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|Title:||Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks.|
|Authors:||SILVA, C. A.|
DEL FRATE, F.
SANO, E. E.
|Affiliation:||CLAUDIA ARANTES SILVA; GIORGIA GUERRISI; FABIO DEL FRATE; EDSON EYJI SANO, CPAC.|
|Citation:||European Journal of Remote Sensing, v. 55, n. 1, 2022.|
|Description:||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.|
Extração automática de imagens
|Type of Material:||Artigo de periódico|
|Appears in Collections:||Artigo em periódico indexado (CPAC)|
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|Sano-Near-real-time-deforestation-detection-in-the.pdf||23,35 MB||Adobe PDF|