Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais

Detalhes bibliográficos
Autor(a) principal: Mendonça,Bruno Araujo Furtado de
Data de Publicação: 2019
Outros Autores: 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
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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spelling 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
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