Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil.
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/949316 |
Resumo: | The objective of this work was to evaluate a simple, semi?automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R2 = 0.89), but poor agreement in municipalities with less than 5% crop cover (R2 = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil. |
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Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil.Índice de vegetaçãoMáscara de culturasÁrea cultivadaTransformada de FourierCropland masksCultivated areavegetation indexThe objective of this work was to evaluate a simple, semi?automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R2 = 0.89), but poor agreement in municipalities with less than 5% crop cover (R2 = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil.DANIEL DE CASTRO VICTORIA, CNPM; ADRIANO ROLIM DA PAZ, UFPB; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JUDE KASTENS, Kansas Applied Remote Sensing; J. CHRISTOPHER BROWN, University of Kansas.VICTORIA, D. de C.PAZ, A. R. daCOUTINHO, A. C.KASTENS, J.BROWN, J. C.2020-02-19T00:36:53Z2020-02-19T00:36:53Z2013-02-1420122020-02-19T00:36:53Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePesquisa Agropecuária Brasileira, Brasília, DF v. 47, n. 9, p. 1270-1278, set. 2012.http://www.alice.cnptia.embrapa.br/alice/handle/doc/949316enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2020-02-19T00:37:00Zoai:www.alice.cnptia.embrapa.br:doc/949316Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542020-02-19T00:37Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil. |
title |
Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil. |
spellingShingle |
Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil. VICTORIA, D. de C. Índice de vegetação Máscara de culturas Área cultivada Transformada de Fourier Cropland masks Cultivated area vegetation index |
title_short |
Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil. |
title_full |
Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil. |
title_fullStr |
Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil. |
title_full_unstemmed |
Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil. |
title_sort |
Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil. |
author |
VICTORIA, D. de C. |
author_facet |
VICTORIA, D. de C. PAZ, A. R. da COUTINHO, A. C. KASTENS, J. BROWN, J. C. |
author_role |
author |
author2 |
PAZ, A. R. da COUTINHO, A. C. KASTENS, J. BROWN, J. C. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
DANIEL DE CASTRO VICTORIA, CNPM; ADRIANO ROLIM DA PAZ, UFPB; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JUDE KASTENS, Kansas Applied Remote Sensing; J. CHRISTOPHER BROWN, University of Kansas. |
dc.contributor.author.fl_str_mv |
VICTORIA, D. de C. PAZ, A. R. da COUTINHO, A. C. KASTENS, J. BROWN, J. C. |
dc.subject.por.fl_str_mv |
Índice de vegetação Máscara de culturas Área cultivada Transformada de Fourier Cropland masks Cultivated area vegetation index |
topic |
Índice de vegetação Máscara de culturas Área cultivada Transformada de Fourier Cropland masks Cultivated area vegetation index |
description |
The objective of this work was to evaluate a simple, semi?automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R2 = 0.89), but poor agreement in municipalities with less than 5% crop cover (R2 = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012 2013-02-14 2020-02-19T00:36:53Z 2020-02-19T00:36:53Z 2020-02-19T00:36:53Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Pesquisa Agropecuária Brasileira, Brasília, DF v. 47, n. 9, p. 1270-1278, set. 2012. http://www.alice.cnptia.embrapa.br/alice/handle/doc/949316 |
identifier_str_mv |
Pesquisa Agropecuária Brasileira, Brasília, DF v. 47, n. 9, p. 1270-1278, set. 2012. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/949316 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
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Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1822721463686266880 |