GSA Annual Meeting in Phoenix, Arizona, USA - 2019

Paper No. 101-11
Presentation Time: 9:00 AM-6:30 PM


GOMEZ, Francisco, Department of Geological Sciences, University of Missouri, 101 Geology Building, Columbia, MO 65211

This study explores the utility of Interferometric Synthetic Aperture Radar (InSAR) time series for identification and kinematic characterization of slow mass movements along the Interstate 70 corridor in Western Colorado. InSAR provides a means of identifying creeping, unstable slopes by measuring small (cm-scale) displacements. InSAR as a remote-sensing technique has been in use since the early 1990s, and earlier studies have demonstrated the applicability of InSAR to measure known mass movements, as well as identifying previously undocumented slope movements. However, prior to the late-2014 launch of the Sentinel-1 satellite mission by the European space agency, use of satellite-based InSAR for regular landslide monitoring was limited owing to infrequent data acquisitions, insufficiently controlled satellite orbits, and limited data aerial coverage. Higher quality data, if available, were often cost prohibitive for monitoring applications on any broad spatial scales, as these were acquired by commercial satellites. Sentinel-1, which has an open data policy, provides regular imaging with large image swaths more than 250 km in width. For western Colorado, imagery has been (and continues to be) acquired at least every 24 days since early 2015, and all satellite orbits are compatible with InSAR analysis (i.e., short satellite baselines). The result is a large data archive that allows robust interferometric analyses. This study applies multi-interferogram techniques that help mitigate known challenges for InSAR, including the estimation of errors in digital elevation models used in the analyses. Ascending and Descending orbits are used to reduce limitations of a single radar line-of-sight. Approaches for atmospheric phase removal are compared, including modeling of meteorological data and spatial filtering limited to scales of likely mass movements. The resulting image time series are used to identify and map likely mass movements, as well as characterize their (displacements rates and temporal variability). The results also provide a basis for continued monitoring going forward, as new data from the Sentinal-1 mission become available.