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| 5. |  | MONTIBELLER, B.; SANCHES, I. D.; LUIZ, A. J. B.; GONÇALVES, F.; AGUIAR, D. A. de. Spectral-temporal profile analysis of maize, soybean and sugarcane based on OLI/Landsat-8 data. Brazilian Journal of Agriculture, Piracicaba, v. 94, n. 3, p. 242-258, 2019.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 10. | | AGUIAR, D. A.; SILVA, W. F.; MELLO, M. P.; SILVA, G. B. S. da; ADAMI, M.; RUDORFF, B. F. T.; CREPANI, E.; VARLEZ, M. A. Monitoramento agroambiental do setor sucroenergético: contribuições do sensoriamento remoto orbital para a certificação ambiental do cultivo da cana-de-açúcar. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: INPE, 2011. p. 0132-0139.| Biblioteca(s): Embrapa Territorial. |
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| 12. | | RONQUIM, C. C.; GUILARDI, V.; AGUIAR, D. A. DE; ROQUE, A. A. DE O.; RUDORFF, B. F. T.; DRUGOWICH, M. I.; MOREIRA, M. A. Uso de geotecnologias para análise da dinâmica das áreas de citros e cana-de-açúcar nos últimos 26 anos na Bacia do Mogi-Pardo. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 3361-3368.| Biblioteca(s): Embrapa Territorial. |
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| 13. | | NOGUEIRA, S. F.; MACEDO, M. C. M.; POCCARD-CHAPUIS, R.; SILVA, G. B. S. da; VICTORIA, D. de C.; ANDRADE, R. G.; AGUIAR, D. A. DE.; BOLFE, E. L. Geotechnologies for monitoring pasture degradation levels in Brazil. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 51., 2014, Barra dos Coqueiros. A produção animal frente às mudanças climáticas e tecnológicas - anais. Barra dos coqueiros: Sociedade Brasileira de Zootecnia, 2014. 1 p.| Biblioteca(s): Embrapa Territorial. |
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| 14. | | FONTANA, A.; PIRES, A. M. M.; VENTURIERI, A.; JESUS, A. H. de; PAULA, A. J. de; MEDEIROS, A. de S.; VARGAS, A. de G.; BERNDT, A.; FERREIRA, A. L.; LUIZ, A. J. B.; ALVES, A. E.; PEREIRA, A. C.; FREITAS, A. F. de; PACKER, A. P.; CAVALCANTE, A. P. C. M.; FRASCA, A. V. da L.; FERNANDES, A.; ZIMBRES, B.; RUDORFF, B. F. T.; DENARDI, B. M.; BORDRON, B.; ALVES, B. J. R.; KRAHEMBUHL, C. B. B. P.; ARAÚJO, C. E. R.; DUBEUX, C. B. S; MAEQUES, C. L.; SILVA, C. R.; BALDI, C.; JANTALIA, C. P.; GESTEIRA NETO, C. J. M.; BACK, C. D.; ANDRADE, C. A. de; AGUIAR, D. A. de; OBERLING, D. F.; OLIVEIRA, D. M. de; CALÇADO, D. M.; GONÇALVES, D. N. S.; MOLLETA, D. G. S.; GAROFALO, D. F. T.; FERREIRA, D.; GOULART, D.; OLIVEIRA, E. C. de; ROSA, E. R.; ALMEIDA, E. G. de; ALBUQUERQUE, E. R. G. M. de; LA ROVERE, E. L.; RIEGELHAUPT, E. M.; HENRIQUE, F. L.; FARIA, F. F. de; PATERNOST, F. F.; CESÁRIO, F. V.; PEREIRA, F. R. de S.; PAREYN, F. G. C.; RODRIGUES, G. A. H.; BERTANI, G.; LEMOS, G. da S.; RIBEIRO, G. H. P. de M.; CABRAL, G. H.; FROES, G. A. M.; GOES, G. V.; ANDRADE, G. S. de; CARVALHO, G. C. de; NASPOLINI, G. F.; CHRISTO, G. L. de; SANTOS, G. V. dos; BARROS, H. H. D. de; HASENACK, H.; TAVARES, H. C.; MARTINS, I. T.; FRANÇA, I. E. R.; ROITMAN, I.; SOARES, I. M. de M.; SIMIONATO, J.; FREITAS, J.; OMETTO, J. P. H. B.; LINARES, J. A. H.; RISSO, J.; CRUZ FILHO, J. L. V. da; QUINTÃO, J. M. B.; SILVA, J. S. O.; LANA, J. T. de O.; ZANATTA, J. A.; MARACAHIPES, L.; RIBEIRO, L. da S.; VICENTE, L. E.; SCHRAMM, L. F. P.; ROSSI, L. M. B.; SILVA, M. G. da; SARAIVA, M.; D'AGOSTO, M. de A.; VIRTUOSO, M. A.; LIGO, M. A. V.; RACHWAL, M. F. G.; ROSA, M.; PICHARILLO, M. E.; SILVA, M. G. L. da; MATSUURA, M. I. da S. F.; DALLA BETTA, M. M.; PEREIRA, M. J.; CARON, M. L.; ASSIS, M. L. R. de; SANTOS, M. M. de O.; ROCHA, M. B.; BUSTAMANTE, M. M. da C.; WALTER, M. C.; AQUINO, M. F. S. de; SALGADO, M. P. G.; SERRUYA, N. M.; MOREIRA, N. de P.; FERREIRA, N. C.; RAMOS, N. P.; CABRAL, O. M. R.; MOSER, P.; COLTURATO, P. D.; GRÜTZMACHER, P.; AUGUSTO, R. C.; GRISOLI, R. P. S.; SOLARI, R.; PAZIANOTTO, R. A. A.; DUPONT, R. A. B.; CANTINHO, R. Z.; PEIXOTO, R. de A.; PADILHA, R. A.; NICOLOSO, R. da S.; OLIVEIRA, R. R. S. de; HIGA, R. C. V.; GUIDOTTI, R. M. M.; NOGUEIRA, S. F.; MAIA, S. M. F.; ASSIS, T. F. de; IGAWA, T. K.; RODRIGUES, T. da S.; CARMO, T. R. L. do; MIRANDA, V. S.; KONDO, V. Y.; SCIVITTARO, W. B.; WILLS, W.; HOLLER, W. A. Inventário nacional de emissões e remoções antrópicas de gases de efeito estufa. In: BRASIL. Ministério da Ciência, Tecnologia e Inovações. Quarta Comunicação Nacional do Brasil à Convenção-Quadro das Nações Unidas sobre Mudança do Clima. Brasília, DF, 2021. cap. 2, p. 80-181.| Biblioteca(s): Embrapa Agrobiologia; Embrapa Amazônia Oriental; Embrapa Florestas; Embrapa Pecuária Sudeste; Embrapa Solos. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
04/10/2011 |
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Data da última atualização: |
24/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
AGUIAR, D. A.; ADAMI, M.; SILVA, W. F.; RUDORFF, B. F. T.; MELLO, M. P.; SILVA, J. dos S. V. da. |
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Afiliação: |
DANIEL ALVES AGUIAR, INPE; MARCOS ADAMI, INPE; WAGNER FERNANDO SILVA, INPE; BERNARDO FRIEDRICH THEODOR RUDORFF, INPE; MARCIO PUPIN MELLO, INPE; JOÃO DOS SANTOS VILA DA SILVA, CNPTIA. |
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Título: |
MODIS time series to assess pasture land. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
In: INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, Honolulu. Remote sensing: global vision for local action. [S.l.]: IEEE, 2010. |
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Páginas: |
p. 2123-2126. |
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Idioma: |
Inglês |
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Notas: |
IGARSS 2010. |
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Conteúdo: |
Land use conversion is a key factor in the mitigation of GHG emission. Maximum mitigation can be achieved when degraded pasture land is converted to biofuel crops. Remote sensing images, and in particular the MODIS time series data, have a great potential to asses degraded pasture land. This work has the objective to identify pasture land and its different levels of degradation in Mato Grosso do Sul state, Brazil. MODIS time series were used to obtain vegetation indices and fraction images. The wavelet technique was applied at various levels of decomposition to extract the input parameters in the WEKA J48 classifier. Pasture land was well distinguished from Cerrado. The distinction among different pasture land presented lower performance with best results for pasture with invasive plants followed by good pasture. Pasture land with bare soil patches and termite mounds were not distinguished from other classes of pasture. made it possible to reduce pasture land without herd reduction. Consequently more land became available for sugarcane. Considering that land use change is a key factor for the benefit of biofuel production to mitigate carbon emission, this benefit can be even higher if sugarcane expansion occurs on degraded pasture land. Remote sensing images have a great potential to evaluate degraded pasture land although is not a trivial task and requires intensive fieldwork. MODIS time series data transformed into vegetation indices or linear spectral mixing model are suitable to represent different pasture land conditions. Under these considerations this work has the objective to use MODIS time series to identify pasture land and its different levels of degradation in Mato Grosso do Sul state, Brazil. MenosLand use conversion is a key factor in the mitigation of GHG emission. Maximum mitigation can be achieved when degraded pasture land is converted to biofuel crops. Remote sensing images, and in particular the MODIS time series data, have a great potential to asses degraded pasture land. This work has the objective to identify pasture land and its different levels of degradation in Mato Grosso do Sul state, Brazil. MODIS time series were used to obtain vegetation indices and fraction images. The wavelet technique was applied at various levels of decomposition to extract the input parameters in the WEKA J48 classifier. Pasture land was well distinguished from Cerrado. The distinction among different pasture land presented lower performance with best results for pasture with invasive plants followed by good pasture. Pasture land with bare soil patches and termite mounds were not distinguished from other classes of pasture. made it possible to reduce pasture land without herd reduction. Consequently more land became available for sugarcane. Considering that land use change is a key factor for the benefit of biofuel production to mitigate carbon emission, this benefit can be even higher if sugarcane expansion occurs on degraded pasture land. Remote sensing images have a great potential to evaluate degraded pasture land although is not a trivial task and requires intensive fieldwork. MODIS time series data transformed into vegetation indices or linear spectral mixing model are sui... Mostrar Tudo |
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Palavras-Chave: |
Degradação de pastagem; Séries temporais MODIS. |
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Thesaurus NAL: |
Degradation; Pastures; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02529nam a2200253 a 4500
001 1902230
005 2020-01-24
008 2010 bl uuuu u00u1 u #d
100 1 $aAGUIAR, D. A.
245 $aMODIS time series to assess pasture land.$h[electronic resource]
260 $aIn: INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, Honolulu. Remote sensing: global vision for local action. [S.l.]: IEEE$c2010
300 $ap. 2123-2126.
500 $aIGARSS 2010.
520 $aLand use conversion is a key factor in the mitigation of GHG emission. Maximum mitigation can be achieved when degraded pasture land is converted to biofuel crops. Remote sensing images, and in particular the MODIS time series data, have a great potential to asses degraded pasture land. This work has the objective to identify pasture land and its different levels of degradation in Mato Grosso do Sul state, Brazil. MODIS time series were used to obtain vegetation indices and fraction images. The wavelet technique was applied at various levels of decomposition to extract the input parameters in the WEKA J48 classifier. Pasture land was well distinguished from Cerrado. The distinction among different pasture land presented lower performance with best results for pasture with invasive plants followed by good pasture. Pasture land with bare soil patches and termite mounds were not distinguished from other classes of pasture. made it possible to reduce pasture land without herd reduction. Consequently more land became available for sugarcane. Considering that land use change is a key factor for the benefit of biofuel production to mitigate carbon emission, this benefit can be even higher if sugarcane expansion occurs on degraded pasture land. Remote sensing images have a great potential to evaluate degraded pasture land although is not a trivial task and requires intensive fieldwork. MODIS time series data transformed into vegetation indices or linear spectral mixing model are suitable to represent different pasture land conditions. Under these considerations this work has the objective to use MODIS time series to identify pasture land and its different levels of degradation in Mato Grosso do Sul state, Brazil.
650 $aDegradation
650 $aPastures
650 $aTime series analysis
653 $aDegradação de pastagem
653 $aSéries temporais MODIS
700 1 $aADAMI, M.
700 1 $aSILVA, W. F.
700 1 $aRUDORFF, B. F. T.
700 1 $aMELLO, M. P.
700 1 $aSILVA, J. dos S. V. da
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