Abstract

Ground deformation is an important index for evaluating the stability and degradation of the permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost area on the Tibetan Plateau is a challenge. Thus, the technique of time-series Interferometric Synthetic Aperture Radar (InSAR) is often adopted for measuring the ground deformation of the permafrost area, the effectiveness of which is however degraded in the areas with geometric distortions in Synthetic Aperture Radar (SAR) images. In this study, a method that integrates InSAR and random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; and, to demonstrate the application of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is studied. First, the ground deformation in the concerned area is studied with InSAR, in which 67 Sentinel-1 scenes taken in the period from 2014 to 2020 are collected and analyzed. Second, the relationship between the environmental factors (i.e., topography, land cover, land surface temperature, and distance-to-road) and the permafrost stability is mapped with the random forest method, based on the high-quality data extracted from initial InSAR analysis. Third, the permafrost stability in the areas where the visibility of SAR images is poor or the InSAR analysis results are not available is mapped with the trained random forest model. Comparative analyses demonstrate that the integration of InSAR and random forest method yields a more effective permafrost stability mapping, compared to the sole application of InSAR analysis.

Highlights

  • Under the influences of global warming and human activities, the mountain ecosystem and cryosphere system have changed limited accessibility, in-situ measurement of the ground deformation of permafrost area on the Tibetan Plateau is a challenge.the technique of time-series Interferometric Synthetic Aperture Radar (InSAR) is often adopted for measuring the ground deformation of the permafrost area, the effectiveness of which is degraded in the areas with geometric distortions in Synthetic Aperture Radar (SAR) images

  • A method that integrates InSAR and random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; and, to demonstrate the application of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is studied

  • The relationship between the environmental factors and the permafrost stability is mapped with the random forest method, based on the highquality data extracted from initial InSAR analysis

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Summary

Introduction

Under the influences of global warming and human activities, the mountain ecosystem and cryosphere system have changed limited accessibility, in-situ measurement of the ground deformation of permafrost area on the Tibetan Plateau is a challenge. The effectiveness of differential InSAR in monitoring the ground deformation and the permafrost stability is, degraded by the spatial decoherence and atmospheric delay induced in the processing of SAR images. The technique of time-series InSAR, such as persistent scatterer InSAR (PS-InSAR) and small baseline subset-InSAR (SBAS-InSAR), has been developed recently and applied to monitoring the ground deformation and the permafrost stability on the Tibetan Plateau (Schaefer et al, 2015; Daout et al, 2017; Lu et al, 2019). The integrated method could take the advantage of the effectiveness of time-series InSAR (in monitoring the ground deformation in the areas with good visibility of input SAR images) and that of the machine learning method To illustrate the application and effectiveness of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is analyzed. To illustrate the application and effectiveness of the integrated method proposed, a small area located in the central region of the

Principle of the integrated method for permafrost stability mapping
Time-series InSAR analysis of the ground deformation
Analysis of geometric distortion in input SAR images using the R-index model
Random forest method-based permafrost stability mapping
Results of the ground deformation with time-series InSAR analysis
Ground deformations obtained in the study area
Influence of the topography on the ground deformation
Influence of the vegetation coverage on the ground deformation
Screening results of the high-quality and low-quality areas
Permafrost stability mapping in the low-quality area with random forest method
Verifications of the ground deformations obtained with InSAR analysis
Verifications of permafrost stability mapping with ascending Sentinel-1 SAR images
Superiority of the proposed method over the sole application of InSAR analysis
Concluding Remarks
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