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TitleAn automated procedure to map breaking river ice with C-band HH SAR data
Authorvan der Sanden, J JORCID logo; Drouin, HORCID logo; Geldsetzer, TORCID logo
SourceRemote Sensing of Environment vol. 252, 112119, 2020 p. 1-14,
Alt SeriesNatural Resources Canada, Contribution Series 20200475
Mediapaper; on-line; digital
File formatpdf; html
Subjectsgeophysics; Science and Technology; Nature and Environment; remote sensing; satellite imagery; surface waters; rivers; ice; mapping techniques; models; hydrologic environment; floods; climate; meteorology; Methodology; synthetic aperture radar surveys (SAR); Automation; Classification; Infrastructures
Illustrationslocation maps; tables; flow diagrams; histograms; plots; models; geoscientific sketch maps; photographs; time series
ProgramCanada Centre for Remote Sensing People Support and Leadership
Released2020 10 07
AbstractThe development of effective strategies to manage the river ice breakup process or the associated risks is hindered by a lack of understanding and information. Radar earth observation satellites offer excellent potential for collecting up-to-date information on the conditions of and changes in river ice cover during the breakup period. This text describes the development, performance and limitations of an automated procedure to map breaking river ice by means of C-band, HH-polarized Synthetic Aperture Radar (SAR) images. An original two-step supervised classification model (IceBC), which uses backscatter intensities, lies at the core of the procedure. First, IceBC identifies three primary classes: water, sheet ice and rubble ice. Next, each primary ice class is divided in three secondary classes that denote top surface roughness scale differences. Input images must have incidence angles from ~27° to ~60°. Below ~36°, IceBC may assign a class labelled 'unclassified' to water or sheet ice pixels. The primary classification model yields overall accuracies of ~86% and ~93% for independent test pixels with incidence angles less than or equal to ~49° and greater than or equal to ~29° or greater than or equal to ~36°, respectively. The associated class accuracies for water, sheet ice and rubble ice are ~97% & 96%, ~69% & 85% and ~97% & 99%. Given its connection to ice jam flood events, the classification accuracy achieved for rubble ice is particularly important. Maps produced by means of IceBC comprise detailed spatial information regarding ice cover conditions and the development of the breakup process. Their quality may be affected by: freezing conditions, wet snow cover, meltwater pools, infrastructure, rapids or high winds. Monitoring is the key to managing the impacts of most of these challenges. An IceBC prototype has been used operationally since 2015.
Summary(Plain Language Summary, not published)
One out of three flood events in Canada is caused by river ice jams. Natural Resources Canada's Emergency Geomatics Services (EGS) helps to protect Canadians from ice jam flood hazards through the provision of near real-time map products to Public Safety Canada and other emergency management organizations. This paper reports on a study that has enabled the operational capability of EGS with respect to river ice breakup mapping. It describes the design, performance and limitations of an automated method to generate ice cover condition maps from image data acquired radar earth observation satellites including Canada's RADARSAT-2 and the RADARSAT Constellation Mission. An original classification model lies at the core of the method. The resulting maps display water and two principal ice cover types referred to as 'sheet ice' and 'rubble ice'. River ice jams are comprised of rubble ice and so information regarding its presence, location, extent and movement is particular interest. A performance test yields classification accuracies up to 96%, 85% and 99% for water, sheet ice and rubble ice, respectively. Freezing conditions, wet snow cover, meltwater pools, infrastructure, rapids and high winds may cause map inaccuracies or ambiguities. These deficiencies can be managed through systematic and repetitive image acquisition and analysis, i.e. monitoring.

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