Remote sensing analysis of depositional landforms in alluvial settings: Method development and application to the Taquari megafan, Pantanal (Brazil)

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dc.contributor.authorZani, Hiran
dc.contributor.authorAssine, Mario Luis [UNESP]
dc.contributor.authorMcGlue, Michael Matthew
dc.contributor.institutionInstituto Nacional de Pesquisas Espaciais (INPE)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversity of Arizona
dc.date.accessioned2013-09-30T18:51:08Z
dc.date.accessioned2014-05-20T14:16:55Z
dc.date.available2013-09-30T18:51:08Z
dc.date.available2014-05-20T14:16:55Z
dc.date.issued2012-08-01
dc.description.abstractTraditional Shuttle Radar Topography Mission (SRTM) topographic datasets hold limited value in the geomorphic analysis of low-relief terrains. To address this shortcoming, this paper presents a series of techniques designed to enhance digital elevation models (DEMs) of environments dominated by low-amplitude landforms, such as a fluvial megafan system. These techniques were validated through the study of a wide depositional tract composed of several megafans located within the Brazilian Pantanal. The Taquari megafan is the most remarkable of these features, covering an area of approximately 49,000 km(2). To enhance the SRTM-DEM, the megafan global topography was calculated and found to be accurately represented by a second order polynomial. Simple subtraction of the global topography from altitude produced a new DEM product, which greatly enhanced low amplitude landforms within the Taquari megafan. A field campaign and optical satellite images were used to ground-truth features on the enhanced DEM, which consisted of both depositional (constructional) and erosional features. The results demonstrate that depositional lobes are the dominant landforms on the megafan. A model linking baselevel change, avulsion, clastic sedimentation, and erosion is proposed to explain the microtopographic features on the Taquari megafan surface. The study confirms the potential promise of enhanced DEMs for geomorphological research in alluvial settings. (c) 2012 Elsevier B.V. All rights reserved.en
dc.description.affiliationInst Nacl Pesquisas Espaciais, Div Sensoriamento Remoto, BR-12227010 Sao Jose Dos Campos, Brazil
dc.description.affiliationUniv Estadual Paulista, Dept Geol Aplicada, BR-13506900 Rio Claro, SP, Brazil
dc.description.affiliationUniv Arizona, Dept Geosci, Tucson, AZ 85721 USA
dc.description.affiliationUnespUniv Estadual Paulista, Dept Geol Aplicada, BR-13506900 Rio Claro, SP, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 06/02381-8
dc.description.sponsorshipIdFAPESP: 07/55987-3
dc.description.sponsorshipIdCNPq: 305108/2009-3
dc.format.extent82-92
dc.identifierhttp://dx.doi.org/10.1016/j.geomorph.2012.04.003
dc.identifier.citationGeomorphology. Amsterdam: Elsevier B.V., v. 161, p. 82-92, 2012.
dc.identifier.doi10.1016/j.geomorph.2012.04.003
dc.identifier.issn0169-555X
dc.identifier.lattes0471102133658128
dc.identifier.urihttp://hdl.handle.net/11449/25075
dc.identifier.wosWOS:000305672700007
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofGeomorphology
dc.relation.ispartofjcr3.308
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectMegafanen
dc.subjectQuaternaryen
dc.subjectDigital elevation model (DEM)en
dc.subjectSRTMen
dc.subjectPantanalen
dc.titleRemote sensing analysis of depositional landforms in alluvial settings: Method development and application to the Taquari megafan, Pantanal (Brazil)en
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.lattes0471102133658128
unesp.author.orcid0000-0002-3097-5832[2]
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Geociências e Ciências Exatas, Rio Claropt

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Artigos - Geologia Aplicada - IGCE