Landscape Ecological Security Pattern and Its Influencing Factors in Xin'an River Basin on the Optimal Scale
Xin'an River Basin is the first pilot area where cross-provincial ecological compensation policy is adopted,and the implementation of the policy has positively promoted the overall urban ecological governance in this region. By analyzing the spatio-temporal evolution of landscape ecological risks and its driving factors in the early, middle,and later periods of the policy implementation, we can provide scientific support for ecological governance and risk prevention in the region and similar regions.This paper seeks the optimal scale for evaluating the ecological risk of landscapes,analyzes its spatial and temporal evolution trends,and calculates the influence degree of spatial factors with the help of geoprobes based on the three-period remote sensing data of land use in the years 2000,2010,and 2020.The optimal study scale in the study area is 70 m in granularity and 11 km in amplitude;From 2000 to 2020,the ecological risk of the landscape in the study area has been significantly decreased,with higher risk areas greatly reduced.The ecological risk shows a significant positive spatial correlation,with the degree of correlation diminishing,and the area of high aggregation is gradually shrinking.Land use intensity and slope have the strongest driving effect on landscape ecological risk in the study area,and the validity is significantly improved when the factors are detected interactively.There are significant spatial differences in landscape ecological risks among the cities in the study area,and it is necessary to strengthen the integrated maintenance of grassland and water landscape to further enhance the urban ecological stability of Xin'an River Basin,and at the same time,the risk management measures should be taken in a targeted manner in view of the regional land use structure and the risk situation.
scale effectlandscape ecological risktemporal and spatial evolutiongeodetectorXin'an River Basin cities
万方数据
维普
