The Caohai Nature Reserve is one of the three major plateau freshwater lakes in China.Since the 1950s,human activities such as land reclamation and population relocation have greatly damaged Caohai.A rapid evaluation ...The Caohai Nature Reserve is one of the three major plateau freshwater lakes in China.Since the 1950s,human activities such as land reclamation and population relocation have greatly damaged Caohai.A rapid evaluation of the spatiotemporal evolution trend of the ecological quality of the Caohai Nature Reserve is significant for the maintenance and construction of the ecosystem in this area.The research is based on the Google Earth Engine(GEE)remote sensing cloud computing platform.Landsat TM/OLI images from May to October in five time periods:2000-2002,2004-2006,2009-2011,2014-2016,and 2019-2021 were obtained to reconstruct the optimal cloud image set by averaging the images in each time period.By constructing four ecological indicators:Greenness(NDVI),Wetness(Wet),Hotness(LST),and Dryness(NDBSI),and using Principal Component Analysis(PCA)method to obtain the Remote Sensing Ecological Index(RSEI)for the corresponding years,the spatiotemporal variation of ecological quality in the Caohai Nature Reserve over 20 years was analyzed.The results indicate:①the mean value of RSEI increased from 0.460 in 2000-2002 to 0.772 in 2019-2021,a 67.83%increase,indicating a significant improvement in the ecological quality of the reserve over the 20 years;②from the perspective of functional zoning of the Caohai Nature Reserve,the ecological quality of the core area showed a degrading trend,while the ecological quality of the buffer zone and experimental zone significantly improved;③with the implementation of ecological restoration projects,the ecological quality of the reserve gradually recovered and improved from 2014 to 2021.The trend of RSEI value changes is well correlated with human interventions,indicating that the PCA-based RSEI model can be effectively used for ecological quality assessment in lake areas.展开更多
基金Supported by Joint Project between Bijie Science and Technology Bureau and Guizhou University of Engineering Science (Bike Lianhe Zi (Guigongcheng)[2021]03)Guizhou Provincial Key Technology R&D Program (Qiankehe[2023]General 211).
文摘The Caohai Nature Reserve is one of the three major plateau freshwater lakes in China.Since the 1950s,human activities such as land reclamation and population relocation have greatly damaged Caohai.A rapid evaluation of the spatiotemporal evolution trend of the ecological quality of the Caohai Nature Reserve is significant for the maintenance and construction of the ecosystem in this area.The research is based on the Google Earth Engine(GEE)remote sensing cloud computing platform.Landsat TM/OLI images from May to October in five time periods:2000-2002,2004-2006,2009-2011,2014-2016,and 2019-2021 were obtained to reconstruct the optimal cloud image set by averaging the images in each time period.By constructing four ecological indicators:Greenness(NDVI),Wetness(Wet),Hotness(LST),and Dryness(NDBSI),and using Principal Component Analysis(PCA)method to obtain the Remote Sensing Ecological Index(RSEI)for the corresponding years,the spatiotemporal variation of ecological quality in the Caohai Nature Reserve over 20 years was analyzed.The results indicate:①the mean value of RSEI increased from 0.460 in 2000-2002 to 0.772 in 2019-2021,a 67.83%increase,indicating a significant improvement in the ecological quality of the reserve over the 20 years;②from the perspective of functional zoning of the Caohai Nature Reserve,the ecological quality of the core area showed a degrading trend,while the ecological quality of the buffer zone and experimental zone significantly improved;③with the implementation of ecological restoration projects,the ecological quality of the reserve gradually recovered and improved from 2014 to 2021.The trend of RSEI value changes is well correlated with human interventions,indicating that the PCA-based RSEI model can be effectively used for ecological quality assessment in lake areas.