Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142848
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Campo DCValorIdioma
dc.contributor.authorSILVA, C. A.
dc.contributor.authorGUERRISI, G.
dc.contributor.authorDEL FRATE, F.
dc.contributor.authorSANO, E. E.
dc.date.accessioned2022-05-10T20:12:48Z-
dc.date.available2022-05-10T20:12:48Z-
dc.date.created2022-05-10
dc.date.issued2022
dc.identifier.citationEuropean Journal of Remote Sensing, v. 55, n. 1, 2022.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1142848-
dc.descriptionOptical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective in the rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV- and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, and the difference between maximum and minimum values ? MMD) were computed from 30 Sentinel-1 images, from 2019, used as input parameters of the NN classifier. The samples were manually selected, including forested and deforested areas. After deforestation, mean backscatter signals decreased on the average of 2 dB for VV and 2.3 dB for VH from May to September?October. A Multi-Layer Perceptron (MLP) network was used for detecting near-real time forest disturbances larger than 2 ha. Case studies were performed for both polarizations considered the following input sets to the MLP: mean; mean and standard deviation; mean and MMD; and mean, standard deviation, and MMD. For the 2019 dataset, the latter showed the best performance of the NN algorithm with accuracy and F1 score of 99%. Automatic extraction using 2018 Sentinel-1 images reached accuracy and F1 score of 89% with the MapBiomas reference data and accuracy of 81% and F1 score of 79% with the PRODES reference data.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectFloresta Amazônica
dc.subjectRede neural
dc.subjectDesflorestamento
dc.subjectExtração automática de imagens
dc.titleNear-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks.
dc.typeArtigo de periódico
dc.subject.thesagroFloresta Tropical
dc.subject.thesagroDesmatamento
dc.format.extent2p. 129-149
riaa.ainfo.id1142848
riaa.ainfo.lastupdate2022-05-10
dc.contributor.institutionCLAUDIA ARANTES SILVA; GIORGIA GUERRISI; FABIO DEL FRATE; EDSON EYJI SANO, CPAC.
Aparece nas coleções:Artigo em periódico indexado (CPAC)

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