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| Registros recuperados : 141 | |
| 101. |  | SILVA, M.; NASCIMENTO, C. P.; COUTINHO, A. C.; ALMEIDA, C. A.; VENTURIERI, A.; ESQUERDO, J. C. D. M. A transformação do espaço amazônico e seus reflexos na condição atual da cobertura e uso da terra. Novos Cadernos NAEA, Belém, PA, v. 16, n. 1, p. 229-248, jun. 2013.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Amazônia Oriental. |
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| 102. | | BROWN, J. C.; KASTENS, J.; WARDLOW, B.; JEPSON, W.; COUTINHO, A. C.; VENTURIERI, A.; LOMAS, J.; PRICE, K. Using MODIS to detect cropping frequency variation in mechanized agriculture in Amazonia. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 13., 2007, Florianópolis. Anais... São José dos Campos: INPE, 2007. p. 99-101.| Biblioteca(s): Embrapa Amazônia Oriental; Embrapa Territorial. |
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| 104. | | ESQUERDO, J. C. D. M.; CRUZ, S. A. B. da; MACÁRIO, C. G. do N.; ANTUNES, J. F. G.; SILVA, J. dos S. V. da; COUTINHO, A. C. Information technologies applied to geospatial data. In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; LUCHIARI JUNIOR, A.; ROMANI, L. A. S. (Ed.). Information and communication technologies and their relations with agriculture. Brasília, DF: Embrapa, 2016. ch. 8, p. 139-155.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 105. | | ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; COUTINHO, A. C.; SANTOS, J. L. dos; PARIZZI, T. N. T.; BERTOLO, L. S. Land use and land cover dynamics in municipalities of the Upper Paraguay. Ra'e Ga: o espaço geográfico em análise, v. 46, n. 3, p. 63-74, set. 2019.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 106. | | ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; SANTOS, J. L. dos; PARIZZI, T. N. T.; BERTOLO, L. S. Análise das mudanças do uso e cobertura da terra no Estado de Mato Grosso por meio do GeoPortal TerraClass. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos. Anais... São José dos Campos: INPE, 2019. p. 2002-2005. Editores: Douglas Francisco Marcolino Gherardi, Ieda Del?Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão. SBSR 2019.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 109. | | TERRA, T. N.; LUCIANO, A. C. dos; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; ANTUNES, J. F. G.; SANTOS, J. L. dos; BERTOLO, L. S. Dinâmica da intensificação da agricultura temporária na Área de Proteção Ambiental Ilha do Bananal-Cantão. In: BRAZILIAN SYMPOSIUM ON GEOINFORMATICS, 21., 2020, São José dos Campos. Proceedings... São José dos Campos: MCTIC/INPE, 2020. p. 246-251. GEOINFO 2020.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 111. | | ESQUERDO, J. C. D. M.; COUTINHO, A. C.; SANCHES, L. B.; RIBEIRO, B. M. de O.; ZAKHAROV, N. Z.; TERRA, T. N.; MANABE, V. D. Dinâmica da agricultura anual na região do Matopiba. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 4583-4588. SBSR 2015.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 112. | | ALMEIDA, C. A. de; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; ADAMI, M.; VENTURIERI, A.; DINIZ, C. G.; DESSAY, N.; DURIEUX, L.; GOMES, A. R. High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data. Acta Amazonica, Manaus, v. 46, n. 3, p. 291-302, July/Sept. 2016.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Amazônia Oriental. |
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| 115. | | ESQUERDO, J. C. D. M.; CRUZ, S. A. B.; MACÁRIO, C. G. do N.; ANTUNES, J. F. G.; SILVA, J. dos S. V. da; COUTINHO, A. C. Tecnologias da informação aplicadas aos dados geoespaciais. In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; LUCHIARI JUNIOR, A.; ROMANI, L. A. S. (Ed.). Tecnologias da informação e comunicação e suas relações com a agricultura. Brasília, DF: Embrapa, 2014. Cap. 8. p. 139-156.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 116. | | WANG, Y.; ZIV, G.; ADAMI, M.; ALMEIDA, C. A. de; ANTUNES, J. F. G.; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; GOMES, A. R.; GALBRAITH, D. Upturn in secondary forest clearing buffers primary forest loss in the Brazilian Amazon. Nature sustainability, v. 3, n. 4, Apr. 2020.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 118. | | BUENO, I. T.; ANTUNES, J. F. G.; TORO, A. P. S. G. D.; WERNER, J. P. S.; COUTINHO, A. C.; FIGUEIREDO, G. K. D. A.; LAMPARELLI, R. A. C.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G. Land use/land cover classification in a heterogeneous agricultural landscape using PlanetScope data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLVIII-M-1-2023, p. 49-55, 2023. Edition of proceedings of the 39th International Symposium on Remote Sensing of Environment (ISRSE-39) "From Human Needs to SDGs", 2023, Antalya, Türkiye.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 119. | | BUENO, I. T.; ANTUNES, J. F. G.; TORO, A. P. S. G. D.; WERNER, J. P. S.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G. Land use/land cover classification and scale effect analysis for a multi-temporal superpixel-based segmentation using PlanetScope data. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 304-307. Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 120. | | REIS, A. A. dos; WERNER, J. P. S.; SILVA, B. C.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; ROCHA, J. V.; MAGALHÃES, P. S. G. Monitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery. Remote Sensing, v. 12, n. 16, p. 1-21, Aug. 2020. Article number: 2534.| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 141 | |
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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: |
17/12/2018 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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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. |
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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. |
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Título: |
Big earth observation time series analysis for monitoring Brazilian agriculture. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
ISPRS Journal of Photogrammetry and Remote Sensing, v. 145, part B, p. 328-339, Nov. 2018. |
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DOI: |
https://doi.org/10.1016/j.isprsjprs.2018.08.007 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Alexandre Coutinho, Julio Esquerdo, João Antunes. |
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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 |
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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. |
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Thesagro: |
Uso da Terra. |
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Thesaurus NAL: |
Land use; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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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.
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