生态环境学报2025，Vol.34Issue(2)：209-221,13.DOI:10.16258/j.cnki.1674-5906.2025.02.004
基于EL-InVEST模型的玛多地震对高寒湿地面积及生境质量影响研究
Effects of Maduo Earthquake on Alpine Wetland Area and Habitat Quality Based on EL-InVEST Model
摘要
Abstract
On May 22,2021,a 7.4-magnitude earthquake struck Maduo County,Guoluo Tibetan Autonomous Prefecture,Qinghai Province,China,causing damages to natural landscapes,such as wetlands,rivers,and lakes.Thus,this study aimed to accurately assess and quantify the impact of earthquakes on the alpine wetlands in the region.To address the research aim,we selected the 6-degree intensity zone of the Maduo earthquake as the research area,using the Google Earth Engine(GEE)as the platform,which was combined with Sentinel-2,Sentinel-1,and SRTM1 to extract research data.Remote sensing features,seven experimental schemes including the original spectrum,and vegetation index,original spectrum and water index,original spectrum and red edge index,original spectrum and texture feature,original spectrum and terrain feature,original spectrum and radar feature,and feature selection of random forest importance ranking.Finally,the habitat quality of the study area before and after the earthquake was systematically evaluated based on the classified alpine wetland data combined with the InVEST model.The influence of remote sensing features on alpine wetland classification was also explored.Changes in the alpine wetlands before and after the earthquake were quantified.Changes in habitat quality in the study area before and after the earthquake were quantitatively analyzed.The results showed that:1)the integrated learning model constructed based on the stacking algorithm was superior to the stochastic forest in terms of classification accuracy,and the stochastic forest was superior to the support vector machine in terms of classification accuracy.When ensemble learning was used for classification,the classification accuracy of the alpine wetlands reached 92.741%,and the kappa coefficient was 0.902.Compared to a single support vector machine or random forest model,the classification accuracy was improved to a certain extent.2)After adding textural features to the original spectrum,the classification accuracy of the lake wetland and river wetland reached 0.987 and 0.933,respectively,and by adding topographic features to the original spectrum,the classification accuracy of swamp wetlands reached 0.857.The results showed that the variance and correlation of texture features,as well as the slope and elevation of terrain features,had the most significant influence on the classification of alpine wetlands.3)The Maduo earthquake resulted in a reduction in wetland area,among which the marsh wetland area decreased by 43.088 km2,with a change rate of-0.254%;the river wetland area decreased by 31.654 km2,with a change rate of-3.522%;and the lake wetland area decreased by 4.971 km2,with a change rate of-0.303%.When the wetland area decreased,the bare land area increased by 36.160 km2 and the rate of change was as high as 13.610%.The earthquake also caused many wetlands to undergo non-wetland transformation.4)The average habitat quality decreased from 0.523 before the earthquake to 0.482 after the earthquake,and the high-grade habitat quality area decreased from 7.399%to 5.993%.The area of low-grade habitat quality increased from 2.191 to 7.658%.In summary,the research results show that the Maduo earthquake had a significant negative impact on alpine wetlands,which not only led to a decrease in wetland areas and an increase in non-wetland areas,but also led to an overall decline in habitat quality.This study provides a scientific basis for the ecological restoration and management of alpine wetlands after disasters and has important practical significance.关键词
遥感分类/Google Earth Engine/地震/高寒湿地/特征优选/哨兵2号Key words
remote sensing classification/google earth engine/earthquake/alpine wetland/characterization preferred/sentinel-2分类
资源环境引用本文复制引用
顾天江,杜凯,毛旭锋,金鑫,于红妍,唐文家,吴艺,刘泽碧..基于EL-InVEST模型的玛多地震对高寒湿地面积及生境质量影响研究[J].生态环境学报,2025,34(2):209-221,13.基金项目
青海省自然科学基金青年项目(2024-ZJ-960) （2024-ZJ-960）
青海师范大学中青年科研基金项目(KJQN2022002) （KJQN2022002）
