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Mapping fragmented agricultural systems in the Sudano-Sahelian environments of Africa using random forest and ensemble metrics of coarse resolution MODIS imagery / E. Vintrou in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 8 (August 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 8 (August 2012) . - pp 839 - 848 Titre : Mapping fragmented agricultural systems in the Sudano-Sahelian environments of Africa using random forest and ensemble metrics of coarse resolution MODIS imagery Type de document : Article/Communication Auteurs : E. Vintrou, Auteur ; M. Soumaré, Auteur ; Serge Bernard, Auteur ; Agnès Bégue, Auteur ; C. Baron, Auteur ; D. Lo Seen, Auteur Année de publication : 2012 Article en page(s) : pp 839 - 848 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse texturale [Termes IGN] carte agricole [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Terra-MODIS [Termes IGN] Mali [Termes IGN] métrique [Termes IGN] Sahel [Termes IGN] zone aride Résumé : (Auteur) We worked on the assumption that agricultural systems shaped the landscape through human cropping practices, and that the resulting landscape can be described with a set of coarse resolution satellite-derived metrics (spectral, textural, temporal, and spatial metrics). A Random Forest classification model was developed at the village scale in South Mali, based on 100 samples, with data on the main type of agricultural system in each village (three-class typology), and 30 MODIS-derived and socio-environmental metrics calculated on agricultural areas. The model was found to perform well (overall accuracy of 60 percent) and was stable. Class A (food crops) and B (intensive agriculture) displayed good producer's accuracy (70 percent and 67 percent, respectively), while class C (mixed agriculture) was less accurate (50 percent). The most important metrics were shown to be the annual mean of NDVI, followed by the phenology transition dates and texture metrics. However, when considering each set of metrics separately, texture emerged as the most discriminating factor (with 53 percent of global accuracy). This result, i.e., that even coarse resolution imagery contains textural information that can be used for crop mapping, is new. Such maps could be used in food security systems as an indicator of system vulnerability, or as spatial inputs for crop yield models. Numéro de notice : A2012-430 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.78.8.839 En ligne : https://doi.org/10.14358/PERS.78.8.839 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31876 [article]