New Radar Interferometric Time Series Analysis Toolbox Released

Agram, P. S.; Jolivet, Romain; Riel, Bryan; Lin, Yunung Nina; Simons, M.; Hetland, E. A.; Doin, Marie‐Pierre; Lasserre, Cécile

doi:10.1002/2013eo070001

Cited by 122 publications

(97 citation statements)

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“…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].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2018

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Abstract:The discontinuous permafrost zone is one of the world's most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using interferometric synthetic aperture radar (InSAR) of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, we mapped extensive permafrost degradation over interior Alaskan boreal forest in Yukon Flats, induced by the 2009 Big Creek wildfire. Our analyses showed that fire-induced permafrost degradation in the second post-fire thawing season contributed up to 20 cm of ground surface subsidence. We generated post-fire deformation time series and introduced a model that exploited the deformation time series to estimate fire-induced permafrost degradation and changes in active layer thickness. The model showed a wildfire-induced increase of up to 80 cm in active layer thickness in the second post-fire year due to pore-ice permafrost thawing. The model also showed up to 15 cm of permafrost degradation due to excess-ice thawing with little or no increase in active layer thickness. The uncertainties of the estimated change in active layer thickness and the thickness of thawed excess ice permafrost are 27.77 and 1.50 cm, respectively. Our results demonstrate that InSAR-derived deformation measurements along with physics models are capable of quantifying fire-induced permafrost degradation in Alaskan boreal forests underlain by discontinuous permafrost. Our results also have illustrated that fire-induced increase of active layer thickness and excess ice thawing contributed to ground surface subsidence.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2018

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“…Our implementation of the dictionary method solves for the parameters of a timedependent function, similarly to the approach proposed in the wavelet domain by Hetland et al [2012] or in the space domain in the Generic Interferometric Toolbox [Agram et al, 2013]. This approach is frequently used in the post-processing of GNSS time series.…”

Section: Three Time Series Analysis Formulationmentioning

confidence: 99%

“…Berardino et al, 2002;Usai , 2003;Hetland et al, 2012;Agram et al, 2013]. For instance, in the field of active tectonics, these methods allow detection of transient slip along active faults or to image slow, longwavelength, strain rates due to interseismic loading across active faults [e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Some concentrate on the actual phase values [Schmidt and Burgmann, 2003] while other methods focus on a geophysically motivated dictionary of time-dependent functions to describe the evolution of the phase [e.g. Hetland et al, 2012;Agram et al, 2013]. The NSBAS approach combines both SBAS and a dictionary approach to overcome limitations posed by spatial and temporal decorrelation [López-Quiroz et al, 2009].…”

Section: Introductionmentioning

confidence: 99%

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A Multipixel Time Series Analysis Method Accounting for Ground Motion, Atmospheric Noise, and Orbital Errors

Jolivet

Simons

2018

Geophysical Research Letters

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Interferometric synthetic aperture radar time series methods aim to reconstruct time‐dependent ground displacements over large areas from sets of interferograms in order to detect transient, periodic, or small‐amplitude deformation. Because of computational limitations, most existing methods consider each pixel independently, ignoring important spatial covariances between observations. We describe a framework to reconstruct time series of ground deformation while considering all pixels simultaneously, allowing us to account for spatial covariances, imprecise orbits, and residual atmospheric perturbations. We describe spatial covariances by an exponential decay function dependent of pixel‐to‐pixel distance. We approximate the impact of imprecise orbit information and residual long‐wavelength atmosphere as a low‐order polynomial function. Tests on synthetic data illustrate the importance of incorporating full covariances between pixels in order to avoid biased parameter reconstruction. An example of application to the northern Chilean subduction zone highlights the potential of this method.

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“…In recent years, many SBI algorithms have become implemented and integrated in open source tool boxes, e.g., the StaMPS/MTI software package [18], and the Generic InSAR Analysis Toolbox (GIAnT) [29], which can be easily accessed by the radar interferometry community. Three SBI strategies, including the conventional SBAS [6], NSBAS [7,22,25], and a temporal analysis method (Timefun) adapted from the MInTS algorithm [8], have been implemented in the GIAnT toolbox.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Small Baseline Interferometric SAR Processors for Estimating Ground Deformation

et al. 2016

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Abstract:The small Baseline Synthetic Aperture Radar (SAR) Interferometry (SBI) technique has been widely and successfully applied in various ground deformation monitoring applications. Over the last decade, a variety of SBI algorithms have been developed based on the same fundamental concepts. Recently developed SBI toolboxes provide an open environment for researchers to apply different SBI methods for various purposes. However, there has been no thorough discussion that compares the particular characteristics of different SBI methods and their corresponding performance in ground deformation reconstruction. Thus, two SBI toolboxes that implement a total of four SBI algorithms were selected for comparison. This study discusses and summarizes the main differences, pros and cons of these four SBI implementations, which could help users to choose a suitable SBI method for their specific application. The study focuses on exploring the suitability of each SBI module under various data set conditions, including small/large number of interferograms, the presence or absence of larger time gaps, urban/vegetation ground coverage, and temporally regular/irregular ground displacement with multiple spatial scales. Within this paper we discuss the corresponding theoretical background of each SBI method. We present a performance analysis of these SBI modules based on two real data sets characterized by different environmental and surface deformation conditions. The study shows that all four SBI processors are capable of generating similar ground deformation results when the data set has sufficient temporal sampling and a stable ground backscatter mechanism like urban area. Strengths and limitations of different SBI processors were analyzed based on data set configuration and environmental conditions and are summarized in this paper to guide future users of SBI techniques.

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New Radar Interferometric Time Series Analysis Toolbox Released

Cited by 122 publications

References 10 publications

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

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

A Multipixel Time Series Analysis Method Accounting for Ground Motion, Atmospheric Noise, and Orbital Errors

Comparison of Small Baseline Interferometric SAR Processors for Estimating Ground Deformation

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