Analysis of Ecological Environment Quality of Tailings Reservoir and Surrounding Areas Based on Improved Remote Sensing Ecological Index
Taking the Sha'an tailings pond and its surrounding area in Wuxuan,Guangxi as the research area,the meas-ured data and remote sensing data are integrated.Based on the information of vegetation,surface humidity,surface temperature,bareness fraction and construction land in the tailings pond and its surrounding area,the soil heavy metal pollution in the con-centrator,tailings pond and its surrounding area is considered,and the soil heavy metal pollution index is included in the re-mote sensing ecological index RSEI,so as to construct an improved remote sensing ecological index IRSEI.Combined with the moving window algorithm and principal component analysis model,an improved remote sensing ecological index IRSEI_mw is constructed.The results show that increasing the Nemero comprehensive index is conducive to more detailed assessment of the ecological environment quality of metal mining areas and surrounding areas.Compared with the overall study area,the internal ecological quality of the tailings reservoir is generally poor.The average values of RSEI,IRSEI and IRSEI_mw in the whole study area are 0.53,0.52 and 0.58,respectively.The average values of the three ecological indexes in the tailings pond are 0.29,0.27 and 0.22,respectively.Compared with RSEI and IRSEI,the discrimination of regional ecological quality results cal-culated by IRSEI_mw index has increased,the range of areas with poor ecological quality grade in tailings area has increased greatly,and the area with excellent external grade in tailings area has increased greatly.In general,the IRSEI_mw index can ef-fectively quantify the ecological environment quality of the tailings pond and the surrounding area,which is conducive to a more detailed assessment of the ecological environment quality of the metal mining area and the surrounding area.
remote sensing ecological indexmoving windowNemerow indextailings reservoirsoil heavy metals
万方数据
维普
