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Registros recuperados : 28 | |
21. | | KUCHLER, P. C.; SIMÕES, M.; FERRAZ, R. P. D.; ARVOR, D.; MACHADO, P. L. O. de A.; ROSA, M.; GAETANO, R.; BÉGUÉ, A. Monitoring complex integrated crop-livestock systems at regional scale in Brazil: a big earth observation data approach. Remote Sensing, v. 14, n. 7, 1648, 2022. Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Solos. |
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22. | | BÉGUÉ, A.; ARVOR, D.; BELLON, B.; BETBEDER, J.; ABELLEYRA, D. de; FERRAZ, R. P. D.; LEBOURGEOIS, V.; LELONG, C.; SIMÕES, M.; VERÓN, S. R. Remote sensing and cropping practices: a review. Remote Sensing, v. 10, n. 1, Jan. 2018. Biblioteca(s): Embrapa Solos. |
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24. | | SIMÕES, M. G.; FERRAZ, R. P. D.; BÉGUÉ, A.; BELLÓN, B.; FREITAS, P. L.; MACHADO, P. L. O. A.; NEVES, M. L.; SKORUPA, L. Satellite based multi-scale methods to support governance of Brazil's low-carbon agriculture (ABC Plan). In: GEOBIA, 6., 2016, Enschede. Solutions & synergies: conference proceedings. Enschede: University of Twente, 2016. Biblioteca(s): Embrapa Meio Ambiente. |
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25. | | SIMÕES, M. G.; FERRAZ, R. P. D.; BÉGUÉ, A.; BELLÓN, B.; FREITAS, P. L.; MACHADO, P. L. O. A.; NEVES, M. L.; SKORUPA, L. Satellite based multi-scale methods to support governance of Brazil's low-carbon agriculture (ABC Plan). In: GEOBIA, 6., 2016, Enschede. Solutions & synergies: conference proceedings. Enschede: University of Twente, 2016. Biblioteca(s): Embrapa Solos. |
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26. | | SIMÕES, M.; FERRAZ, R. P. D.; FREITAS, P. L.; SKORUPAE, L.; MANZATTO, C.; PEREIRA, S.; EVANGELISTA, B.; XAUD, H.; XAUD, M.; MACHADO, P. L. O. A.; BÉGUÉ, A.; BELLÓN, B.; BARON, C.; LO SEEN, D.; COSTA, G. Methodologies and technological innovation for satellite monitoring of low carbon agriculture in support to Brazil's ABC Plan - GeoABC Project. Rio de Janeiro: Embrapa Solos, 2016. 1 folder. Biblioteca(s): Embrapa Pesca e Aquicultura; Embrapa Solos. |
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27. | | WALDNER, F.; SCHUCKNECHT, A.; LESIV, M.; GALLEGO, J.; SEE, L.; PÉREZ-HOYOS, A.; D'ANDRIMONT, R.; DE MAET, T.; LASO BAYAS, J. C.; FRITZ, S.; LEO, O.; KERDILES, H.; DÍEZ, M.; VAN TRICHT, K.; GILLIAMS, S.; SHELESTOV, A.; LAVRENIUK, M.; SIMÕES, M.; FERRAZ, R. P. D.; BELLÓN, B.; BÉGUÉ, A.; HAZEU, G.; STONACEK, V.; KOLOMAZNIK, J.; MISUREC, J.; VERÓN, S. R.; ABELLEYRA, D. de; PLOTNIKOV, D.; MINGYONG, L.; SINGHA, M.; PATIL, P.; ZHANG, M.; DEFOURNY, P. Conflation of expert and crowd reference data to validate global binary thematic maps. Remote Sensing of Environment, v. 221, p. 235-246, Feb. 2019. Biblioteca(s): Embrapa Solos. |
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28. | | JOLIVOT, A.; LEBOURGEOIS, V.; LEROUX, L.; AMELINE, M.; ANDRIAMANGA, V.; BELLÓN, B.; CASTETS, M.; CRESPIN-BOUCAUD, A.; DEFOURNY, P.; DIAZ, S.; DIEYE, M.; DUPUY, S.; FERRAZ, R. P. D.; GAETANO, R.; GELY, M.; JAHEL, C.; KABORE, B.; LELONG, C.; LE MAIRE, G.; LO SEEN, D.; MUTHONI, M.; NDAO, B.; NEWBY, T.; SANTOS, C. L. M. de O.; RASOAMALALA, E.; SIMÕES, M.; THIAW, I.; TIMMERMANS, A.; TRAN, A.; BÉGUÉ, A. Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries. Earth System Science Data, v. 13, n. 2, p. 5951-5967, 2021. Biblioteca(s): Embrapa Solos. |
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Registros recuperados : 28 | |
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Registro Completo
Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Solos. |
Data corrente: |
04/04/2022 |
Data da última atualização: |
05/04/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
KUCHLER, P. C.; SIMÕES, M.; FERRAZ, R. P. D.; ARVOR, D.; MACHADO, P. L. O. de A.; ROSA, M.; GAETANO, R.; BÉGUÉ, A. |
Afiliação: |
PATRICK CALVANO KUCHLER, UERJ; MARGARETH GONCALVES SIMOES, CNPS; RODRIGO PECANHA DEMONTE FERRAZ, CNPS; DAMIEN ARVOR, UNIVERSITÉ RENNES; PEDRO LUIZ OLIVEIRA DE A MACHADO, CNPAF; MARCOS ROSA, UNIVERSIDADE ESTADUAL DE FEIRA DE SANTANA; RAFFAELE GAETANO, CIRAD; AGNÈS BÉGUÉ, CIRAD. |
Título: |
Monitoring complex integrated crop-livestock systems at regional scale in Brazil: a big earth observation data approach. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Remote Sensing, v. 14, n. 7, 1648, 2022. |
DOI: |
https://doi.org/10.3390/rs14071648 |
Idioma: |
Inglês |
Conteúdo: |
Due to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop-livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop-Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-cotton and soy-cereal); 0.77 for Level 3, the iCL level (iCL1 soy-pasture and iCL2 soy-pasture mixed with corn). The F-scores for DC, iCL and iCL1 cropping systems presented high accuracy (0.89, 0.85 and 0.84), while iCL2 was more difficult to classify (0.63). This approach will next be applied across the entire Brazilian soybean corridor, leading to an operational tool for monitoring the adoption of sustainable intensification practices recognized by Brazil's Agriculture Low Carbon Plan (ABC PLAN). MenosDue to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop-livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop-Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-c... Mostrar Tudo |
Palavras-Chave: |
Big data; Hierarchical classification; Machine learning; MODIS; Samples balancing; Satellite image time series; Training sample designs. |
Thesagro: |
Agricultura Sustentável. |
Thesaurus NAL: |
Cropping systems; Double cropping; Sustainable agriculture. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1141783/1/Monitoring-complex-integrated-crop-livestock-systems-at-regional-scale-in-Brazil-2022.pdf
|
Marc: |
LEADER 03062naa a2200349 a 4500 001 2141783 005 2022-04-05 008 2022 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3390/rs14071648$2DOI 100 1 $aKUCHLER, P. C. 245 $aMonitoring complex integrated crop-livestock systems at regional scale in Brazil$ba big earth observation data approach.$h[electronic resource] 260 $c2022 520 $aDue to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop-livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop-Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-cotton and soy-cereal); 0.77 for Level 3, the iCL level (iCL1 soy-pasture and iCL2 soy-pasture mixed with corn). The F-scores for DC, iCL and iCL1 cropping systems presented high accuracy (0.89, 0.85 and 0.84), while iCL2 was more difficult to classify (0.63). This approach will next be applied across the entire Brazilian soybean corridor, leading to an operational tool for monitoring the adoption of sustainable intensification practices recognized by Brazil's Agriculture Low Carbon Plan (ABC PLAN). 650 $aCropping systems 650 $aDouble cropping 650 $aSustainable agriculture 650 $aAgricultura Sustentável 653 $aBig data 653 $aHierarchical classification 653 $aMachine learning 653 $aMODIS 653 $aSamples balancing 653 $aSatellite image time series 653 $aTraining sample designs 700 1 $aSIMÕES, M. 700 1 $aFERRAZ, R. P. D. 700 1 $aARVOR, D. 700 1 $aMACHADO, P. L. O. de A. 700 1 $aROSA, M. 700 1 $aGAETANO, R. 700 1 $aBÉGUÉ, A. 773 $tRemote Sensing$gv. 14, n. 7, 1648, 2022.
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