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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Main Authors: Ariadne Barbosa Gonçalves, Raquel de Faria Godoi, Antonio Conceição Paranhos Filho, Marcelo Theophilo Folhes, Hemerson Pistori
Format: Article
Language:English
Published: Universidade Federal do Rio de Janeiro 2018-12-01
Series:Anuário do Instituto de Geociências
Subjects:
Online Access:http://www.anuario.igeo.ufrj.br/2018_03/2018_3_24_36.pdf
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spelling 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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