FOREST/VEGETATION TYPES DISCRIMINATION IN AN ALPINE AREA USING RADARSAT2 AND ALOS PALSAR POLARIMETRIC DATA AND NEURAL NETWORKS

摘要

The potential of SAR data in discriminating vegetation/forest types it is here explored using Neural Networks (NN) in an Alpine environment. Amplitude data from two SAR polarimetric sensors, namely RADARSAT2 Standard Quad Polarization (SQP) and ALOS PALSAR Fine Beam Dual (FBD), were used separately and in conjunction to discriminate four vegetation types: conifer forest, broadleaved forest, riparian vegetation, and dwarf pine and shrubs (mainly composed by Pinus mugo species). Results indicate successful separation of needle-leaved from broadleaved and/or riparian vegetation, but scarce ability to discriminate the other two types. ALOS PALSAR produced better results in separating vegetation types with respect to RADARSAT2 reaching in the best case a K Cohen's coefficient equal to 0.88. Results obtained from combination of the two SAR data were successful, but still in the range of those obtained by single scene usage.