The area with SGS advance accounts for 32.1% (Figures 3d and 3g). It is mainly distributed in the eastern mountain interval, the front of alluvial-proluvial fan in the central area, the northern slope of Qilian Mountains and both ends of southern slope of Kunlun Mountains, in which the area with significant SGS advance (P<0.01 and 0.01≤P< 0.05) accounts for 1.6%, and 0.5% of the region advances more than 2 d , such as the northern slope of Qilian Mountains from Har Lake to the Da Haleteng River and the mountain valley of the upper Xiangride River.
Figure 3 The spatial pattern (a-c) and interannual variation (d-i) of phenology in Qaidam Basin during 2000-2015
EGS mainly occurs on 270th-310th d, with the mean being 294th d (Figure 3b). In the eastern low mountains, the oasis in the front of alluvial-proluvial fan and the mountain interval valley in northern and southern sides of the basin, EGS is late than 300th d, which appears after October. Other areas accounting for 47.7% of vegetation coverage show a vertical differentiation law consistent with SGS. That is, EGS is delayed with the increase of elevation (Figures 3e and 3h). The EGS of the vegetation in the basin is delayed by 0.04 d/16 a as a whole. EGS is delayed in 13.9% of the area, which is mainly distributed in the front of alluvial and diluvium fan in the basin center, the eastern low mountainous area, the northern slope of Qilian Mountains and the intervale of Kunlun Mountains. The area where SGS is delayed accounts for 52.3%. But only in 9.7% of the area, SGS advances significantly, which is distributed in the eastern edge of Qaidam Basin and the southern slope of Kunlun Mountains.
Under the interaction of SGS and EGS, LGS mainly concentrates in 120 d-170 d, with the average being 138 d (Figure 3c). The area where LGS is longer than 150 d includes the west bank of Gaskule Lake, east bank of Sugan Lake, some small basins in mountains lying in the east of Qaidam Basin and the front of alluvial-proluvial fan in the basin center, and is covered by 24.2% of the vegetation. The area where LGS is shorter than 130 d accounts for 35.7% lies in the southern slope of Kunlun Mountains and the mountains around Hela Lake of Qilian Mountains. The area where LGS is among 130 d-150 d is widely distributed in the regions between 150 d and 130 d, such as valleys, floodplains, lacustrine plains, the river downstream in alluvial- proluvial fan and slow slopes. The changing range of LGS is very large, from 2000 to 2015 (Figures 3f and 3i). It is shown that a shortening trend appears in 53.2% of vegetation area and only 4.1% of the area shows s significantly shortening tendency (P<0.01 and 0.01≤P<0.05). On the contrary, the area where LGS is extended accounts for 46.8% of the vegetation area and the significantly extended area reaches to 6.3%, whose extended amplitude is over 2 d/16 a. Among it, the area where LGS is lower than 130 d mainly locates in Har Lake downstream of the north of Qilian Mountains, with its extended trend being obvious.
All the analysis shows that phenology of Qaidam Basin is influenced greatly by topography, so that the spatial and temporal variation of plant phenology presents the characteristic of gradient distribution from low to high altitude above sea level. Relational analysis between plant phenology and elevation in Qaidam Basin is made as follows.

4.2 The altitude distribution of plant phenology

With a line fit between the elevation data and the plant phenology in a unit of 100 m, the variation features of multi-year average of plant phenology is obtained during 2000 to 2015 in Qaidam Basin along the altitude gradient. Phenology presents significant spatial heterogeneity and altitude dependence along with the altitude rising (Figure 4). Specifically, at an altitude from 2600 m to 3000 m, SGS fluctuates from 149th d to 159th d, which mainly locates in the ecotone from oasis to mountain or agricultural planting area. Starting from 3000 m, phenology shows a regular delayed trend. SGS reached 164th d at 5200 m. Interannual variability analysis of SGS at different altitudes shows that vegetation turn green in advance below 3000 m and above 4800 m, and the area between 3000 m and 4800 m is delayed. The delay rate reached 1.15 d/16 a at 3400 m. This confirms the conclusion that the SGS delayed area in the previous study is larger than the advance region.
Figure 4 The relations between phenology and altitude of Qaidam Basin during 2000-2015
With 3300 m as a boundary, EGS shows two different trends: (1) Between 2600 m and 3300 m, EGS has a slightly fluctuating state from advance to delay. (2) Above 3300 m, EGS tends to advance from 306th d to 275th d, and the trend is faster and faster, reaching 3.96 d/16 a at 5300 m. For the interannual variation, the EGS is delayed below 4000 m and advance above it.
For LGS, 3500 m as a node, the fluctuation range of phenophase is 146 d to 154 d below 3500 m. Starting from 3500 m, LGS is from 153 d to 108 d or shorter. In terms of interannual variation, LGS performs an extension trend below 4300 m, and above 4300 m it is shortened.
To sum up, plant phenology is closely related to elevation in Qaidam Basin. Generally, SGS is delayed, EGS advances, and LGS is shortened along with altitude rise. And the elevation is mainly through the difference of hydrothermal composition and other environmental factors at different altitude to impact phenology. Among them, changes in the climatic factors that occur with elevation are important reasons for phenological changes.

4.3 Response of plant phenology to major climatic factors

4.3.1 Variation characteristics of temperature and precipitation in Qaidam Basin
The spatial and temporal distribution of temperature and precipitation from 2000 to 2015 is obtained by interpolation processing of 18 meteorological stations inside and around Qaidam Basin (Figure 5). The result shows that the multi-year average temperature (2000-2015) is 1.58°C in the Basin (Figure 5a). It is shown that the temperature rose significantly in Qaidam Basin at the beginning of the new century. Even the average temperature reached 3.45°C in 2004, and then declined for four consecutive years until it dropped to 0.29°C in 2008. From the beginning of 2009, temperature fluctuated around the average. By excluding the impact of the three abnormal temperature year 2004, 2005 and 2008, Qaidam Basin shows a warming trend over 16 years. The spatial distribution of the mean temperature is highly consistent with the elevation distribution of the region (Figure 5c). That is, there is a ring declining trend from the lowland of the basin center to the mountains around the basin, as well as from the small mountain basin or valley to the ridge and mountain peak. In terms of precipitation, the average precipitation of 16 a is 131.63 mm, showing a tendency to increase first and then decrease (Figure 5b). Qaidam Basin is located in the inland and surrounded by mountains, so it is difficult for water vapor to reach there. The mid-west presents an extremely arid climatic characteristic, while the eastern low mountainous area and the southern slope of Kunlun Mountains is closer to the monsoon region, so the precipitation is much more. In summary, the mean annual precipitation of Qaidam Basin performs a semi-circular decreasing trend from southeast to northwest (Figure 5d).
In general, the climate tends to be increasingly warm-humid in Qaidam Basin. Interannual variation of temperature and precipitation of 16 a in Qaidam Basin are obtained with trend analysis method. The result shows that the overall trend of annual variation of temperature in Qaidam Basin is not very obvious (Figure 5e). Only the south slope of the eastern Kunlun Mountains passes the 0.05 significance test. The vegetation area is mainly characterized by no significant warming, while the temperature declines in the basin center where desert is distributed widely. In terms of interannual variation of precipitation, the precipitation of the eastern Basin, represented by the eastern Qilian Mountains and eastern Kunlun Mountains, is significantly increasing. To the west of the Basin, precipitation presents a trend of gradient decline. The significance test of the interannual variation of precipitation is conducted, and the result shows that precipitation increases significantly in the eastern Kunlun Mountains (Figure 5f).
4.3.2 The relationship between SGS and temperature and precipitation
The SGS is affected by both temperature and precipitation. In order to obtain the correlation between one of the dependent variables and the independent variable, the mean temperature and available precipitation in spring (from March to May) during 2000 to 2015 are extracted. And then with partial correlation analysis, the partial correlation coefficients between SGS and temperature and precipitation are obtained to conduct T tests (Figure 6). (1) According to the results of T tests, the area where temperature and SGS are significantly correlated (P<0.01 and 0.01≤P<0.05) accounts for 7.45% of vegetation coverage area (Figure 6a). Of the area, the part with a significant positive correlation (P<0.01 and 0.01≤P<0.05) accounts for 3.14%, which is distributed in the northeast of the basin and the high altitude areas of the southern slope of Qilian Mountains and Kunlun Mountains. The temperature, based on the above analysis, of these areas, rises obviously. However, since the rare precipitation there has been excluded in the analysis, unilateral warming can lead to the increasing evaporation and the destruction of soil moisture to postpone SGS. The areas with the significant negative correlation (P<0.01 and 0.01≤P<0.05) accounts for 4.31%, which is distributed in the north, southwest of Qaidam Basin and alluvial and diluvium fan in the basin center, such as the northern slope of Qilian Mountains and some river valleys in the west of Kunlun Mountains. There is much runoff supply. Therefore temperature rising is beneficial to the increase of the runoff amount, which mainly comes from the melted-water, and thereby the soil moisture will be improved and SGS will advance.
(2) The area where there is a significant correlation (P<0.01 and 0.01≤P<0.05) between SGS and spring precipitation only accounts for only 7.41% (Figure 6b), which is probably related to the poor and unstable precipitation. In spite of this, with precipitation increasing, there are still 6.6% of the areas whose SGS advances in the vegetation area. The areas are distributed in the valley area of southern slope of Kunlun Mountains and Qilian Mountains. It is of proof once again that climate warming leads to SGS being delayed without runoff supply in the Qaidam Basin where precipitation is poor. When the precipitation becomes abundant in some years, soil moisture will become better to drive SGS ahead of time.
Figure 5 Spatial and temporal distribution of temperature and precipitation (a-d), and their significance (e, f) in Qaidam Basin during 2000-2015
Figure 6 The relationship between SGS and spring temperature (a), as well as spring precipitation (b) and winter temperature (c) in Qaidam Basin
(3) Studies have shown that winter temperature on Qinghai-Tibet Plateau has a greater impact on SGS, even greater than spring temperature (Li et al., 2010; Ding et al., 2011). On the basis of the analysis, it is revealed that the areas where winter temperatures relate to SGS in Qaidam Basin account for 4.17% (Figure 6c). It can be revealed that the impact of the two on SGS is roughly close to each other. Nevertheless, the affected areas are slightly different, and the obvious areas such as the high altitude ridge of Kunlun Mountains where the temperature rising will cause the SGS to postpone in the following year. Water in these areas is dominated by precipitation, while the winter precipitation is scarce or even none. At this time, warm winter will exacerbate the evaporation to lead to the spring drought, so that SGS will be delayed. The negative correlation between winter temperature and SGS is mainly in the eastern part of the basin. Due to the abundant water resources in this area, soil moisture is better, and warm winter may help drive the initial period at 0°C and 5°C ahead of time, so that SGS may be advanced.
4.3.3 The relationship between EGS and temperature and precipitation
EGS is closely related to the temperature and precipitation in both summer and autumn. Within the time frame of this study, the EGS in Qaidam Basin concentrates on the range of 270 d and 310 d (around October). In order to make the analysis result more accurately reflect the relationship between climate and phenology, in this paper, the monthly mean temperature and precipitation of September and October (2000-2015) of Qaidam Basin are selected to conduct partial correlation analysis (Figure 7). The result shows, (1) the areas with significant positive correlation between EGS and autumn temperature account for 3.69% (P<0.01 and 0.01≤P<0.05), which are distributed in the upstream valley of the southeast and northeast of the basin (Figure 7a). Research conducted by Dai et al. (2013) shows that most rivers’ discharge in Qaidam area increases year by year due to the rising temperature, which causes snow and ice to melt, and thus directly increases the flow of underground and aboveground runoff. Plant growth conditions have been improved, thus delaying EGS. The areas where EGS and autumn temperature are significantly negatively correlated account for 1.29% (P<0.01 and 0.01≤P<0.05), and are mainly distributed in the eastern part of Kunlun Mountains. The elevation is about 4500 m above sea level, and the alpine meadow distributed here is extremely sensitive to the climate. Significant warming and intense evaporation lead to water shortage, and thus vegetation advances into EGS. (2) By 0.01 and 0.05 significance tests, it is shown that the areas where EGS and autumn precipitation are significantly positively correlated accounts for 6.89%. They are distributed in the northern slope of Qilian Mountains such as the upstream of Bayinguole River and Dahaleteng River, Har Lake around and the eastern part of Kunlun Mountains (Figure 7b). In particular, the significantly increased precipitation in the eastern section of Kunlun Mountains provides favorable hydrothermal conditions for vegetation growth.
In general, in Qaidam Basin, there are extreme drought and significant differentiation in altitude, so that increased autumn temperature may cause severe evaporation. And then due to lack of water, the plants are withered in advance. In contrast, precipitation can quickly and effectively replenish the water required for the plant, and then delay EGS significantly.
4.3.4 The relationship between LGS and temperature and precipitation
LGS can reflect the status of vegetation growth in a region, and indirectly reflect the perennial hydrothermal condition there. Analysis shows that the area where there is a positive correlation between LGS and annual mean temperature is larger than with a negative correlation (Figure 8). 6.43% of the vegetation coverage area is significantly positively correlated with temperature (P<0.01 and 0.01≤P<0.05), and it is concentrated in the eastern mountains and the upstream of some valleys in Qilian Mountains and Kunlun Mountains (Figure 8a). The negative correlation with annual mean temperature is mainly represented by the southeastern margin of Kunlun Mountains, with a significant correlation accounting for 2.23% (P<0.01 and 0.01≤P<0.05). It can be revealed from the above analysis that the warming of the eastern Kunlun Mountains in the basin is significant, and the interannual variation trend of LGS there is significantly shortened. Heat plays a leading role in this trend. This is caused, though the SGS change here is not significant, by the temperature increases and the evapotranspiration increases, which lead to the advancing of EGS and then the shortening of LGS.
Figure 7 Partial correlations between EGS and autumn temperature (a), as well as autumn precipitation (b) in Qaidam Basin
Figure 8 Partial correlations between LGS and annual temperature (a), as well as annual precipitation (b) in Qaidam Basin
In contrast to the temperature, there are more areas where there are negative correlations between precipitation and LGS, accounting for 13.5% of vegetation coverage. These areas are represented by the eastern low mountains, lacustrine plain, river upstream and so on (Figure 8b). Maybe due to the relatively abundant precipitation and water sources, the areas are not sensitive to the weak increase or decrease of precipitation. Other areas account for about 3.1% of the vegetation cover area, such as the eastern part of the Kunlun Mountains, where precipitation is significantly increased. If the interference of temperature rising is excluded, LGS there will extend with precipitation increasing.

5 Conclusions

(1) The spatial variation of plant phenology in Qaidam Basin shows a significant banded structure and vertical gradient characteristics, the differences from the mountains on the basin edge, to river-valley, alluvial and diluvial fan, and then to the oasis in the basin center. Meanwhile, phenology has a regular change from the eastern low mountains and central oasis areas to the mountains located in the north and south. From the east and center of the basin to the north and south sides SGS is gradually delayed, EGS gradually advanced, and LGS is gradually shortened, especially in the southeast of Qaidam Basin, LGS is shortened significantly. In addition, SGS is delayed and LGS is shortened in the oasis and river downstream where there is insufficient runoff supply.
(2) In Qaidam Basin, SGS and EGS overall delayed, and LGS shortened. Specific analysis showed that SGS postponed 0.36 d/16 a, EGS delayed 0.04 d/16 a, LGS shortened 0.32 d/16 a. This matches the conclusion from the study on phenology of southern Qinghai of Zhang et al. (1999) and Yu et al. (2004). It mainly reflects spatial difference. LGS generally shows a shortened trend except for the central oasis, east of the basin, the northern low mountainous areas of Qilian Mountains, etc.
(3) Plant phenology in Qaidam Basin presents significant spatial heterogeneity and altitude dependence. Due to different hydrothermal combinations at different elevations, SGS is delayed, EGS is advanced, and LGS is shortened along with the altitude rising. The occurrence of inflection points may be related to the changes of hydrothermal combination along with altitude rising. The phenology has a big variation in the area below 3000 m and above 5000 m. One reason for this is the foothills or oasis area has a wide variety of vegetation types or is the ecotone of agriculture and animal husbandry. And the other reason is that the vegetation species in the area above 5000 m are simple, whose growing power is very weak and the growth and development is sensitive to hydrothermal variations and climate changes. These two reasons lead to different LGS at the same elevation.
(4) A significant correlation between temperature distribution and elevation is found in Qaidam Basin. The trend presents gradual diminishing rings from the basin center to the edge. From 2000 to 2015, temperature’s overall trend matches that of the global trend, and with a slight increase along with a big fluctuation. The temperature in vegetation coverage area is dominated by warming. The plateau of the eastern part of Kunlun Mountains represents a significant warming trend. There is an obvious interannual precipitation increment with gradual decrease from the eastern edge to the center and the west of Qaidam Basin.
(5) On the whole, the effect of precipitation on plant phenology of Qaidam Basin is greater than that of temperature, but there are differences in the degree of the effect in different regions. Firstly, areas represented by the valley, basin, lacustrine plain, and oasis, with their good soil moisture content, are highly sensitive to temperature, but not to precipitation. The increased temperature is favorable to the dormant plants in their passing through 0°C and 5°C in advance and can prompt them to sprout and turn green. Secondly, in the high-altitude mountain areas of the basin edge and the outer zone of some oasis areas, the plant phenology has a significant response to the changes of both temperature and precipitation. In some areas, precipitation is not enough, and the unilateral increase of temperature leads to evaporation increases, which may be the reason for SGS being delayed and EGS being advanced in these areas. And in other areas, the precipitation increases significantly, which can improve the hydrothermal condition. Meanwhile, SGS will be advanced and EGS will be delayed.
In this study, due to the lack of ground-based verification of the phenological time series, to some extent, the reliability of phenology data sources and monitoring indicators could be affected. The spatial and temporal changes of plant phenology are the result of comprehensive factors, while there are only the temperature and precipitation selected in this study without solar radiation quantity, evapotranspiration, humidity, hydrology, soil temperature and humidity, human activities and so on, which could be added in future studies for further analysis.

The authors have declared that no competing interests exist.

Chen J, Jönsson P, Tamura Met al., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay Filter.Remote Sensing of Environment, 91(3/4): 332-344.Although the Normalized Difference Vegetation Index (NDVI) time-series data, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, has been successfully used in research regarding global environmental change, residual noise in the NDVI time-series data, even after applying strict pre-processing, impedes further analysis and risks generating erroneous results. Based on the assumptions that NDVI time-series follow annual cycles of growth and decline of vegetation, and that clouds or poor atmospheric conditions usually depress NDVI values, we have developed in the present study a simple but robust method based on the Savitzky鈥揋olay filter to smooth out noise in NDVI time-series, specifically that caused primarily by cloud contamination and atmospheric variability. Our method was developed to make data approach the upper NDVI envelope and to reflect the changes in NDVI patterns via an iteration process. From the results obtained by applying the newly developed method to a 10-day MVC SPOT VGT-S product, we provide optimized parameters for the new method and compare this technique with the BISE algorithm and Fourier-based fitting method. Our results indicate that the new method is more effective in obtaining high-quality NDVI time-series.


Chen X Q, Hu B, Yu R, 2005. Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China.Global Change Biology, 11: 1118-1130.Abstract Using phenological and normalized difference vegetation index (NDVI) data from 1982 to 1993 at seven sample stations in temperate eastern China, we calculated the cumulative frequency of leaf unfolding and leaf coloration dates for deciduous species every 5 days throughout the study period. Then, we determined the growing season beginning and end dates by computing times when 50% of the species had undergone leaf unfolding and leaf coloration for each station year. Next, we used these beginning and end dates of the growing season as time markers to determine corresponding threshold NDVI values on NDVI curves for the pixels overlaying phenological stations. Based on a cluster analysis, we determined extrapolation areas for each phenological station in every year, and then implemented the spatial extrapolation of growing season parameters from the seven sample stations to all possible meteorological stations in the study area. Results show that spatial patterns of growing season beginning and end dates correlate significantly with spatial patterns of mean air temperatures in spring and autumn, respectively. Contrasting with results from similar studies in Europe and North America, our study suggests that there is a significant delay in leaf coloration dates, along with a less pronounced advance of leaf unfolding dates in different latitudinal zones and the whole area from 1982 to 1993. The growing season has been extended by 1.4鈥3.6 days per year in the northern zones and by 1.4 days per year across the entire study area on average. The apparent delay in growing season end dates is associated with regional cooling from late spring to summer, while the insignificant advancement in beginning dates corresponds to inconsistent temperature trend changes from late winter to spring. On an interannual basis, growing season beginning and end dates correlate negatively with mean air temperatures from February to April and from May to June, respectively.


Chen X Q, Wang L H, 2009. Progress in remote sensing phenological research.Progress in Geography, 28(1): 33-40. (in Chinese)Plant phenological phenomena are the most salient and sensitive bio-indicators of the environmental change at seasonal and interannual scales. Timings of plant phenological phenomena can indicate the rapid response of terrestrial ecosystems to climate change. Since the remote sensed phenology observation is characterized by multi-temporal, broad coverage, spatial continu- ality, and relatively long time series, recently, it has been an important means for detecting re- sponses and feedbacks of vegetation dynamics to global climate change. On the basis of introduc- ing remote sensing data sets and processing methods for monitoring plant phenology, we systemati- cally reviewed important progresses in remote sensing phenology during the last five years world- wide focusing on identification of the phenological growing season, plant phenology and climate change, plant phenology and net primary production, plant phenology and land cover, and plant phenology and crop yield estimate, and so on. Then, we pointed out some existing problems in the current research, and tried to propose some main research aspects in the near future as follows: (1) developing a kind of more general technique for identifying the phenological growing season using remote sensing data; (2) unifying surface observed and satellite derived spatial information by car- rying out plant community phenology observations and selecting appropriate scale transition meth- ods; (3) analyzing quantitatively response mechanisms of plant phenology to human activities; (4) implementing amalgamation of remote sensing data with different spatial resolutions using suitable mathematical methods and models; and (5) estimating possible responses of plant phenology to fu- ture climate change by dynamic simulations.


Chen Y Y, Jiang F C, 2009. Responses of the environment to climate variations in Gonghe basin.Geographical Research, 28(2): 363-370. (in Chinese)This paper studied the responses of the environment to the climatic changes by using the temperature and precipitation data at Gonghe station covering the period from 1953 to 2005.The results show that:(1)The annual average temperatures in the area showed a marked increasing trend,with a rate of about 0.28鈩 per 10 years,higher than the mean values for China and the whole world.The seasonal temperatures and precipitation showed different increasing trends.The temperatures rose more significantly in autumn(from September to November)and winter(from December to next February),while spring(from March to May)and summer(from June to August)witnessed a slower temperature increase.The annual precipitation showed a slight increasing trend.In summer and autumn,the precipitation showed a tendency to decrease year by year,and more marked decrease was recorded in autumn.However,in spring and winter the precipitation shows a tendency to increase year by year.(2)The warmer and drier climate is the key factor leading to the environmental problems such as desertification,grassland degradation,frequent disasters and decreasing runoff,which have had great effects on the socio-economic development and environment in this area.(3)The Gonghe basin is one of the sensitive and vulnerable areas to the climate change on the Tibetan Plateau and even in the world.


Chmielewski F M, Rotzer T, 2001. Response of tree phenology to climate change across Europe.Agricultural & Forest Meteorology, 108(2): 101-112.To investigate the impact of recent climatic changes on the plant development in Europe, this study uses phenological data of the International Phenological Gardens for the period 1969–1998. For this study, the leafing dates of four tree species ( Betula pubescens , Prunus avium , Sorbus aucuparia and Ribes alpinum ) were combined in an annual leaf unfolding index to define the beginning of growing season. The end of growing season was defined using the average leaf fall of B. pubescens , P. avium , Salix smithiana and R. alpinum . A nearly Europe-wide warming in the early spring (February–April) over the last 30 years (1969–1998) led to an earlier beginning of growing season by 8 days. The observed trends in the onset of spring corresponded well with changes in air temperature and circulation ( North Atlantic Oscillation Index (NAO-index)) across Europe. In late winter and early spring, the positive phase of NAO increased clearly, leading to prevailing westerly winds and thus to higher temperatures in the period February–April. Since the end of the 1980s the changes in circulation, air temperature and the beginning of spring time were striking. The investigation showed that a warming in the early spring (February–April) by 1°C causes an advance in the beginning of growing season of 7 days. The observed extension of growing season was mainly the result of an earlier onset of spring. An increase of mean annual air temperature by 1°C led to an extension of 5 days.


Dai S, Shen H Y, Li Let al., 2013. Analysis on climatic transition characteristic warm-dry to warm-wet in Tsaidam Basin.Plateau Meteorology, 32(1): 211-220. (in Chinese)Using the meteorological,hydrological and vegetation data in the Tsaidam basin from 1961 to 2010,the characteristic of climate from warm-dry turn to warm-wet in the Tsaidam basin is studied.The results show that,it is the fact that many characteristics of climate transformation appeared in the last of 1980's,such as temperature rising,precipitation and run-off increasing,sandstorm day decreasing,potential evapotranspiration decreasing,lake level rising and vegetation coverage increasing.The main reason of climate transformation are global warming,water cycle accelerating,water vapor in upper level and black carbon aerosol increasing.

Ding M J, Zhang Y L, Liu L S, 2011. Spatiotemporal changes of commencement of vegetation regreening and its response to climate change on Tibetan Plateau.Advances in Climate Change Research, 7(5): 317-323.Detecting the growing season variability of terrestrial vegetation is crucial for identifying responses of ecosystems to recent climate change.Based on the normalized difference vegetation index(NDVI) and the air temperature and precipitation data,we have extracted the commencement of growing season(CGS) and the commencement of dekadly average temperature(CDAT) of 0 and 5℃ at typical weather stations on Tibetan Plateau from 1982-2009,and analyzed their spatiotemporal changes and the relationships between the CGS of vegetation and the air temperature and precipitation of winter and spring.The results show: the CDAT of 0 and 5℃ on the whole showed an advanced tendency,and the tendency of 5℃ was more significant than that of 0℃;the multiple years average of CGS near typical weather stations gradually delayed from south to north and from east to west,and from 1982 to 2009,the CGS displayed an advanced tendency at all typical weather stations;and the CGS had a negative relation with the air temperature and precipitation of winter and spring,furthermore,the correlation of CGS with temperature was stronger than with precipitation.Different seasonal temperatures had different effects on CGS and the winter temperature affected CGS more than spring temperature.


Ding M J, Zhang Y L, Sun X Met al., 2013. Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009.Chinese Science Bulletin, 58(3): 396-405. doi: 10.1007/s11434-012- 5407-5


Eklundh L, Jönsson P, 2015. Timesat3.2 Software Manual, Lund and Malmö University.

Fisher J I, Mustard J F, Vadeboncoeur M A, 2006. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite.Remote Sensing of Environment, 100: 265-279.Despite the large number of in situ, plot-level phenological measurements and satellite-derived phenological studies, there has been little success to date in merging these records temporally or spatially. In this research, we bridge this scale gap through higher resolution satellite records (Landsat) and quantify the accuracy of satellite-derived metrics with direct field measurements. We compiled fifty-seven Landsat scenes from southern New England (P12 R51) from 1984 to 2002. Green vegetation areal abundance for each scene was derived from spectral mixture analysis and a single set of endmembers. The leaf area signal was fit with a logistic-growth simulating sigmoid curve to derive phenological markers (half-maximum leaf-onset and offset). Spring leaf-onset dates in homogenous stands of deciduous forests displayed significant and persistent local variability. The local variability was validated with multiple springtime ground observations ( r 2 = 0.91). The highest degree of verified small-scale variation occurred where contiguous forests displayed leaf-onset gradients of 10鈥14 days over short distances (< 500 m). These dramatic gradients occur in of low-relief (< 40 m) upland regions. The patterns suggest that microclimates resulting from springtime cold-air drainage may be influential in governing the start of leaf growth; every 4.16 m loss in elevation delayed spring leaf onset by 1 day. These microclimates may be of crucial importance in interpreting in situ records and interpolating phenology from satellite data. Regional patterns from the Landsat analyses suggest topographic, coastal, and land-use controls on phenology. Our results indicate that deciduous forests in the Providence, RI metropolitan area leaf out 5鈥7 days earlier than comparable rural areas. The platform-independent curve-fit methodology may be extended across platforms and field data. The methodologically consistent approach, in tandem with Landsat data, allows an effective scaling from plot to satellite phenological observations.


Han G J, Wang Y L, Fang S B, 2011. Analysis on climate change of the Qinghai-Tibet Plateau in the recent 50 years.Resources Science, 33(10): 1969-1975. (in Chinese)

Jönsson P, Eklundh L, 2002. Seasonality extraction by function fitting to time-series of satellite sensor data.Geoscience & Remote Sensing IEEE Transactions on, 40(8): 1824-1832.

Jönsson P, Eklundh L, 2004. Timesat: A program for analyzing time-series of satellite sensor data.Computers and Geosciences, 30: 833-845.Three different least-squares methods for processing time-series of satellite sensor data are presented. The first method uses local polynomial functions and can be classified as an adaptive Savitzky鈥揋olay filter. The other two methods are more clear cut least-squares methods, where data are fit to a basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting seasonal parameters related to the growing seasons. The methods are implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, giving spatially coherent images of seasonal parameters such as beginnings and ends of growing seasons, seasonally integrated NDVI and seasonal amplitudes. Based on general principles, the TIMESAT program can be used also for other types of satellite-derived time-series data.


Li H M, Ma Y S, Wang Y L, 2010. Influences of climate warming on plant phenology in Qinghai Plateau.Journal of Applied Meteorological Science, 21(4): 500-505. (in Chinese)Long period data observed at selected meteorological stations are analyzed to investigate the response of plant phonology to climate warming in Qinghai Plateau.The Plteau is divided into eastern agriculture region,around Qinghai Lake region,source of three rivers and Qaidam Basin,with their representative areas of Huangyuan,Menyuan,Qumalai and Delingha,and the analysis period is 1983—2007,1987—2007, 1997—2007 and 1987—2007 respectively.Monthly and annual mean temperature,precipitation,plant development period and other related data are analyzed statistically,showing that annual temperature increases in all of the 4 regions with the ratio of 0.61℃/10 a,0.59℃/10 a,1.52℃/10a,0.62℃/10 a respectively. Annual precipitation of around Qinghai Lake region shows a decreasing trend,and the value is -11.58 mm/10 a.Annual precipitation of eastern agriculture region,source of three rivers and Qaidam Basin increases by 0.86 mm /10 a,107.37 mm /10 a and 12.95 mm /10 a respectively.The date of turning green in Qaidam Basin region has delayed by 8.2 d,but in eastern agriculture region,around Qinghai Lake region and source of three rivers it is 8.6 d,10.5 d and 9.8 d earlier than before.In these 4 regions the flowing stage are respectively 3.2 d,7.8 d,1.7 d and 10.9 d earlier.The yellow stage is respectively 6.5 d,16.2 d,6.6 d and 6.2 d later,so the plant growth stage is extended in the eastern agriculture region, around Qinghai Lake region and the source of three rivers,but the plant growth stage is shortened a little in Qaidam Basin.The changing tendency of turning green data and yellow stage is found well correlated with annual mean temperature.There are many factors affecting the flowing of plant,so the correlation between plant flowing and mean annual temperature is less significant.The annual precipitation and stage precipitation have some influences on key periods of plant growing,but the influences are less important compared with the temperature.


Li L, Chen X G, Wang Z Yet al., 2010. Climate change and its regional differences over the Tibetan Plateau.Advances in Climate Change Research, 6(3): 181-186. (in Chinese)Using the meteorological observation data including temperature and precipitation at 66 stations over the Tibetan Plateau from 1961 to 2007,by means of the typical climate zoning,the spatio-temporal variations of climatic variables over the plateau in the recent 47 years were systematically studied,and the differences of climate change in different sub-regions were shown.The results indicate that the climate over the Tibetan Plateau overally showed a significant warming trend in the recent 47 years,the annual mean temperature rose at a rate of 0.37 鈩/10a;the warming was more remarkable in nighttime than in daytime,and in winter than in other seasons.The change from colder to warmer was the most significant in February,but the least in August,when climate showed a cooling trend in some areas.Climate warming at the edge of the plateau was more obvious than in the hinterland.The northern of Qinghai,especially the Qaidam Basin was a most sensitive area to climate change.Precipitation showed a rising trend at a tendency rate of 9.1 mm/10a;but regional differences were very apparent,the western of Sichuan,which lies in the southeast of Tibet,was the most significant in precipitation-increasing in the whole Tibetan Plateau;Precipitation in winter and spring(from December to May of the next year) in all areas increased with the climate warming;but the drought trend in the upstream of Yellow River was obvious after 1987.


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Lu N, Jin X M, 2015. Laws of vegetation distribution and evolutionary trend and analysis of the influencing factors in Qaidam Basin.Yellow River, 37(1): 94-98. (in Chinese)

Ma J F, Lv C H, Wang Q, 2008. Land-cover classification of Qaidam Basin based on MODIS NDVI time series data.Arid Land Geography, 31(3): 442-448. (in Chinese)The Normalized Difference Vegetation Index (NDVI) can well reflect the status of vegetation and land-cover. Its time series has great significance for land-cover classification since they are very sensitive to vegetation’s temporal changes and can more precisely distinguish the vegetation type, which is a very important characteristic of the land-cover. In this paper, using MODIS remote sensing image of the Qaidam basin in the year of 2003 as the source data, we calculate the NDVI and compile the NDVI time series. To avoid the impact of the cloud and guarantee the accuracy of the calculated NDVI time series, a maximum value composing (MVC) process is implemented after the geometrical and radiometer calibration for the MODIS data. In the year of 2003, we invested the Qaidam basin and made samplings of its major vegetation types. Abundant field survey data were got. Based on the NDVI time series and field survey data, this paper analyzed the temporal changes and spatial distribution of the NDVI in the Qaidam Basin. It gave the NDVI time variation curves and the coefficient of variation(COV)of major land-cover types in the Qaidam Basin for the year of 2003 which were then employed as the base for the land-cover classification using a newly innovative method namely the NDVI time variation curve’s shape matching method. The method compares the NDVI time variation curve of an unclassified region to the curve of each of 9 given land-cover types. The land-cover type whose curve most matches the curve of the unclassified region determines the region’s classification type. To make the shape match more accurate, the monthly NDVI variation curves were transferred into a twelve-dimension space where one vector with a particular angle represents the original curve with the aid of ENVI software. The extent of similarity and the classes the vectors belong to were determined by comparing the angle of vectors. With the approach, a relatively precise land-cover classification result whose precision was validated by the ETM remote sensing image was obtained .The result of this paper gives a relatively new depiction of the Qaidam basin’s land-cover status and proves the NDVI time variation curve’s shape matching method a practicable approach for classifying the land-cover using MODIS data.


Ma T, Zhou C, 2012. Climate-associated changes in spring plant phenology in China.International Journal of Biometeorology, 56(2): 269-275.The timing of phenological events is highly responsive to global environmental change, and shifts in a phenological phase can affect terrestrial ecosystems, agriculture and economics. We analyzed changes in phenology for the spring season in China that occurred between the 1960’s and the 2000’s using four methods: species-level observations, meta-analysis, satellite measurements and phenology modeling. Previous analyses have rarely been reported due to sparse observations. Our results suggest that spring in China has started on average 2.8802days earlier per decade in response to spring warming by 614.9302days per degree Celsius over the last three decades. The shift towards an earlier start of spring was faster in two forest biomes (spring started on average 3.9002days earlier per decade) than in three grassland biomes (spring started on average 0.9502day earlier per decade). This difference was probably due to increased precipitation impacts in the grassland biomes. Interannual variations in the start of spring were most likely attributed to annual fluctuations in spring temperature (6540%) and in large-scale circulation anomalies (6520%).


Ma X F, Chen S Y, Deng Jet al., 2016. Vegetation phenology dynamic and its response to climate change on the Tibetan Plateau.Acta Prataculturae Sinica, 25(1): 13-21. (in Chinese)We extracted vegetation phenology parameters(start,end and duration of the growing season)using GIMMS NDVI remote sensing data from 1982-2005 from the Tibetan Plateau and analyzed the spatial distribution and inter-annual variation of vegetation phenology in order to explore the mechanisms of the response of phenology to climate change.The spatial distribution of vegetation phenology was closely related to hydrothermal conditions on the Tibetan Plateau.Along a transect running from the southeast to the northwest,the start of the growing season was gradually delayed,the end of growth season advanced while the length of growing season reduced.The inter-annual variability of the start and the end of the growing season tended to increase while that of the growing season length decreased.Elevation plays an important role in the regional heterogeneity of phenology,but a boundary was identified at approximately 3400 maltitude.Below 3400 m phenology fluctuated irregularly with altitude whereas above 3400 mphenology was closely associated with altitude.Based on climate data from meteorological stations,the relationship between vegetation phenology and the key meteorological factors under the different grassland types were analyzed;the results show that temperature is more important to the change of vegetation phenology than precipitation on the Tibetan Plateau.


Ma Y G, Zhang C, Tiyip T, 2014. Spatial-temporal change of vegetation phenology in arid zone of Central Asia and Xinjiang, China.Advances in Climate Change Research, 10(2): 95-102. (in Chinese)Based on GIMMS(Global Inventory Modeling and Mapping Studies) data from 1982 to 2006,we calculated the vegetation phenological data in arid zone of Central Asia and Xinjiang,China using threshold method with the Timesat software.The Mann-Kendall trend analysis and Theil Sen slope tools were used to assess the spatial-temporal change.Combined with land cover data and digital elevation model,varieties of phenological metrics were also analyzed for different vegetation cover types and different elevation zones.According to our results,no significant phenological change was detected for the whole study area,but obvious change was found in some local areas.The area where the start of growth season(SOS) had advanced was also found to have a significant extended length of growth season(LOS).Different vegetation cover types showed remarkably different patterns in phonological changes.The end of growth season(EOS) of Deciduous Broadleaf Forest delayed significantly.Except for the Open Shrub,Closed Shrub and Bare Ground,most vegetation cover types showed an extended LOS.The phenology in different elevation varied obviously.Analyses further showed that the water and heat conditions in the 2000-3000 m elevation zone were improved,which may result in the most advanced SOS,delayed EOS and prolonged LOS in this zone.


Menzel A, 2003. Plant phenological anomalies in Germany and their relation to air temperature and NAO.Climatic Change, 57(3): 243-263.This paper analyses long-term (1951–2000) phenological observations of20 plant seasonal phases recorded within the phenological network of the German Weather Service in relation to climate data and NAO. Phenological inter-annual variability and temporal trends were determined by using mean anomaly curves for Germany. For all phases, the mean trends derived by this method are similar to German averages of linear trends of single station records. Trend analysis using anomaly curves appears to be effective in relating seasonal phenological trends to climate or satellite data: Spring and summer phenological anomalies, such as leaf unfolding and flowering of different species, strongly correlate with temperature of the preceding months (R 2 between 0.65 and 0.85, best one-variable model) andtheir onsets have advanced by 2.5 to 6.7 days per ° C warmer spring. Fruit ripening of Sambucus nigra and Aesculus hippocastanum , keyphenophases of early and mid autumn, correlate well with summer temperature (R 2 0.74 and 0.84) and also advance by 6.5and 3.8 days per ° C (April–June). But the response of autumn colouringto warmer climate is more complex because two opposing factors influence autumn colouring dates. Higher spring and early summer temperatures advance leaf colouring, whereas warmer autumn temperatures delay leaf colouring. The percentage of variance explained by temperature (R 2 0.22 to 0.51,best one-variable model) is less than for spring and summer phases. The length of the growing season is mainly increased by warmer springs (R 2 0.48to 0.64, best one-variable model) and lengthened by 2.4 to 3.5 days/° C (February–April). The North Atlantic Oscillation Index (NAO) of January–March correlates with spring phenological anomalies(R 2 0.37 to 0.56, best one-variable model), summer to mid autumn phases respond to NAO of February–March (R 2 0.23 to 0.36) (both negativecorrelations). Leaf colouring is delayed by higher NAO of (August) September (R 2 0.10to 0.18). NAO of January–February explains 0.41 to 0.44% of thevariance of the length of the growing season.


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Reed B C, Brown J F, VanderZee Det al., 1994. Measuring phenological variability from satellite imagery.Journal of Vegetation Science, 5: 703-714.Abstract Abstract. Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large-area land cover mapping and monitoring. The utility of remotely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.


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Schwartz M D, 1998.Green-wave phenology.Nature, 394: 839-840.

Song C Q, You S C, Ke L Het al., 2012. Phenological variation of typical vegetation types in northern Tibet and its response to climate changes.Acta Ecologica Sinica, 32(4): 1045-1055. (in Chinese)Vegetation phenology is closely related to seasonal dynamics of the lower atmosphere and,therefore,is thus considered as an important indicator that influences the feedback mechanisms of global climate variation to the terrestrial ecosystem.Detecting vegetation phenological metrics using time series remote sensing dataset at the regional or global scale has become an active research topic in geography and ecology.This paper presents a dynamic thresholding method to extract the vegetation phenological metrics(greenup date,growing season length,maximum EVI and wilting date) of typical grassland,including alpine shrub meadow in the eastern region,alpine meadow in the central region,alpine steppe and alpine desert steppe in the western region of northern Tibetan Plateau,using a time-series TERRA/MODIS EVI dataset from 2001 to 2010 which was reconstructed using the asymmetric Guassian function fitting method.Furthermore,the relationship between inter-annual variation of phenological metrics and climatic factors was analyzed based on the measured data of weather stations close to the sampling areas,the results showed that:(1) The ordinal relationship of these typical vegetation samples in phenological metrics was in the sequence of alpine shrub meadow,alpine meadow,alpine steppe,and alpine desert steppe(for greenup date and the wilting date,the sequence respectively referred to be earlier and later).(2) The annual,spring,summer and winter average temperature of northern Tibetan Plateau all showed significant ascending trend,the increased rate was between 0.8鈩/10a and 3.9鈩/10a.While the decreased trend of precipitation was not apparent.Due to the warming and drier climate,the greenup date of four types of typical vegetation was advanced(approximately 7.2鈥15.5d/10a earlier) and the growing season length became longer(approximately 8.4鈥19.2d/10a),whereas the wilting date of four types of vegetation cover did not show apparent advanced or lingering trend;(3) The maximum EVI of different typical vegetation cover types was affected by different climatic factors.As follows,alpine shrub meadow was mainly affected by spring average temperature and spring precipitation,and precipitation had more important influence on phenological variation than temperature;Alpine meadow was affected by both spring and summer average temperature and precipitation;Alpine steppe and alpine desert steppe were primarily influenced by summer average temperature and precipitation,while for alpine steppe vegetation the inhibition of summer average temperature was more intense than the promotion effect of summer precipitation,among four types of vegetation cover,alpine desert steppe had the closest relationship with summer precipitation(the correlation coefficient,-0.752);(4) As for greenup date,alpine shrub meadow was mainly affected by autumn precipitation of the previous year,the correlation coefficient was closed to-0.579;however,other three types of vegetation cover were mainly influenced by spring average temperature,and the character was the most obvious for desert steppe(the correlation coefficient,-0.559).That was due to that high temperature in spring expedited snow melting and tundra soil thawing which are indispensable for vegetation greenup,especially for high-altitude regions in northern and western Tibet.In addition,the autumn precipitation and winter average temperature in the previous year also had some impact on the vegetation greenup of alpine steppe and alpine desert steppe.


Xu H J, Yang T B, 2013. Climate factors change and its impact on lake area and vegetation growth in Qaidam Basin during 1981-2010.Progress in Geography, 32(6): 868-879. (in Chinese)Changes of climate factors in the Qaidam Basin during 1981-2010 were analyzed based on the monthly temperature and precipitation data from meteorological stations.Rate of linear tendency and Mann-Kendall test were used to illustrate the change trends and detect abrupt changes of meteorological elements respectively.In addition,lake area changes and vegetation activity changes in the Qaidam Basin in response to climate changes were discussed using Landsat TM/ETM+ images,NOAA/AVHRR-NDVI and EOS/MODIS-NDVI products.The results showed that(1) The Qaidam Basin experienced an overall rise in temperature,especially in autumn and winter during 1981-2010.Temperature changed abruptly from low to high in 1987 and the increase has become more rapid since 1998.(2) The annual available precipitation showed a "decrease-increase-decrease-increase" change in recent 30 years,but the inter-annual trend of annual available precipitation was similar to annual mean temperature at a rate of 5.65 mm/10a.Available precipitations were low in 1981-1985,1990-2001,but high in 1985-1990,2001-2010.An abrupt change to warm-wet happened in the mid-late 1980s.However,an overall trend of warm-dry was detected from the mid-1990s to the end of the 20th century.A significant warm-wet trend of climate has arrived once again since the early 21th century.(3) Variations of lake area and vegetation growth were significantly affected by the change of wet and dry climate.The "expansion-atrophy-expansion" pattern in lake areas correlated with summer available precipitations which happened in 1985-1990,1990-2001,2001-2010 respectively.(4) The "degradation-amelioration-degradation-amelioration" pattern in vegetation growth was closely related with growing season available precipitation which happened in 1981-1985,1985-1990,1990-2001,2001-2010.


Yao T D, Zhu L P, 2006. The response of environmental changes on Tibetan Plateau to global changes and adaptation strategy.Advances in Earth Science, 21(5): 459-464. (in Chinese)The environmental changes of Tibetan Plateau possess sensitive response and strong effect to global changes.The interaction between modern environment and land surface processes on the plateau induces a series of variations in the cryosphere,water resources and ecological system,which produce important influence on the human living circumstance and economic-society development on the plateau itself and periphery regions.As a region focused by international scientific research,the plateau experienced three developing stages.The first is focusing the systemic studies of the key problems in the key areas, the second is focusing the monitoring studies centered with surface processes,and the third is focusing the interactions among different spheres influenced by global changes.This project possesses very important significance to the study of environmental changes on Tibetan Plateau and the contribution for international scientific frontier as well as local economics-society development.In this project,a series of objectives are planned to be achieved: discovering key tectonic and environmental events from the plateau's formation to its present landform structures;reconstructing climatic and environmental sequences with different time scales in different areas and clarifying their space-time features;elucidating the variation characteristics of cryosphere,lakes,dominant ecosystems and land covers under different climatic conditions on the plateau;revealing the responses of the environmental changes and land surface processes of the plateau to global changes,and the effects of the plateau's thermo and dynamical processes to different climatic systems.Some methods and contents are performed in this project: By using geomorphologic and sedimentary methods,we study the formation processes of present landform and environment frames.By collecting lake cores,ice cores and tree rings,we study key events during the past environment changes and their linkages to global changes.By analyzing the space-time variations of glaciers,permafrost and snow accumulations together with observation of boundary layers,we study the variations of cryosphere and cycling processes of energy and water.By monitoring the glaciers,lakes and atmosphere together with using climatic models,we study the potential mechanism of environmental changes.By detecting the variations of carbon sources and sinks in the dominant ecosystems,we study the response of the main ecosystem to environmental changes. By a series of integrated analyses,we study the effects of the environmental changes and water sources changes on the Tibetan Plateau and adaptation strategies under global changes.


Yin G, Hu Z Y, Chen Xet al., 2016. Vegetation dynamics and its response to climate change in Central Asia.Journal of Arid Land, 8(3): 375-388.


Yu F, Price K P, Ellis Jet al., 2004. Satellite observations of the seasonal vegetation growth in Central Asia: 1982-1990.Photogrammetric Engineering & Remote Sensing, 70(4): 461-469.ABSTRACT A new method called Derivation and Threshold Approach (DATA) was developed to estimate the onset of vegetation green-up (OVG) for the Mongolian Plateau from 1982 to 1990. The estimates of OVG date were highly correlated with geographic patterns and climatic variations across time and space within our study area. The date of OVG was earliest for the forest and meadow steppe, later for the typical steppe, and latest for the desert steppe. Our findings also showed that ecosystems in the Mongolian Plateau exhibited high spa-tiotemporal variability in the date of OVG during the study period. The date of onset was found to be most consistent for the forest ecosystem, while the desert steppe was characterized by high year-to-year variations in onset date. A comparison of the derived OVG date estimates with meteorological data showed a clear linkage between onset date and the beginning of spring precipitation in the dry region of the Mongolian Plateau.


Zhang G L, Xu X L, Zhou C P, 2011. Responses of vegetation changes to climatic variations in Hulun Buir Grassland in past 30 years.Acta Geographica Sinica, 66(1): 47-58. (in Chinese)Global warming,a global concern,has led to significant vegetation changes especially in the past 30 years.The Hulun Buir Grassland in Inner Mongolia,one of the world's three prairies,is undergoing a process of prominent warming and drying.It is necessary to investigate the effects of climatic variations(temperature and precipitation) on vegetation changes for a better understanding of acclimation to climatic change.NDVI(Normalized Difference Vegetation Index),which can reflect characteristics of plant growth,vegetation coverage,biomass,and so on,is used as an indicator in monitoring vegetation changes.GIMMS NDVI from 1981 to 2006 and MODIS NDVI from 2000 to 2009 were adopted and integrated in this study to extract the time series characteristics of vegetation change conditions in Hulun Buir.The responses of vegetation coverage changes to climatic variations from the yearly,seasonal and monthly time scales were analyzed combined with temperature and precipitation data of seven meteorological sites.In the past 30 years,vegetation coverage change was closely correlated with climatic factors,and the correlations were different on different time scales.Annual average of vegetation change was better correlated with precipitation,suggesting that rainfall was the main factor for driving vegetation change.Correlations between seasonal average of vegetation coverage and climatic factors showed that the sensitivity of vegetation growth to hydrothermal condition change was different in different seasons.The sensitivity of vegetation growth to temperature in summer was higher than in the other seasons,while that of vegetation growth to rainfall in both summer and autumn was higher,especially in summer.Correlations between monthly average of vegetation coverage and climatic factors during growing seasons showed that the response of vegetation change to temperature in April and May was stronger,indicating that the temperature effect occurred in the early stage of vegetation growth.Correlations between NDVI of the current month and precipitation of the month before the current month were better from May to August,showing a hysteresis response of vegetation growth to rainfall.Grasses turned green and began to grow in April,and the impacts of temperature on grass growth was obvious,therefore,the increase of NDVI in April might be due to an advanced growing season caused by climatic warming.In summary,relationships between annual variation of monthly vegetation and climatic factors represent temporal rhythm controls of temperature and precipitation on grass growth.


Zhang G S, Li L, Wang C Cet al., 1999. Effects of climatic changes of south Qinghai plateau on the alpine meadow.Acta Prataculturae Sinica, (3): 1-10. (in Chinese)The climatic variations since 1961 on Qinghai south plateau were analyzed in this paper. It indicated that the annual and autumn precipitation did not have significant tendency of variation. The winter (from December to February) and spring (from March to May) tended to increase by year, that tendency ratio of climate is 2.6 mm/10 a and 3.6 mm/10 a respectively. However, summer rainfall was decreased by year with the tendency ratio of 6.5 mm/10 a. The rate of air temperature rising at turning green period of pasture was increased by year. As the results, the height of Kobresia, the dominant species on the Qinghai south plateau, was decreased by 3 cm in the late 1980s. The development percentage of florescence and seed maturity of herbage by 20%~50% and greenness and dry yield of grassland declined by 70%~80%.

Zhang W X, 1988. The xeric succession process of soil cover in Qaidam Basin.Journal of Arid Land Resources and Environment, (3): 79-86. (in Chinese)Qaidam Basin is a typical continental plateau basin which featured byarid salt desert. In the environmental evolution of Qaidam Basin which takedesertification as background, the succession process of soil cover in the ba-sin has undergone hydromorphic stage, semi-hydrmorphic stage and desertiza-tion or salinization stage in turns. In the xeric succession of soil cover, twokinds of mechanism including of desiccation and salinization undergone at thesame time, which resulted in the appearence of two kinds of climax soils,i. e. autogenic type and halogenic type. The succession of soil cover is thus.characterized by double climax. Ithas deeply influenced on soil developmentand soil cover succession in Qaidam Basin that the physical geography processwhich featured by desertization and unreasonable humam activities.

Zheng J Y, Ge Q S, Hao Z X, 2002. Effects of climate warming on plant phenology changes in China in recent 40 years.Chinese Science Bulletin, 47(21): 1826-1831.姝 Based on plant phenology data from 26 stations of the Chinese Phenology Observation Network of the Chinese Academy of Sciences and the climate data, the change of plant phenophase in spring and the impact of climate warming on the plant phenophase in China for the last 40 years are analyzed. Furthermore, the geographical distribution models of phenophase in every decade are reconstructed, and the impact of climate warming on geographical distribution model of phenophase is studied as well. The results show that (i) the response of phenophase advance or delay to temperature change is nonlinear. Since the 1980s, at the same amplitude of temperature change, phenophase delay amplitude caused by temperature decrease is greater than phenophase advance amplitude caused by temperature increase; the rate of phenophase advance days decreases with temperature increase amplitude, and the rate of phenophase delay days increases with temperature decrease amplitude. (ii) The geographical distribution model between phe


Zhu K Z, Wan M W, 1999. Phenology. Changsha: Hunan Education Publishing House, 1-4. (in Chinese)