Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin
The increasing incidence of retrogressive thaw slumps (RTSs) in permafrost regions underscores the need for detailed spatial and temporal analysis using InSAR technology to monitor and predict dynamic changes in the process of RTSs. Nevertheless, current InSAR deformation forecasting methods employi...
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ftdoajarticles:oai:doaj.org/article:bd08de9130b044c49ecd0df6bf4761fc 2024-09-15T18:29:55+00:00 Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin Jing Wang Xiwei Fan Zhijie Zhang Xuefei Zhang Wenyu Nie Yuanmeng Qi Nan Zhang 2024-05-01T00:00:00Z https://doi.org/10.3390/rs16111891 https://doaj.org/article/bd08de9130b044c49ecd0df6bf4761fc EN eng MDPI AG https://www.mdpi.com/2072-4292/16/11/1891 https://doaj.org/toc/2072-4292 doi:10.3390/rs16111891 2072-4292 https://doaj.org/article/bd08de9130b044c49ecd0df6bf4761fc Remote Sensing, Vol 16, Iss 11, p 1891 (2024) retrogressive thaw slumps InSAR deformation prediction spacetimeformer permafrost degradation Science Q article 2024 ftdoajarticles https://doi.org/10.3390/rs16111891 2024-08-05T17:49:12Z The increasing incidence of retrogressive thaw slumps (RTSs) in permafrost regions underscores the need for detailed spatial and temporal analysis using InSAR technology to monitor and predict dynamic changes in the process of RTSs. Nevertheless, current InSAR deformation forecasting methods employing deep learning strategies such as the traditional long short-term memory (LSTM) and recent transformer models encounter difficulties in effectively capturing temporal features. Moreover, they are limited in their ability to directly integrate spatial information. In this paper, an innovative deep learning approach named Spacetimeformer is proposed for predicting medium- and short-term InSAR deformation of RTSs in the Chumar River area. This method employs a transformer architecture with a spatiotemporal attention mechanism, which enhances the long-term prediction capabilities of time series models and dynamic spatial modeling. It is applicable to multivariate InSAR spatiotemporal deformation prediction problems. The findings include a list of 72 RTSs compiled based on derived InSAR deformation maps and Sentinel-2 optical images, of which 64 have an average deformation rate exceeding 10 mm/year, indicating signs of permafrost degradation. The density distribution of the displacement maps predicted by the Spacetimeformer model aligned well with the InSAR deformation maps obtained from the small baseline subset (SBAS) method, with the overall prediction deviation controlled within 20 mm. In addition, the point-scale prediction results were compared with LSTM and transformer models. This study indicates that the Spacetimeformer network achieved good results in predicting the deformation of RTSs, with a root mean square error of 1.249 mm. The Spacetimeformer method for deformation prediction with the spacetime mechanism presented in this study can serve as a general framework for multivariate deformation prediction based on InSAR results. It can also quantitatively assess the spatial deformation characteristics and ... Article in Journal/Newspaper permafrost Directory of Open Access Journals: DOAJ Articles Remote Sensing 16 11 1891 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
retrogressive thaw slumps InSAR deformation prediction spacetimeformer permafrost degradation Science Q |
spellingShingle |
retrogressive thaw slumps InSAR deformation prediction spacetimeformer permafrost degradation Science Q Jing Wang Xiwei Fan Zhijie Zhang Xuefei Zhang Wenyu Nie Yuanmeng Qi Nan Zhang Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin |
topic_facet |
retrogressive thaw slumps InSAR deformation prediction spacetimeformer permafrost degradation Science Q |
description |
The increasing incidence of retrogressive thaw slumps (RTSs) in permafrost regions underscores the need for detailed spatial and temporal analysis using InSAR technology to monitor and predict dynamic changes in the process of RTSs. Nevertheless, current InSAR deformation forecasting methods employing deep learning strategies such as the traditional long short-term memory (LSTM) and recent transformer models encounter difficulties in effectively capturing temporal features. Moreover, they are limited in their ability to directly integrate spatial information. In this paper, an innovative deep learning approach named Spacetimeformer is proposed for predicting medium- and short-term InSAR deformation of RTSs in the Chumar River area. This method employs a transformer architecture with a spatiotemporal attention mechanism, which enhances the long-term prediction capabilities of time series models and dynamic spatial modeling. It is applicable to multivariate InSAR spatiotemporal deformation prediction problems. The findings include a list of 72 RTSs compiled based on derived InSAR deformation maps and Sentinel-2 optical images, of which 64 have an average deformation rate exceeding 10 mm/year, indicating signs of permafrost degradation. The density distribution of the displacement maps predicted by the Spacetimeformer model aligned well with the InSAR deformation maps obtained from the small baseline subset (SBAS) method, with the overall prediction deviation controlled within 20 mm. In addition, the point-scale prediction results were compared with LSTM and transformer models. This study indicates that the Spacetimeformer network achieved good results in predicting the deformation of RTSs, with a root mean square error of 1.249 mm. The Spacetimeformer method for deformation prediction with the spacetime mechanism presented in this study can serve as a general framework for multivariate deformation prediction based on InSAR results. It can also quantitatively assess the spatial deformation characteristics and ... |
format |
Article in Journal/Newspaper |
author |
Jing Wang Xiwei Fan Zhijie Zhang Xuefei Zhang Wenyu Nie Yuanmeng Qi Nan Zhang |
author_facet |
Jing Wang Xiwei Fan Zhijie Zhang Xuefei Zhang Wenyu Nie Yuanmeng Qi Nan Zhang |
author_sort |
Jing Wang |
title |
Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin |
title_short |
Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin |
title_full |
Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin |
title_fullStr |
Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin |
title_full_unstemmed |
Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin |
title_sort |
spatiotemporal mechanism-based spacetimeformer network for insar deformation prediction and identification of retrogressive thaw slumps in the chumar river basin |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/rs16111891 https://doaj.org/article/bd08de9130b044c49ecd0df6bf4761fc |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Remote Sensing, Vol 16, Iss 11, p 1891 (2024) |
op_relation |
https://www.mdpi.com/2072-4292/16/11/1891 https://doaj.org/toc/2072-4292 doi:10.3390/rs16111891 2072-4292 https://doaj.org/article/bd08de9130b044c49ecd0df6bf4761fc |
op_doi |
https://doi.org/10.3390/rs16111891 |
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Remote Sensing |
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16 |
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11 |
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1891 |
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1810471397409947648 |