Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Árvore (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622019000400206 |
Resumo: | ABSTRACT The Iron Quadrangle (IQ) region in Minas Gerais is remarkably geobiodiverse, despite a long history of anthropogenic pressures such as mining and urbanization, but still lacks detailed studies on the distribution of its remaining native vegetation in different substrates. In this study, we utilized Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, besides Gamma-spectrometry (Gamma) survey data associated with existing geological mapping (GM) and extensive fieldwork, to discriminate and quantify remnants of vegetation on ferruginous substrates in the IQ. The Maximum Likelihood (ML) algorithm was used to classify the vegetation types, thus named: open Rupestrian Field, shrubby Rupestrian Field, Capão Forest, Cerrado stricto sensu, Cerrado Field, Seasonal Forests, Pastures and Reforestation (the latter three regardless of substrate type) associated with the predominant substrates (ferruginous ironstone, phyllites, and quartzite). The use of ASTER images alone did not allow a reliable separation of ferruginous and non-ferruginous substrates, but the integration of all different data (ASTER-ML + Gamma + GM) allowed the provisional mapping of the vegetation associated with ferruginous substrates, potentially ferruginous and non-ferruginous substrates. The resulting map shows that the vegetation on ferruginous and potentially ferruginous substrates cover 8.7% and 6.9% of the IQ, respectively. The detailed analysis of the distribution and fragmentation of phytophysiognomies on ferruginous substrates is of great importance for developing strategies to conserve the geobiodiversity of the IQ, and need to be further refined by checking and field mapping by novel approaches. |
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Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas GeraisSupervised classificationRemote sensingAerogeophysical dataABSTRACT The Iron Quadrangle (IQ) region in Minas Gerais is remarkably geobiodiverse, despite a long history of anthropogenic pressures such as mining and urbanization, but still lacks detailed studies on the distribution of its remaining native vegetation in different substrates. In this study, we utilized Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, besides Gamma-spectrometry (Gamma) survey data associated with existing geological mapping (GM) and extensive fieldwork, to discriminate and quantify remnants of vegetation on ferruginous substrates in the IQ. The Maximum Likelihood (ML) algorithm was used to classify the vegetation types, thus named: open Rupestrian Field, shrubby Rupestrian Field, Capão Forest, Cerrado stricto sensu, Cerrado Field, Seasonal Forests, Pastures and Reforestation (the latter three regardless of substrate type) associated with the predominant substrates (ferruginous ironstone, phyllites, and quartzite). The use of ASTER images alone did not allow a reliable separation of ferruginous and non-ferruginous substrates, but the integration of all different data (ASTER-ML + Gamma + GM) allowed the provisional mapping of the vegetation associated with ferruginous substrates, potentially ferruginous and non-ferruginous substrates. The resulting map shows that the vegetation on ferruginous and potentially ferruginous substrates cover 8.7% and 6.9% of the IQ, respectively. The detailed analysis of the distribution and fragmentation of phytophysiognomies on ferruginous substrates is of great importance for developing strategies to conserve the geobiodiversity of the IQ, and need to be further refined by checking and field mapping by novel approaches.Sociedade de Investigações Florestais2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622019000400206Revista Árvore v.43 n.4 2019reponame:Revista Árvore (Online)instname:Universidade Federal de Viçosa (UFV)instacron:SIF10.1590/1806-90882019000400006info:eu-repo/semantics/openAccessMendonça,Bruno Araujo Furtado deFernandes Filho,Elpído InácioAssis,Luciano Mozer deSchaefer,Carlos Ernesto Gonçalves ReynaudBrandão,Pedro ChristoFaria,Maola MoniqueSantos,Eliana Elizabet dosPereira,Aianã Francisco Santoseng2020-02-18T00:00:00Zoai:scielo:S0100-67622019000400206Revistahttp://www.scielo.br/revistas/rarv/iaboutj.htmPUBhttps://old.scielo.br/oai/scielo-oai.php||r.arvore@ufv.br1806-90880100-6762opendoar:2020-02-18T00:00Revista Árvore (Online) - Universidade Federal de Viçosa (UFV)false |
dc.title.none.fl_str_mv |
Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais |
title |
Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais |
spellingShingle |
Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais Mendonça,Bruno Araujo Furtado de Supervised classification Remote sensing Aerogeophysical data |
title_short |
Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais |
title_full |
Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais |
title_fullStr |
Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais |
title_full_unstemmed |
Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais |
title_sort |
Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais |
author |
Mendonça,Bruno Araujo Furtado de |
author_facet |
Mendonça,Bruno Araujo Furtado de Fernandes Filho,Elpído Inácio Assis,Luciano Mozer de Schaefer,Carlos Ernesto Gonçalves Reynaud Brandão,Pedro Christo Faria,Maola Monique Santos,Eliana Elizabet dos Pereira,Aianã Francisco Santos |
author_role |
author |
author2 |
Fernandes Filho,Elpído Inácio Assis,Luciano Mozer de Schaefer,Carlos Ernesto Gonçalves Reynaud Brandão,Pedro Christo Faria,Maola Monique Santos,Eliana Elizabet dos Pereira,Aianã Francisco Santos |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Mendonça,Bruno Araujo Furtado de Fernandes Filho,Elpído Inácio Assis,Luciano Mozer de Schaefer,Carlos Ernesto Gonçalves Reynaud Brandão,Pedro Christo Faria,Maola Monique Santos,Eliana Elizabet dos Pereira,Aianã Francisco Santos |
dc.subject.por.fl_str_mv |
Supervised classification Remote sensing Aerogeophysical data |
topic |
Supervised classification Remote sensing Aerogeophysical data |
description |
ABSTRACT The Iron Quadrangle (IQ) region in Minas Gerais is remarkably geobiodiverse, despite a long history of anthropogenic pressures such as mining and urbanization, but still lacks detailed studies on the distribution of its remaining native vegetation in different substrates. In this study, we utilized Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, besides Gamma-spectrometry (Gamma) survey data associated with existing geological mapping (GM) and extensive fieldwork, to discriminate and quantify remnants of vegetation on ferruginous substrates in the IQ. The Maximum Likelihood (ML) algorithm was used to classify the vegetation types, thus named: open Rupestrian Field, shrubby Rupestrian Field, Capão Forest, Cerrado stricto sensu, Cerrado Field, Seasonal Forests, Pastures and Reforestation (the latter three regardless of substrate type) associated with the predominant substrates (ferruginous ironstone, phyllites, and quartzite). The use of ASTER images alone did not allow a reliable separation of ferruginous and non-ferruginous substrates, but the integration of all different data (ASTER-ML + Gamma + GM) allowed the provisional mapping of the vegetation associated with ferruginous substrates, potentially ferruginous and non-ferruginous substrates. The resulting map shows that the vegetation on ferruginous and potentially ferruginous substrates cover 8.7% and 6.9% of the IQ, respectively. The detailed analysis of the distribution and fragmentation of phytophysiognomies on ferruginous substrates is of great importance for developing strategies to conserve the geobiodiversity of the IQ, and need to be further refined by checking and field mapping by novel approaches. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622019000400206 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622019000400206 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1806-90882019000400006 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade de Investigações Florestais |
publisher.none.fl_str_mv |
Sociedade de Investigações Florestais |
dc.source.none.fl_str_mv |
Revista Árvore v.43 n.4 2019 reponame:Revista Árvore (Online) instname:Universidade Federal de Viçosa (UFV) instacron:SIF |
instname_str |
Universidade Federal de Viçosa (UFV) |
instacron_str |
SIF |
institution |
SIF |
reponame_str |
Revista Árvore (Online) |
collection |
Revista Árvore (Online) |
repository.name.fl_str_mv |
Revista Árvore (Online) - Universidade Federal de Viçosa (UFV) |
repository.mail.fl_str_mv |
||r.arvore@ufv.br |
_version_ |
1750318003402047488 |