Abstract
Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) time series (1982–2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal (MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends (P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.
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Foundation item: Under the auspices of National Natural Science Foundation of China (No. 41771179, 41871103, 41771138), the National Key Research and Development Project (No. 2016YFA0602301)
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Guo, M., Li, J., He, H. et al. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982–2015 Time Period. Chin. Geogr. Sci. 28, 907–919 (2018). https://doi.org/10.1007/s11769-018-1002-2
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DOI: https://doi.org/10.1007/s11769-018-1002-2