Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Outros Autores: | , , , |
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. |
id |
UFRJ-21_f1dedd2d3ac1f6af01e7c5db3f5b307b |
---|---|
oai_identifier_str |
oai:www.revistas.ufrj.br:article/29505 |
network_acronym_str |
UFRJ-21 |
network_name_str |
Anuário do Instituto de Geociências (Online) |
repository_id_str |
|
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) instacron:UFRJ |
instname_str |
Universidade Federal do Rio de Janeiro (UFRJ) |
instacron_str |
UFRJ |
institution |
UFRJ |
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|| |
_version_ |
1797053541188108288 |