The Qinghai Province, situated in the northwest of China, is experiencing a continuous warming which is approximately three times more than the rate of global warming. This ongoing warming has a direct connection to vegetation cover, with significant societal and economic impacts in this region. In the present study, we investigate the correlation between climate change and vegetation cover in Qinghai Province. Analysis shows that in the Qinghai Province, order of NDVI is highest in summer followed by autumn, spring and winter. By calculating the average annual and seasonal-NDVI values, it is deduced that the main type of vegetation cover in the Qinghai Province has an upward trend at the rate of 0.013/10a, 0.016/10a, 0.035/10a and 0.058/10a for annual, winter, spring and summer, respectively. While a downward trend at a rate of 0.056/10a is present in autumn-NDVI. At the 0.01% significance level, a significant positive relationship of winter-NDVI with mean winter precipitation and temperature is revealed. Mean NDVI of spring and autumn show a significant positive relationship with respective seasonal mean precipitation. However, a significant difference is present between mean summer-NDVI and mean summer precipitation. Furthermore, mean NDVI of summer and autumn has a significant negative relationship with respective seasonal mean temperature.
Land Cover Change Climate Factors NDVI Qinghai Province1. Introduction
Global mean temperature has increased by 0.85˚C over the period of 1880 to 2012 and this increase in temperature is likely due to anthropogenic activities that have increased the concentrations of greenhouse gases to unprecedented levels [1] . Over the last 50 years, the Qinghai Province of China is experiencing a continuous warming which is approximately three times more than the global warming rate. The increasing trend in temperature is one of the main reasons for climate change which consequently lead to droughts and negative effects on vegetation cover due to the increase in evapotranspiration in Qinghai Province [2] . A plausible warmer world with longer and more severe droughts could lead to rapid collapse of tropical forest communities converting them from a net carbon sink to a large carbon source with cascading ecosystem effects affecting global climate-vegetation feedbacks [3] . Due to the monsoon climate interacted with the complicated geographical landscapes; high-frequency severe droughts are the most devastating natural disasters in China. According to statistics, the drought affected and damaged areas have greatly increased in the past 50 years [4] . In the 2000s, extreme droughts occurred frequently in China, for example, the winter-spring drought in southwest China during 2009-2010 [5] and the spring-summer drought over the middle and lower reaches of Yangtze River in 2011 [6] . The drought has especially affected the agricultural areas in northern China [7] . The regional geological, geomorphologic and ecological systems are complex and diverse. These natural factors and climate change are intertwined, making regional economic and social developments extremely challenging. The regional economic community has demonstrated a high degree of sensitivity to these changes. The literature shows that there has been a significant warming trend in Northwest China since 1951 [8] . Thus, droughts are difficult to pinpoint in time and space since it is very complex to identify the moment when a drought starts and ends, and also to quantify its duration, magnitude and spatial extent [9] . China is on the top in the world in paddy rice, wheat and fresh vegetable production. In total, China ranks number 1 in the world in the production of 45 agricultural commodities [10] . Series of geological disasters and environmental problems of agricultural biological disasters in the Qinghai Province will consequently influence the societal and economic conditions [11] . Global climate change and anthropogenic activities are the main driving forces of terrestrial ecosystems [12] . The quantitative evaluation of direct economic loss of the grassland and livestock due to drought and snow disaster comes true. The evaluation model is in line with grass pasture growth law and livestock production characteristics. The evaluation is accord with the actual loss of animal husbandry. Therefore, the evaluation can be used in the grassland animal husbandry assessment [13] . China has about 328 million people involved in agricultural labour, and a vast majority of them are small and marginal farmers (operating 0.4 ha on average). Further, the vast majorities of farmers depend on rain-fed crops and are, therefore, particularly vulnerable to the vagaries of the climate. The government of China (GoC) recognizes the importance of revitalizing the agricultural insurance industry to meet the needs of farmers throughout China in a better way [14] . The Department of Crop Production and Ministry of Agriculture in China have initiated schemes having significant contributions in anti-disaster and disaster relief. The agricultural sectors have strengthened monitoring and early warning of disasters [15] .
The aim of this study is to investigate the relationship between climate factors (precipitation and temperature) and vegetation cover in Qinghai Province of China over the period of 2001 to 2013.
2. Material and Methods2.1. Study Area
Figure 1 shows the geographical location and topography of Qinghai Province. This province has a large variety of ecosystems, from the sub-tropical rain forest in the south-east to the alpine desert in the north-west. Among all types of land cover vegetation, alpine grassland is the dominant ecosystem, combined cover an area of 715823.8 km2, extending from the latitude of 31˚40' - 39˚30'N and longitude of 89˚25' - 103˚04'E and altitude 1721 - 8500 m. The total irrigated areas in Qinghai Province, as reports in [16] are 259.3 (103 ha) out of which 182.4 (103 ha) is effective irrigated area, 31.2(103 ha) is woodland area, 6.8 (103 ha) orchard area, 38.9 (103 ha) is pasture land and 155 (103 ha) is actual effective irrigated area, respectively. The major economic indices are number of enterprises, employment in the year (person), business income (103 RMB) and Tax payment (103 RMB): 20, 2966, 1273 and 1035, respectively.
2.2. Normalized Difference Vegetation Index (NDVI)
In this research, we utilize the MODIS data of 16-day temporal and 250 m spatial resolution (MOD13Q1, collection 5), for period of 2001-2013. This data is obtained from NASA website (ftp://ladsweb.nascom.nasa.gov/allData/5/MOD13Q1). This data maintains by the NASA in Land Processes Distributed Active Archive Center (LP DAAC) at the USGS/Earth Resources Observation and Science (EROS) Centre. A vegetation index is an indicator that describes the greenness, relative density and health of vegetation for each picture element (pixel). Although there are several vegetation indices, NDVI is one of the most widely used vegetation indices and its range varies from 1.0 to −1.0. More details of NDVI and its utilization can be found in [17] .
2.3. Linear Trend (Slope)
Analysis of linear regression trend is carried out by using ArcGIS v 10.2 Software, which can simulate trends in each grid [18] . ArcGIS can be used to reflect different periods of vegetation cover characteristics. In this study, the relative slope, in Formula (1), is used to indicate the relative NDVI change in every pixel point.
θ Slope = n × ∑ i = 1 n i NDVI i − ∑ i = 1 n i ∑ i = 1 n NDVI i n × ∑ i = 1 n i 2 − ( ∑ i = 1 n i ) 2 (1)
where i is the annual number; n is monitoring period (the cumulative number of years); NDVI as NDVI mean value of the i year; slope is each pixel NDVI trend of the slope, if θ Slope > 0 then the pixel NDVI value in n years is increasing, otherwise it is decreasing. This study categorizes into a significant increase, slight increase, essentially the same, slightly reduced and a significant reduction, and the statistics of the study area in 2001-2013 vegetation changes and the percentage of each class area.
2.4. Correlation
It is possible to explain the closeness of a relationship between geographic features, and closely related to the degree of mutual determination between geographical elements, mainly through the correlation coefficient calculation. In this research, study of NDVI correlation with average annual temperature and precipitation is carried out by-pixel spatial correlation, the correlation coefficient used to reflect the sequence of climatic factors and NDVI degree of correlation, the range of correlation coefficient varies from −1 to 1. Formula is given as below:
r x y = ∑ i = 1 n ( x i − x ¯ ) ( y i − y ¯ ) ∑ i = 1 n ( x i − x ¯ ) 2 ∑ i = 1 n ( y i − y ¯ ) 2 (2)
where: n is the number of time series, x and y are two elements of the correlation, and represent the average of the two elements of the sample values, and finally delineated thresholds based on the number of data, the results of the correlation level of significant.
3. Result and Discussions
Figure 2 shows the vegetation on the monthly average time scale in the Qinghai Province, the NDVI value distribution show that May to August are the center to both sides of vegetation cover in a year, which corresponds to the average monthly vegetation NDVI values: 0.12, 0.12, 0.13, 0.15, 0.26, 0.35, 0.36, 0.28, 0.15, 0.12, 0.12 and 0.12 for January to December, respectively. Overall, the rainy season (April to September) has higher vegetation covers.
3.1. Spatial Distribution of Vegetation
Spatial distribution of mean NDVI in Qinghai Province is shown in Figure 3 which clearly exhibits the mean annual and seasonal-NDVI, a high value is in the eastern and southern part of the Qinghai Province. The vegetation cover is
high in the eastern and southern part due to humid climate zone. The vegetation cover is widely distributed in woodland, grasslands and crop land. The north- west part has a low vegetation cover value due to the location in the desert area which is covered by rock and sands with low precipitation.
Figure 4 shows the calculation of NDVI; each season of the Qinghai Province is ranked on the basis of NDVI. Summer season has the highest NDVI followed by autumn, spring and winter. Analysis of average annual and seasonal-NDVI shows that the main vegetation cover type in the Qinghai Province has an upward trend at the rate of 0.013/10a, 0.016/10a, 0.035/10a and 0.058/10a for annual, winter, spring and summer, respectively. While an autumn-NDVI has downward trend at a rate of 0.056/10a. The plain and other highland mountain areas have different values due to seasonal differences in the degree of green crops and woodland, grassland due to seasonal changes in climate factors. [13] analyzed and investigated the data of pasture, precipitation and disaster monitoring in 20 grassland ecological monitoring sites in Qinghai Province from 2003 to 2011.They found that, at 0.001% significance level, grassland herbage yield and precipitation have a close relationship. A good correlation is found between the forage yield at the end of August and the precipitation from May to August. However, a decline in 2009-NDVI is found due to the increase in precipitation in that particular year, as reports on the website of China Statistical Information Network: http://www.tjcn.org/help/3574.html. In 2009, the prices of agricultural production materials decline to 2.2% compared with the previous year. Producer Price Index (PPI) declined to 8.7%. [13] reported that the snow disasters in December 2008 in Doulan (middle part of Qinghai Province) cause the death of livestock which is 0.11% of the total Loss (18,455 × 10³ RMB).
3.2. Spatial Variation of Vegetation Cover3.2.1. Annual
Based on the calculation of principle θ slope value, the spatial variation is reclassified into seven categories from low to high value: significant degradation, moderate degradation, mild degradation, no change, mild improvement, moderate improvement and significant improvement. Figure 5 and Table 1 show
Statistical result of trend of mean annual-NDVI change simulated in Qinghai Province over the period of 2001-2013
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