Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil

Detalhes bibliográficos
Autor(a) principal: Carvalho Júnior, Osmar Abílio de
Data de Publicação: 2014
Outros Autores: Guimarães, Renato Fontes, Montgomery, David R., Gillespie, Alan R., Gomes, Roberto Arnaldo Trancoso, Martins, Éder de Souza, Silva, Nilton Correia
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Institucional da UnB
Texto Completo: http://repositorio.unb.br/handle/10482/16183
https://dx.doi.org/10.3390/rs6010330
Resumo: 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 nr 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 liigher-resolution LiDAR-generated DEMs.
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spelling Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, BrasilKarstCalcárioModelo digital de terrenoSistemas de informação geográficaSensoriamento remotoRemote 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 nr 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 liigher-resolution LiDAR-generated DEMs.MDPI - Multidisciplinar Digital Publishing2014-09-03T11:36:02Z2014-09-03T11:36:02Z2014info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfCARVALHO JÚNIOR, Osmar Abílio de at al. Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil. Remote Sensing, Basel, Suíça, v. 6, p. 330-351, 2014. Disponível em: <http://www.mdpi.com/2072-4292/6/1/330>. Acesso em: 18 ago. 2014.http://repositorio.unb.br/handle/10482/16183https://dx.doi.org/10.3390/rs6010330Remote Sensing - © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).info:eu-repo/semantics/openAccessCarvalho Júnior, Osmar Abílio deGuimarães, Renato FontesMontgomery, David R.Gillespie, Alan R.Gomes, Roberto Arnaldo TrancosoMartins, Éder de SouzaSilva, Nilton Correiaporreponame:Repositório Institucional da UnBinstname:Universidade de Brasília (UnB)instacron:UNB2023-05-27T00:32:48Zoai:repositorio.unb.br:10482/16183Repositório InstitucionalPUBhttps://repositorio.unb.br/oai/requestrepositorio@unb.bropendoar:2023-05-27T00:32:48Repositório Institucional da UnB - Universidade de Brasília (UnB)false
dc.title.none.fl_str_mv Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil
title Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil
spellingShingle Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil
Carvalho Júnior, Osmar Abílio de
Karst
Calcário
Modelo digital de terreno
Sistemas de informação geográfica
Sensoriamento remoto
title_short Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil
title_full Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil
title_fullStr Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil
title_full_unstemmed Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil
title_sort Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil
author Carvalho Júnior, Osmar Abílio de
author_facet Carvalho Júnior, Osmar Abílio de
Guimarães, Renato Fontes
Montgomery, David R.
Gillespie, Alan R.
Gomes, Roberto Arnaldo Trancoso
Martins, Éder de Souza
Silva, Nilton Correia
author_role author
author2 Guimarães, Renato Fontes
Montgomery, David R.
Gillespie, Alan R.
Gomes, Roberto Arnaldo Trancoso
Martins, Éder de Souza
Silva, Nilton Correia
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Carvalho Júnior, Osmar Abílio de
Guimarães, Renato Fontes
Montgomery, David R.
Gillespie, Alan R.
Gomes, Roberto Arnaldo Trancoso
Martins, Éder de Souza
Silva, Nilton Correia
dc.subject.por.fl_str_mv Karst
Calcário
Modelo digital de terreno
Sistemas de informação geográfica
Sensoriamento remoto
topic Karst
Calcário
Modelo digital de terreno
Sistemas de informação geográfica
Sensoriamento remoto
description 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 nr 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 liigher-resolution LiDAR-generated DEMs.
publishDate 2014
dc.date.none.fl_str_mv 2014-09-03T11:36:02Z
2014-09-03T11:36:02Z
2014
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv CARVALHO JÚNIOR, Osmar Abílio de at al. Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil. Remote Sensing, Basel, Suíça, v. 6, p. 330-351, 2014. Disponível em: <http://www.mdpi.com/2072-4292/6/1/330>. Acesso em: 18 ago. 2014.
http://repositorio.unb.br/handle/10482/16183
https://dx.doi.org/10.3390/rs6010330
identifier_str_mv CARVALHO JÚNIOR, Osmar Abílio de at al. Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil. Remote Sensing, Basel, Suíça, v. 6, p. 330-351, 2014. Disponível em: <http://www.mdpi.com/2072-4292/6/1/330>. Acesso em: 18 ago. 2014.
url http://repositorio.unb.br/handle/10482/16183
https://dx.doi.org/10.3390/rs6010330
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language por
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dc.publisher.none.fl_str_mv MDPI - Multidisciplinar Digital Publishing
publisher.none.fl_str_mv MDPI - Multidisciplinar Digital Publishing
dc.source.none.fl_str_mv reponame:Repositório Institucional da UnB
instname:Universidade de Brasília (UnB)
instacron:UNB
instname_str Universidade de Brasília (UnB)
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institution UNB
reponame_str Repositório Institucional da UnB
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repository.name.fl_str_mv Repositório Institucional da UnB - Universidade de Brasília (UnB)
repository.mail.fl_str_mv repositorio@unb.br
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