[Objective]The three prefectures of Sichuan(Liangshan Yi Autonomous Prefecture,Ganzi Tibetan Autonomous Prefecture and Aba Tibetan and Qiang Autonomous Prefecture)have fragile ecological environments and frequent geohazards,whereas slope-surface vegeta-tion has the role of water storage and foundation consolidation.The study of the dynamic change of vegetation cover and key drivers in this re-gion can help predict the robustness of ecological structure and prevent geohazards.[Method]MODIS remote sensing data from 2002 to 2022 were selected to analyze the development trend and transfer of vegetation cover based on transfer matrix and trend analysis method,and the influ-ence of each driving factor on vegetation cover was studied through correlation analysis and geodetection model.[Result]The different vegetation cover in the study area had a tendency to evolve to higher cover,but the overall tendency was stabilized with the increase of time,and the influ-ence of meteorological factors,such as air temperature,was slightly larger than that of rainfall,and both factors had a facilitating effect on the vegetation cover;The combined effect of natural and human factors was the main reason for the differences in the spatial distribution of vegeta-tion cover in the three states of Sichuan,and elevation and land use were the two controlling factors of vegetation cover.Elevation and land use were the two controlling factors of vegetation cover,and natural factors had a more significant impact on vegetation cover in the study area,and the superposition of different drivers explained vegetation cover more strongly than the role of a single factor.[Conclusion]The study suggest that the recovery of vegetation cover at high altitude should focus on the temperature tolerance of vegetation and strictly control the over-exploitation and utilization of natural space by human beings,and that the factors affecting vegetation cover should be improved in a multi-dimensional way.
Vegetation coverageGeodetector modelSpatial distributionDisaster preventionMODIS remote sensing data
NETL
NSTL
万方数据
