Crop area estimate from original and simulated spatial resolution data and landscape metrics
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000500003 |
Resumo: | Images acquired at the same day by the ETM+/Landsat-7 (30 m of spatial resolution) and MODIS/Terra (250 m) sensors were used to estimate areas of three major crops (soybean, sugarcane, and corn) with different landscape patterns in Southeastern Brazil. Majority filtering of ETM + classification results was applied to describe the behavior of 15 selected landscape metrics at distinct simulated spatial resolutions (90, 150, 210 and 270 m). By using regression models, the performance of MODIS and derived metrics to predict adequately the crop area, considering ETM+ data as reference, were analyzed. Results showed that the MODIS instrument overestimated the areas of soybean (15%) and sugarcane (1%), and underestimated the area of corn (12%). Multiple regression results indicated that coarse spatial resolution sensors can be used to predict adequately the area viewed by the 30 m spatial resolution instruments only for crops with low fragmentation pattern such as soybean. These sensors cannot be used to predict the area of corn due to aggregation pixel effects of the less fragmented crops (soybean and sugarcane) over the most fragmented one (corn), as demonstrated by the spatial resolution simulation using majority filtering of the ETM+ image. Landscape metrics improved MODIS area estimates only for sugarcane, as indicated by higher values of R² for multiple than for simple regression. Only a small set of metrics was select to compose the multiple regression models because most of them were not preserved across different spatial resolutions (30 m and 250 m). |
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oai:scielo:S0103-90162008000500003 |
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Scientia Agrícola (Online) |
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Crop area estimate from original and simulated spatial resolution data and landscape metricsMODISremote sensingregressionsoybeansugarcaneImages acquired at the same day by the ETM+/Landsat-7 (30 m of spatial resolution) and MODIS/Terra (250 m) sensors were used to estimate areas of three major crops (soybean, sugarcane, and corn) with different landscape patterns in Southeastern Brazil. Majority filtering of ETM + classification results was applied to describe the behavior of 15 selected landscape metrics at distinct simulated spatial resolutions (90, 150, 210 and 270 m). By using regression models, the performance of MODIS and derived metrics to predict adequately the crop area, considering ETM+ data as reference, were analyzed. Results showed that the MODIS instrument overestimated the areas of soybean (15%) and sugarcane (1%), and underestimated the area of corn (12%). Multiple regression results indicated that coarse spatial resolution sensors can be used to predict adequately the area viewed by the 30 m spatial resolution instruments only for crops with low fragmentation pattern such as soybean. These sensors cannot be used to predict the area of corn due to aggregation pixel effects of the less fragmented crops (soybean and sugarcane) over the most fragmented one (corn), as demonstrated by the spatial resolution simulation using majority filtering of the ETM+ image. Landscape metrics improved MODIS area estimates only for sugarcane, as indicated by higher values of R² for multiple than for simple regression. Only a small set of metrics was select to compose the multiple regression models because most of them were not preserved across different spatial resolutions (30 m and 250 m).Escola Superior de Agricultura "Luiz de Queiroz"2008-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000500003Scientia Agricola v.65 n.5 2008reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/S0103-90162008000500003info:eu-repo/semantics/openAccessSoares,Dênis de MouraGalvão,Lênio SoaresFormaggio,Antônio Robertoeng2008-09-16T00:00:00Zoai:scielo:S0103-90162008000500003Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2008-09-16T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Crop area estimate from original and simulated spatial resolution data and landscape metrics |
title |
Crop area estimate from original and simulated spatial resolution data and landscape metrics |
spellingShingle |
Crop area estimate from original and simulated spatial resolution data and landscape metrics Soares,Dênis de Moura MODIS remote sensing regression soybean sugarcane |
title_short |
Crop area estimate from original and simulated spatial resolution data and landscape metrics |
title_full |
Crop area estimate from original and simulated spatial resolution data and landscape metrics |
title_fullStr |
Crop area estimate from original and simulated spatial resolution data and landscape metrics |
title_full_unstemmed |
Crop area estimate from original and simulated spatial resolution data and landscape metrics |
title_sort |
Crop area estimate from original and simulated spatial resolution data and landscape metrics |
author |
Soares,Dênis de Moura |
author_facet |
Soares,Dênis de Moura Galvão,Lênio Soares Formaggio,Antônio Roberto |
author_role |
author |
author2 |
Galvão,Lênio Soares Formaggio,Antônio Roberto |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Soares,Dênis de Moura Galvão,Lênio Soares Formaggio,Antônio Roberto |
dc.subject.por.fl_str_mv |
MODIS remote sensing regression soybean sugarcane |
topic |
MODIS remote sensing regression soybean sugarcane |
description |
Images acquired at the same day by the ETM+/Landsat-7 (30 m of spatial resolution) and MODIS/Terra (250 m) sensors were used to estimate areas of three major crops (soybean, sugarcane, and corn) with different landscape patterns in Southeastern Brazil. Majority filtering of ETM + classification results was applied to describe the behavior of 15 selected landscape metrics at distinct simulated spatial resolutions (90, 150, 210 and 270 m). By using regression models, the performance of MODIS and derived metrics to predict adequately the crop area, considering ETM+ data as reference, were analyzed. Results showed that the MODIS instrument overestimated the areas of soybean (15%) and sugarcane (1%), and underestimated the area of corn (12%). Multiple regression results indicated that coarse spatial resolution sensors can be used to predict adequately the area viewed by the 30 m spatial resolution instruments only for crops with low fragmentation pattern such as soybean. These sensors cannot be used to predict the area of corn due to aggregation pixel effects of the less fragmented crops (soybean and sugarcane) over the most fragmented one (corn), as demonstrated by the spatial resolution simulation using majority filtering of the ETM+ image. Landscape metrics improved MODIS area estimates only for sugarcane, as indicated by higher values of R² for multiple than for simple regression. Only a small set of metrics was select to compose the multiple regression models because most of them were not preserved across different spatial resolutions (30 m and 250 m). |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000500003 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000500003 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-90162008000500003 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.65 n.5 2008 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936461021872128 |