2012 · 11 citations · 4 references
Natural SciencesGeographyMarine SciencesCoastal GeographyCoastal OceanographyRemote SensingLand Cover ClassificationLand Cover MappingMulti-temporal Image AnalysisRemote Sensing ImagerySalt MarshesSeafloor MappingClassification AlgorithmsHigh-resolution Satellite Image AnalysisIntertidal EnvironmentsMulti-temporal Remote Sensing AnalysisRadar Satellite Data FusionRoughnessSatellite Imagery AnalysisSupervised Classification Algorithms
This contribution explores the potential of high-resolution satellite SAR imagery (TerraSAR-X, ALOS-PALSAR) for mapping coastal habitats in complement of optical data (SPOT-5). It addresses X- and L-band SAR signatures over intertidal flats and coastal salt-marshes by investigating the mean backscattering coefficient σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> over the major environmental units and its variation associated with physical parameters (soil roughness, soil moisture, tidal inundation) and instrumental configurations. The major findings outline a great potential of TerraSAR-X data to detect oyster beds and discriminate Zostera noltii seabed from salt-marsh vegetation species. Based on these results, a multi-sensor multi-temporal mapping strategy was tested running simple supervised classification algorithms. These tests show that mapping performance is greatly enhanced when running Mahalanobis classifier on a 6-band concatenated image. Further work is needed to account with green macro-algae deposits and microphytobenthos in order to demonstrate the full capabilities of spaceborne sensors to provide an exhaustive mapping of intertidal environments.
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