Synergistic Usage of ERS, ASAR and PALSAR Data for PS InSAR Based Mining Induced Subsidence Monitoring

Thursday, 18 December 2014
Christian Joachim Thiel, Nesrin Salepci Jr., Christiane Schmullius and Arvid Kuehl Jr., Friedrich Schiller University of Jena, Jena, Germany
For the derivation of the temporal evolution and the spatial pattern of the mining induced subsidence in Sondershausen, Germany, this study employs persistent scatterer interferometry (PSI) to multiple sets of SAR scenes from different sensors. The work is a part of the ongoing INFLUINS project funded by the German Federal Ministry of Education and Research. In order to improve the model of subsidence derived from a single sensor, a novel methodology for integration of multiple PSI data sets is introduced. The proposed approach allows the combination of subsidence information derived from different SAR data sets with different temporal coverage and/or spatial resolution.

After long and intensive mining activities, the subsidence rate in Sondershausen has gradually increased almost to a rate of 250 mm/year reaching its maximum in the early 1990s. However, since 1996 the mine is backfilled leading to a gradual decrease in the subsidence rate in the undermined part of the city. The SAR scenes cover the period of backfilling between 1995 and 2010. The displacement rates for the first ten years (1995 -2005) are derived by ERS-1/2 scenes and by an ENVISAT-ASAR stack for the following years (2004-2010). In addition, for the span of 3 years from 2007 to 2010 an ALOS-PALSAR stack is processed in order to exploit the different information provided by different sensors.

The vertical linear deformation rates derived by PSI clearly indicate the regions of subsidence in each set. Furthermore, they confirm the influence of backfilling, i.e. gradual decreasing of subsidence rates. These results are compared and validated with leveling measurements collected by the mine management company during 1995-2010.

After the validation, the results are integrated in order to improve the quality of the subsidence model for the entire time span. With the objective of spatially refining the model derived from a single sensor, the approach exploits the PS points available in the other stacks by transferring them with a scaling factor.