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

Abstract

Meteorological conditions have an important impact on changes in vegetation in karst ecologically fragile areas. This study aims to explore a method for quantitative evaluation of these meteorological conditions. We analyzed the changing trend characteristics of vegetation during 2000 to 2018 and the correlations between vegetation change and various meteorological factors in the karst rocky areas of Guangxi. The characteristics of meteorological factors in vegetation areas with varying degrees of improvement were also analyzed. Key meteorological factors at seasonal scale were selected for meteorological condition evaluation. A quantitative evaluation model of comprehensive influence of meteorological factors on vegetation was built using partial least-square regression (PLS). About 91.45% of the vegetation tended to be improved, while only 8.55% of the vegetation 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 and high temperature in summer and autumn were unfavorable conditions. Climate Vegetation Index (CVI) model was established by PLS using the maximum temperature, minimum temperature, average temperature, vapor pressure, rainfall, and air pressure as key meteorological factors. (Ehanced Vegetation Index (EVI) was well fitted by the CVI model, with R2 and RMSE average of 0.856 and 0.042, respectively. Finally, the assessment model of comprehensive meteorological conditions was built based on 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.