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dc.contributor.authorSILVA, M. A. S. da
dc.contributor.authorMATOS, L. N.
dc.contributor.authorSANTOS, F. E. DE O.
dc.contributor.authorDOMPIERI, M. H. G.
dc.contributor.authorMOURA, F. R. DE
dc.date.accessioned2023-01-10T13:01:19Z-
dc.date.available2023-01-10T13:01:19Z-
dc.date.created2023-01-10
dc.date.issued2022
dc.identifier.citationIEEE Latin America Transactions, v. 20, n. 11, nov., 2022.
dc.identifier.isbn1548-0992
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1150825-
dc.descriptionABSTRACT - In Brazil, agribusiness has a considerable role in the country?s GDP. Because of this, the State needs territorial planning to minimize the impacts on natural resources, especially in the Pantanal and Amazon biomes, where agribusiness has expanded. The lower the agricultural diversification, the lower the pattern of land use homogeneity, generally associated with agribusiness, especially when it occupies large areas with more technological productive units. This paper investigates the relationship between spatial diversification patterns and the dynamics of native vegetation in Brazil. We propose a feature engineering and clustering approach for 5570 Brazilian municipalities between 1999 and 2018. It was based on the unsupervised artificial neural network Self-Organizing Map (SOM) to divide the municipalities into homogeneous groups of agricultural products diversity trends. The results were compared with the change in vegetation area using data from the national land use-mapping project called Mapbiomas. The analysis allowed the identification of three different regimes of modification in native vegetation, particularly related to municipalities in Brazil?s Midwest and North regions, indicating substantial changes in the Cerrado and Amazon biomes.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectClustering
dc.subjectSelf-Organizing Maps
dc.subjectShannon's entropy
dc.subjectSpatial panel data
dc.subjectSustainability
dc.titleTracking the Connection between Brazilian Agricultural Diversity and Native Vegetation Change by a Machine Learning Approach.
dc.typeArtigo de periódico
dc.format.extent2p. 2371-2380.
riaa.ainfo.id1150825
riaa.ainfo.lastupdate2023-01-10
dc.identifier.doihttps://latamt.ieeer9.org/index.php/transactions/article/view/6813
dc.contributor.institutionMARCOS AURELIO SANTOS DA SILVA, CPATC; LEONARDO NOGUEIRA MATOS, UNIVERSIDADE FEDERAL DE SERGIPE; FLÁVIO EMANUEL DE OLIVEIRA SANTOS, UNIVERSIDADE FEDERAL DO SERGIPE; MARCIA HELENA GALINA DOMPIERI, CNPM; FÁBIO RODRIGUES DE MOURA, UNIVERSIDADE FEDERAL DE SERGIPE.
Aparece nas coleções:Artigo em periódico indexado (CNPM)

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