Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139789
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorKUCHLER, P. C.
dc.contributor.authorSIMÕES, M.
dc.contributor.authorBEGUE, A.
dc.contributor.authorFERRAZ, R. P. D.
dc.date.accessioned2022-02-08T15:01:35Z-
dc.date.available2022-02-08T15:01:35Z-
dc.date.created2022-02-08
dc.date.issued2021
dc.identifier.citationIn: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. WCCLF 2021 proceedings. Brasília, DF: Embrapa, 2021. p. 904-909. WCCLF 2021. Evento online.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1139789-
dc.descriptionThe adoption of crop-livestock (iCL) integrated systems has been pointed out as an important strategy for increasing production based on sustainable intensification of land use in Brazil. Mapping and monitoring the iCL areas would allow us to know the expansion rates and the adoption level of the integrated system, being an important instrument for public policy management. However, due to the time-space variability from integrated production systems, developing methods based on remote sensing remains a major challenge. In this sense, this work discusses the application of Big Data and machine learning concepts in Earth Observation Data as a strategy to compose a methodology for monitoring the iCL in Brazil. We tested the capacity of the Random Forest (RF) classifier applied to MODIS time series to iCL detection in the Mato Grosso State, Brazil. For this, we evaluated the classification accuracy for the years between 2012 and 2019, totaling 3,864 images processed. The overall accuracy founded was between 0.77 and 0.89 and an fscore average of 0.85 was found for the iCL class. The generated maps showed a trajectory of sustainable intensification, with the expansion of the iCL area from 1,100,000 ha in 2012/2013 to 2,597,000 ha in 2018/2019, an increase of 135%. The results indicate that the use of the RF classification technique with MODIS times series has great potential to compose an iCL monitoring methodology, requiring parallel and cloud computing applied to advanced algorithms.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectMODIS time series
dc.subjectMachine learning
dc.titleBig earth observation data and machine learning for mapping crop-livestock integrated system in Brazil.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroAgricultura Sustentável
dc.subject.nalthesaurusSustainable agricultural intensification
riaa.ainfo.id1139789
riaa.ainfo.lastupdate2022-02-08
dc.contributor.institutionPATRICK CALVANO KUCHLER, UERJ; MARGARETH GONCALVES SIMOES, CNPS; AGNÈS BEGUE, CIRAD; RODRIGO PECANHA DEMONTE FERRAZ, CNPS.
Aparece nas coleções:Artigo em anais de congresso (CNPS)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
Big-earth-observation-data-and-machine-learning-2021.pdf313.59 kBAdobe PDFThumbnail
Visualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace