Response of vegetation to extreme climate in the karst core areas of southwest China
The karst core areas of southwest China were chosen as the research area for exploring the interrelationship between vegetation change and extreme climate conditions.Based on NDVI data from 1982 to 2019 and daily temperature and precipitation data from meteorological stations and using extreme climate indices defined by the Expert Group on Climate Change Monitoring and Indicators(ETCCDI),trend analysis and correlation analysis methods were applied to analyze the spatiotemporal changes in the NDVI and extreme climate indices and their response mechanisms.The study can provide foundation for ecological protection and management in karst area.The results showed that:from 1982 to 2019,the overall(83.13％)NDVI exhibited a significant upward trend in the southwest karst core areas,with only a few areas(0.51％)showing a downward trend.Among the extreme climate indices,the extreme high-temperature indices showed a significant increasing trend in both temporally and spatially(P＜0.05),and the extreme low-temperature indices exhibited a significant decreasing trend(P＜0.05),and the extreme precipitation indices exhibited nonsignificant changes(P＞0.05).At the annual scale,there were a positive correlations between NDVI and extreme precipitation indices and extreme warm temperature indices,and a negative correlation with extreme cold temperature indices in the southwest karst core areas.The correlations between the NDVI and seven extreme temperature indices(TN90P,TX90P,TNX,TXX,TN10P,TX10P,and TNN)were significant relationships(P＜0.05),revealing significant spatial heterogeneity.The three main factors influencing the growth of each type of vegetation were TN90P,TX90P and TN1 0P,which characterize the frequency of extreme temperatures.The results of this study can guide ecological protection and management in karst areas.
normalized difference vegetation indexextreme climatespatiotemporal changesvegetation typesthe karst core areas of southwest China
万方数据
维普
