Forest Ecological Vulnerability Assessment of Guangdong Province Based on SRP Model
The comprehensive evaluation of forest ecological environment vulnerability in Guangdong province and the knowledge of their temporal and spatial distribution characteristics are helpful to provide theoretical sup-port for the optimization of commonweal forest layout and ecological restoration.According to the 3S technology,and on the supply of fundamental geographic data of the third national land survey,comprehensive monitoring results for forest,grassland,wetland,ecological conservation redline,and rocky desertification,DEM data,meteorological data,and soil data,we derived 17 assessment indicators such as population density,construc-tion land per unit area,proportion of cultivated land,fraction vegetation coverage,area size with severe rocky desertification,annual average precipitation,plant net primary productivity,and biological abundance and so on to construct the evaluation index system for forest ecological vulnerability in Guangdong province by com-bined with ecological SRP model and Analytic Hierarchy Process method to evaluate the forest ecological vulner-ability of Guangdong province.The results indicate that the ecological environment vulnerability of forest land in Guangdong province is mainly slightly,mildly,and moderately,together accounting for 92.02%of the total forest land area in the province.Divided by administrative division,the severely vulnerable areas and extremely vulnerable areas are mainly distributed in 7 cities,including Qingyuan,Shaoguan,Meizhou,Heyuan,Zhao-qing,Maoming,and Jieyang.Divided by spatial layout,the severely vulnerable areas and extremely vulnerable areas are relatively concentrated in the main rocky desertification areas,key control areas and key prevention areas of water and soil loss,densely populated areas in the Pearl River Delta,coastal trunk forest belts,and surrounding islands.
forest ecological vulnerabilitySRP modelAnalytic Hierarchy Process3S technologyGuang-dong Province
NETL
NSTL
万方数据
