Toward consistent global physiognomic vegetation mapping using ERS/JERS SAR classification

Recent research identified a small number of vegetation characteristics that are essential to describe parameters needed for global atmosphere-biosphere models. Efforts to derive some of these characteristics from satellite remote sensing focussed on the use of AVHRR NDVI datasets, and global land cover characteristics data bases were produced. The usefulness of this dataset is hampered by the fact, that low spatial resolution of the AHVRR data results in the necessary definition of mixed herbaceous/shrub/tree classes, where the % mixture of these basic physiogomic classes are unknown. Radar is known to be very sensitive to vegetation physiognomy and biomass. In a study at the University of Michigan the potential of the existing orbital SAR imaging systems JERS-1 and ERS-1/2 for vegetation mapping has been investigated. Both sensors have mapped the global land masses within a period of four years. Using the complimentary characteristics of frequency (L-, C-Band) and polarization (hh, vv), a classification scheme was developed to produce vegetation maps at a scale of ca. 1:200,000 with classes based on physiognomic characteristics of vegetation. The approach uses unsupervised clustering techniques and class assignment based on radar signatures, hence consistent, automatic classification is possible. The combination of the high spatial resolution of JERS/ERS SAR composites and the high temporal resolution of the AVHRR based datasets could be the winning combination to describe vegetation distribution and vegetation dynamics.