El Yacoubi S. auteur aut IRD Fargette Mireille auteur aut IRD Faye A. auteur aut IRD De Carvalho W. Jr auteur aut IRD Libourel T. auteur aut IRD Loireau Maud auteur aut IRD text journalArticle eng text/pdf born digital access 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. specialized DESERTIFICATION ERODIBILITE DU SOL SURFACE DU SOL IMAGE SATELLITE CORRELATION MODELISATION GESTION DE L'ENVIRONNEMENT SYSTEME D'INFORMATION GEOGRAPHIQUE RESEAU NEURONAL VULNERABILITE SENEGAL ZONE SAHELIENNE 068EROSOL 020MATH01 126TELAPP03 34 1 art. no 1434175 [ 3-12] 2019 1744-5760 https://www.documentation.ird.fr/hor/fdi:010072589 1744-5760 https://www.documentation.ird.fr/hor/fdi:010072589 https://www.documentation.ird.fr/intranet/publi/depot/2019-06-24/010072589.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2018-06-26 2019-06-28 fdi:010072589 fre