Changes in Vegetation and Assessment of Meteorological Conditions in Ecologically Fragile Karst Areas

Meteorological conditions have an important impact on changes of vegetation in ecologically fragile karst areas.This study aims to explore a method for quantitative evaluation of these meteorological conditions. We analyzed the changing trend of vegetation during 2000–2018 and the correlations between vegetation changes and various meteorological factors in karst rocky areas of Guangxi Zhuang Autonomous Region, China. Key meteorological factors in vegetation areas with varying degrees of improvement were selected and evaluated at seasonal timescale. A quantitative evaluation model of comprehensive influences of meteorological factors on vegetation was built by using the partial least-square regression(PLS). About 91.45% of the vegetation tended to be improved, while only the rest 8.55% showed a trend of degradation from 2000 to 2018. Areas with evident vegetation improvement were mainly distributed in the middle and northeast, and those with obvious vegetation degradation were scattered. Meteorological factors affecting vegetation were significantly different among the four seasons. Overall, high air humidity, small temperature difference in spring and autumn, and low daily minimum temperature and air pressure were favorable conditions. Low temperature in winter as well as high temperature in summer and autumn were unfavorable conditions. The Climate Vegetation Index(CVI) model was established by PLS using the maximum, minimum, and average temperatures;vapor pressure;rainfall;and air pressure as key meteorological factors. The Enhanced Vegetation Index(EVI) was well fitted by the CVI model, with the average coefficient of determination(r2) and root mean square error(RMSE) of 0.856 and 0.042, respectively. Finally, an assessment model of comprehensive meteorological conditions was built based on the interannual differences in CVI. The meteorological conditions in the study area in 2014 were successfully evaluated by combining the model and selected seasonal key meteorological factors.