Preliminary assessment of AVHRR/NOAA data to detect deforestation in the Pantanal
Autor(a) principal: | |
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Data de Publicação: | 1998 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Pesquisa Agropecuária Brasileira (Online) |
Texto Completo: | https://seer.sct.embrapa.br/index.php/pab/article/view/5055 |
Resumo: | This paper presents preliminary results of deforestation detection in the Pantanal, using AVHRR/NOAA image data. This initial analysis is part of the MULPAN Project, which investigates the usefulness of data gathered by different sensors for surveying thematic information of the Pantanal physical environment. This Project results from cooperation between INPE (Instituto Nacional de Pesquisas Espaciais) and Embrapa (Empresa Brasileira de Pesquisa Agropecuária). A full resolution AVHRR/NOAA image, channel 2 (0.72-1.1 µm) and channel 3 (3.5-3.9 µm) from Sept. 15, 1990, was used to detect deforestation. The image was geometrically corrected, remapped to conform to the cylindrical equidistant projection, and classified using a maximum likelihood algorithm. SPRING-1.1, a geographical information and image processing system, was used for both geometric correction and classification. Classification assessment was based on previous maps derived from Landsat-5/TM from 1990/91. Results indicated that the methodology is not efficient to detect deforestation, mainly because Pantanal is a region of a high occurence of savannas. The AVHRR/NOAA image discriminated only 20% of the total amount of 112 points of deforestation identified with Landsat-5/TM data. The remaining points of deforestation were wrongly classified as savanna or dense savanna. |
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Preliminary assessment of AVHRR/NOAA data to detect deforestation in the PantanalAvaliação preliminar da utilização de imagens AVHRR/NOAA na detecção de desmatamento no Pantanalsensoriamento remoto; meio físico; Cerradoremote sensing; physical environment; SavannaThis paper presents preliminary results of deforestation detection in the Pantanal, using AVHRR/NOAA image data. This initial analysis is part of the MULPAN Project, which investigates the usefulness of data gathered by different sensors for surveying thematic information of the Pantanal physical environment. This Project results from cooperation between INPE (Instituto Nacional de Pesquisas Espaciais) and Embrapa (Empresa Brasileira de Pesquisa Agropecuária). A full resolution AVHRR/NOAA image, channel 2 (0.72-1.1 µm) and channel 3 (3.5-3.9 µm) from Sept. 15, 1990, was used to detect deforestation. The image was geometrically corrected, remapped to conform to the cylindrical equidistant projection, and classified using a maximum likelihood algorithm. SPRING-1.1, a geographical information and image processing system, was used for both geometric correction and classification. Classification assessment was based on previous maps derived from Landsat-5/TM from 1990/91. Results indicated that the methodology is not efficient to detect deforestation, mainly because Pantanal is a region of a high occurence of savannas. The AVHRR/NOAA image discriminated only 20% of the total amount of 112 points of deforestation identified with Landsat-5/TM data. The remaining points of deforestation were wrongly classified as savanna or dense savanna.Apresentam-se resultados preliminares da detecção de áreas desmatadas no Pantanal a partir de dados de imagens AVHRR/NOAA. Esta primeira análise compreende uma das abordagens do Projeto MULPAN, que avalia a potencialidade dos dados obtidos por diferentes sistemas sensores para levantamento de dados temáticos do meio físico no Pantanal. Este Projeto é resultado da cooperação entre o INPE (Instituto Nacional de Pesquisas Espaciais) e a Embrapa (Empresa Brasileira de Pesquisa Agropecuária). Na detecção de desmatamentos utilizou-se imagem AVHRR/NOAA de 15/09/90, nos canais 2 (0,72-1,1 µm) e 3 (3,5-3,9 µm). A imagem foi inicialmente corrigida geometricamente, registrada em um mapa, e classificada com o algoritmo Maxver. No procedimento de registro e classificação da imagem utilizou-se o Sistema de Processamento de Informações Georreferenciadas – SPRING-1.1. A avaliação da classificação baseou-se no mapeamento realizado com imagens TM/Landsat de 1990/91, e observou-se que na região do Pantanal, com alta incidência de cerrados, a metodologia adotada não foi eficiente para discriminar os desmatamentos. Do total de 112 pontos de desmatamento, identificados no mapeamento TM/Landsat, apenas cerca de 20 % foi registrado pela imagem AVHRR/NOAA. A maior parte dos demais pontos de desmatamento foi classificada como Cerrado ou Cerradão.Pesquisa Agropecuaria BrasileiraPesquisa Agropecuária BrasileiraMantovani, Angelica Carvalho Di MaioAmaral, Silvana1998-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.sct.embrapa.br/index.php/pab/article/view/5055Pesquisa Agropecuaria Brasileira; v.33, n. especial, out. 1998; 1683-1690Pesquisa Agropecuária Brasileira; v.33, n. especial, out. 1998; 1683-16901678-39210100-104xreponame:Pesquisa Agropecuária Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAporhttps://seer.sct.embrapa.br/index.php/pab/article/view/5055/7201info:eu-repo/semantics/openAccess2015-01-21T16:31:09Zoai:ojs.seer.sct.embrapa.br:article/5055Revistahttp://seer.sct.embrapa.br/index.php/pabPRIhttps://old.scielo.br/oai/scielo-oai.phppab@sct.embrapa.br || sct.pab@embrapa.br1678-39210100-204Xopendoar:2015-01-21T16:31:09Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Preliminary assessment of AVHRR/NOAA data to detect deforestation in the Pantanal Avaliação preliminar da utilização de imagens AVHRR/NOAA na detecção de desmatamento no Pantanal |
title |
Preliminary assessment of AVHRR/NOAA data to detect deforestation in the Pantanal |
spellingShingle |
Preliminary assessment of AVHRR/NOAA data to detect deforestation in the Pantanal Mantovani, Angelica Carvalho Di Maio sensoriamento remoto; meio físico; Cerrado remote sensing; physical environment; Savanna |
title_short |
Preliminary assessment of AVHRR/NOAA data to detect deforestation in the Pantanal |
title_full |
Preliminary assessment of AVHRR/NOAA data to detect deforestation in the Pantanal |
title_fullStr |
Preliminary assessment of AVHRR/NOAA data to detect deforestation in the Pantanal |
title_full_unstemmed |
Preliminary assessment of AVHRR/NOAA data to detect deforestation in the Pantanal |
title_sort |
Preliminary assessment of AVHRR/NOAA data to detect deforestation in the Pantanal |
author |
Mantovani, Angelica Carvalho Di Maio |
author_facet |
Mantovani, Angelica Carvalho Di Maio Amaral, Silvana |
author_role |
author |
author2 |
Amaral, Silvana |
author2_role |
author |
dc.contributor.none.fl_str_mv |
|
dc.contributor.author.fl_str_mv |
Mantovani, Angelica Carvalho Di Maio Amaral, Silvana |
dc.subject.por.fl_str_mv |
sensoriamento remoto; meio físico; Cerrado remote sensing; physical environment; Savanna |
topic |
sensoriamento remoto; meio físico; Cerrado remote sensing; physical environment; Savanna |
description |
This paper presents preliminary results of deforestation detection in the Pantanal, using AVHRR/NOAA image data. This initial analysis is part of the MULPAN Project, which investigates the usefulness of data gathered by different sensors for surveying thematic information of the Pantanal physical environment. This Project results from cooperation between INPE (Instituto Nacional de Pesquisas Espaciais) and Embrapa (Empresa Brasileira de Pesquisa Agropecuária). A full resolution AVHRR/NOAA image, channel 2 (0.72-1.1 µm) and channel 3 (3.5-3.9 µm) from Sept. 15, 1990, was used to detect deforestation. The image was geometrically corrected, remapped to conform to the cylindrical equidistant projection, and classified using a maximum likelihood algorithm. SPRING-1.1, a geographical information and image processing system, was used for both geometric correction and classification. Classification assessment was based on previous maps derived from Landsat-5/TM from 1990/91. Results indicated that the methodology is not efficient to detect deforestation, mainly because Pantanal is a region of a high occurence of savannas. The AVHRR/NOAA image discriminated only 20% of the total amount of 112 points of deforestation identified with Landsat-5/TM data. The remaining points of deforestation were wrongly classified as savanna or dense savanna. |
publishDate |
1998 |
dc.date.none.fl_str_mv |
1998-12-01 |
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://seer.sct.embrapa.br/index.php/pab/article/view/5055 |
url |
https://seer.sct.embrapa.br/index.php/pab/article/view/5055 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://seer.sct.embrapa.br/index.php/pab/article/view/5055/7201 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira Pesquisa Agropecuária Brasileira |
publisher.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira Pesquisa Agropecuária Brasileira |
dc.source.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira; v.33, n. especial, out. 1998; 1683-1690 Pesquisa Agropecuária Brasileira; v.33, n. especial, out. 1998; 1683-1690 1678-3921 0100-104x reponame:Pesquisa Agropecuária Brasileira (Online) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Pesquisa Agropecuária Brasileira (Online) |
collection |
Pesquisa Agropecuária Brasileira (Online) |
repository.name.fl_str_mv |
Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
pab@sct.embrapa.br || sct.pab@embrapa.br |
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