This study aimed to reveal the spatiotemporal variations and the influencing factors of vegetation in the Qiangtang grassland during 2001-2020,and to predict the change trends of vegetation under climate change sce-narios.Based on the data of MODIS NDVI,temperature,precipitation,and wind speed,we explored the relation-ship between vegetation changes and meteorological factors.Furthermore,NDVI prediction models were establish with three machine learning algorithms of random forest,support vector machine,and random gradient descent re-gression.The optimal model with the best simulation accuracy was selected to simulate vegetation changes under multiple scenarios.We found that NDVI of the Qiangtang grassland showed a slight increasing trend with a growth rate of 0.0003 a-1 from 2001 to 2020.The response of NDVI to temperature lagged by 3 months,precipitation lagged by 0-1 months.NDVI was negatively correlated with wind speed without lag.The random forest algorithm had the highest simulation accuracy(Adjusted R2=0.958).The scenario for improvement of vegetation coverage in the future included 1.0 ℃ increase in temperature,25％increase in precipitation,and 25％decrease in wind speed.This study contributed to early warning of vegetation degradation,which would help vegetation conservation under climate change.
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