Increasing interannual variability of global vegetation greenness
Increasing interannual variability of global vegetation greenness
复制标题
全球植被绿度的年际变化不断增加
DOI：
10.1088/1748-9326/ab4ffc
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发表时间：
2019-12-01
影响因子：
6.7
通讯作者：
Zhang, Yafeng
中科院分区：
文献类型：
--
作者：
Chen, Chen;He, Bin;Zhang, Yafeng
尽管在之前的调查中发现了全球植被的长期绿化趋势，但人们对植被绿度年际变化（IAV）随时间的变化仍然知之甚少。利用1982-2015年全球清单建模与制图研究归一化植被指数（NDVI）第三代数据和相应的气象数据，研究了全球范围内植被绿度变异系数表示的植被绿度IAV的变化和驱动因素。干燥和高纬度地区的 NDVI 变异性较高，而潮湿地区的 NDVI 变异性相对较低。我们使用 15 年移动窗口检测到全球植被绿度 IAV 随着时间的推移而增加。在空间上，我们观察到全球超过 45% 的植被区域的植被绿度 IAV 显着增加，并下降了 21%。我们对生态模型的比较表明，在模拟植被变异性的空间差异方面表现良好，但在捕捉植被绿度 IAV 变化方面表现相对较差。此外，利用主成分回归和偏最小二乘回归在空间上确定了控制植被绿度 IAV 变化的主要气候变量。这两种方法得出了相似的模式，表明温度对植被绿度 IAV 变化的影响最大，其次是太阳辐射和降水。这项研究提供了对全球植被变化的见解，这将有助于理解气候变化背景下的植被动态。
Despite the long-term greening trend in global vegetation identified in previous investigations, changes in the interannual variability (IAV) of vegetation greenness over time is still poorly understood. Using Global Inventory Modeling and Mapping Studies normalized difference vegetation index (NDVI) third generation data and corresponding meteorological data from 1982 to 2015, we studied the changes and drivers of the IAV of vegetation greenness as indicated by the coefficient of variation of vegetation greenness at a global scale. Dry and high-latitude areas exhibited high NDVI variability whereas humid areas exhibited relatively low NDVI variability. We detected an increase in the global IAV of vegetation greenness over time using a 15 year moving window. Spatially, we observed significant increases in the IAV of vegetation greenness in greater than 45% of vegetated areas globally and decreases in 21%. Our comparison of ecological models suggests good performance in terms of simulating spatial differences in vegetation variability, but relatively poor performance in terms of capturing changes in the IAV of vegetation greenness. Furthermore, the dominant climate variables controlling changes in the IAV of vegetation greenness were determined spatially using principal component regression and partial least squares regression. The two methods yielded similar patterns, revealing that temperature exerted the biggest influence on changes in the IAV of vegetation greenness, followed by solar radiation and precipitation. This study provides insights into global vegetation variability which should contribute to an understanding of vegetation dynamics in the context of climate change.