<div class="csl-bib-body">
<div class="csl-entry">Roth, F., Bauer-Marschallinger, B., Tupas, M. E., Reimer, C., Salamon, P., & Wagner, W. (2023). Sentinel-1-based analysis of the severe flood over Pakistan 2022. <i>Natural Hazards and Earth System Sciences</i>, <i>23</i>(10), 3305–3317. https://doi.org/10.5194/nhess-23-3305-2023</div>
</div>
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dc.identifier.issn
1561-8633
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/189403
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dc.description.abstract
In August and September 2022, Pakistan was hit by a severe flood, and millions of people were impacted. The Sentinel-1-based flood mapping algorithm developed by Technische Universität Wien (TU Wien) for the Copernicus Emergency Management Service (CEMS) global flood monitoring (GFM) component was used to document the propagation of the flood from 10 August to 23 September 2022. The results were evaluated using the flood maps from the CEMS rapid mapping component. Overall, the algorithm performs reasonably well with a critical success index of up to 80 %, while the detected differences can be primarily attributed to the time difference of the algorithm's results and the corresponding reference. Over the 6-week time span, an area of 30 492 km² was observed to be flooded at least once, and the maximum extent was found to be present on 30 August. The study demonstrates the ability of the TU Wien flood mapping algorithm to fully automatically produce large-scale results and how key data of an event can be derived from these results.
en
dc.description.sponsorship
European Commission
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dc.language.iso
en
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dc.publisher
COPERNICUS GESELLSCHAFT MBH
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dc.relation.ispartof
Natural Hazards and Earth System Sciences
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
Flood Mapping
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dc.subject
SAR
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dc.subject
Sentinel-1
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dc.subject
Natural hazards
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dc.subject
Hydrology
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dc.subject
Remote Sensing
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dc.title
Sentinel-1-based analysis of the severe flood over Pakistan 2022
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dc.type
Article
en
dc.type
Artikel
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.contributor.affiliation
University of the Philippines Diliman, Philippines (the)
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dc.contributor.affiliation
EODC Earth Observation Data Centre for Water Resources Monitoring GmbH, Austria