Seasonal InSAR Displacements Documenting the Active Layer Freeze and Thaw Progression in Central-Western Spitsbergen, Svalbard

Rouyet, Line; Liu, Lingli; Strand, Sarah Marie; Christiansen, Hanne H.; Lauknes, Tom Rune; Larsen, Yngvar

doi:10.3390/rs13152977

Cited by 11 publications

(14 citation statements)

References 74 publications

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“…The subsidence values are similar to those on the ice-rich Eboling Mountain in the northeastern region of the Tibetan Plateau [28], and those in the Hohxil region from Wudaoliang to Tuotuohe [29]. In comparison to other permafrost areas in Arctic and subarctic regions [18][19][20][22][23][24]27,30,46,47,54,55,[57][58][59][60][61][62][63][64][65][66][67][68][69], generally, the seasonal deformation value on the Tibetan Plateau is smaller, but the intra-annual subsidence rate is not a small value. (3) We compared two important deformation indices over permafrost terrain, i.e., longterm deformation trend and seasonal deformation magnitude, derived by direct calculation of deformation time series and model approximations using the sinusoidal and degree-day models.…”

Section: Discussionmentioning

confidence: 58%

Permafrost Ground Ice Melting and Deformation Time Series Revealed by Sentinel-1 InSAR in the Tanggula Mountain Region on the Tibetan Plateau

et al. 2022

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In this study, we applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) to monitor the ground surface deformation from 2017 to 2020 in the permafrost region within an ~400 km × 230 km area covering the northern and southern slopes of Mt. Geladandong, Tanggula Mountains on the Tibetan Plateau. During SBAS-InSAR processing, we inverted the network of interferograms into a deformation time series using a weighted least square estimator without a preset deformation model. The deformation curves of various permafrost states in the Tanggula Mountain region were revealed in detail for the first time. The study region undergoes significant subsidence. Over the subsiding terrain, the average subsidence rate was 9.1 mm/a; 68.1% of its area had a subsidence rate between 5 and 20 mm/a, while just 0.7% of its area had a subsidence rate larger than 30 mm/a. The average peak-to-peak seasonal deformation was 19.7 mm. There is a weak positive relationship (~0.3) between seasonal amplitude (water storage in the active layer) and long-term deformation velocity (ground ice melting). By examining the deformation time series of subsiding terrain with different subsidence levels, we also found that thaw subsidence was not restricted to the summer and autumn thawing times but could last until the following winter, and in this circumstance, the winter uplift was greatly weakened. Two import indices for indicating permafrost deformation properties, i.e., long-term deformation trend and seasonal deformation magnitude, were extracted by direct calculation and model approximations of deformation time series and compared with each other. The comparisons showed that the long-term velocity by different calculations was highly consistent, but the intra-annual deformation magnitudes by the model approximations were larger than those of the intra-annual highest-lowest elevation difference. The findings improve the understanding of deformation properties in the degrading permafrost environment.

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Section: Discussionmentioning

confidence: 58%

Permafrost Ground Ice Melting and Deformation Time Series Revealed by Sentinel-1 InSAR in the Tanggula Mountain Region on the Tibetan Plateau

et al. 2022

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“…Previous studies have shown that seasonal deformation has apparent spatial heterogeneity [8,71,78]. This study used geographical detection to analyze the influencing factor of SDA and the correlation analysis method to analyze the relationship between SDA and time characteristics.…”

Section: Influencing Factor Analysismentioning

confidence: 99%

“…The Stamps-InSAR and SBAS-InSAR algorithms are based on permanent scatterers [116] and distributed scatterers [117]. They have been widely used to obtain ground deformation in permafrost regions [60,78,118,119], and their reliability and accuracy have been proved. Because of the requirements for the scattering characteristics in the permafrost region, the SDA obtained by the two algorithms may be different.…”

Section: Influencing Factors Of Seasonal Deformation Amplitudementioning

confidence: 99%

“…Benefits from the revisiting period, Sentinel-1 SAR data have been proven to detect the F-T cycle of AL [8,[74][75][76][77]. The latest research in the Arctic shows that the SBAS-InSAR technology can obtain the DOY subsidence maxima of AL based on seasonal displacement progression [78]. However, the time-related characteristics of F-T in AL, such as the onset of freezing/thawing and thawing duration, are the main factors determining vegetation growth and energy exchange between land and air [76,79] and have not been explored yet.…”

Section: Introductionmentioning

confidence: 99%

“…However, the time-related characteristics of F-T in AL, such as the onset of freezing/thawing and thawing duration, are the main factors determining vegetation growth and energy exchange between land and air [76,79] and have not been explored yet. Many factors influence the amplitude and temporal variability of seasonal deformation amplitude (SDA), such as spatial location [71,78,80,81], topography [82][83][84][85], vegetation [70,75], ground thermal regime variability [76,79], soil water content [8,74,77,80,86], soil particle size [87,88], active layer thickness (ALT) [8,64,76,89,90], etc. These factors also lack detailed analysis as well.…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of Freeze–Thaw Cycles in an Endorheic Basin on the Qinghai-Tibet Plateau Based on SBAS-InSAR Technology

et al. 2022

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The freeze–thaw (F-T) cycle of the active layer (AL) causes the “frost heave and thaw settlement” deformation of the terrain surface. Accurately identifying its amplitude and time characteristics is important for climate, hydrology, and ecology research in permafrost regions. We used Sentinel-1 SAR data and small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain the characteristics of F-T cycles in the Zonag Lake-Yanhu Lake permafrost-affected endorheic basin on the Qinghai-Tibet Plateau from 2017 to 2019. The results show that the seasonal deformation amplitude (SDA) in the study area mainly ranges from 0 to 60 mm, with an average value of 19 mm. The date of maximum frost heave (MFH) occurred between November 27th and March 21st of the following year, averaged in date of the year (DOY) 37. The maximum thaw settlement (MTS) occurred between July 25th and September 21st, averaged in DOY 225. The thawing duration is the thawing process lasting about 193 days. The spatial distribution differences in SDA, the date of MFH, and the date of MTS are relatively significant, but there is no apparent spatial difference in thawing duration. Although the SDA in the study area is mainly affected by the thermal state of permafrost, it still has the most apparent relationship with vegetation cover, the soil water content in AL, and active layer thickness. SDA has an apparent negative and positive correlation with the date of MFH and the date of MTS. In addition, due to the influence of soil texture and seasonal rivers, the seasonal deformation characteristics of the alluvial-diluvial area are different from those of the surrounding areas. This study provides a method for analyzing the F-T cycle of the AL using multi-temporal InSAR technology.

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Disparate permafrost terrain changes after a large flood observed from space

Zwieback,

McClernan,

Kanevskiy

et al. 2023

Permafrost & Periglacial

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The 2015 spring flood of the Sagavanirktok River inundated large swaths of tundra as well as infrastructure near Prudhoe Bay, Alaska. Its lasting impact on permafrost, vegetation, and hydrology is unknown but compels attention in light of changing Arctic flood regimes. We combined InSAR and optical satellite observations to quantify subdecadal permafrost terrain changes and identify their controls. While the flood locally induced quasi‐instantaneous ice‐wedge melt, much larger areas were characterized by subtle, spatially variable post‐flood changes. Surface deformation from 2015 to 2019 estimated from ALOS‐2 and Sentinel‐1 InSAR varied substantially within and across terrain units, with greater subsidence on average in flooded locations. Subsidence exceeding 5 cm was locally observed in inundated ice‐rich units and also in inactive floodplains. Overall, subsidence increased with deposit age and thus ground ice content, but many flooded ice‐rich units remained stable, indicating variable drivers of deformation. On average, subsiding ice‐rich locations showed increases in observed greenness and wetness. Conversely, many ice‐poor floodplains greened without deforming. Ice wedge degradation in flooded locations with elevated subsidence was mostly of limited intensity, and the observed subsidence largely stopped within 2 years. Based on remote sensing and limited field observations, we propose that the disparate subdecadal changes were influenced by spatially variable drivers (e.g., sediment deposition, organic layer), controls (ground ice and its degree of protection), and feedback processes. Remote sensing helps quantify the heterogeneous interactions between permafrost, vegetation, and hydrology across permafrost‐affected fluvial landscapes. Interdisciplinary monitoring is needed to improve predictions of landscape dynamics and to constrain sediment, nutrient, and carbon budgets.

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Seasonal InSAR Displacements Documenting the Active Layer Freeze and Thaw Progression in Central-Western Spitsbergen, Svalbard

Cited by 11 publications

References 74 publications

Permafrost Ground Ice Melting and Deformation Time Series Revealed by Sentinel-1 InSAR in the Tanggula Mountain Region on the Tibetan Plateau

Permafrost Ground Ice Melting and Deformation Time Series Revealed by Sentinel-1 InSAR in the Tanggula Mountain Region on the Tibetan Plateau

Characteristics of Freeze–Thaw Cycles in an Endorheic Basin on the Qinghai-Tibet Plateau Based on SBAS-InSAR Technology

Disparate permafrost terrain changes after a large flood observed from space

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