TanDEM-X is a German spaceborne Synthetic Aperture Radar (SAR) mission, whose main goal is the generation of a global high-precision Digital Elevation Model (DEM) using twin satellites flying in close orbit formation, acting as a single-pass interferometer.
In interferometric SAR acquisitions, forested areas are typically subjected to volume decorrelation, whose intensity depends on several factors such as the acquisition geometry, the sensor parameters, and the canopy density. The data provided by the TanDEM-X mission allows to retrieve the bistatic interferometric coherence, from which the volume decorrelation contribution can be isolated in order to achieve a classification of forested/non-forested areas on a global scale. Such a global product is of high interest for the scientific community.
The objective of the proposed thesis is to develop an algorithm for efficient combination of multiple coherence quicklook images over the same ground areas and for classification of these areas as either forest or non-forest via a theoretical model of the volume decorrelation. In addition, a map of forest density could be delivered as an interesting by-product.
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