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hal.structure.identifierUMR 228 Espace-Dev, Espace pour le développement
dc.contributor.authorENGUEHARD, Pauline
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
hal.structure.identifierLaboratoire d'études en Géophysique et océanographie spatiales [LEGOS]
dc.contributor.authorFRAPPART, Frédéric
hal.structure.identifierLaboratoire d'études en Géophysique et océanographie spatiales [LEGOS]
dc.contributor.authorZEIGER, Pierre
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorBLAREL, Fabien
hal.structure.identifierUMR 228 Espace-Dev, Espace pour le développement
dc.contributor.authorSATGÉ, Frédéric
hal.structure.identifierUMR 228 Espace-Dev, Espace pour le développement
dc.contributor.authorBONNET, Marie-Paule
dc.date.accessioned2024-04-08T11:42:56Z
dc.date.available2024-04-08T11:42:56Z
dc.date.issued2023-04-27
dc.identifier.issn2214-5818
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195089
dc.description.abstractEnStudy region: The Curuaí floodplain in the low Amazon river in the Pará state of Brazil and Juruá basin, a major Solimões tributary. Study focus: Characterizing the hydrological dynamics of Amazon floodplains is essential to better understand and preserve these environments providing important resources to local populations. Radar altimetry is an effective remote sensing tool for monitoring water levels of continental hydrosystems, including floodplains. An unsupervised classification approach on radar echoes to determine hydrological regimes has recently been tested and showed a strong potential on the Congo River basin. This method is adapted to Envisat and Saral satellite radar altimetry data on two study areas in the Amazon Basin. The aim is to improve inland water detection along altimeter tracks to automatically generate water level time series (WLTS) over rivers, lakes, and poorly monitored floodplains and wetlands. New hydrological insights: Results show a good agreement with land cover maps obtained with optical imagery over selected Amazonian wetlands (70-80% accuracies with Envisat data and 50-60% with Saral data). Automatically generated WLTS are strongly correlated to the manually generated WLTS (R 2 ≈ 0.9; RMSE < 1 m). Compared to the manual method, the automatic method is faster, more efficient and replicable. Densifying the WL network in the floodplains bring crucial information on the connectivity dynamic between lakes and rivers.
dc.description.sponsorshipBalancing biOdiversity conservatioN with Development in Amazon wetlandS - ANR-18-EBI4-0006
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enRadar altimetry
dc.subject.enAmazon
dc.subject.enFloodplains
dc.subject.enUnsupervised classification
dc.subject.enAutomatic generation of water level gauges
dc.subject.enHydrological connectivity
dc.title.enContribution of automatically generated radar altimetry water levels from unsupervised classification to study hydrological connectivity within Amazon floodplains
dc.typeArticle de revue
dc.identifier.doi10.1016/j.ejrh.2023.101397
dc.subject.halSciences de l'environnement
bordeaux.journalJournal of Hydrology: Regional Studies
bordeaux.page101397
bordeaux.volume47
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-04185774
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04185774v1
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