Global Forest Classification from Tandem-x Interferometric Data: Potentials and First Results

This paper presents a method to generate forest/nonforest maps from TanDEM-X interferometric SAR data. Among the several contributions which may affect the quality of interferometric products, the coherence loss caused by volume scattering represents the contribution which is predominantly affected by the presence of vegetation, and is therefore here exploited as main indicator for forest classification. Due to the strong dependency of the considered InSAR quantity on the geometric acquisition configuration, namely the incidence angle and the interferometric baseline, a multi-fuzzy clustering classification approach is used. Some examples are provided which show the potential of the proposed method. Further, additional features such as urban settlements, water, and critical areas affected by geometrical distortions (e.g. shadow and layover) need to be extracted, and possible approaches are presented as well. Very promising results are shown, which demonstrate the potentials of TanDEM-X bistatic data not only for forest identification, but, more in general, for the generation of a global land classification map as a next step.