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| Registros recuperados : 90 | |
| 7. |  | SILVEIRA, H. L. F. da; VETTORAZZI, C. A.; VALENTE, R. de O. A. Avaliação multicriterial no mapeamento de risco de incêndios florestais, em ambiente SIG, na bacia do Rio Corumbataí, SP. Revista Árvore, Viçosa, MG, v. 32, n. 2, p. 259-268, mar./abr. 2008.| Biblioteca(s): Embrapa Florestas; Embrapa Solos. |
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| 13. | | SILVA, M. S. L. da; NASCIMENTO, A. F. do; SILVEIRA, H. L. F. da. Spatial variability of soil attributes in a cultivated area of underground dam in the State of the Paraíba, Brazil. In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2019. v. 2, p. 267. WCSS 2018.| Biblioteca(s): Embrapa Solos. |
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| 20. | | SILVA, A. B. da; GOMES, E. C; ACCIOLY, L. J. de O.; SILVEIRA, H. L. F. da. [Mapa] solos do município de Feliz Deserto, Alagoas. In: REUNIÃO BRASILEIRA DE MANEJO E CONSERVAÇÃO DO SOLO E DA ÁGUA, 17., 2008, Rio de Janeiro. Manejo e conservação do solo e da água no contexto das mudanças ambientais. Rio de Janeiro: SBCS: Embrapa Solos: Embrapa Agrobiologia, 2008. (Embrapa Solos. Documentos, 101). 1 mapa color.| Biblioteca(s): Embrapa Solos. |
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| Registros recuperados : 90 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente; Embrapa Solos. |
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Data corrente: |
29/08/2017 |
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Data da última atualização: |
17/01/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
MONTIBELLER, B.; LUIZ, A. J. B.; SANCHES, I. D. A.; SILVEIRA, H. L. F. da. |
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Afiliação: |
BRUNO MONTIBELLER, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; IEDA DEL'ARCO SANCHES, INPE; HILTON LUIS FERRAZ DA SILVEIRA, CNPS. |
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Título: |
Análise da variabilidade espectro-temporal intraespecífica do milho. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18.,2017, Santos. Anais... Santos: Inpe, 2017. Trabalho: 59410. |
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Páginas: |
p. 2011-2018. |
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Idioma: |
Português |
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Conteúdo: |
Remote sensing data has been widely used worldwide to estimate crop field?s parameters such as area. For that purpose, we use automatic classification algorithms to identify different land uses and land covers (e.g. agricultural and native vegetation), groups of crops (e.g. annual and perennial crops) or crops species (e.g. maize, sugarcane or soybean). For agricultural applications, the ultimate goal is to be able to use remote sensing technology to map crops in the specie level, and then to monitor them. One essential input data used in the classifications algorithms is the spectral information of the ground targets (e.g. reflectance and vegetation indices). Therefore, it is important to know the spectral behavior of all targets. However, the ability of one classifier to distinguish between plant species is probably dependent on the amount of intraspecific variability. In other words, if a crop specie has high intraspecific spectral variation, it will be difficult to classify this specie among others. Thus, the aim of this work is to analyze the intraspecific spectral temporal variability of maize crop. To accomplish that, spectral data (OLI/Landsat-8) were acquired from first and second harvest maize plots, cultivated over distinct management systems (irrigated and non-irrigated), along two agricultural crop years, (2014/2015 and 2015/2016). We concluded that maize fields harvested in different years, sowed in different seasons, irrigated or not, have a high temporal spectral variation, which cannot be associated with these known characteristics. MenosRemote sensing data has been widely used worldwide to estimate crop field?s parameters such as area. For that purpose, we use automatic classification algorithms to identify different land uses and land covers (e.g. agricultural and native vegetation), groups of crops (e.g. annual and perennial crops) or crops species (e.g. maize, sugarcane or soybean). For agricultural applications, the ultimate goal is to be able to use remote sensing technology to map crops in the specie level, and then to monitor them. One essential input data used in the classifications algorithms is the spectral information of the ground targets (e.g. reflectance and vegetation indices). Therefore, it is important to know the spectral behavior of all targets. However, the ability of one classifier to distinguish between plant species is probably dependent on the amount of intraspecific variability. In other words, if a crop specie has high intraspecific spectral variation, it will be difficult to classify this specie among others. Thus, the aim of this work is to analyze the intraspecific spectral temporal variability of maize crop. To accomplish that, spectral data (OLI/Landsat-8) were acquired from first and second harvest maize plots, cultivated over distinct management systems (irrigated and non-irrigated), along two agricultural crop years, (2014/2015 and 2015/2016). We concluded that maize fields harvested in different years, sowed in different seasons, irrigated or not, have a high temporal spec... Mostrar Tudo |
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Palavras-Chave: |
Agricultural monitoring; Dado multitemporal; Monitoramento agrícola; Multitemporal data; OLI-Landsat-8; Reflectância de superfície; Surface reflectance. |
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Thesagro: |
Milho; Sensoriamento remoto. |
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Thesaurus NAL: |
Remote sensing. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170682/1/2017AA32.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/163104/1/2017-021.pdf
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Marc: |
LEADER 02476nam a2200277 a 4500
001 2084705
005 2023-01-17
008 2017 bl uuuu u00u1 u #d
100 1 $aMONTIBELLER, B.
245 $aAnálise da variabilidade espectro-temporal intraespecífica do milho.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18.,2017, Santos. Anais... Santos: Inpe, 2017. Trabalho: 59410.$c2017
300 $ap. 2011-2018.
520 $aRemote sensing data has been widely used worldwide to estimate crop field?s parameters such as area. For that purpose, we use automatic classification algorithms to identify different land uses and land covers (e.g. agricultural and native vegetation), groups of crops (e.g. annual and perennial crops) or crops species (e.g. maize, sugarcane or soybean). For agricultural applications, the ultimate goal is to be able to use remote sensing technology to map crops in the specie level, and then to monitor them. One essential input data used in the classifications algorithms is the spectral information of the ground targets (e.g. reflectance and vegetation indices). Therefore, it is important to know the spectral behavior of all targets. However, the ability of one classifier to distinguish between plant species is probably dependent on the amount of intraspecific variability. In other words, if a crop specie has high intraspecific spectral variation, it will be difficult to classify this specie among others. Thus, the aim of this work is to analyze the intraspecific spectral temporal variability of maize crop. To accomplish that, spectral data (OLI/Landsat-8) were acquired from first and second harvest maize plots, cultivated over distinct management systems (irrigated and non-irrigated), along two agricultural crop years, (2014/2015 and 2015/2016). We concluded that maize fields harvested in different years, sowed in different seasons, irrigated or not, have a high temporal spectral variation, which cannot be associated with these known characteristics.
650 $aRemote sensing
650 $aMilho
650 $aSensoriamento remoto
653 $aAgricultural monitoring
653 $aDado multitemporal
653 $aMonitoramento agrícola
653 $aMultitemporal data
653 $aOLI-Landsat-8
653 $aReflectância de superfície
653 $aSurface reflectance
700 1 $aLUIZ, A. J. B.
700 1 $aSANCHES, I. D. A.
700 1 $aSILVEIRA, H. L. F. da
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Embrapa Meio Ambiente (CNPMA) |
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