Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1141783
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dc.contributor.authorKUCHLER, P. C.
dc.contributor.authorSIMÕES, M.eng
dc.contributor.authorFERRAZ, R. P. D.eng
dc.contributor.authorARVOR, D.eng
dc.contributor.authorMACHADO, P. L. O. de A.eng
dc.contributor.authorROSA, M.eng
dc.contributor.authorGAETANO, R.eng
dc.contributor.authorBÉGUÉ, A.eng
dc.date.accessioned2022-04-04T15:00:45Z-
dc.date.available2022-04-04T15:00:45Z-
dc.date.created2022-04-04
dc.date.issued2022
dc.identifier.citationRemote Sensing, v. 14, n. 7, 1648, 2022.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1141783-
dc.descriptionDue 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).
dc.language.isoeng
dc.rightsopenAccess
dc.subjectMODISeng
dc.subjectSamples balancingeng
dc.subjectSatellite image time serieseng
dc.subjectMachine learningeng
dc.subjectBig dataeng
dc.subjectHierarchical classificationeng
dc.subjectTraining sample designseng
dc.titleMonitoring complex integrated crop-livestock systems at regional scale in Brazil: a big earth observation data approach.
dc.typeArtigo de periódico
dc.subject.nalthesaurusSustainable agriculture
dc.subject.nalthesaurusCropping systemseng
dc.subject.nalthesaurusDouble croppingeng
riaa.ainfo.id1141783
riaa.ainfo.lastupdate2022-04-04
dc.identifier.doihttps://doi.org/10.3390/rs14071648
dc.contributor.institutionPATRICK CALVANO KUCHLER, UERJ
dc.contributor.institutionMARGARETH GONCALVES SIMOES, CNPSeng
dc.contributor.institutionRODRIGO PECANHA DEMONTE FERRAZ, CNPSeng
dc.contributor.institutionDAMIEN ARVOR, UNIVERSITÉ RENNESeng
dc.contributor.institutionPEDRO LUIZ OLIVEIRA DE A MACHADO, CNPAFeng
dc.contributor.institutionMARCOS ROSA, UNIVERSIDADE ESTADUAL DE FEIRA DE SANTANAeng
dc.contributor.institutionRAFFAELE GAETANO, CIRADeng
dc.contributor.institutionAGNÈS BÉGUÉ, CIRAD.eng
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