Small-Baseline Interferometric Synthetic Aperture Radar (SBAS-InSAR) has been widely used in the field of permafrost deformation monitoring. However, previous research has mainly relied on short-term interferograms, which may cause phase bias or signal fading. In this study, we propose an improved SBAS-InSAR algorithm based on a two-stage optimal interferometric pair selection strategy that integrates both short-term and long-term interferograms. The central Qing-Tibet Plateau, characterized by its high elevation and low vegetation cover, provides an ideal region for permafrost monitoring using InSAR technology. By comparing the results of the SBAS-InSAR technique based on three different interferogram networks, our method can detect 129 additional thawing permafrost areas with local subsidence in the QTP study areas. In contrast, these subsidence areas are missed by SBAS-InSAR based on two other interferogram networks.
As a global warning, the Qinghai-Tibet Plateau (QTP) permafrost is undergoing degradation, which can be a threat to the high-altitude infrastructure. Multitemporal interferometric synthetic aperture radar (MT-InSAR) is an effective tool to monitor freeze-thaw deformation cycles of permafrost. In this study, we employ small baseline synthetic aperture radar interferometry (SBAS) in the northern Qinghai-Tibet Railway (QTR), including Tuotuohe, Beiluhe, Wudaoliang and Xidatan regions with 73 scenes of L-band ALOS-2 stripmap SAR images between 2015 to 2020. The experimental results show that the maximum deformation of permafrost from Tuotuohe to Beiluhe area can reach -35 mm/year. Besides we combined optical data (GF-1, GF-2) found that the permafrost of some regions is undergoing degradation, which may cause thermal melting landslides. And our results can provide significant insight for monitoring permafrost deformation in QTP.
As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments.
Peatlands in Southeast Asia have been undergoing extensive and rapid degradation in recent years. Interferometric Synthetic Aperture Radar (InSAR) technology has shown excellent performance in monitoring surface deformation. However, due to the characteristics of high vegetation cover and large dynamic changes in peatlands, it is difficult for classical InSAR technology to achieve satisfactory results. Therefore, an adaptive high coherence temporal subsets (HCTSs) small baseline subset (SBAS)-InSAR method is proposed in this paper, which captures the high coherence time range of pixels to establish adaptive temporal subsets and calculates the deformation results in corresponding time intervals, combining with the time-weighted strategy. Ninety Sentinel-1 SAR images (2019–2022) in South Sumatra province were processed based on the proposed method. The results showed that the average deformation rate of peatlands ranged from approximately −567 to 347 mm/year and was affected by fires and the changes in land cover. Besides, the dynamic changes of peatlands’ deformation rate a long time after fires were revealed, and the causes of changes were analyzed. Furthermore, the deformation results of the proposed method observed 2 to 127 times as many measurement points as the SBAS-InSAR method. Pearson’s r (ranged from 0.44 to 0.75) and Root Mean Square Error (ranged from 50 to 75 mm/year) were calculated to verify the reliability of the proposed method. Adaptive HCTSs SBAS-InSAR can be considered an efficient method for peatland degradation monitoring, which provides the foundation for investigating the mechanisms of peatland degradation and monitoring it in broader regions.
With the global warming, thaw slump activity has increased in permafrost regions of Qinghai-Tibet Plateau (QTP), which influence the stability of human infrastructure and carbon cycling. However, the intrinsic dynamic process of surface displacement of the retrogression thaw slump (RTS) is still less understood. Here, we employed the spaceborne interferometric synthetic aperture radar (InSAR) based on L-band ALOS-2 PLASAR2 data acquired from December 2015 to May 2022 to monitor the surface subsidence trends of thaw slump-derived thermokarst in permafrost terrain of Qinghai-Tibet Plateau (QTP). The InSAR analysis reveals the thermokarst subsidence of -81~46 mm/year between 2015 and 2022. A large number of RTS were distributed in the slopes, with the large annual average sedimentation rates. Besides, the time-series seasonal deformation reveals that during the period from January 2019 to March 2019, with the mean temperature below 0 °C , RTS' deformation still represents large seasonal subsidence, which indicates the intrinsic pattern of surface displacement of the thaw slump is not simple cold-season freeze heaving.
In the context of global warming, the accelerated degradation of circum-Arctic permafrost is releasing a significant amount of carbon. InSAR can indirectly reflect the degradation of permafrost by monitoring its deformation. This study selected three typical permafrost regions in North America: Alaskan North Slope, Northern Great Bear Lake, and Southern Angikuni Lake. These regions encompass a range of permafrost landscapes, from tundra to needleleaf forests and lichen-moss, and we used Sentinel-1 SAR data from 2018 to 2021 to determine their deformation. In the InSAR process, due to the prolonged snow cover in the circum-Arctic permafrost, we used only SAR data collected during the summer and applied a two-stage interferogram selection strategy to mitigate the resulting temporal decorrelation. The Alaskan North Slope showed pronounced subsidence along the coastal alluvial plains and uplift in areas with drained thermokarst lake basins. Northern Great Bear Lake, which was impacted by wildfires, exhibited accelerated subsidence rates, revealing the profound and lasting impact of wildfires on permafrost degradation. Southern Angikuni Lake’s lichen and moss terrains displayed mild subsidence. Our InSAR results indicate that more than one-third of the permafrost in the North American study area is degrading and that permafrost in diverse landscapes has different deformation patterns. When monitoring the degradation of large-scale permafrost, it is crucial to consider the unique characteristics of each landscape.
With global warming, permafrost is undergoing degradation, which may cause thawing subsidence, collapse, and emission of greenhouse gases preserved in previously frozen permafrost, change the local hydrology and ecology system, and threaten infrastructure and indigenous communities. The Qinghai-Tibet Plateau (QTP) is the world’s largest permafrost region in the middle and low latitudes. Permafrost status monitoring in the QTP is of great significance to global change and local economic development. In this study, we used 66 scenes of ALOS data (2007–2009), 73 scenes of ALOS-2 data (2015–2020) and 284 scenes of Sentinel-1 data (2017–2021) to evaluate the spatial and temporal permafrost deformation over the 83,000 km2 in the northern QTP, passing through the Tuotuohe, Beiluhe, Wudaoliang and Xidatan regions. We use the SBAS-InSAR method and present a coherence weighted least squares estimator without any hypothetical model to calculate long-term deformation velocity (LTDV) and maximum seasonal deformation (MSD) without any prior knowledge. Analysis of the ALOS results shows that the LTDV ranged from −20 to +20 mm/year during 2007–2009. For the ALOS-2 and Sentinel-1 results, the LTDV ranged from −30 to 30 mm/year during 2015–2021. Further study shows that the expansion areas of permafrost subsidence are concentrated on braided stream plains and thermokarst lakes. In these areas, due to glacial erosion, surface runoff and river alluvium, the contents of water and ground ice are sufficient, which could accelerate permafrost subsidence. In addition, by analyzing LTDV and MSD for the different periods, we found that the L-band ALOS-2 is more sensitive to the thermal collapse of permafrost than the C-band sensor and the detected collapse areas (LTDV < −10 mm/year) are consistent with the GF-1/2 thermal collapse dataset. This research indicates that the InSAR technique could be crucial for monitoring the evolution of permafrost and freeze-thaw disasters.
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