Abstract
When severe flooding occurs in Canada, the Emergency Geomatics Service (EGS) is tasked with creating and disseminating maps that depict flood extents in near real time. EGS flood mapping methods were created with efficiency and robustness in mind, to allow maps to be published quickly, and therefore have the potential to generate high-repeat water products that can enhance frequent wetland monitoring. The predominant imagery currently used is synthetic aperture radar (SAR) from RADARSAT-2 (R2). With the commissioning phase of the RADARSAT Constellation Mission (RCM) complete, the EGS is adapting its methods for use with this new source of SAR data. The introduction of RCM’s circular-transmit linear-receive (CTLR) beam mode provides the option to exploit compact polarimetric (CP) information not previously available with R2. The aim of this study was to determine the most effective CP parameters for use in mapping open water and flooded vegetation, using current EGS methodologies, and compare these products to those created by using R2 data. Nineteen quad-polarization R2 scenes selected from three regions containing wetlands prone to springtime flooding were used to create reference flood maps, using existing EGS tools. These scenes were then used to simulate 22 RCM CP parameters at different noise floors and spatial resolutions representative of the three RCM beam modes. Using multiple criteria, CP parameters were ranked in order of importance and entered into a stepwise classification procedure, for evaluation against reference R2 products. The top four CP parameters —m-chi-volume or m-delta-volume, RR intensity, Shannon Entropy intensity (SEi), and RV intensity—achieved a maximum agreement with baseline R2 products of upward of 98% across all 19 scenes and three beam modes. Separability analyses between flooded vegetation and other land-cover classes identified four candidate CP parameters—RH intensity, RR intensity, SEi, and the first Stokes parameter (SV0)—suitable for flooded-vegetation-region growing. Flooded-vegetation-region-growing CP thresholds were found to be dependent on incidence angle for each of these four parameters. After region growing using each of the four candidate CP parameters, RH intensity was deemed best to map flooded vegetation, based on our evaluations. The results of the study suggest a set of suitable CP parameters to generate flood maps from RCM data, using current EGS methodologies that must be validated further as real RCM data become available.
Highlights
Wetlands supply clean water and support food production for human populations worldwide, in addition to the intrinsic value they provide by sustaining wildlife
Commission, and agreement of the RADARSAT Constellation Mission (RCM) products with RADARSAT-2 satellite (R2)-derived open-water products should enable the selection of an optimal set of compact polarimetric (CP) parameters to classify open water
An examination of intensity beneath known flooded-vegetation targets revealed a dependence on incidence angle, with two separate groups occurring at greater than and less than 30 degrees
Summary
Wetlands supply clean water and support food production for human populations worldwide, in addition to the intrinsic value they provide by sustaining wildlife. Surface-water mapping with high repeat frequency can help locate wetlands and monitor their changes due to land use and climate. EGS flood maps must be highly accurate, to ensure that information used in emergency management is correct, and they must be generated quickly and efficiently, to minimize the latency between image acquisition and product availability to end-users to four hours or less. The open-water and flooded-vegetation mapping methods developed by EGS for this purpose have been applied to historical satellite imagery in [3,4], to generate products depicting surface water dynamics, and as such can serve to map wetland dynamics with high repeat frequency. Following the recent launch of the RADARSAT Constellation Mission (RCM) in the summer of 2019, the EGS is adapting its tools and methods for implementation with new data from RCM, to ensure that operations transition seamlessly once RCM begins acquiring imagery
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