|
|
Registro Completo |
Biblioteca(s): |
Embrapa Uva e Vinho. |
Data corrente: |
23/10/2008 |
Data da última atualização: |
22/10/2019 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
MELLO, L. M. R. de; GARAGORRY, F. L.; CHAIB FILHO, H. |
Afiliação: |
LOIVA MARIA RIBEIRO DE MELLO, CNPUV; FERNANDO LUIS GARAGORRY CASSALES, SIRE; Homero Chaib Filho, Embrapa Cerrados. |
Título: |
Spatial dynamics of grapes production in Brazil between 1975 and 2005. |
Ano de publicação: |
2008 |
Fonte/Imprenta: |
In: CONGRESSO MONDIALE DELLA VIGNA E DEL VINO, 31.; ASSEMBLEA GENERALE DELL'O.I.V., 6., 2008, Verona, Italia. Riassunti delle comunicazioni. [Verona]: OIV: Ministero Politiche Agricole, Alimentari e Forestali, 2008. |
Páginas: |
p. 363. |
Idioma: |
Inglês |
Notas: |
Resumo P.III.02. |
Conteúdo: |
Viticulture is an activity which creates employment and is adequate to provide economic and social sustaunability to small family based farms. |
Palavras-Chave: |
Concentração regional; Dinâmica agrícola; Distância. |
Thesagro: |
Agricultura Familiar; Produção; Uva; Viticultura. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/203405/1/10134-2008-p.363.pdf
|
Marc: |
LEADER 00994nam a2200241 a 4500 001 1543076 005 2019-10-22 008 2008 bl uuuu u00u1 u #d 100 1 $aMELLO, L. M. R. de 245 $aSpatial dynamics of grapes production in Brazil between 1975 and 2005.$h[electronic resource] 260 $aIn: CONGRESSO MONDIALE DELLA VIGNA E DEL VINO, 31.; ASSEMBLEA GENERALE DELL'O.I.V., 6., 2008, Verona, Italia. Riassunti delle comunicazioni. [Verona]: OIV: Ministero Politiche Agricole, Alimentari e Forestali$c2008 300 $ap. 363. 500 $aResumo P.III.02. 520 $aViticulture is an activity which creates employment and is adequate to provide economic and social sustaunability to small family based farms. 650 $aAgricultura Familiar 650 $aProdução 650 $aUva 650 $aViticultura 653 $aConcentração regional 653 $aDinâmica agrícola 653 $aDistância 700 1 $aGARAGORRY, F. L. 700 1 $aCHAIB FILHO, H.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Uva e Vinho (CNPUV) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
| Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
17/12/2018 |
Data da última atualização: |
07/01/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
PICOLI, M. C. A.; CAMARA, G.; SANCHES, I.; SIMÕES, R.; CARVALHO, A.; MACIEL, A.; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; BEGOTTI, R. A.; ARVOR, D.; ALMEIDA, C. |
Afiliação: |
MICHELLE CRISTINA ARAUJO PICOLI, Inpe; GILBERTO CAMARA, Inpe; IEDA SANCHES, Inpe; ROLF SIMÕES, Inpe; ALEXANDRE CARVALHO, Ipea; ADELINE MACIEL, Inpe; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; RODRIGO ANZOLIN BEGOTTI, Inpe; DAMIEN ARVOR, Universite de Rennes; CLAUDIO ALMEIDA, Inpe. |
Título: |
Big earth observation time series analysis for monitoring Brazilian agriculture. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
ISPRS Journal of Photogrammetry and Remote Sensing, v. 145, part B, p. 328-339, Nov. 2018. |
DOI: |
https://doi.org/10.1016/j.isprsjprs.2018.08.007 |
Idioma: |
Inglês |
Notas: |
Na publicação: Alexandre Coutinho, Julio Esquerdo, João Antunes. |
Conteúdo: |
This paper presents innovative methods for using satellite image time series to produce land use and land cover classification over large areas in Brazil from 2001 to 2016. We used Moderate Resolution Imaging Spectroradiometer (MODIS) time series data to classify natural and human-transformed land areas in the state of Mato Grosso, Brazil?s agricultural frontier. Our hypothesis is that building high-dimensional spaces using all values of the time series, coupled with advanced statistical learning methods, is a robust and efficient approach for land cover classification of large data sets. We used the full depth of satellite image time series to create large dimensional spaces for statistical classification. The data consist of MODIS MOD13Q1 time series with 23 samples per year per pixel, and 4 bands (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), near-infrared (nir) and mid-infrared (mir)). By taking a series of labelled time series, we fed a 92-dimensional attribute space into a support vector machine model. Using a 5-fold cross validation, we obtained an overall accuracy of 94% for discriminating among nine land cover classes: forest, cerrado, pasture, soybeanfallow, fallow-cotton, soybean-cotton, soybean-corn, soybean-millet, and soybean-sunflower. Producer and user accuracies for all classes were close to or better than 90%. The results highlight important trends in agricultural intensification in Mato Grosso. Double crop systems are now the most common production system in the state, sparing land from agricultural production. Pasture expansion and intensification has been less studied than crop expansion, although it has a stronger impact on deforestation and greenhouse gas (GHG) emissions. Our results point to a significant increase in the stocking rate in Mato Grosso and to the possible abandonment of pasture areas opened in the state?s frontier. The detailed land cover maps contribute to an assessment of the interplay between production and protection in the Brazilian Amazon and Cerrado biomes. MenosThis paper presents innovative methods for using satellite image time series to produce land use and land cover classification over large areas in Brazil from 2001 to 2016. We used Moderate Resolution Imaging Spectroradiometer (MODIS) time series data to classify natural and human-transformed land areas in the state of Mato Grosso, Brazil?s agricultural frontier. Our hypothesis is that building high-dimensional spaces using all values of the time series, coupled with advanced statistical learning methods, is a robust and efficient approach for land cover classification of large data sets. We used the full depth of satellite image time series to create large dimensional spaces for statistical classification. The data consist of MODIS MOD13Q1 time series with 23 samples per year per pixel, and 4 bands (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), near-infrared (nir) and mid-infrared (mir)). By taking a series of labelled time series, we fed a 92-dimensional attribute space into a support vector machine model. Using a 5-fold cross validation, we obtained an overall accuracy of 94% for discriminating among nine land cover classes: forest, cerrado, pasture, soybeanfallow, fallow-cotton, soybean-cotton, soybean-corn, soybean-millet, and soybean-sunflower. Producer and user accuracies for all classes were close to or better than 90%. The results highlight important trends in agricultural intensification in Mato Grosso. Double crop systems are now ... Mostrar Tudo |
Palavras-Chave: |
Big earth observation data; Bioma Amazônia; Bioma Cerrado; Crop expansion; Imagem de satélite; Land use science; Satellite image time series; Séries temporais; Statistical learning; Tropical deforestation. |
Thesagro: |
Uso da Terra. |
Thesaurus NAL: |
Land use; Time series analysis. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 03421naa a2200433 a 4500 001 2101776 005 2020-01-07 008 2018 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1016/j.isprsjprs.2018.08.007$2DOI 100 1 $aPICOLI, M. C. A. 245 $aBig earth observation time series analysis for monitoring Brazilian agriculture.$h[electronic resource] 260 $c2018 500 $aNa publicação: Alexandre Coutinho, Julio Esquerdo, João Antunes. 520 $aThis paper presents innovative methods for using satellite image time series to produce land use and land cover classification over large areas in Brazil from 2001 to 2016. We used Moderate Resolution Imaging Spectroradiometer (MODIS) time series data to classify natural and human-transformed land areas in the state of Mato Grosso, Brazil?s agricultural frontier. Our hypothesis is that building high-dimensional spaces using all values of the time series, coupled with advanced statistical learning methods, is a robust and efficient approach for land cover classification of large data sets. We used the full depth of satellite image time series to create large dimensional spaces for statistical classification. The data consist of MODIS MOD13Q1 time series with 23 samples per year per pixel, and 4 bands (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), near-infrared (nir) and mid-infrared (mir)). By taking a series of labelled time series, we fed a 92-dimensional attribute space into a support vector machine model. Using a 5-fold cross validation, we obtained an overall accuracy of 94% for discriminating among nine land cover classes: forest, cerrado, pasture, soybeanfallow, fallow-cotton, soybean-cotton, soybean-corn, soybean-millet, and soybean-sunflower. Producer and user accuracies for all classes were close to or better than 90%. The results highlight important trends in agricultural intensification in Mato Grosso. Double crop systems are now the most common production system in the state, sparing land from agricultural production. Pasture expansion and intensification has been less studied than crop expansion, although it has a stronger impact on deforestation and greenhouse gas (GHG) emissions. Our results point to a significant increase in the stocking rate in Mato Grosso and to the possible abandonment of pasture areas opened in the state?s frontier. The detailed land cover maps contribute to an assessment of the interplay between production and protection in the Brazilian Amazon and Cerrado biomes. 650 $aLand use 650 $aTime series analysis 650 $aUso da Terra 653 $aBig earth observation data 653 $aBioma Amazônia 653 $aBioma Cerrado 653 $aCrop expansion 653 $aImagem de satélite 653 $aLand use science 653 $aSatellite image time series 653 $aSéries temporais 653 $aStatistical learning 653 $aTropical deforestation 700 1 $aCAMARA, G. 700 1 $aSANCHES, I. 700 1 $aSIMÕES, R. 700 1 $aCARVALHO, A. 700 1 $aMACIEL, A. 700 1 $aCOUTINHO, A. C. 700 1 $aESQUERDO, J. C. D. M. 700 1 $aANTUNES, J. F. G. 700 1 $aBEGOTTI, R. A. 700 1 $aARVOR, D. 700 1 $aALMEIDA, C. 773 $tISPRS Journal of Photogrammetry and Remote Sensing$gv. 145, part B, p. 328-339, Nov. 2018.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|