Association Analysis Between Spatiotemporal Variation of Vegetation Greenness and Precipitation/Temperature in the Yangtze River Basin (China)
Document Type
Article
Publication Date
5-23-2018
Abstract
The variation in vegetation greenness provides good understanding of the sustainable management and monitoring of land surface ecosystems. The present paper discusses the spatial-temporal changes in vegetation and controlling factors in the Yangtze River Basin (YRB) using Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) for the period 2001–2013. Theil-Sen Median trend analysis, Pearson correlation coefficients, and residual analysis have been used, which shows decreasing trend of the annual mean NDVI over the whole YRB. Spatially, the regions with significant decreasing trends were mainly located in parts of central YRB, and pronounced increasing trends were observed in parts of the eastern and western YRB. The mean NDVI during spring and summer seasons increased, while it decreased during autumn and winter seasons. The seasonal mean NDVI shows spatial heterogeneity due to the vegetation types. The correlation analysis shows a positive relation between NDVI and temperature over most of the YRB, whereas NDVI and precipitation show a negative correlation. The residual analysis shows an increase in NDVI in parts of eastern and western YRB and the decrease in NDVI in the small part of Yangtze River Delta (YRD) and the mid-western YRB due to human activities. In general, climate factors were the principal drivers of NDVI variation in YRB in recent years.
Recommended Citation
Cui, L., Wang, L., Singh, R.P. et al. Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China). Environ Sci Pollut Res 25, 21867–21878 (2018). https://doi.org/10.1007/s11356-018-2340-4
Copyright
Springer
Comments
This article was originally published in Environmental Science and Pollution Research volume, volume 25, in 2018. https://doi.org/10.1007/s11356-018-2340-4
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