Monitoring permafrost subsidence on Alaska’s North slope: assessing the feasibility of InSAR with LiCSAR and LiCSBAS Algorithms

Abstract

The thawing and degradation of permafrost, due to climate change, results in ground subsidence. Interferometric synthetic aperture radar (InSAR) is a remote sensing technique that can be utilised to derive widespread and long-term deformation. However, no studies have derived long-term deformation over Alaska’s North Slope using open-source interferograms (LiCSAR) and software (LiCSBAS). This study assessed the feasibility of this on the main LiCSBAS algorithm alongside an adapted version, which was designed to mitigate temporal decorrelation from seasonal snowfall.

Both methods observed that approximately 30% of the region experienced permafrost degradation from 2018-2024. Yet the reliability of these observations are questioned due to localised processing and environmental challenges. This included data quality issues such as unwrapping errors from geometric distortions, interferogram misregistration, and rapid seasonal landscape changes (snowfall and soil moisture content) which resulted in low coherence interferograms. The main LiCSBAS method produced a more reliable result (lower residuals and higher coherence), although notable processing challenges were observed over the Brooks Mountain Range. In contrast, the second method’s more rigorous selection process (seasonal filtering and two-stage loop closure check) resulted in a sparser interferometric network and produced a model with higher residuals and lower confidence. In conclusion, localised environmental and processing challenges remain prevalent due to temporal decorrelation, which limits the reliability of long-term velocities derived from InSAR over Alaska’s North Slope. This study highlights the need for local considerations and improvements in global open-source datasets and software, which will enable the more reliable monitoring of permafrost in Alaska.