LT-1 SAR Satellite Constellation for Permafrost Deformation Monitoring Along the Tibetan Plateau Engineering Corridor

The Tibetan Plateau stands as one of China's largest middle and low latitude permafrost regions. However, the effects of global warming and human activities have led to permafrost thawing, inducing surface instability and posing significant threats to infrastructure and indigenous communities. The deployment of Lu Tan-1 (LT-1), China's premier L-band synthetic aperture radar (SAR) satellite constellation, offers a novel opportunity to assess these changes. This paper evaluates the deformation of critical engineering corridors, such as the Qinghai-Tibet Railway (QTR) and the Qinghai-Tibet Highway (QTH), utilizing time-series InSAR techniques with LT-1 SAR constellation data. We introduce both Stacking InSAR and a multi-baseline persistent scatterer multitemporal (MT-InSAR) method to enhance permafrost and engineering corridor deformation detection capabilities. Results obtained through the MT-InSAR approach reveal line-of-sight (LOS) deformation velocities of permafrost in the Beiluhe region ranging from -90 mm/y to approximately 70 mm/y, with an average velocity amplitude of 0.06 m/y. Differential displacement between alpine meadows and alpine deserts across the Beiluhe region is successfully discerned using LT-1 SAR data. Deformation velocities of QTR, QTH were found to be lower than that of permafrost, with average velocities of 0.027 m/y. These findings underscore the LT-1 SAR constellation's potential to serve as a valuable SAR data source for monitoring engineering corridor deformation within the Tibetan Plateau permafrost region

Time-Series InSAR Monitoring of Permafrost Freeze-Thaw Seasonal Displacement over Qinghai–Tibetan Plateau Using Sentinel-1 Data

Permafrost is widely distributed in the Tibetan Plateau. Seasonal freeze−thaw cycles of permafrost result in upward and downward surface displacement. Multitemporal interferometric synthetic aperture radar (MT-InSAR) observations provide an effective method for monitoring permafrost displacement under difficult terrain and climatic conditions. In this study, a seasonal sinusoidal model-based new small baselines subset (NSBAS) chain was adopted to obtain a deformation time series. An experimental study was carried out using 33 scenes of Sentinel-1 data (S-1) from 28 November 2017 to 29 December 2018 with frequent revisit (12 days) observations. The spatial and temporal characteristics of the surface displacements variation combined with different types of surface land cover, elevation and surface temperature factors were analyzed. The results revealed that the seasonal changes observed in the time series of ground movements, induced by freeze−thaw cycles were observed on flat surfaces of sedimentary basins and mountainous areas with gentle slopes. The estimated seasonal oscillations ranged from 2 mm to 30 mm, which were smaller in Alpine deserts than in Alpine meadows. In particular, there were significant systematic differences in seasonal surface deformation between areas near mountains and sedimentary basins. It was also found that the time series of deformation was consistent with the variation of surface temperature. Based on soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data, the deformation analysis influenced by soil moisture factors was also carried out. The comprehensive analysis of deformation results and auxiliary data (elevation, soil moisture and surface temperature et al.) provides important insights for the monitoring of the seasonal freeze-thaw cycles in the Tibetan Plateau

CONTRIBUTIONS OF OPTICAL REMOTE SENSING TO PERMAFROST MAPPING IN DONNELLY TRAINING AREA, ALASKA

AN ABSTRACT OF THE THESIS OFKiran Thapa, for the Master of Science degree in Geography and Environmental Resources, presented on April 8, 2020, at Southern Illinois University Carbondale.TITLE: CONTRIBUTIONS OF OPTICAL REMOTE SENSING TO PERMAFROST MAPPING IN DONNELLY TRAINING AREA, ALASKA MAJOR PROFESSOR: Dr. Guangxing Wang Permafrost occupies about a quarter of the northern hemisphere land with 25.5 million ha. Global warming and anthropogenic activities affect the dynamics of permafrost. Snow and permafrost, in turn, serve as an indicator of climate change and human activity disturbance. The dynamics of permafrost are often estimated using interferometric Synthetic Aperture Radar (InSAR) methods. However, acquiring and processing InSAR images is costly and computation intensive. Due to various spectral variables and indices available from optical images, Landsat satellite images that are free-downloadable provide the potential for studying and monitoring changes of permafrost. The overall objective of this study was to explore the use of optical images as a cost-effective method to map permafrost in Donnelly Training Area (DTA) - an installation located in Alaska. First, Landsat 8 OLI/TIRS images from January 2014 to December 2018 were used to calculate various remote sensing variables. The variables included Land Surface Temperature (LST), albedo, Soil Moisture index (SMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Snow Index (NDSI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water index (NDWI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI), Normalized Burn Ratio (NBR), Triangular Vegetation Index(TVI), Visible Atmospherically Resistant Index (VARI), and Active Layer Thickness (ALT). Moreover, elevation, slope, and aspect were obtained from a digital elevation model (DEM). The variables were used to estimate the probabilities of permafrost presence (POP) for DTA. The logistic and linear models were respectively selected and optimized based on logistic and linear stepwise regression for the estimation of and ALT. A total of 414 field observations that were collected from 1994 to 2012 were utilized for validation of models.The results showed that the POP in DTA was significantly affected by all the factors except aspect and EVI. The factor that was most correlated with ln((1-POP)/POP) was elevation, then NDVI, albedo, ALT, LST, NDWI, NDSI, slope, TVI, RSR, SMI, NDBI, SR, SAVI, NBR and VARI. A total of six prediction models were obtained. The elevation, NDVI, LST, TVI, ALT, SLOPE, RSR, SMI, NBR, and NDSI were finally chosen in the best model 5.6 with the smallest relative root mean square error (RMSE) and Akaike information criterion (AIC). The albedo used in previous studies was excluded in the final model, implying that the albedo was not critical to the prediction of POP. In addition to the previously used elevation, NDVI and SMI, other predictors including LST, TVI, ALT, SLOPE, RSR, NBR, and NDSI could not be ignored in the prediction of POP. The model generated reasonable spatial distribution of POP in which POP had greater values in the east, northeast, north, and northwest parts and smaller in the south and southwest parts. Except for NDVI, NDWI, NDSI, aspect, and RSR, moreover, all other predictors showed significant contributions to the prediction of ALT. The SMI, ELEVATION, SAVI, NDBI, SLOPE, LST, SR, EVI, VARI, and TVI were finally selected in the best model 5.14 with the smallest relative RMSE and AIC. The ALT highly varied over the study area with the spatial patterns inversely consistent with those of POP.The results are essential for the governments, policymakers, and other concerned stakeholders to estimate the degradation of permafrost in DTA and minimize the risk of policy decision-making for land use management and planning. This study will help to understand the global climate change, changing ecosystems, increasing concentration in the atmosphere, and human activity-induced disturbance