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dc.contributor.authorCARVALHO JÚNIOR, O. A. dept_BR
dc.contributor.authorGUIMARÃES, R. F.pt_BR
dc.contributor.authorMONTGOMERY, D. R.pt_BR
dc.contributor.authorGILLESPIE, A. R.pt_BR
dc.contributor.authorGOMES, R. A. T.pt_BR
dc.contributor.authorMARTINS, E. de S.pt_BR
dc.contributor.authorSILVA, N. C.pt_BR
dc.date.accessioned2015-02-12T11:11:11Zpt_BR
dc.date.available2015-02-12T11:11:11Zpt_BR
dc.date.created2015-02-12pt_BR
dc.date.issued2014pt_BR
dc.identifier.citationRemote sensing, v. 6, p. 330-351, 2014.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1008550pt_BR
dc.descriptionAbstract: Remote sensing has been used in karst studies to identify limestone terrain, describe exokarst features, analyze karst depressions, and detect geological structures important to karst development. The aim of this work is to investigate the use of ASTER-, SRTM- and ALOS/PRISM-derived digital elevation models (DEMs) to detect and quantify natural karst depressions along the São Francisco River near Barreiras city, northeast Brazil. The study area is a karst landscape characterized by karst depressions (dolines), closed depressions in limestone, many of which contain standing water connected with the ground-water table. The base of dolines is typically sealed with an impermeable clay layer covered by standing water or herbaceous vegetation. We identify dolines by combining the extraction of sink depth from DEMs, morphometric analysis using GIS, and visual interpretation. Our methodology is a semi-automatic approach involving several steps: (a) DEM acquisition; (b) sink-depth calculation using the difference between the raw DEM and the corresponding DEM with sinks filled; and (c) elimination of falsely identified karst depressions using morphometric attributes. The advantages and limitations of the applied methodology using different DEMs are examined by comparison with a sinkhole map generated from traditional geomorphological investigations based on visual interpretation of the high-resolution remote sensing images and field surveys. The threshold values of the depth, area size and circularity index appropriate for distinguishing dolines were identified from the maximum overall accuracy obtained by comparison with a true doline map. Our results indicate that the best performance of the proposed methodology for meso-scale karst feature detection was using ALOS/PRISM data with a threshold depth > 2 m; areas > 13,125 m2 and circularity indexes > 0.3 (overall accuracy of 0.53). The overall correct identification of around half of the true dolines suggests the potential to substantially improve doline identification using higher-resolution LiDAR-generated DEMs.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectAnálise DEMpt_BR
dc.subjectBrasilpt_BR
dc.titleKarst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí Group, Brazil.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2015-02-12T11:11:11Zpt_BR
dc.subject.thesagroSensoriamento remotopt_BR
dc.subject.thesagroCalcáriopt_BR
dc.subject.thesagroSistema de Informação Geográficapt_BR
dc.subject.nalthesaurusKarstspt_BR
dc.subject.nalthesaurusLimestonept_BR
dc.subject.nalthesaurusGeographic information systemspt_BR
dc.subject.nalthesaurusRemote sensingpt_BR
dc.subject.nalthesaurusBrazilpt_BR
riaa.ainfo.id1008550pt_BR
riaa.ainfo.lastupdate2015-02-12pt_BR
dc.identifier.doi10.3390/rs6010330pt_BR
dc.contributor.institutionOSMAR ABÍLIO DE CARVALHO JÚNIOR; RENATO FONTES GUIMARÃES; DAVID R. MONTGOMERY; ALAN R. GILLESPIE; ROBERTO ARNALDO TRANCOSO GOMES; EDER DE SOUZA MARTINS, CPAC; NILTON CORREIA SILVA.pt_BR
Aparece en las colecciones:Artigo em periódico indexado (CPAC)

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