Preprints
https://doi.org/10.5194/tc-2022-9
https://doi.org/10.5194/tc-2022-9
24 Jan 2022
 | 24 Jan 2022
Status: this preprint was under review for the journal TC but the revision was not accepted.

Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method

Fumeng Zhao, Wenping Gong, Tianhe Ren, Jun Chen, Huiming Tang, and Tianzheng Li

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.

Fumeng Zhao, Wenping Gong, Tianhe Ren, Jun Chen, Huiming Tang, and Tianzheng Li

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-9', Anonymous Referee #1, 17 Feb 2022
    • AC1: 'Reply on RC1', Fumeng Zhao, 15 Jun 2022
  • RC2: 'Comment on tc-2022-9', Anonymous Referee #2, 28 Mar 2022
    • AC2: 'Reply on RC2', Fumeng Zhao, 15 Jun 2022
  • RC3: 'Comment on tc-2022-9', Anonymous Referee #3, 05 May 2022
    • AC3: 'Reply on RC3', Fumeng Zhao, 15 Jun 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-9', Anonymous Referee #1, 17 Feb 2022
    • AC1: 'Reply on RC1', Fumeng Zhao, 15 Jun 2022
  • RC2: 'Comment on tc-2022-9', Anonymous Referee #2, 28 Mar 2022
    • AC2: 'Reply on RC2', Fumeng Zhao, 15 Jun 2022
  • RC3: 'Comment on tc-2022-9', Anonymous Referee #3, 05 May 2022
    • AC3: 'Reply on RC3', Fumeng Zhao, 15 Jun 2022
Fumeng Zhao, Wenping Gong, Tianhe Ren, Jun Chen, Huiming Tang, and Tianzheng Li
Fumeng Zhao, Wenping Gong, Tianhe Ren, Jun Chen, Huiming Tang, and Tianzheng Li

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Short summary
In this study, a new permafrost stability mapping is obtained by integrating time-series InSAR and machine learning method, this method provides another alternative for measuring permafrost degradation when the ground temperature is limited to the site-specific measurements. Also, the influences of topography and vegetation coverage on the ground deformations are studied to illustrate that the permafrost stability is high related to the environmental factors.