Seasonal Deformation of Permafrost in Wudaoliang Basin in Qinghai-Tibet Plateau Revealed by StaMPS-InSAR

Lü, Ping; Han, Jiangping; Tu, Hao; Li, Rongxing; Qiao, Gang

doi:10.1080/01490419.2019.1698480

Cited by 31 publications

(32 citation statements)

References 39 publications

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“…The time lags from TerraSAR-X and Sentinel-1 data between the date of the maximum uplift and the date of the minimum temperature (01/20/2019) were 86 days and 96 days, respectively, and the time lags from between the date of the maximum subsidence and the date of the maximum temperature (07/28/2019) were 86 days and 96 days, respectively. The time lags between the temperature and the deformation of frozen soil in the present study are consistent with the conclusions of previous studies [13,20]. Although the surface temperature of permafrost may increase to its highest or fall to its lowest, the bottom of the active layer continues freezing or thawing until it reaches its maximum freezing or thawing depth after this time delay.…”

Section: Freeze-thaw Cycles Of Permafrost In the Beiluhe Basinsupporting

confidence: 92%

“…In recent years, Synthetic Aperture Radar interferometry (InSAR) has begun to provide an opportunity to retrieve the surface deformation of the frozen soil on the QTP with the development of multiple, high resolution sensors with wide coverage and frequent acquisitions [12][13][14]. In the past two decades, by overcoming those limitations-such as temporal and geometrical decorrelations [15] and atmospheric delay anomalies [16]-time-series InSAR technology (TSInSAR) has been widely applied to monitor the freezing and thawing processes of the active layer in permafrost areas [14,[17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Small-Baseline Approach for Monitoring the Freezing and Thawing Deformation of Permafrost on the Beiluhe Basin, Tibetan Plateau Using TerraSAR-X and Sentinel-1 Data

Wang

Zhang

et al. 2020

Sensors

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The dynamic changes of the thawing and freezing processes of the active layer cause seasonal subsidence and uplift over a large area on the Qinghai–Tibet Plateau due to ongoing climate warming. To analyze and investigate the seasonal freeze–thaw process of the active layer, we employ the new small baseline subset (NSBAS) technique based on a piecewise displacement model, including seasonal deformation, as well as linear and residual deformation trends, to retrieve the surface deformation of the Beiluhe basin. We collect 35 Sentinel-1 images with a 12 days revisit time and 9 TerraSAR-X images with less-than two month revisit time from 2018 to 2019 to analyze the type of the amplitude of seasonal oscillation of different ground targets on the Beiluhe basin in detail. The Sentinel-1 results show that the amplitude of seasonal deformation is between −62.50 mm and 11.50 mm, and the linear deformation rate ranges from −24.50 mm/yr to 5.00 mm/yr (2018–2019) in the study area. The deformation trends in the Qinghai–Tibet Railway (QTR) and Qinghai–Tibet Highway (QTH) regions are stable, ranging from −18.00 mm to 6 mm. The InSAR results of Sentinel-1 and TerraSAR-X data show that seasonal deformation trends are consistent, exhibiting good correlations 0.78 and 0.84, and the seasonal and linear deformation rates of different ground targets are clearly different on the Beiluhe basin. Additionally, there are different time lags between the maximum freezing uplift or thawing subsidence and the maximum or minimum temperature for the different ground target areas. The deformation values of the alpine meadow and floodplain areas are higher compared with the alpine desert and barren areas, and the time lags of the freezing and thawing periods based on the Sentinel-1 results are longest in the alpine desert area, that is, 86 days and 65 days, respectively. Our research has important reference significance for the seasonal dynamic monitoring of different types of seasonal deformation and the extensive investigations of permafrost in Qinghai Tibet Plateau.

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Section: Freeze-thaw Cycles Of Permafrost In the Beiluhe Basinsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Small-Baseline Approach for Monitoring the Freezing and Thawing Deformation of Permafrost on the Beiluhe Basin, Tibetan Plateau Using TerraSAR-X and Sentinel-1 Data

Wang

Zhang

et al. 2020

Sensors

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“…where λ is the wavelength, B ⊥ denotes the perpendicular baseline, θ and R denote the incidence angle and slant range distance, respectively, and t represents the time interval between two SAR scenes. In recent studies, The seasonal subsidence with a short time interval was successfully monitored based on the PSI (StaMPS-InSAR) [60] or DInSAR [61] method using Sentinel-1 data. The linear model with a short time interval used in seasonal subsidence monitoring is acceptable.…”

Section: Nsbas Methodsmentioning

confidence: 99%

Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data

Zhang

Wang

et al. 2020

IEEE Access

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Increasing near-surface temperature over the Qinghai-Tibet Plateau (QTP) has led to permafrost degradation and increasing active layer thickness (ALT). In this study, the ALT was estimated based on ground subsidence monitored by multitemporal interferometric synthetic aperture radar (MT-InSAR) and temporal-spatial multilayer soil moisture data. For the ground subsidence monitoring, a modified Stefan piecewise elevation change model based on air temperature data was integrated into a new small baseline subset (NSBAS) chain. A total of 33 scenes of Sentinel-1 data (S-1) were collected over one year to build the MT-InSAR analysis network. Moreover, both soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data and ERA-Interim reanalysis data were used to build an ALT retrieval model. In particular, the global-scaled soil moisture data (SMAP and ERA-Interim) fraction was separated based on the Sentinel-1 amplitude-based land cover classification results and in situ soil moisture data. A typical ALT estimation method based on the point scale groundwater information was also performed to evaluate the performance of the proposed method. Based on the validation of the ground-based ALT observations, the proposed method outperformed the traditional point scale groundwater information-based method, with a correlation coefficient of 0.67, RMSE of 0.70 and ubRMSE of 0.51, respectively. The ERA-Interim-based estimation results were underestimated due to the overestimation of the ERA-Interim soil moisture data. Obvious differences were observed between the ALT of the alpine meadow areas and alpine desert areas. Our results demonstrate that the combination of temporal-spatial multilayer soil moisture data and the MT-InSAR method with S-1 images is a promising approach for the large-scale characterization of ALT.

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“…The Xiakou landslide, the subject of this study, is located in the Longxi River basin that administratively belongs to Ya'an City, approximately 130 km from Chengdu, the capital city of Sichuan Province (Figure 1). This region, like other sites in the Qinghai‐Tibetan Plateau, is currently facing dynamic climate change, which may further amplify the influences of natural hazards, such as accelerated ground deformations resulting from increased air and ground temperatures (Lu et al, 2020; Lu, Han, Hao, Li, & Qiao, 2020). The Longxi River basin is a long and narrow valley with an area of approximately 180 km 2 .…”

Section: Study Sitementioning

confidence: 99%

Modern reactivations of a thick colluvial landslide reconstructed from dendrochronology: the example of the Xiakou landslide, China

Bai

Lü

2021

Land Degrad Dev

Self Cite

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In this study, we investigated two types of growth disturbance (GD) in tree‐ring sequences, namely, the abrupt growth reduction in annual tree‐ring width, and the formation of reaction wood, in tree stems growing on the thick colluvial Xiakou landslide in China. In total, 96 GDs were identified in 183 core samples that were acquired from the 81 trees on the Xiakou landslide, revealing nine reactivation phases of the landslide body over the past 30 years. These reactivations were found to have occurred in the years of 1987, 1988, 1989, 1992, 1993, 1995, 1997, 2006, and 2007. Further hazard analysis indicated that the probability of landslide reactivation in 20 years was highest in the upper body of the landslide and on the boundaries where the new and old landslides meet. In contrast, the most stable region was the lower landslide body. Under the circumstances of an extreme lack of historical data, this work demonstrated the usefulness of dendrochronology in reconstructing modern reactivations over the past 30 years for a giant, deep‐seated, colluvial landslide with a volume exceeding approximately 10 million m3. This study has broader impacts because landslides in colluvium are a globally common types of land degradation.

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Seasonal Deformation of Permafrost in Wudaoliang Basin in Qinghai-Tibet Plateau Revealed by StaMPS-InSAR

Cited by 31 publications

References 39 publications

Small-Baseline Approach for Monitoring the Freezing and Thawing Deformation of Permafrost on the Beiluhe Basin, Tibetan Plateau Using TerraSAR-X and Sentinel-1 Data

Small-Baseline Approach for Monitoring the Freezing and Thawing Deformation of Permafrost on the Beiluhe Basin, Tibetan Plateau Using TerraSAR-X and Sentinel-1 Data

Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data

Modern reactivations of a thick colluvial landslide reconstructed from dendrochronology: the example of the Xiakou landslide, China

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