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

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...

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Main Authors: Zhao, Fumeng, Gong, Wenping, Ren, Tianhe, Chen, Jun, Tang, Huiming, Li, Tianzheng
Format: Text
Language:English
Published: 2022
Subjects:
Online Access:https://doi.org/10.5194/tc-2022-9
https://tc.copernicus.org/preprints/tc-2022-9/
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spelling ftcopernicus:oai:publications.copernicus.org:tcd100781 2023-05-15T17:55:30+02:00 Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method Zhao, Fumeng Gong, Wenping Ren, Tianhe Chen, Jun Tang, Huiming Li, Tianzheng 2022-01-24 application/pdf https://doi.org/10.5194/tc-2022-9 https://tc.copernicus.org/preprints/tc-2022-9/ eng eng doi:10.5194/tc-2022-9 https://tc.copernicus.org/preprints/tc-2022-9/ eISSN: 1994-0424 Text 2022 ftcopernicus https://doi.org/10.5194/tc-2022-9 2022-01-31T17:22:16Z 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. Text permafrost Copernicus Publications: E-Journals
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
description 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.
format Text
author Zhao, Fumeng
Gong, Wenping
Ren, Tianhe
Chen, Jun
Tang, Huiming
Li, Tianzheng
spellingShingle Zhao, Fumeng
Gong, Wenping
Ren, Tianhe
Chen, Jun
Tang, Huiming
Li, Tianzheng
Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method
author_facet Zhao, Fumeng
Gong, Wenping
Ren, Tianhe
Chen, Jun
Tang, Huiming
Li, Tianzheng
author_sort Zhao, Fumeng
title Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method
title_short Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method
title_full Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method
title_fullStr Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method
title_full_unstemmed Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method
title_sort permafrost stability mapping on the tibetan plateau by integrating time-series insar and random forest method
publishDate 2022
url https://doi.org/10.5194/tc-2022-9
https://tc.copernicus.org/preprints/tc-2022-9/
genre permafrost
genre_facet permafrost
op_source eISSN: 1994-0424
op_relation doi:10.5194/tc-2022-9
https://tc.copernicus.org/preprints/tc-2022-9/
op_doi https://doi.org/10.5194/tc-2022-9
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