Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks
| Autor: | SILVA, C. A., GUERRISI, G., DEL FRATE, F., SANO, E. E. |
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| Přispěvatelé: | CLAUDIA ARANTES SILVA, GIORGIA GUERRISI, FABIO DEL FRATE, EDSON EYJI SANO, CPAC. |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice) Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
| Popis: | 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. Made available in DSpace on 2022-05-10T20:12:48Z (GMT). No. of bitstreams: 1 Sano-Near-real-time-deforestation-detection-in-the.pdf: 23911273 bytes, checksum: 22125fb817d881a7ac998134c825fdd2 (MD5) Previous issue date: 2022 |
| Databáze: | OpenAIRE |
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