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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente; Embrapa Unidades Centrais. |
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Data corrente: |
13/05/2010 |
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Data da última atualização: |
05/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
EPIPHANIO, R. D. V.; FORMAGGIO, A. R.; RUDORFF, B. F. T.; MAEDA, E. E.; LUIZ, A. J. B. |
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Afiliação: |
Rui Dalla Valle Epiphanio, Louis Dreyfus Commodities Brasil S.A.; Antonio Roberto Formaggio, INPE; Bernardo Friedrich Theodor Rudorff, INPE; Eduardo Eiji Maeda, University of Helsinki; ALFREDO JOSE BARRETO LUIZ, CNPMA. |
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Título: |
Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF v. 45, n. 1, p. 72-80, 2010. |
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Idioma: |
Inglês
Português |
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Conteúdo: |
Abstract ? The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas. |
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Palavras-Chave: |
Mapa temático; Thematic map. |
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Thesagro: |
Estatística Agrícola; Glycine Max; Sensoriamento Remoto. |
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Thesaurus Nal: |
Accuracy; Agricultural statistics; Classification; Remote sensing. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/AI-SEDE-2010/47610/1/45n01a10.pdf
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Marc: |
LEADER 02214naa a2200277 a 4500
001 1872457
005 2023-01-05
008 2010 bl uuuu u00u1 u #d
100 1 $aEPIPHANIO, R. D. V.
245 $aEstimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil.
260 $c2010
520 $aAbstract ? The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.
650 $aAccuracy
650 $aAgricultural statistics
650 $aClassification
650 $aRemote sensing
650 $aEstatística Agrícola
650 $aGlycine Max
650 $aSensoriamento Remoto
653 $aMapa temático
653 $aThematic map
700 1 $aFORMAGGIO, A. R.
700 1 $aRUDORFF, B. F. T.
700 1 $aMAEDA, E. E.
700 1 $aLUIZ, A. J. B.
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 45, n. 1, p. 72-80, 2010.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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| Registros recuperados : 267 | |
| 117. |  | SOUSA, P. G.; SOARES SOBRINHO, J.; LUIZ, A. J. B.; RUMIATTO, M.; SOUZA, E. B. de. Cultivares de trigo em nível estadual de experimentação. In: REUNIÃO DA COMISSÃO CENTRO-SUL-BRASILEIRA DE PESQUISA DE TRIGO, 5., 1989, Cornélio Procópio. Resultados de pesquisa com trigo - 1988. Dourados: EMBRAPA-UEPAE Dourados, 1989. p. 31-34. (EMBRAPA-UEPAE Dourados. Documentos, 39). Projeto 004.87.0116-8 - Competição de cultivares de trigo na Região Sul de Mato Grosso do Sul.| Biblioteca(s): Embrapa Agropecuária Oeste. |
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| 118. | | SCHULTZ, B.; FORMAGGIO, A. R.; ATZBERGER, C.; LUIZ, A. J. B.; GOLTZ, E.; MELLO, M. P. Dynamic of sugarcane harvested areas in São Paulo state, Brazil, over the last two decades. GLOBAL LAND PROJECT, OPEN SCIENCE MEETING, 2., 2014, Berlim. Land Transformations: between global challenges and local realities. Proceedings... Berlim: International Geosphere-Biosphere Programme, 2014. p. 512-513.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Meio Ambiente. |
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| 119. | | SCHULTZ, B.; BERTANI, G.; FORMAGGIO, A. R.; EBERHARDT, D. S.; LUIZ, A. J. B.; ATZBERGER, C. Data mining and object based image analysis applied to soybean areas classification through time-series TM/ETM+ images. In: GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS CONFERENCE, 5., 2014, Tessalônica. Proceedings... Tessalônica: Aristotle University of Thessaloniki, 2014. Ref. O.T9 - 085. p. 122.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Meio Ambiente. |
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| Registros recuperados : 267 | |
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