Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods
Permafrost degradation can significantly affect vegetation, infrastructure, and sustainable development on the Qinghai-Tibet Plateau (QTP). The permafrost on the QTP faces a risk of widespread degradation due to climate change and ecosystem disturbances; thus, monitoring its changes is critical. In...
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ftmdpi:oai:mdpi.com:/1424-8220/23/3/1215/ 2023-08-20T04:09:08+02:00 Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods Ping Zhou Weichao Liu Xuefei Zhang Jing Wang 2023-01-20 application/pdf https://doi.org/10.3390/s23031215 EN eng Multidisciplinary Digital Publishing Institute Radar Sensors https://dx.doi.org/10.3390/s23031215 https://creativecommons.org/licenses/by/4.0/ Sensors; Volume 23; Issue 3; Pages: 1215 Qinghai-Tibet Plateau deformation prediction LSTM MT-InSAR permafrost degradation Text 2023 ftmdpi https://doi.org/10.3390/s23031215 2023-08-01T08:24:46Z Permafrost degradation can significantly affect vegetation, infrastructure, and sustainable development on the Qinghai-Tibet Plateau (QTP). The permafrost on the QTP faces a risk of widespread degradation due to climate change and ecosystem disturbances; thus, monitoring its changes is critical. In this study, we conducted a permafrost surface deformation prediction over the Tuotuo River tributary watershed in the southwestern part of the QTP using the Long Short-Term Memory model (LSTM). The LSTM model was applied to the deformation information derived from a time series of Multi-Temporal Interferometry Synthetic Aperture Radar (MT-InSAR). First, we designed a quadtree segmentation-based Small BAseline Subset (SBAS) to monitor the seasonal permafrost deformation from March 2017 to April 2022. Then, the types of frozen soil were classified using the spatio-temporal deformation information and the temperature at the top of the permafrost. Finally, the time-series deformation trends of different types of permafrost were predicted using the LSTM model. The results showed that the deformation rates in the Tuotuo River Basin ranged between −80 to 60 mm/yr. Permafrost, seasonally frozen ground, and potentially degraded permafrost covered 7572.23, 900.87, and 921.70 km2, respectively. The LSTM model achieved high precision for frozen soil deformation prediction at the point scale, with a root mean square error of 4.457 mm and mean absolute error of 3.421 mm. The results demonstrated that deformation monitoring and prediction using MT-InSAR technology integrated with the LSTM model can be used to accurately identify types of permafrost over a large region and quantitatively evaluate its degradation trends. Text permafrost MDPI Open Access Publishing Sensors 23 3 1215 |
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English |
topic |
Qinghai-Tibet Plateau deformation prediction LSTM MT-InSAR permafrost degradation |
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Qinghai-Tibet Plateau deformation prediction LSTM MT-InSAR permafrost degradation Ping Zhou Weichao Liu Xuefei Zhang Jing Wang Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods |
topic_facet |
Qinghai-Tibet Plateau deformation prediction LSTM MT-InSAR permafrost degradation |
description |
Permafrost degradation can significantly affect vegetation, infrastructure, and sustainable development on the Qinghai-Tibet Plateau (QTP). The permafrost on the QTP faces a risk of widespread degradation due to climate change and ecosystem disturbances; thus, monitoring its changes is critical. In this study, we conducted a permafrost surface deformation prediction over the Tuotuo River tributary watershed in the southwestern part of the QTP using the Long Short-Term Memory model (LSTM). The LSTM model was applied to the deformation information derived from a time series of Multi-Temporal Interferometry Synthetic Aperture Radar (MT-InSAR). First, we designed a quadtree segmentation-based Small BAseline Subset (SBAS) to monitor the seasonal permafrost deformation from March 2017 to April 2022. Then, the types of frozen soil were classified using the spatio-temporal deformation information and the temperature at the top of the permafrost. Finally, the time-series deformation trends of different types of permafrost were predicted using the LSTM model. The results showed that the deformation rates in the Tuotuo River Basin ranged between −80 to 60 mm/yr. Permafrost, seasonally frozen ground, and potentially degraded permafrost covered 7572.23, 900.87, and 921.70 km2, respectively. The LSTM model achieved high precision for frozen soil deformation prediction at the point scale, with a root mean square error of 4.457 mm and mean absolute error of 3.421 mm. The results demonstrated that deformation monitoring and prediction using MT-InSAR technology integrated with the LSTM model can be used to accurately identify types of permafrost over a large region and quantitatively evaluate its degradation trends. |
format |
Text |
author |
Ping Zhou Weichao Liu Xuefei Zhang Jing Wang |
author_facet |
Ping Zhou Weichao Liu Xuefei Zhang Jing Wang |
author_sort |
Ping Zhou |
title |
Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods |
title_short |
Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods |
title_full |
Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods |
title_fullStr |
Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods |
title_full_unstemmed |
Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods |
title_sort |
evaluating permafrost degradation in the tuotuo river basin by mt-insar and lstm methods |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/s23031215 |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Sensors; Volume 23; Issue 3; Pages: 1215 |
op_relation |
Radar Sensors https://dx.doi.org/10.3390/s23031215 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/s23031215 |
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Sensors |
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23 |
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3 |
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1215 |
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1774721871829270528 |