Landscape ecological risk assessment plays a vital role in identifying vulnerable ecosystem areas for targe-ted management.While current methods primarily rely on land-use change data for ecological risk analysis,they of-ten lack a comprehensive evaluation of multiple factors,especially the prediction of landscape ecological risk dy-namics under climate change scenarios integrating climate variations and socio-economic trends.To tackle this is-sue,we constructed a predictive model for ecological landscape risk influenced by diverse factors by integrating tra-ditional landscape ecological risk assessment models with deep learning technique,and further applied this model to simulating the change in landscape ecological risks of Hanjiang River Basin.Findings reveal that:1)during the baseline period(2000-2015),higher ecological risk levels predominantly clustered in the downstream of Dan-jiangkou reservoir;2)both SSP370 and SSP585 scenarios exhibited elevated ecological risk levels,particularly concentrated in the downstream of Danjiangkou;3)the high ecological risk area in Hanjiang River basin signifi-cantly expanded under the 2042 scenario for SSP370 and SSP585,with an average increase of 14.58％per decade under the SSP370 scenario.The proposed landscape ecological risk prediction approach in consideration of multiple factors serves as a valuable reference for ecological risk assessment in the basin under changing climatic conditions and the formulation of ecological compensation policies.
关键词
气候变化/景观生态风险/深度学习/SSP-RCP/汉江流域
Key words
climate change/landscape ecological risk/deep learning/SSP-RCP/Hanjiang River Basin
维普
万方数据