Monitoring rapid permafrost thaw using elevation models generated from satellite radar interferometry
Vast areas of the Arctic host ice-rich permafrost, which is becoming increasingly vulnerable to terrain-altering thermokarst in a warming climate. Among the most rapid and dramatic changes are retrogressive thaw slumps. These slumps evolve by a retreat of the slump headwall during the summer months,...
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ftethz:oai:www.research-collection.ethz.ch:20.500.11850/453189 2023-05-15T15:04:56+02:00 Monitoring rapid permafrost thaw using elevation models generated from satellite radar interferometry Bernhard, Philipp Zwieback, Simon Leinss, Silvan Hajnsek, Irena 2020-06-12 application/application/pdf https://hdl.handle.net/20.500.11850/453189 https://doi.org/10.3929/ethz-b-000453189 en eng Copernicus info:eu-repo/semantics/altIdentifier/doi/10.5194/egusphere-egu2020-6965 http://hdl.handle.net/20.500.11850/453189 doi:10.3929/ethz-b-000453189 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International CC-BY EGUsphere info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion 2020 ftethz https://doi.org/20.500.11850/453189 https://doi.org/10.3929/ethz-b-000453189 https://doi.org/10.5194/egusphere-egu2020-6965 2022-04-25T14:17:12Z Vast areas of the Arctic host ice-rich permafrost, which is becoming increasingly vulnerable to terrain-altering thermokarst in a warming climate. Among the most rapid and dramatic changes are retrogressive thaw slumps. These slumps evolve by a retreat of the slump headwall during the summer months, making them detectable by comparing digital elevation models over time using the volumetric change as an indicator. Despite the availability of many topographic InSAR observations to generate digital elevation models, there is currently no method to map and analyze retrogressive thaw slumps. Here, we present and assess a method to detect and monitor thaw slumps using time-series of elevation models (DEMs), generated from single-pass InSAR observations, which have been acquired across the Arctic at high resolution since 2011 by the TanDEM-X satellite pair. At least three observations over this timespan are available with a spatial resolution of about 12 meter and the height sensitivity of 0.5-2 meter. We first difference the generated digital elevation and detect significant elevation changes taking the uncertainty estimates of each elevation measurement into account. In the implementation of the processing chain we focused on making it as automated as much as possible to be able to cover large areas of the northern hemisphere. This includes detecting common problems with the data and apply appropriate algorithms to obtain DEMs with high accuracy. Additionally we implemented methods to deal with problematic features like wet-snow, vegetation and water bodies. After generating the DEMs we us DEM differencing followed by a blob detection and cluster algorithm to detect active thaw slumps. To improve the accuracy of our method we apply and compare different machine learning methods, namely a simple threshold method, a Random Forest and a Support-Vector-Machine. To estimate the accuracy of our method we use data from past studies as well as a classification based on optical satellite data. The obtained locations of thaw slumps can be used as a starting point to extract important slump properties, like the headwall height and volumetric change, which are currently not available on regional scales. Additionally to the thaw slump detection, we show first results of the thaw slump property extraction for thaw slumps located in Northern Canada (Peel Plateau, Mackenzie River Delta, Banks Island, Ellesmere Island). Conference Object Arctic Banks Island Ellesmere Island Ice Mackenzie river permafrost Thermokarst ETH Zürich Research Collection Arctic Canada Ellesmere Island Mackenzie River |
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Open Polar |
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ETH Zürich Research Collection |
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language |
English |
description |
Vast areas of the Arctic host ice-rich permafrost, which is becoming increasingly vulnerable to terrain-altering thermokarst in a warming climate. Among the most rapid and dramatic changes are retrogressive thaw slumps. These slumps evolve by a retreat of the slump headwall during the summer months, making them detectable by comparing digital elevation models over time using the volumetric change as an indicator. Despite the availability of many topographic InSAR observations to generate digital elevation models, there is currently no method to map and analyze retrogressive thaw slumps. Here, we present and assess a method to detect and monitor thaw slumps using time-series of elevation models (DEMs), generated from single-pass InSAR observations, which have been acquired across the Arctic at high resolution since 2011 by the TanDEM-X satellite pair. At least three observations over this timespan are available with a spatial resolution of about 12 meter and the height sensitivity of 0.5-2 meter. We first difference the generated digital elevation and detect significant elevation changes taking the uncertainty estimates of each elevation measurement into account. In the implementation of the processing chain we focused on making it as automated as much as possible to be able to cover large areas of the northern hemisphere. This includes detecting common problems with the data and apply appropriate algorithms to obtain DEMs with high accuracy. Additionally we implemented methods to deal with problematic features like wet-snow, vegetation and water bodies. After generating the DEMs we us DEM differencing followed by a blob detection and cluster algorithm to detect active thaw slumps. To improve the accuracy of our method we apply and compare different machine learning methods, namely a simple threshold method, a Random Forest and a Support-Vector-Machine. To estimate the accuracy of our method we use data from past studies as well as a classification based on optical satellite data. The obtained locations of thaw slumps can be used as a starting point to extract important slump properties, like the headwall height and volumetric change, which are currently not available on regional scales. Additionally to the thaw slump detection, we show first results of the thaw slump property extraction for thaw slumps located in Northern Canada (Peel Plateau, Mackenzie River Delta, Banks Island, Ellesmere Island). |
format |
Conference Object |
author |
Bernhard, Philipp Zwieback, Simon Leinss, Silvan Hajnsek, Irena |
spellingShingle |
Bernhard, Philipp Zwieback, Simon Leinss, Silvan Hajnsek, Irena Monitoring rapid permafrost thaw using elevation models generated from satellite radar interferometry |
author_facet |
Bernhard, Philipp Zwieback, Simon Leinss, Silvan Hajnsek, Irena |
author_sort |
Bernhard, Philipp |
title |
Monitoring rapid permafrost thaw using elevation models generated from satellite radar interferometry |
title_short |
Monitoring rapid permafrost thaw using elevation models generated from satellite radar interferometry |
title_full |
Monitoring rapid permafrost thaw using elevation models generated from satellite radar interferometry |
title_fullStr |
Monitoring rapid permafrost thaw using elevation models generated from satellite radar interferometry |
title_full_unstemmed |
Monitoring rapid permafrost thaw using elevation models generated from satellite radar interferometry |
title_sort |
monitoring rapid permafrost thaw using elevation models generated from satellite radar interferometry |
publisher |
Copernicus |
publishDate |
2020 |
url |
https://hdl.handle.net/20.500.11850/453189 https://doi.org/10.3929/ethz-b-000453189 |
geographic |
Arctic Canada Ellesmere Island Mackenzie River |
geographic_facet |
Arctic Canada Ellesmere Island Mackenzie River |
genre |
Arctic Banks Island Ellesmere Island Ice Mackenzie river permafrost Thermokarst |
genre_facet |
Arctic Banks Island Ellesmere Island Ice Mackenzie river permafrost Thermokarst |
op_source |
EGUsphere |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/egusphere-egu2020-6965 http://hdl.handle.net/20.500.11850/453189 doi:10.3929/ethz-b-000453189 |
op_rights |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/20.500.11850/453189 https://doi.org/10.3929/ethz-b-000453189 https://doi.org/10.5194/egusphere-egu2020-6965 |
_version_ |
1766336717430718464 |