Mapping Slumgullion Landslide in Colorado, USA Using Airborne Repeat-Pass InSAR
Abstract:Interferometric Synthetic Aperture Radar (InSAR) uses two or more SAR images over the same area to determine landscape topography or ground deformation. An interferogram, generated by the phase components of two coherent SAR images, depicts range changes between the radar and the ground resolution elements, and can be used to derive both landscape topography and subtle changes in surface elevation. However, spaceborne repeat-pass interferometry has two main drawbacks: effects due to differences in atmospheric temperature, pressure, and water vapour at two observation times, and loss of coherence due to long spatial and temporal baselines between observations. Airborne repeat-pass interferometry does not suffer from these drawbacks. The atmospheric effect in case of airborne DInSAR becomes negligible due to smaller swath coverage, and the coherence can be maintained by using smaller spatial and temporal baselines. However, the main technical limitation concerning airborne DInSAR is the need of precise motion compensation with an accurate navigation system to correct for the significant phase errors due to typical flight instability from air turbulence.
Here, we present results from a pilot study conducted on July 2015 using both X-band and L-band SlimSAR airborne system over the Slumgullion landslide in Colorado in order to (1) acquire the differential interferograms from the airborne platform, (2) understand their source of errors, and (3) pave a way to improve the precision of the derived surface deformation. The landslide movement estimated from airborne DInSAR is also compared with coincident GPS, terrestrial laser scanning (TLS), airborne LiDAR, and spaceborne DInSAR measurements using COSMO-SkyMed images. The airborne DInSAR system has a potential to provide time-transient variability in land surface topography with high-precision and high-resolution, and provide researchers with greater flexibility in selecting the temporal and spatial baselines of the data sets. Furthermore, it is expected that the airborne DInSAR system will greatly expand the number of academic researchers that will have access to the observational data they need to better characterize, quantify, and understand the behaviour of many natural hazards.