GIScience & Remote Sensing (Nov 2019)
Separability analysis of wetlands in Canada using multi-source SAR data
Abstract
Accurately classifying and monitoring wetlands using new technologies is important because of many services that wetlands provide to the environment. In this regard, Synthetic Aperture Radar (SAR) systems provide valuable data to separate different wetland classes. Using large amount of field samples collected over three years, 78 SAR features extracted from multi-source satellites were investigated to select the most important features and decomposition methods for discriminating five wetland classes: Bog, Fen, Marsh, Swamp, and Shallow Water. The results indicated that the ratio features obtained from the diagonal elements of the covariance matrix (extracted from full polarimetric data RADARSAT-2 imagery) and the intensity layers of the dual polarimetric data (i.e., the data acquired by Sentinel-1 and ALOS-2) were most useful for distinguishing wetland class pairs as well as all wetland classes. In this regard, the ratio of HH and HV channels had the highest potential especially for discriminating herbaceous (Bog, Fen, Marsh) and woody (e.g., Swamp) wetlands. Moreover, the features derived from eigenvalues of the coherency matrix (e.g., Anisotropy, serd, normalized serd, and normalized derd) were among the most optimum features for wetland classification. Regarding the decomposition techniques, the H/A/Alpha and Freeman-Durden methods were selected as the best to discriminate wetlands. In terms of scattering mechanisms, it was observed that the volume component was generally the most useful element to discriminate wetland classes compared to the two other components (i.e., single- and double-bounce). This study comprehensively discusses the efficiency of various SAR features/decomposition methods for wetland studies and the results are expected to help with creating sustainable policies and management for wetland protection and monitoring using remote sensing methods.
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