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| 1. |  | WENDT, S. N.; SOUSA, V. A. de; SEBBENN, A. M.; STURION, J. A.; LUÍS, F.; QUOIRIN, M. Polinização efetiva em pomar de Ilex paraguariensis St. Hil. In: REGO, G. M.; NEGRELLE, R. R. B.; MORELLATO, L. P. C. (ed.). Fenologia: ferramenta para conservação, melhoramento e manejo de recursos vegetais arbóreos. Colombo: Embrapa Florestas, 2007. p. 345-362. 1 CD-ROM.| Biblioteca(s): Embrapa Florestas. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
26/03/2018 |
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Data da última atualização: |
11/11/2021 |
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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: |
EL YACOUBI, S.; FARGETTE, M.; FAYE, A.; CARVALHO JUNIOR, W. de; LIBOUREL, T.; LOIREAU, M. |
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Afiliação: |
SAMIRA EL YACOUBI, UNIVERSITY OF PERPIGNAN; MIREILLE FARGETTE, INSTITUTE OF RESEARCH FOR DEVELOPMENT; ABDOULAYE FAYE, CENTRE DE SUIVI ÉCOLOGIQUE; WALDIR DE CARVALHO JUNIOR, CNPS; THÉRÈSE LIBOUREL, INSTITUTE OF RESEARCH FOR DEVELOPMENT; MAUD LOIREAU, INSTITUTE OF RESEARCH FOR DEVELOPMENT. |
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Título: |
A multilayer perceptron model for the correlation between satellite data and soil vulnerability in the Ferlo, Senegal. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
International Journal of Parallel, Emergent and Distributed Systems, v. 34, n. 1, p. 3-12, 2019. |
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DOI: |
https://doi.org/10.1080/17445760.2018.1434175 |
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Idioma: |
Inglês |
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Conteúdo: |
Soil erosion processes which contribute to desertification and land degradation, constitute major environmental and social issues for the coming decades. This is particularly true in arid areas where rural populations mostly depend on soil ability to support crop production. Assessment of soil erosion across large and quite diverse areas is very difficult but crucial for planning and management of the natural resources. The purpose of this paper is to investigate a prediction model for soil vulnerability to erosion based on the use of the information contained in satellite images. Based on neural networks models, the used approach in this paper aims at checking a correlation between the digital content of satellite images and soil vulnerability factors: erosivity (R), the soil erodibility (K), and the slope length and steepness (LS); vulnerability (V) as described in the RUSLE model. Significant results have been obtained for R and K factors. This promising pilot study was conducted in South Ferlo, Senegal, a region with Sahelian environmental characteristics. |
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Palavras-Chave: |
Imagens de satélite; Redes neurais; Vulnerabilidade do solo. |
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Thesagro: |
Desertificação. |
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Thesaurus NAL: |
Desertification; Neural networks. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
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001 2089751
005 2021-11-11
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1080/17445760.2018.1434175$2DOI
100 1 $aEL YACOUBI, S.
245 $aA multilayer perceptron model for the correlation between satellite data and soil vulnerability in the Ferlo, Senegal.$h[electronic resource]
260 $c2019
520 $aSoil erosion processes which contribute to desertification and land degradation, constitute major environmental and social issues for the coming decades. This is particularly true in arid areas where rural populations mostly depend on soil ability to support crop production. Assessment of soil erosion across large and quite diverse areas is very difficult but crucial for planning and management of the natural resources. The purpose of this paper is to investigate a prediction model for soil vulnerability to erosion based on the use of the information contained in satellite images. Based on neural networks models, the used approach in this paper aims at checking a correlation between the digital content of satellite images and soil vulnerability factors: erosivity (R), the soil erodibility (K), and the slope length and steepness (LS); vulnerability (V) as described in the RUSLE model. Significant results have been obtained for R and K factors. This promising pilot study was conducted in South Ferlo, Senegal, a region with Sahelian environmental characteristics.
650 $aDesertification
650 $aNeural networks
650 $aDesertificação
653 $aImagens de satélite
653 $aRedes neurais
653 $aVulnerabilidade do solo
700 1 $aFARGETTE, M.
700 1 $aFAYE, A.
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aLIBOUREL, T.
700 1 $aLOIREAU, M.
773 $tInternational Journal of Parallel, Emergent and Distributed Systems$gv. 34, n. 1, p. 3-12, 2019.
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