New approaches have been developed for mapping and monitoring Great Lakes coastal land cover/ land use, wetlands and the invasive species Phragmites using a fusion of multi-sensor, multi-temporal satellite radar imagery (JERS and Radarsat) and traditional multi-spectral data(Landsat). Wetlands have historically been one of the most difficult ecosystems to classify using remotely sensed data. This difficulty is partially due to the high variability in wetland morphology. Our results show how SAR and multi-spectral sensors complement each other in the classification of wetland ecosystems, allowing greater definition of wetland and non-wetland categories including the invasive species Phragmites. While multi-spectral data measure spectral reflectance and emittance characteristics of various cover types and wetness in open canopied ecosystems, SAR is sensitive to variations in biomass, structure, dielectric properties and flood condition of landscapes including forests and other closed canopy ecosystems.
The multi-sensor, multi-temporal techniques have been demonstrated at test sites on Lake Michigan, Lake St. Clair, and Lake Ontario. While the methods were developed for land cover and land use classification in the Great Lakes they are also being applied over vast peatland complexes in Canada. New techniques using object-based classification and including not only multi-temporal Radarsat and JERS, but also PALSAR data are being investigated. The cross-polarized PALSAR channel is aiding tremendously in discerning the bogs from fens and also shrub versus forested bogs of boreal Alberta.