Historically,Lop Nur was a large and famous salt lake that acted as an important geographic position along the ancient‘Silk Road’,and was associated with the surrounding old civilizations,such as Loulan and ***,it d...
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Historically,Lop Nur was a large and famous salt lake that acted as an important geographic position along the ancient‘Silk Road’,and was associated with the surrounding old civilizations,such as Loulan and ***,it dried up before *** shows a clear‘Ear’feature on synthetic aperture radar(SAR)*** objective of this paper is to interpret Lop Nur’s environmental evolution during its drying-up process based on an analysis of its sodium sedimentary *** genetic algorithm-partial least squares approach is introduced as a modeling method to retrieve the subsurface sodium content from polarimetricparameters obtained by Cloude *** a result,the R2 and root-mean-square error can reach 0.7 and 9.1 g/*** is suggested that the subsurface salt content was the primary reason for the bright-grey strips textures on SAR ***,our results show that the sodium content along the same strip changed,with its distribution exhibiting consistency with the lake body’s movement driven by the strong *** future,high-precision topographical data will be considered,and should be helpful in the analysis of lake body *** method of this paper can also be applied in other similar dried salt lakes.
The accurate classification of marsh vegetation is an important prerequisite for wetland management and pro-tection. In this study, the Honghe National Nature Reserve was used as the research area. The VV and VH polar...
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The accurate classification of marsh vegetation is an important prerequisite for wetland management and pro-tection. In this study, the Honghe National Nature Reserve was used as the research area. The VV and VH polarized backscattering coefficients of Sentinel-1B, the polarimetric decomposition parameters of Sentinel-1B, and Sentinel-2A multi-spectral images from June and September were selected to construct 18 multi-dimensional data sets. A highly correlated variable elimination algorithm, a recursive feature elimination vari-able selection algorithm (RFE-RF), and an optimized random forest algorithm (RF) were used to construct a marsh vegetation identification model. In this study, we searched for an RF model to achieve the accurate classification of marsh vegetation and find the best feature for identifying various types of vegetation. Addi-tionally, the applicability of different optimized RF models to the task of the identification of wetland vegetation and the stability of the identification of marsh vegetation using different classification models were quantita-tively analyzed. The results show the following: (1) RFE-RF variable selection and RF parameter optimization can reduce the data dimensionality, improve the accuracy and stability of the wetland vegetation classification model, and achieve a training accuracy of up to 85.39%. (2) The RF model integrating multi-spectral data, backscattering coefficients, and polarimetric decomposition parameters for June and September can obtain the highest overall accuracy (91.16%), and the model has the strongest applicability. (3) The importance of multi-spectral variables in wetland vegetation classification is higher than that of backscattering coefficients and polarimetric decomposition parameters. The visible bands and vegetation index are the most important vari-ables, while the cross-polarized backscattering coefficient (Mean_VH), polarimetricdecomposition eigenvalue (Mean_l1, Mean_l2), and calculated eigenva
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