Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí group, Brasil
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , , |
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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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 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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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UNB |
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UNB |
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Repositório Institucional da UnB |
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Repositório Institucional da UnB |
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Repositório Institucional da UnB - Universidade de Brasília (UnB) |
repository.mail.fl_str_mv |
repositorio@unb.br |
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