InSAR processing for volcano monitoring and other near‐real time applications

Abstract

Radar interferometry (InSAR, interferometric synthetic aperture radar) is routinely used to measure surface deformation prior to, during, and after volcanic events, although not in a monitoring capacity. The improved data availability of some current satellite missions presents us with the opportunity to do just that. We present here a fast and flexible algorithm to estimate coherence and select points on an interferogram‐by‐interferogram basis, which overcomes limitations of the conventional boxcar ensemble method in areas of marginal coherence. Time series methods, which offer an alternative way to select coherent points, are typically slow, and do not allow for insertion of new data without reprocessing the entire data set. Our new algorithm calculates the coherence for each point based on an ensemble of points with similar amplitude behavior throughout the data set. The points that behave similarly are selected prior to new images being acquired, on the assumption that the behavior of these nearby points does not change rapidly through time. The resulting coherence estimate is superior in resolution and noise level to the boxcar method. In contrast to most other time series methods, we select a different set of coherent points for each interferogram, avoiding the selection compromise inherent to other time series methods. The relative simplicity of this strategy compared to other time series techniques means we can process new images in about 1 h for a typical setup.

A New Method for Large-Scale Landslide Classification from Satellite Radar

“…Unlike the ARIA method, the new method is not based on the change in coherence through time and so is expected to have fewer false positives caused by variations in temporal coherence unrelated to landsliding. As an alternative to the boxcar coherence described in Section 2.1 and used in the absolute and ARIA methods, SAR coherence can be calculated based on ensembles of 'sibling' pixels, which exhibit As an alternative to the boxcar coherence described in Section 2.1 and used in the absolute and ARIA methods, SAR coherence can be calculated based on ensembles of 'sibling' pixels, which exhibit similar behaviour to the target pixel, e.g., [42][43][44]. Here, we use the RapidSAR algorithm of Spaans and Hooper [44] for this process.…”

“…As an alternative to the boxcar coherence described in Section 2.1 and used in the absolute and ARIA methods, SAR coherence can be calculated based on ensembles of 'sibling' pixels, which exhibit As an alternative to the boxcar coherence described in Section 2.1 and used in the absolute and ARIA methods, SAR coherence can be calculated based on ensembles of 'sibling' pixels, which exhibit similar behaviour to the target pixel, e.g., [42][43][44]. Here, we use the RapidSAR algorithm of Spaans and Hooper [44] for this process. For every pixel, a search is performed within a window of a given size, centred on that pixel, for pixels behaving similarly in terms of amplitude and amplitude variability throughout a time series of pre-event imagery.…”

Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China

“…Time series InSAR analysis only keeps those pixels that show a high level of coherence in all the interferograms. Coherent pixels in highly coherent interferograms might not be selected because of the low coherence of the same points in other interferograms [41]. As a result, detailed information in high coherence interferograms might be neglected.…”

Evaluation of the Stability of the Darbandikhan Dam after the 12 November 2017 Mw 7.3 Sarpol-e Zahab (Iran–Iraq Border) Earthquake

“…The imaging specifications in terms of spatial and temporal resolution have improved significantly, and this has contributed to remarkable expansion in the applications of InSAR. Spaceborne InSAR has been successfully applied for large-scale applications such as topographic mapping [21][22][23][24][25][26][27][28], earthquake deformation [19,[29][30][31][32], volcano monitoring [33][34][35][36][37][38], landslides [39][40][41][42][43][44], and even for generic nationwide InSAR deformation mapping [45]. Recently, SENTINEL-1 SAR data have been used for the dynamic monitoring of ground displacement [46,47] and to forecast the landslide time of failure [48].…”