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| 8. |  | TONINI, H.; MORALES, M. M.; PORFIRIO-DA-SILVA, V. Biomassa e qualidade da madeira do eucalipto em monocultivo e sistema silvipastoril. In: FARIAS NETO, A. L. de; NASCIMENTO, A. F. do; ROSSONI, A. L.; MAGALHÃES, C. A. de S.; ITUASSU, D. R.; HOOGERHEIDE, E. S. S.; IKEDA, F. S.; FERNANDES JUNIOR, F.; FARIA, G. R.; ISERNHAGEN, I.; VENDRUSCULO, L. G.; MORALES, M. M.; CARNEVALLI, R. A. (Ed.). Embrapa Agrossilvipastoril: primeiras contribuições para o desenvolvimento de uma agropecuária sustentável. Brasília, DF: Embrapa, 2019. pt. 4, cap. 9, p. 226-230.| Biblioteca(s): Embrapa Agrossilvipastoril; Embrapa Florestas; Embrapa Pecuária Sul. |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
12/02/2015 |
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Data da última atualização: |
12/02/2015 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 3 |
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Autoria: |
CARVALHO JÚNIOR, O. A. de; GUIMARÃES, R. F.; MONTGOMERY, D. R.; GILLESPIE, A. R.; GOMES, R. A. T.; MARTINS, E. de S.; SILVA, N. C. |
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Afiliação: |
OSMAR 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. |
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Título: |
Karst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí Group, Brazil. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Remote sensing, v. 6, p. 330-351, 2014. |
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DOI: |
10.3390/rs6010330 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: 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. MenosAbstract: 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... Mostrar Tudo |
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Palavras-Chave: |
Análise DEM; Brasil. |
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Thesagro: |
Calcário; Sensoriamento remoto; Sistema de Informação Geográfica. |
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Thesaurus NAL: |
Brazil; Geographic information systems; Karsts; Limestone; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/117884/1/Karst-depression-Eder.pdf
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Marc: |
LEADER 03057naa a2200325 a 4500
001 2008550
005 2015-02-12
008 2014 bl uuuu u00u1 u #d
024 7 $a10.3390/rs6010330$2DOI
100 1 $aCARVALHO JÚNIOR, O. A. de
245 $aKarst depression detection using ASTER, ALOS/PRISM and SRTM-Derived digital elevation models in the Bambuí Group, Brazil.
260 $c2014
520 $aAbstract: 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.
650 $aBrazil
650 $aGeographic information systems
650 $aKarsts
650 $aLimestone
650 $aRemote sensing
650 $aCalcário
650 $aSensoriamento remoto
650 $aSistema de Informação Geográfica
653 $aAnálise DEM
653 $aBrasil
700 1 $aGUIMARÃES, R. F.
700 1 $aMONTGOMERY, D. R.
700 1 $aGILLESPIE, A. R.
700 1 $aGOMES, R. A. T.
700 1 $aMARTINS, E. de S.
700 1 $aSILVA, N. C.
773 $tRemote sensing$gv. 6, p. 330-351, 2014.
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