2020
DOI: 10.3390/rs12193150
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Spatiotemporal Variation of Vegetation on the Qinghai–Tibet Plateau and the Influence of Climatic Factors and Human Activities on Vegetation Trend (2000–2019)

Abstract: Vegetation is the terrestrial ecosystem component most sensitive to climate change. The Qinghai–Tibet Plateau (QTP), characterized by a cold climate and vulnerable ecosystems, has experienced significant warming in previous decades. Identifying the variation in vegetation coverage and elucidating its main driving factors are critical for ecological protection on the QTP. In this study, MOD13A2 Normalized Difference Vegetation Index (NDVI) data in the growing season (May to September) was used to represent QTP … Show more

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Cited by 100 publications
(109 citation statements)
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“…The NDVI also has a significant growth trend since 2000 with a change rate of 0.001/year, which is similar to Li, et al [83] and Chen, et al [84]. Combining the interannual changes in GED, the influence of NDVI on LST shows a downward trend (−0.0013/year) during the daytime.…”
Section: Discussionsupporting
confidence: 82%
“…The NDVI also has a significant growth trend since 2000 with a change rate of 0.001/year, which is similar to Li, et al [83] and Chen, et al [84]. Combining the interannual changes in GED, the influence of NDVI on LST shows a downward trend (−0.0013/year) during the daytime.…”
Section: Discussionsupporting
confidence: 82%
“…Several studies [81][82][83] have shown that, whether in the 1980s or after 2000, the growing season NDVI of the QTP showed an overall increasing trend, and grassland dominated, with some regions being degraded, showing an "overall improvement and local degradation" pattern [84], which was consistent with the results of this study. The climate change trend showed that before 2014, the annual precipitation and annual mean temperature of the QTP both increased, and the regional climate was "warming and humid."…”
Section: Discussionsupporting
confidence: 91%
“…The slope is the trend of variables. We used the ordinary least squares method [39][40][41][42] to determine the trends in climate, VI, and VGEG in time series:…”
Section: Trend Analysismentioning
confidence: 99%