NWIPB OpenIR
Large discrepancies of global greening: Indication of multi-source remote sensing data
Wang, ZQ; Wang, H; Wang, TF; Wang, LN; Liu, X; Zheng, K; Huang, XT
2022
发表期刊GLOBAL ECOLOGY AND CONSERVATION
卷号34
摘要Global warming has a great impact on the activities of terrestrial vegetation. A consensus has been reached that the global vegetation is greening from the 1980-2010s. However, the trends of global vegetation are highly uncertain after 2000. Therefore, we used multi-source remote sensing vegetation index (VI), climate data, and Mann-Kendall trend analysis to explore the global vegetation trend and its uncertainty from 2001 to 2016. The effects of climate on the changes in vegetation were also investigated. We found that GIMMS-based VIs exhibited decreasing trends. By contrast, MODIS-based VIs and GLOBMAP LAI tended to increase. Evergreen broad-leaf forest contributed the most to the uncertainty of global vegetation trends, and the uncertainty of December-January-February and September-October-November was higher than that in the other seasons. The correlation of forest VI and temperature was the highest in March-April-May, whereas the correlation of non-forest VI and precipitation was higher than that of the forest. The anomalies of GIMMS-based VIs and mean annual precipitation were more consistent in the evergreen broad-leaf forest, woody savannas, mixed forest, evergreen needle-leaf forest, and deciduous needle-leaf forest than those in biomes under the impact of 2015-2016 El Nino.
收录类别SCIE
文献类型期刊论文
条目标识符http://210.75.249.4/handle/363003/61256
专题中国科学院西北高原生物研究所
推荐引用方式
GB/T 7714
Wang, ZQ,Wang, H,Wang, TF,et al. Large discrepancies of global greening: Indication of multi-source remote sensing data[J]. GLOBAL ECOLOGY AND CONSERVATION,2022,34.
APA Wang, ZQ.,Wang, H.,Wang, TF.,Wang, LN.,Liu, X.,...&Huang, XT.(2022).Large discrepancies of global greening: Indication of multi-source remote sensing data.GLOBAL ECOLOGY AND CONSERVATION,34.
MLA Wang, ZQ,et al."Large discrepancies of global greening: Indication of multi-source remote sensing data".GLOBAL ECOLOGY AND CONSERVATION 34(2022).
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