Journal of Geographical Sciences >
Interannual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010
^{*}Corresponding author: Jiang Weiguo (1976), PhD, Email: jiangweiguo@bnu.edu.cn
Author: Cao Ran (1990), Graduate Student, specialized in ecological remote sensing and natural hazard and risk analysis. Email: caoran_1990@163.com.
Received date: 20140530
Accepted date: 20140710
Online published: 20140620
Supported by
National Natural Science Foundation of China, No.41171318.National Key Technology Support Program, No.2012BAH32B03.No.2012BAH33B05.The Remote Sensing Investigation and Assessment Project for DecadeChange of the National Ecological Environment (20002010)
Copyright
To understand the variations in vegetation and their correlation with climate factors in the upper catchments of the Yellow River, China, Normalized Difference Vegetation Index (NDVI) time series data from 2000 to 2010 were collected based on the MOD13Q1 product. The coefficient of variation, TheilSen median trend analysis and the MannKendall test were combined to investigate the volatility characteristic and trend characteristic of the vegetation. Climate data sets were then used to analyze the correlation between variations in vegetation and climate change. In terms of the temporal variations, the vegetation in this study area improved slightly from 2000 to 2010, although the volatility characteristic was larger in 20002005 than in 20062010. In terms of the spatial variation, vegetation which is relatively stable and has a significantly increasing trend accounts for the largest part of the study area. Its spatial distribution is highly correlated with altitude, which ranges from about 2000 to 3000 m in this area. Highly fluctuating vegetation and vegetation which showed a significantly decreasing trend were mostly distributed around the reservoirs and in the reaches of the river with hydropower developments. Vegetation with a relatively stable and significantly decreasing trend and vegetation with a highly fluctuating and significantly increasing trend are widely dispersed. With respect to the response of vegetation to climate change, about 2030% of the vegetation has a significant correlation with climatic factors and the correlations in most areas are positive: regions with precipitation as the key influencing factor account for more than 10% of the area; regions with temperature as the key influencing factor account for less than 10% of the area; and regions with precipitation and temperature as the key influencing factors together account for about 5% of the total area. More than 70% of the vegetation has an insignificant correlation with climatic factors.
CAO Ran , JIANG Weiguo , YUAN Lihua , WANG Wenjie , LV Zhongliang , CHEN Zheng . Interannual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010[J]. Journal of Geographical Sciences, 2014 , 24(6) : 963 979 . DOI: 10.1007/s1144201411311
Figure 1 Location of the LongyangxiaLiujiaxia basin 
Figure 2 Interannual change in vegetation on the regional scale from 2000 to 2010 
Table 1 Classification of coefficient of variation and corresponding proportions of area in the LongLiu Basin 
CV  Volatility level  Area (%) 

0.0150.095  Lowest fluctuation  19.90 
0.0950.109  Lower fluctuation  20.63 
0.1090.189  Moderate fluctuation  53.86 
0.1890.650  Higher fluctuation  4.08 
0.6503.317  Highest fluctuation  1.53 
Figure 3 Spatial distribution of level of vegetation volatility in the LongLiu Basin 
Table 2 Classification of TheilSen median slope analysis and MannKendall test and corresponding area proportions in the LongLiu Basin 
S_{NDVI}  Z  Trend level  Area (%) 

<0  < 1.96  Significant decreasing  1.89 
<0  1.96 to 1.96  Insignificant decreasing  17.15 
≥0  1.96 to 1.96  Insignificant increasing  36.90 
≥0  ≥1.96  Significant increasing  44.06 
Figure 4 Spatial distribution of trend in vegetation levels in the LongLiu Basin 
Table 3 Classification of coefficient of variation, TheilSen median slope analysis, the MannKendall test and the corresponding area proportions in the LongLiu bain 
CV  S_{NDVI}  Z  Variation type  Area (%) 

0.015 to 0.189  <0  < 1.96  Relatively stable and significantly decreasing  0.95 
0.189 to 3.317  <0  < 1.96  Highly fluctuating and significantly decreasing  0.63 
0.015 to 0.189  >0  >1.96  Relatively stable and significantly increasing  42.14 
0.189 to 3.317  >0  >1.96  Highly fluctuating and significantly increasing  1.76 
    1.96 to 1.96  Insignificant trend  54.52 
Figure 5 Spatial distribution of vegetation variation types in the LongLiu bain 
Table 4 Classification of correlation of NDVI with precipitation and corresponding area proportion in the LongLiu Basin 
r_{p}  Correlation level  Area (%) 

0.99 to 0.735  Highly significant and negative correlation^{**}  0.78 
0.735 to 0.602  Significant and negative correlation^{*}  2.13 
0.602 to 0  Insignificant and negative correlation  30.64 
0 to 0.602  Insignificant and positive correlation  54.11 
0.602 to 0.735  Significant and positive correlation^{*}  8.36 
0.735 to 0.99  Highly significant and positive correlation^{**}  3.98 
Note: r_{p} is the partial correlation coefficient of NDVI with precipitation.^{*} Correlation coefficient is significant at a level of 0.05.^{ **} Correlation coefficient is significant at a level of 0.01. 
Figure 6 Spatial distribution of the correlation levels of NDVI with precipitation in the LongLiu Basin 
Table 5 Classification of correlation of NDVI with temperature and corresponding area proportion in the LongLiu Basin 
r_{t}  Correlation level  Area (%) 

0.99 to 0.735  Highly significant and negative correlation^{**}  0.16 
0.735 to 0.602  Significant and negative correlation^{*}  0.71 
0.602 to 0  Insignificant and negative correlation  21.75 
0 to 0.602  Insignificant and positive correlation  60.56 
0.602 to 0.735  Significant and positive correlation^{*}  10.9 
0.735 to 0.99  Highly significant and positive correlation^{**}  5.92 
Note: r_{t} represents the partial correlation coefficient of NDVI with temperature.^{*} Correlation coefficient is significant at a level of 0.05.^{**} Correlation coefficient is significant at a level of 0.01. 
Table 6 Classification of correlation of NDVI with precipitation and temperature and corresponding area proportions in the LongLiu Basin 
R  Correlation type  Area (%) 

r_{p}≥0.602  Significant correlation of NDVI with precipitation^{*}  8.96 
r_{t}≥0.602  Significant correlation of NDVI with temperature^{*}  11.21 
r_{p}≥0.602 and r_{t}≥0.602  Significant correlation of NDVI with precipitation and temperature^{*}  4.25 
r_{p}≤0.602 or r_{t}≤0.602  Insignificant correlation  75.59 
Note: r_{p} represents the partial correlation of NDVI with precipitation; r_{t} represents the partial correlation of NDVI with temperature.^{*}The correlation coefficient is significant at a level of 0.05. 
Figure 7 Spatial distribution of the correlation levels of NDVI with temperature in the LongLiu Basin 
Figure 8 Spatial distribution of the correlation types in the LongLiu Basin Note: P = precipitation; T = temperature 
Figure 9 Proportion of correlation types in variation types in the LongLiu Basin. Note: SD = vegetation with relatively stable and significantly decreasing trend; FD = vegetation with a highly fluctuating and significant decreasing trend; SI = vegetation with relatively stable and significantly increasing trend; FI = vegetation with highly fluctuating and significant increasing trend; P = precipitation; T=temperature; Insig. = insignificant correlation 
The authors have declared that no competing interests exist.
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