The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system on board the Terra (EOS AM-1) satellite has been a source of stereoscopic images covering the whole globe at 15-m resolution with consistent quality for over 16 years. The potential of these data in terms of geomorphological analysis and change detection in three dimensions is unrivaled and should be exploited more. Due to uncorrected errors in the image geometry due to sensor motion (“jitter”), however, the quality of the DEMs and orthoimages currently available is often insufficient for a number of applications, including surface change detection. We have therefore developed a series of algorithms packaged under the name MicMac ASTER (MMASTER). It is composed of a tool to compute Rational Polynomial Coefficient (RPC) models from the ASTER metadata, a method that improves the quality of the matching by identifying and correcting jitter-induced cross-track parallax errors and a correction for along-track jitter when computing differences between DEMs (either with another MMASTER DEM or with another data source). Our method outputs more precise DEMs with less unmatched areas and reduced overall noise compared to NASA’s standard AST14DMO product. The algorithms were implemented in the open source photogrammetric library and software suite MicMac. Here, we briefly examine the potential of MMASTER-produced DEMs to investigate a variety of geomorphological changes, including river erosion, seismic deformation, changes in biomass, volcanic deformation and glacier mass balance.
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