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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. |
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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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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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