Título:	Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks.
Autoria:	SILVA, C. A. GUERRISI, G. DEL FRATE, F. SANO, E. E.
Afiliação:	CLAUDIA ARANTES SILVA; GIORGIA GUERRISI; FABIO DEL FRATE; EDSON EYJI SANO, CPAC.
Ano de publicação:	2022
Referência:	European Journal of Remote Sensing, v. 55, n. 1, 2022.
Páginas:	p. 129-149
Conteúdo:	Optical-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.
Thesagro:	Floresta Tropical Desmatamento
Palavras-chave:	Floresta Amazônica Rede neural Desflorestamento Extração automática de imagens
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CPAC)