Multiscale InSAR Time Series (MInTS) analysis of surface deformation

Abstract

.[1] We present a new approach to extracting spatially and temporally continuous ground deformation fields from interferometric synthetic aperture radar (InSAR) data. We focus on unwrapped interferograms from a single viewing geometry, estimating ground deformation along the line-of-sight. Our approach is based on a wavelet decomposition in space and a general parametrization in time. We refer to this approach as MInTS (Multiscale InSAR Time Series). The wavelet decomposition efficiently deals with commonly seen spatial covariances in repeat-pass InSAR measurements, since the coefficients of the wavelets are essentially spatially uncorrelated. Our time-dependent parametrization is capable of capturing both recognized and unrecognized processes, and is not arbitrarily tied to the times of the SAR acquisitions. We estimate deformation in the wavelet-domain, using a cross-validated, regularized least squares inversion. We include a model-resolution-based regularization, in order to more heavily damp the model during periods of sparse SAR acquisitions, compared to during times of dense acquisitions. To illustrate the application of MInTS, we consider a catalog of 92 ERS and Envisat interferograms, spanning 16 years, in the Long Valley caldera, CA, region. MInTS analysis captures the ground deformation with high spatial density over the Long Valley region.

Modeling Wildfire-Induced Permafrost Deformation in an Alaskan Boreal Forest Using InSAR Observations

“…Generally, small baseline differential interferograms are generated first and then time series analysis is conducted (e.g., [40]). In this research, the TimeFun method, adapted from the Multiscale InSAR Time Series (MInTS) algorithm [36] and implemented in the Generic InSAR Analysis Toolbox (GIAnT) [41], was used to generate deformation time series. TimeFun adapts the same inversion strategy as that used in MInTS, but it is implemented in the data domain [36,42].…”

“…In this research, the TimeFun method, adapted from the Multiscale InSAR Time Series (MInTS) algorithm [36] and implemented in the Generic InSAR Analysis Toolbox (GIAnT) [41], was used to generate deformation time series. TimeFun adapts the same inversion strategy as that used in MInTS, but it is implemented in the data domain [36,42]. Since it uses a singular value decomposition (SVD) approach with a minimum-norm criterion, TimeFun is capable of inverting networks that are not fully connected [42].…”

“…Then, using the small baseline subset (SBAS) method (particularly, the TimeFun algorithm [36]), we inverted the measured InSAR phases to deformation time series. Finally, we applied our model to estimate fire-induced changes in ALT using the deformation time series.…”

Comparison of Small Baseline Interferometric SAR Processors for Estimating Ground Deformation

“…These methods include, the classic SBAS algorithm [6], the New Small Baseline Subset (NSBAS) approach [7,22], and the Multiscale InSAR Time-Series (MInTS) method [8]. Each algorithm has its own strengths or pre-requirements in order to successfully extract ground deformation signals.…”

“…techniques that focus on Persistent Scatterers (PS), namely, Persistent Scatterer interferometry (PSI) approaches (e.g., [1][2][3][4][5]); and methods relying on Distributed Scatterers (DS) that commonly exploit small baseline interferograms (SBI) (e.g., [6][7][8][9][10][11]). The characteristics of PS and DS targets are vastly different (e.g., scatterer size, relative geometry between scatterer and satellite, material composition of scatterers [12][13][14]).…”