Caracterização de Fitofisionomias Urbanas Usando NDVI em Imagens de Satélite e Software Livre
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 w...
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Universidade Federal do Rio de Janeiro
2018-12-01
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doaj-c1a80699f9cb4e6abc4b3cc04476b7372020-11-25T03:04:01ZengUniversidade Federal do Rio de JaneiroAnuário do Instituto de Geociências0101-97591982-39082018-12-014132436http://dx.doi.org/10.11137/2018_3_24_36Caracterização de Fitofisionomias Urbanas Usando NDVI em Imagens de Satélite e Software LivreAriadne Barbosa Gonçalves0Raquel de Faria Godoi1Antonio Conceição Paranhos Filho2Marcelo Theophilo Folhes3Hemerson Pistori4Universidade Católica Dom BoscoUniversidade Federal de Mato Grosso do SulUniversidade Federal de Mato Grosso do SulIbiGeo Ciências AplicadasUniversidade Católica Dom BoscoThese 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.http://www.anuario.igeo.ufrj.br/2018_03/2018_3_24_36.pdfRemote sensingUrban modellingLandsat 8Rapideye |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ariadne Barbosa Gonçalves Raquel de Faria Godoi Antonio Conceição Paranhos Filho Marcelo Theophilo Folhes Hemerson Pistori |
spellingShingle |
Ariadne Barbosa Gonçalves Raquel de Faria Godoi Antonio Conceição Paranhos Filho Marcelo Theophilo Folhes Hemerson Pistori Caracterização de Fitofisionomias Urbanas Usando NDVI em Imagens de Satélite e Software Livre Anuário do Instituto de Geociências Remote sensing Urban modelling Landsat 8 Rapideye |
author_facet |
Ariadne Barbosa Gonçalves Raquel de Faria Godoi Antonio Conceição Paranhos Filho Marcelo Theophilo Folhes Hemerson Pistori |
author_sort |
Ariadne Barbosa Gonçalves |
title |
Caracterização de Fitofisionomias Urbanas Usando NDVI em Imagens de Satélite e Software Livre |
title_short |
Caracterização de Fitofisionomias Urbanas Usando NDVI em Imagens de Satélite e Software Livre |
title_full |
Caracterização de Fitofisionomias Urbanas Usando NDVI em Imagens de Satélite e Software Livre |
title_fullStr |
Caracterização de Fitofisionomias Urbanas Usando NDVI em Imagens de Satélite e Software Livre |
title_full_unstemmed |
Caracterização de Fitofisionomias Urbanas Usando NDVI em Imagens de Satélite e Software Livre |
title_sort |
caracterização de fitofisionomias urbanas usando ndvi em imagens de satélite e software livre |
publisher |
Universidade Federal do Rio de Janeiro |
series |
Anuário do Instituto de Geociências |
issn |
0101-9759 1982-3908 |
publishDate |
2018-12-01 |
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. |
topic |
Remote sensing Urban modelling Landsat 8 Rapideye |
url |
http://www.anuario.igeo.ufrj.br/2018_03/2018_3_24_36.pdf |
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