Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil
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 elevatio...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-12-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/6/1/330 |
| _version_ | 1818488264097529856 |
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| author | Osmar Abílio de Carvalho Renato Fontes Guimarães David R. Montgomery Alan R. Gillespie Roberto Arnaldo Trancoso Gomes Éder de Souza Martins Nilton Correia Silva |
| author_facet | Osmar Abílio de Carvalho Renato Fontes Guimarães David R. Montgomery Alan R. Gillespie Roberto Arnaldo Trancoso Gomes Éder de Souza Martins Nilton Correia Silva |
| author_sort | Osmar Abílio de Carvalho |
| collection | DOAJ |
| 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 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. |
| first_indexed | 2024-12-10T16:48:42Z |
| format | Article |
| id | doaj.art-de89fd2591bd40749b160cef9ce89a36 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T16:48:42Z |
| publishDate | 2013-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-de89fd2591bd40749b160cef9ce89a362022-12-22T01:40:58ZengMDPI AGRemote Sensing2072-42922013-12-016133035110.3390/rs6010330rs6010330Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, BrazilOsmar Abílio de Carvalho0Renato Fontes Guimarães1David R. Montgomery2Alan R. Gillespie3Roberto Arnaldo Trancoso Gomes4Éder de Souza Martins5Nilton Correia Silva6Departamento de Geografia, Campus Universitário Darcy Ribeiro, Universidade de Brasília (UnB), Asa Norte, Brasília, DF 70910-900, BrazilDepartamento de Geografia, Campus Universitário Darcy Ribeiro, Universidade de Brasília (UnB), Asa Norte, Brasília, DF 70910-900, BrazilDepartment of Earth and Space Sciences, University of Washington, Seattle, WA 98195, USADepartment of Earth and Space Sciences, University of Washington, Seattle, WA 98195, USADepartamento de Geografia, Campus Universitário Darcy Ribeiro, Universidade de Brasília (UnB), Asa Norte, Brasília, DF 70910-900, BrazilEMBRAPA Cerrados, Planaltina, DF 73310-970, BrazilFaculdade de Engenharias do Gama, Universidade de Brasília (UnB), Gama, DF-72444-240, BrazilRemote 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.http://www.mdpi.com/2072-4292/6/1/330KarstlimestoneDEM analysisGISremote sensingBrazil |
| spellingShingle | Osmar Abílio de Carvalho Renato Fontes Guimarães David R. Montgomery Alan R. Gillespie Roberto Arnaldo Trancoso Gomes Éder de Souza Martins Nilton Correia Silva Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil Remote Sensing Karst limestone DEM analysis GIS remote sensing Brazil |
| title | Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil |
| title_full | Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil |
| title_fullStr | Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil |
| title_full_unstemmed | Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil |
| title_short | Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil |
| title_sort | karst depression detection using aster alos prism and srtm derived digital elevation models in the bambui group brazil |
| topic | Karst limestone DEM analysis GIS remote sensing Brazil |
| url | http://www.mdpi.com/2072-4292/6/1/330 |
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