Research On Remote Sensing Ecological Quality Evaluation Method Combined With Regional Scale Optimization

Accelerated urbanisation has led to a significant increase in the amount of builtup area on natural landscapes,causing ecosystem disturbances at all scales,with considerable impact on the global carbon cycle,leading to unprecedented challenges for the world’s ecosystems.Cities,as the mainstay of urbanisation,will be under the most direct duress from human activities in terms of their ecological quality.How to accurately and effectively quantify urban ecological and environmental quality is directly related to the livelihood of urban residents.In this paper,the most popular Remote Sensing Ecological Index(RSEI)is improved by combining the "regional scale" effect in landscape ecology with remote sensing,geographic information spatial analysis theory and machine learning.RORSEI is based on the "regional scale" effect in landscape ecology,combined with remote sensing,geographic information spatial analysis theory and machine learning methods,and Regional Optimized Remote Sensing Ecological Index(RO-RSEI)with regional scale optimization.The first principal component(PC1)of the central image element is taken as the final result,and the optimized ecological result is obtained at the regional scale after the traversal is completed.In order to solve the problem of random changes in the positive and negative directions of the ecological monitoring results in PC1 due to the different directions and uniqueness of the feature vectors in the PCA algorithm,this paper uses the index optimisation method to give the basis for parameter selection in quantitative form while using the contribution direction of the optimised index to PC1 as a reference for spatial mapping of the images to ensure the accuracy and Spatial continuity.In this paper,using Landsat image data,the study area is Shuangyang District,Changchun,and by analysing the data of the last 21 periods,the results of the study show that.(1)the RSEI calculated with the four parameters NDVI,WET,NDBSI and LST does not maintain the highest correlation with the original four parameters themselves.The feasibility of the method of quantifying the ecological quality of the Shuangyang area with fixed indices is questionable,and there is a need to select the optimal parameters for the study area according to the special conditions of the study area.(2)The optimal parameters obtained by index selection,for ecological environment monitoring on different phases of the same study area,only need to make index selection for data of any one year to obtain the most relevant parameters applicable to the study area,and there is no need to extract indexes year by year.(3)In terms of the direction of the eigenvectors,it was found that NDVI,WET,NDBSI and LST do not follow the same direction of greenness and humidity and the same direction of dryness and heat by using a moving window to calculate the PCA within the window image by image,but instead,the study after millions of calculations showed that they differed greatly in the direction of eigenvector contribution.The direction of eigenvector contribution of each parameter to the first principal component after index selection is consistent,and all windows obey the "isotropic" theory,which makes the RO-RSEI model more generally applicable.(4)The RSEI model is correct only when the contributions of NDVI and WET are positive and those of NDBSI and LST are negative,and when the contributions of NDVI and WET are negative and those of NDBSI and LST are positive,the results need to be normalised and inverted manually,which adds a lot of tedious work,and the method is not this adds a great deal of complexity and is not feasible when using moving windows to calculate PCA in the millions or tens of millions.Using the RO-RSEI model,the PCA calculated in each window is normalised,inverted and mapped in the same proportion as the value in the original window according to the direction of the eigenvector of the input metric,without any manual intervention.(5)RO-RSEI takes into account ecological effects,and the evaluation levels are more continuous in space,especially in the main urban area with sparse vegetation cover,and the ecological monitoring results are more accurate;in time,the monitoring results of RO-RSEI are more stable compared to the RSEI model,which are consistent with the reality and more in line with the physical and ecological meanings.(6)The proposed RO-RSEI model provides a technical possibility for multiseasonal and multitemporal studies of remote sensing ecological quality monitoring models.The model can be corrected automatically by simply correcting the direction of the eigenvector contribution of the parameters,which enhances the degree of model automation.