1985—2015年中国不同生态系统NDVI时空变化及其对气候因子的响应

植被是陆地生态系统不可或缺的部分，气候是影响其动态变化的重要驱动因素。因此，探究植被的时空变化及其与气候因子的响应关系，有助于理解陆地生态系统的内在演化机制。目前，不同生态系统尺度下的植被动态变化与气候因子的时间响应关系仍未被完整剖析。因此，为了厘清过去30年不同生态系统植被生长对气候因子的响应关系，利用GIMMS NDVI3g数据和气候资料数据，通过Theil-Sen Median趋势分析和Mann-Kendall检验分析了1985—2015年中国陆地NDVI的时空变化特征，结合时间序列相关分析探究了NDVI变化与降水、温度和饱和水汽压差的内部关联，探讨了中国不同生态系统植被与气候因子间的时间响应机制。结果表明：(1) 1985—2015年中国陆地植被呈现改善趋势，年均NDVI先减小后增加，拐点时间在1995年左右，整体变化率为0.5×10^-3/a。农田、森林和草地生态系统的植被显著改善的程度最高，湿地生态系统的植被退化趋势最显著。(2)中国陆地植被NDVI与气候因子的相关性存在明显的空间异质性，且受不同生态系统分区影响。内蒙古高原中部草地生态系统NDVI与降水...

Spatiotemporal dynamics of NDVI in China from 1985 to 2015: ecosystem variation, regional differences, and response to climatic factors

Vegetation plays an important role in linking water, atmosphere, and soil. Accurate evaluation of the relationships between climatic factors and vegetation is essential for the formulation of scientific ecological restoration strategies. The response between vegetation and climate has been a hot topic in recent research. However, the heterogeneity of various ecosystems and regions is rarely considered in China, which calls for a comprehensive and comparative understanding. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI), climate reanalysis data (temperature, precipitation, and vapor pressure deficit (VPD)), and land cover data, this study analyzed the climate dynamics of the spatiotemporal variations of vegetation NDVI in China from 1985 to 2015. The Theil-Sen estimator and Mann-Kendall test were employed to investigate the relationships between NDVI and climatic factors to further explore the temporal response mechanism between different ecosystems and climatic factors in China. The results showed that:(1) The annual mean NDVI in China gradually increased by 0.5×10^-3 every year from 1985 to 2015. The NDVI declined before 1995 and then increased. A significant vegetation improvement was observed in cropland, forest, and grassland ecosystems, and the obvious vegetation degradation was mainly in the wetland ecosystem. (2) The response of vegetation to climatic factors varied in different ecosystems. Pixel clusters of positive correlation between precipitation and vegetation were mainly distributed in grassland ecosystems in Central Inner Mongolia Plateau, while the pixel clusters of negative correlation were mainly distributed in forest ecosystems and the Qinghai-Tibet Plateau. The linear correlation relationship between vegetation and temperature was significant in farmland ecosystem, and the negative correlation relationship was detected in cold and arid regions of West China. Vegetation and VPD were positively correlated in farmland ecosystem of East China, and negative correlations were evident in Northern Xinjiang and Central Inner Mongolia Plateau dominated by grassland ecosystems. (3) Time-lag effects of NDVI responses to climate change were detected at the month level. The forest ecosystem had a long lag cumulation period to precipitation, and the cropland ecosystem responded fastest to precipitation. Responses of different ecosystems to temperature exhibited the highest correlations within the current month or the 1-month period. Generally, VPD in different ecosystems had a two-month cumulative effect on vegetation growth, and the correlation gradually decreased with the increasing lag time. These findings are of great significance for understanding the climate dynamics of vegetation growth and it can provide a theoretical basis for the protection of the regional ecological environment under the background of global climate change.