Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method
The ground deformation rate is an important index for evaluating the stability and degradation of permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost areas on the Tibetan Plateau is a challenge. Thus, the technique of time-series interferometric synt...
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ftmdpi:oai:mdpi.com:/2072-4292/15/9/2294/ 2023-08-20T04:09:08+02:00 Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method Fumeng Zhao Wenping Gong Tianhe Ren Jun Chen Huiming Tang Tianzheng Li agris 2023-04-27 application/pdf https://doi.org/10.3390/rs15092294 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs15092294 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 9; Pages: 2294 permafrost stability InSAR random forest method Tibetan Plateau data scarcity Text 2023 ftmdpi https://doi.org/10.3390/rs15092294 2023-08-01T09:51:48Z The ground deformation rate is an important index for evaluating the stability and degradation of permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost areas 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 rate of the permafrost area, the effectiveness of which is, however, degraded in areas with geometric distortions in synthetic aperture radar (SAR) images. In this study, a method that integrates InSAR and the random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; 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 rate 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 the initial InSAR analysis. Third, the permafrost stability in the whole study area is mapped with the trained random forest model, and the issue of data scarcity in areas where the terrain visibility of SAR images is poor or InSAR results are not available in permafrost stability mapping can be overcome. Comparative analyses demonstrate that the integration of the InSAR and the random forest method yields a more effective permafrost stability mapping compared with the sole application of InSAR analysis. Text permafrost MDPI Open Access Publishing Remote Sensing 15 9 2294 |
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English |
topic |
permafrost stability InSAR random forest method Tibetan Plateau data scarcity |
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permafrost stability InSAR random forest method Tibetan Plateau data scarcity Fumeng Zhao Wenping Gong Tianhe Ren Jun Chen Huiming Tang Tianzheng Li Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method |
topic_facet |
permafrost stability InSAR random forest method Tibetan Plateau data scarcity |
description |
The ground deformation rate is an important index for evaluating the stability and degradation of permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost areas 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 rate of the permafrost area, the effectiveness of which is, however, degraded in areas with geometric distortions in synthetic aperture radar (SAR) images. In this study, a method that integrates InSAR and the random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; 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 rate 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 the initial InSAR analysis. Third, the permafrost stability in the whole study area is mapped with the trained random forest model, and the issue of data scarcity in areas where the terrain visibility of SAR images is poor or InSAR results are not available in permafrost stability mapping can be overcome. Comparative analyses demonstrate that the integration of the InSAR and the random forest method yields a more effective permafrost stability mapping compared with the sole application of InSAR analysis. |
format |
Text |
author |
Fumeng Zhao Wenping Gong Tianhe Ren Jun Chen Huiming Tang Tianzheng Li |
author_facet |
Fumeng Zhao Wenping Gong Tianhe Ren Jun Chen Huiming Tang Tianzheng Li |
author_sort |
Fumeng Zhao |
title |
Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method |
title_short |
Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method |
title_full |
Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method |
title_fullStr |
Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method |
title_full_unstemmed |
Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method |
title_sort |
permafrost stability mapping on the tibetan plateau by integrating time-series insar and the random forest method |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15092294 |
op_coverage |
agris |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Remote Sensing; Volume 15; Issue 9; Pages: 2294 |
op_relation |
https://dx.doi.org/10.3390/rs15092294 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs15092294 |
container_title |
Remote Sensing |
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15 |
container_issue |
9 |
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2294 |
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