Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software

Detalhes bibliográficos
Autor(a) principal: Gonçalves, Ariadne Barbosa
Data de Publicação: 2019
Outros Autores: Godoi, Raquel de Faria, Filho, Antonio Conceição Paranhos, Folhes, Marcelo Theophilo, Pistori, Hemerson
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anuário do Instituto de Geociências (Online)
Texto Completo: https://revistas.ufrj.br/index.php/aigeo/article/view/29505
Resumo: These paper reports applications using satellite images to the identification of vegetation types in the Campo Grande city. This identification allows studies of urban vegetation, palynology and environmental changes. Images from Landsat 8 and Rapideye satellites from the Campo Grande urban area were used. A soil coverage map was done for each one of the seven sub-regions. The Normalized Difference Vegetation Index was applied. In addition, a field survey was carried out to confirm the vegetation types sites through satellite images. Satellite images and in situ data validation allowed the distinction of the following features: water, urban structure, herbaceous, open and dense vegetation. For the identification of urban vegetation, Rapideye images were the most suitable for this type of study. The Rapideye satellite sensor detected 6.55% more dense vegetation area than Landsat 8 images.
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spelling Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free SoftwareRemote sensing; Urban modelling; Landsat 8; RapideyeThese paper reports applications using satellite images to the identification of vegetation types in the Campo Grande city. This identification allows studies of urban vegetation, palynology and environmental changes. Images from Landsat 8 and Rapideye satellites from the Campo Grande urban area were used. A soil coverage map was done for each one of the seven sub-regions. The Normalized Difference Vegetation Index was applied. In addition, a field survey was carried out to confirm the vegetation types sites through satellite images. Satellite images and in situ data validation allowed the distinction of the following features: water, urban structure, herbaceous, open and dense vegetation. For the identification of urban vegetation, Rapideye images were the most suitable for this type of study. The Rapideye satellite sensor detected 6.55% more dense vegetation area than Landsat 8 images.Universidade Federal do Rio de JaneiroGonçalves, Ariadne BarbosaGodoi, Raquel de FariaFilho, Antonio Conceição ParanhosFolhes, Marcelo TheophiloPistori, Hemerson2019-10-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/2950510.11137/2018_3_24_36Anuário do Instituto de Geociências; Vol 41, No 3 (2018); 24-36Anuário do Instituto de Geociências; Vol 41, No 3 (2018); 24-361982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/29505/16563Copyright (c) 2019 Anuário do Instituto de Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2020-08-12T18:46:40Zoai:www.revistas.ufrj.br:article/29505Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2020-08-12T18:46:40Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false
dc.title.none.fl_str_mv Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software
title Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software
spellingShingle Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software
Gonçalves, Ariadne Barbosa
Remote sensing; Urban modelling; Landsat 8; Rapideye
title_short Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software
title_full Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software
title_fullStr Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software
title_full_unstemmed Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software
title_sort Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software
author Gonçalves, Ariadne Barbosa
author_facet Gonçalves, Ariadne Barbosa
Godoi, Raquel de Faria
Filho, Antonio Conceição Paranhos
Folhes, Marcelo Theophilo
Pistori, Hemerson
author_role author
author2 Godoi, Raquel de Faria
Filho, Antonio Conceição Paranhos
Folhes, Marcelo Theophilo
Pistori, Hemerson
author2_role author
author
author
author
dc.contributor.none.fl_str_mv

dc.contributor.author.fl_str_mv Gonçalves, Ariadne Barbosa
Godoi, Raquel de Faria
Filho, Antonio Conceição Paranhos
Folhes, Marcelo Theophilo
Pistori, Hemerson
dc.subject.none.fl_str_mv
dc.subject.por.fl_str_mv Remote sensing; Urban modelling; Landsat 8; Rapideye
topic Remote sensing; Urban modelling; Landsat 8; Rapideye
description These paper reports applications using satellite images to the identification of vegetation types in the Campo Grande city. This identification allows studies of urban vegetation, palynology and environmental changes. Images from Landsat 8 and Rapideye satellites from the Campo Grande urban area were used. A soil coverage map was done for each one of the seven sub-regions. The Normalized Difference Vegetation Index was applied. In addition, a field survey was carried out to confirm the vegetation types sites through satellite images. Satellite images and in situ data validation allowed the distinction of the following features: water, urban structure, herbaceous, open and dense vegetation. For the identification of urban vegetation, Rapideye images were the most suitable for this type of study. The Rapideye satellite sensor detected 6.55% more dense vegetation area than Landsat 8 images.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-16
dc.type.none.fl_str_mv

dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/29505
10.11137/2018_3_24_36
url https://revistas.ufrj.br/index.php/aigeo/article/view/29505
identifier_str_mv 10.11137/2018_3_24_36
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/29505/16563
dc.rights.driver.fl_str_mv Copyright (c) 2019 Anuário do Instituto de Geociências
http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2019 Anuário do Instituto de Geociências
http://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
dc.source.none.fl_str_mv Anuário do Instituto de Geociências; Vol 41, No 3 (2018); 24-36
Anuário do Instituto de Geociências; Vol 41, No 3 (2018); 24-36
1982-3908
0101-9759
reponame:Anuário do Instituto de Geociências (Online)
instname:Universidade Federal do Rio de Janeiro (UFRJ)
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reponame_str Anuário do Instituto de Geociências (Online)
collection Anuário do Instituto de Geociências (Online)
repository.name.fl_str_mv Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)
repository.mail.fl_str_mv anuario@igeo.ufrj.br||
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