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| Registros recuperados : 41 | |
| 20. |  | LICHTEMBERG, L. A.; MALBURG, J. L.; HINZ, R. H. Effect of planting density on yield and cycle duration of `Nanicao' banana Curitiba, PR: IAPAR/SBF, 1996 p.438 In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 14. REUNIAO INTERAMERICANA DE HORTICULTURA TROPICAL, 42, 1996, Curitiba, PR.| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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| Registros recuperados : 41 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
10/05/2022 |
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Data da última atualização: |
10/05/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
SILVA, C. A.; GUERRISI, G.; DEL FRATE, F.; SANO, E. E. |
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Afiliação: |
CLAUDIA ARANTES SILVA; GIORGIA GUERRISI; FABIO DEL FRATE; EDSON EYJI SANO, CPAC. |
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Título: |
Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
European Journal of Remote Sensing, v. 55, n. 1, 2022. |
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Páginas: |
p. 129-149 |
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Idioma: |
Inglês |
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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. |
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Palavras-Chave: |
Desflorestamento; Extração automática de imagens; Floresta Amazônica; Rede neural. |
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Thesagro: |
Desmatamento; Floresta Tropical. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1142848/1/Sano-Near-real-time-deforestation-detection-in-the.pdf
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Marc: |
LEADER 02044naa a2200241 a 4500
001 2142848
005 2022-05-10
008 2022 bl uuuu u00u1 u #d
100 1 $aSILVA, C. A.
245 $aNear-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks.$h[electronic resource]
260 $c2022
300 $ap. 129-149
520 $aOptical-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.
650 $aDesmatamento
650 $aFloresta Tropical
653 $aDesflorestamento
653 $aExtração automática de imagens
653 $aFloresta Amazônica
653 $aRede neural
700 1 $aGUERRISI, G.
700 1 $aDEL FRATE, F.
700 1 $aSANO, E. E.
773 $tEuropean Journal of Remote Sensing$gv. 55, n. 1, 2022.
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Embrapa Cerrados (CPAC) |
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