Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr
[Display omitted] •Combination of Landsat and Sentinel-2 imagery in LandTrendr.•Adaptation of LandTrendr to rapid permafrost disturbances.•Detection of retrogressive thaw slumps across a 8.1×106km2 region in North Siberia.•Annually affected area of retrogressive thaw slumps increases 2000–2019. Perm...
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•Combination of Landsat and Sentinel-2 imagery in LandTrendr.•Adaptation of LandTrendr to rapid permafrost disturbances.•Detection of retrogressive thaw slumps across a 8.1×106km2 region in North Siberia.•Annually affected area of retrogressive thaw slumps increases 2000–2019.
Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Retrogressive thaw slumps (RTS) are abrupt permafrost disturbances that expand by several meters each year and lead to an increased soil organic carbon release. Local Remote Sensing studies identified increasing RTS activity in the last two decades by increasing number of RTS or heightened RTS growth rates. However, a large-scale assessment across diverse permafrost regions and at high temporal resolution allowing to further determine RTS thaw dynamics and its main drivers is still lacking.
In this study we apply the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to North Siberia (8.1×106km2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.609). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very high-resolution RapidEye and PlanetScope imagery.
Our study presents the first automated detection and assessment of RTS and their temporal dynamics at large-scale for 2001–2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across North Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158ha, 2001–2019: 66,699ha). Contrary to this, 5 focus sites show spatio-temporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period, indicating that the increase in RTS-affected area was mainly caused by enlarging existing RTS an |
| doi_str_mv | 10.1016/j.rse.2021.112752 |
| format | Article |
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•Combination of Landsat and Sentinel-2 imagery in LandTrendr.•Adaptation of LandTrendr to rapid permafrost disturbances.•Detection of retrogressive thaw slumps across a 8.1×106km2 region in North Siberia.•Annually affected area of retrogressive thaw slumps increases 2000–2019.
Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Retrogressive thaw slumps (RTS) are abrupt permafrost disturbances that expand by several meters each year and lead to an increased soil organic carbon release. Local Remote Sensing studies identified increasing RTS activity in the last two decades by increasing number of RTS or heightened RTS growth rates. However, a large-scale assessment across diverse permafrost regions and at high temporal resolution allowing to further determine RTS thaw dynamics and its main drivers is still lacking.
In this study we apply the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to North Siberia (8.1×106km2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.609). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very high-resolution RapidEye and PlanetScope imagery.
Our study presents the first automated detection and assessment of RTS and their temporal dynamics at large-scale for 2001–2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across North Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158ha, 2001–2019: 66,699ha). Contrary to this, 5 focus sites show spatio-temporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period, indicating that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by new RTS. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. Obtaining such consistent disturbance products will help to parametrise regional and global climate change models.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2021.112752</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Algorithms ; Automation ; Calibration ; Climate change ; Climate change models ; Climate models ; Disturbances ; Dynamics ; Filtration ; Global climate ; Ground truth ; Growth rate ; Image resolution ; Image segmentation ; Landsat ; Learning algorithms ; Machine learning ; Mosaics ; Multi-spectral analysis ; Organic carbon ; Organic soils ; Permafrost ; Permafrost thaw ; Regional climates ; Remote sensing ; Retrogressive thaw slumps ; Sentinel-2 ; Temporal resolution ; Temporal variability ; Thermo-erosion ; Time series ; Workflow</subject><ispartof>Remote sensing of environment, 2022-01, Vol.268, p.112752, Article 112752</ispartof><rights>2021</rights><rights>Copyright Elsevier BV Jan 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-6cee804073c58f0871f3e0cf0adafced10e116701f639848b59299cbac5c213</citedby><cites>FETCH-LOGICAL-c368t-6cee804073c58f0871f3e0cf0adafced10e116701f639848b59299cbac5c213</cites><orcidid>0000-0001-5895-2141 ; 0000-0001-5047-0105 ; 0000-0002-1165-6852</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rse.2021.112752$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids></links><search><creatorcontrib>Runge, Alexandra</creatorcontrib><creatorcontrib>Nitze, Ingmar</creatorcontrib><creatorcontrib>Grosse, Guido</creatorcontrib><title>Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr</title><title>Remote sensing of environment</title><description>[Display omitted]
•Combination of Landsat and Sentinel-2 imagery in LandTrendr.•Adaptation of LandTrendr to rapid permafrost disturbances.•Detection of retrogressive thaw slumps across a 8.1×106km2 region in North Siberia.•Annually affected area of retrogressive thaw slumps increases 2000–2019.
Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Retrogressive thaw slumps (RTS) are abrupt permafrost disturbances that expand by several meters each year and lead to an increased soil organic carbon release. Local Remote Sensing studies identified increasing RTS activity in the last two decades by increasing number of RTS or heightened RTS growth rates. However, a large-scale assessment across diverse permafrost regions and at high temporal resolution allowing to further determine RTS thaw dynamics and its main drivers is still lacking.
In this study we apply the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to North Siberia (8.1×106km2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.609). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very high-resolution RapidEye and PlanetScope imagery.
Our study presents the first automated detection and assessment of RTS and their temporal dynamics at large-scale for 2001–2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across North Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158ha, 2001–2019: 66,699ha). Contrary to this, 5 focus sites show spatio-temporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period, indicating that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by new RTS. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. Obtaining such consistent disturbance products will help to parametrise regional and global climate change models.</description><subject>Algorithms</subject><subject>Automation</subject><subject>Calibration</subject><subject>Climate change</subject><subject>Climate change models</subject><subject>Climate models</subject><subject>Disturbances</subject><subject>Dynamics</subject><subject>Filtration</subject><subject>Global climate</subject><subject>Ground truth</subject><subject>Growth rate</subject><subject>Image resolution</subject><subject>Image segmentation</subject><subject>Landsat</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Mosaics</subject><subject>Multi-spectral analysis</subject><subject>Organic carbon</subject><subject>Organic soils</subject><subject>Permafrost</subject><subject>Permafrost thaw</subject><subject>Regional climates</subject><subject>Remote sensing</subject><subject>Retrogressive thaw slumps</subject><subject>Sentinel-2</subject><subject>Temporal resolution</subject><subject>Temporal variability</subject><subject>Thermo-erosion</subject><subject>Time series</subject><subject>Workflow</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKs_wFvA866T7DeepPgFBUF7D2kysVm62TXJWvrv3bKePc3lfWeeeQi5ZZAyYOV9m_qAKQfOUsZ4VfAzsmB11SRQQX5OFgBZnuS8qC7JVQgtACvqii3I5gO7PiIN6IJ1X1Q6N8o91UcnO6sC7Q31crCaDug7aXwfIo07eaDahjj6rXQKAz3YuKNr6fTGo9P-mlwYuQ948zeX5PP5abN6TdbvL2-rx3WisrKOSakQa8ihylRRG5h4TIagDEgtjULNABkrK2CmzJo6r7dFw5tGbaUqFGfZktzNWwfff48Yomj70bvpoOAlB-BVWZxSbE6piT14NGLwtpP-KBiIkzrRikmdOKkTs7qp8zB3cIL_sehFUBanT7X1qKLQvf2n_QtC53dG</recordid><startdate>202201</startdate><enddate>202201</enddate><creator>Runge, Alexandra</creator><creator>Nitze, Ingmar</creator><creator>Grosse, Guido</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0001-5895-2141</orcidid><orcidid>https://orcid.org/0000-0001-5047-0105</orcidid><orcidid>https://orcid.org/0000-0002-1165-6852</orcidid></search><sort><creationdate>202201</creationdate><title>Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr</title><author>Runge, Alexandra ; Nitze, Ingmar ; Grosse, Guido</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-6cee804073c58f0871f3e0cf0adafced10e116701f639848b59299cbac5c213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Automation</topic><topic>Calibration</topic><topic>Climate change</topic><topic>Climate change models</topic><topic>Climate models</topic><topic>Disturbances</topic><topic>Dynamics</topic><topic>Filtration</topic><topic>Global climate</topic><topic>Ground truth</topic><topic>Growth rate</topic><topic>Image resolution</topic><topic>Image segmentation</topic><topic>Landsat</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Mosaics</topic><topic>Multi-spectral analysis</topic><topic>Organic carbon</topic><topic>Organic soils</topic><topic>Permafrost</topic><topic>Permafrost thaw</topic><topic>Regional climates</topic><topic>Remote sensing</topic><topic>Retrogressive thaw slumps</topic><topic>Sentinel-2</topic><topic>Temporal resolution</topic><topic>Temporal variability</topic><topic>Thermo-erosion</topic><topic>Time series</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Runge, Alexandra</creatorcontrib><creatorcontrib>Nitze, Ingmar</creatorcontrib><creatorcontrib>Grosse, Guido</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Runge, Alexandra</au><au>Nitze, Ingmar</au><au>Grosse, Guido</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr</atitle><jtitle>Remote sensing of environment</jtitle><date>2022-01</date><risdate>2022</risdate><volume>268</volume><spage>112752</spage><pages>112752-</pages><artnum>112752</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>[Display omitted]
•Combination of Landsat and Sentinel-2 imagery in LandTrendr.•Adaptation of LandTrendr to rapid permafrost disturbances.•Detection of retrogressive thaw slumps across a 8.1×106km2 region in North Siberia.•Annually affected area of retrogressive thaw slumps increases 2000–2019.
Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Retrogressive thaw slumps (RTS) are abrupt permafrost disturbances that expand by several meters each year and lead to an increased soil organic carbon release. Local Remote Sensing studies identified increasing RTS activity in the last two decades by increasing number of RTS or heightened RTS growth rates. However, a large-scale assessment across diverse permafrost regions and at high temporal resolution allowing to further determine RTS thaw dynamics and its main drivers is still lacking.
In this study we apply the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to North Siberia (8.1×106km2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.609). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very high-resolution RapidEye and PlanetScope imagery.
Our study presents the first automated detection and assessment of RTS and their temporal dynamics at large-scale for 2001–2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across North Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158ha, 2001–2019: 66,699ha). Contrary to this, 5 focus sites show spatio-temporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period, indicating that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by new RTS. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. Obtaining such consistent disturbance products will help to parametrise regional and global climate change models.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2021.112752</doi><orcidid>https://orcid.org/0000-0001-5895-2141</orcidid><orcidid>https://orcid.org/0000-0001-5047-0105</orcidid><orcidid>https://orcid.org/0000-0002-1165-6852</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Automation Calibration Climate change Climate change models Climate models Disturbances Dynamics Filtration Global climate Ground truth Growth rate Image resolution Image segmentation Landsat Learning algorithms Machine learning Mosaics Multi-spectral analysis Organic carbon Organic soils Permafrost Permafrost thaw Regional climates Remote sensing Retrogressive thaw slumps Sentinel-2 Temporal resolution Temporal variability Thermo-erosion Time series Workflow |
| title | Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr |
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