A multilayer perceptron model for the correlation between satellite data and soil vulnerability in the Ferlo, Senegal

Samira El Yacoubi 1 Mireille Fargette 1 Abdoulaye Faye 2 Waldir de Carvalho Junior 3 Therese Libourel 1 Maud Loireau 1
1 UMR 228 Espace-Dev, Espace pour le développement
2 CES - centre de suivi écologique
3 Empresa Brasileira de Pesquisa Agropecuária
Abstract : 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.
Keywords : Soil vulnerability satellite images neural networks desertification
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Journal articles
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Submitted on : Wednesday, February 14, 2018 - 4:48:45 PM
Last modification on : Friday, May 17, 2019 - 1:01:15 AM

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UNIV-PERP | TDS-MACS | UNIV-AG | ESPACE-DEV | AGROPOLIS | MIPS | UNIV-MONTPELLIER | UNIV-AVIGNON

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Samira El Yacoubi, Mireille Fargette, Abdoulaye Faye, Waldir de Carvalho Junior, Therese Libourel, et al.. A multilayer perceptron model for the correlation between satellite data and soil vulnerability in the Ferlo, Senegal. International Journal of Parallel, Emergent and Distributed Systems, Taylor & Francis, 2018, pp.1 - 10. ⟨10.1080/17445760.2018.1434175⟩. ⟨hal-01709224⟩

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