Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery
Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamic...
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ftdoajarticles:oai:doaj.org/article:e677cc35a9ed43499a6f782fbdc11b8c 2023-05-15T14:50:22+02:00 Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery Chandi Witharana Mahendra R. Udawalpola Anna K. Liljedahl Melissa K. Ward Jones Benjamin M. Jones Amit Hasan Durga Joshi Elias Manos 2022-08-01T00:00:00Z https://doi.org/10.3390/rs14174132 https://doaj.org/article/e677cc35a9ed43499a6f782fbdc11b8c EN eng MDPI AG https://www.mdpi.com/2072-4292/14/17/4132 https://doaj.org/toc/2072-4292 doi:10.3390/rs14174132 2072-4292 https://doaj.org/article/e677cc35a9ed43499a6f782fbdc11b8c Remote Sensing, Vol 14, Iss 4132, p 4132 (2022) Arctic permafrost retrogressive thaw slump satellite images deep learning Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14174132 2022-12-30T22:07:30Z Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents. Article in Journal/Newspaper Arctic Axel Heiberg Island Banks Island Ellesmere Island permafrost Directory of Open Access Journals: DOAJ Articles Arctic Axel Heiberg Island ENVELOPE(-91.001,-91.001,79.752,79.752) Canada Ellesmere Island Heiberg ENVELOPE(13.964,13.964,66.424,66.424) Remote Sensing 14 17 4132 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic permafrost retrogressive thaw slump satellite images deep learning Science Q |
spellingShingle |
Arctic permafrost retrogressive thaw slump satellite images deep learning Science Q Chandi Witharana Mahendra R. Udawalpola Anna K. Liljedahl Melissa K. Ward Jones Benjamin M. Jones Amit Hasan Durga Joshi Elias Manos Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery |
topic_facet |
Arctic permafrost retrogressive thaw slump satellite images deep learning Science Q |
description |
Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents. |
format |
Article in Journal/Newspaper |
author |
Chandi Witharana Mahendra R. Udawalpola Anna K. Liljedahl Melissa K. Ward Jones Benjamin M. Jones Amit Hasan Durga Joshi Elias Manos |
author_facet |
Chandi Witharana Mahendra R. Udawalpola Anna K. Liljedahl Melissa K. Ward Jones Benjamin M. Jones Amit Hasan Durga Joshi Elias Manos |
author_sort |
Chandi Witharana |
title |
Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery |
title_short |
Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery |
title_full |
Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery |
title_fullStr |
Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery |
title_full_unstemmed |
Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery |
title_sort |
automated detection of retrogressive thaw slumps in the high arctic using high-resolution satellite imagery |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14174132 https://doaj.org/article/e677cc35a9ed43499a6f782fbdc11b8c |
long_lat |
ENVELOPE(-91.001,-91.001,79.752,79.752) ENVELOPE(13.964,13.964,66.424,66.424) |
geographic |
Arctic Axel Heiberg Island Canada Ellesmere Island Heiberg |
geographic_facet |
Arctic Axel Heiberg Island Canada Ellesmere Island Heiberg |
genre |
Arctic Axel Heiberg Island Banks Island Ellesmere Island permafrost |
genre_facet |
Arctic Axel Heiberg Island Banks Island Ellesmere Island permafrost |
op_source |
Remote Sensing, Vol 14, Iss 4132, p 4132 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/17/4132 https://doaj.org/toc/2072-4292 doi:10.3390/rs14174132 2072-4292 https://doaj.org/article/e677cc35a9ed43499a6f782fbdc11b8c |
op_doi |
https://doi.org/10.3390/rs14174132 |
container_title |
Remote Sensing |
container_volume |
14 |
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17 |
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4132 |
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1766321401533300736 |