Time-Frequency Optimization And Application Of RSEI

Evaluating regional ecological environmental quality(EEQ)using remote sensing is important for achieving sustainable development.The remote sensing ecological index(RSEI)is a typical EEQ evaluation model used to comprehensively reflect regional ecological quality.The Yangtze River Basin has a wide range and complicated topography.In recent years,under the background of climate and land cover change,the ecological response of the whole ecological quality of the Yangtze River Basin is still unknown.To reveal the spatiotemporal changes in ecological quality in the Yangtze River Basin from 2001 to 2019 and their relationship with environmental and topographical factors,this study used the Google Earth Engine(GEE)platform to calculate the remote sensing ecological index(RSEI)based on the Moderate-resolution Imaging Spectroradiometer(MODIS)product image set,combined with the digital elevation data set and statistical yearbook.The data evaluated the ecological quality of the Yangtze River Basin and analyzed its causes.However,owing to its complete dependence on remote sensing image information,the RSEI also has inherent issues,including unstable time series and inconsistent resolutions of its four sub-indices.Therefore,based on GEE cloud platform,this study adopts three common data reconstruction algorithms firstly,namely:Savitzky-Golay filter(SG),Harmonic analysis of time series(HANTS),Whittaker Smoother(WS),which are used to reconstruct the original MODIS time series data in the Yangtze River Basin(YRB)from 2000 to 2020,in order to optimize the calculation process of RSEI.At the same time,three indicators(correlation coefficient(R),standard deviation(STD),root mean square error(RMSE))are used for the accuracy evaluation,and the data reconstruction method applicable to RSEI is determined.Finally,the optimization concept of HANTS coupled random forest(RF)is proposed,and the accuracy and image quality of the optimized RSEI(RSEI_o)in the YRB are determined.The results showed that:(1)The average RSEI of the Yangtze River Basin showed an overall upward trend,the growth rate was 0.027/year,and the variation ranged from 0.5 to 0.568.The overall ecological quality rank was mainly neutral and slightly good;The ecological quality of 85.7%of the Yangtze River Basin remains stable.A total of 11.2%of the regional ecological quality is improving,and 3.1%of the regional ecological quality is declining.Areas with reduced ecological quality are concentrated in the Hengduan Mountains.The dominant LST factor drives the deterioration of its ecological quality at a rate of-1.06/year.The areas with improved ecological quality are concentrated in the upper and middle reaches of the Yangtze River.The dominant WET factor drives its ecological quality to improve at a rate of 0.27/year;From the perspective of topography,the ecological quality of the Yangtze River basin shows a wave-like decline and first rises and then falls in elevation and slope(the elevation is bounded by 2000 m and 6000 m,and the slope is bounded by 15?).The average RSEI of the Yangtze River Basin is the highest on the northwest slope(0.554),and the ecological quality of sunny slopes is generally higher than that of shady slopes.The research shows that from 2001 to 2019,the overall ecological quality of the Yangtze River Basin has improved and evolved,but the ecological quality of the Hengduan Mountains has declined.Therefore,implementing different ecological protection policies in different regions is an important strategy for enhancing the stability of the ecosystem.(2)Data reconstruction can fill gaps in RSEI,the reconstruction performance of WS and SG for four parameters is better than HANTS,and the four SG reconstructed sequences have the strongest correlation with the original sequences(R between 0.8～1),while the WS reconstruction sequence has the lowest error value(both STD and RMSE are less than 1),both of them can correct the pixel value,which is conducive to maintaining the stability of RSEI in the temporal dimension;the RSEI produced by HANTS has the best accuracy,that is,R,STD,RMSE are respectively 0.898,0.130,0.104.As shown by the research,it is necessary to de-noise each parameter before synthesizing RSEI.This study can provide a theoretical basis for applying time-frequency algorithms to optimize the ecological monitoring performance of RSEI.(3)HANTS could fill the gaps in the images,effectively reducing the noise and discrete degrees of the four indices and thereby improving the stability of the RSEI.The R between the RSEI and RSEI_o was 0.93,with a RMSE of 0.13,indicating that RSEI_o is reliable.The image quality assessment(based on contrast and information entropy)indicates that HANTS combined with the RF model can produce RSEI_o images with higher definitions and richer information at a constant spatial resolution.Further,the pixel-by-pixel coefficient of variation evaluation indicates that the RSEI_o image was highly stabilised,yielding higher numbers of effective RSEI_oimages without changing the temporal resolution.Compared with traditional RSEI calculations,the optimization proposal herein could highlight ecological differences caused by topographic changes in the Yangtze River Basin,which would produce an RSEI closer to actual surface conditions.Further,this proposed method could be used to obtain more detailed ecological information at a constant spatiotemporal resolution,thereby meeting the needs of long-term ecological monitoring in large scale regions.