Abstract
Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.
Highlights
We downloaded 257 Sentinel-1 single look complex (SLC) images from descending track 81 acquired between January 2015 and May 2020 over the Hatfield Moors test site in the UK (Longitude, latitude: −1.01, 53.58), and performed initial Interferometric Synthetic Aperture Radar (InSAR) processing by forming interferograms with every 2 consecutive pairs of images using the Interferometric synthetic aperture radar Scientific Computing Environment (ISCE) software [26,27]
Previous InSAR time series detectors relied on machine learning methods to train an algorithm on specific changes in a time series resulting from ground deformation, for example volcano inflation (e.g., [8,18,21,40])
In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series
Summary
Volcanoes may remain dormant for many years before a sudden or gradual onset of ground deformation preceding an eruption, or ground subsidence may continue slowly at the same rate for many years before suddenly accelerating. Each of these produce distinct deformation patterns that can be detected in InSAR time series
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