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Discrimination of deciduous tree species from time series of unmanned aerial system imagery / Jonathan Lisein in Plos one, vol 10 n° 11 (November 2015)  

Public [article]  inPlos one > vol 10 n° 11 (November 2015) . - n° 0141006 Titre : Discrimination of deciduous tree species from time series of unmanned aerial system imagery Type de document : Article/Communication Auteurs : Jonathan Lisein , Auteur ; Adrien Michez, Auteur ; Hugues Claessens, Auteur ; Philippe Lejeune, Auteur Année de publication : 2015 Article en page(s) : n° 0141006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse discriminante [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] drone [Termes IGN] houppier [Termes IGN] image aérienne [Termes IGN] orthoimage [Termes IGN] orthophotoplan numérique [Termes IGN] phénologie [Termes IGN] variation saisonnière Résumé : (auteur) Technology advances can revolutionize Precision Forestry by providing accurate and fine forest information at tree level. This paper addresses the question of how and particularly when Unmanned Aerial System (UAS) should be used in order to efficiently discriminate deciduous tree species. The goal of this research is to determine when is the best time window to achieve an optimal species discrimination. A time series of high resolution UAS imagery was collected to cover the growing season from leaf flush to leaf fall. Full benefit was taken of the temporal resolution of UAS acquisition, one of the most promising features of small drones. The disparity in forest tree phenology is at the maximum during early spring and late autumn. But the phenology state that optimized the classification result is the one that minimizes the spectral variation within tree species groups and, at the same time, maximizes the phenologic differences between species. Sunlit tree crowns (5 deciduous species groups) were classified using a Random Forest approach for monotemporal, two-date and three-date combinations. The end of leaf flushing was the most efficient single-date time window. Multitemporal datasets definitely improve the overall classification accuracy. But single-date high resolution orthophotomosaics, acquired on optimal time-windows, result in a very good classification accuracy (overall out of bag error of 16%). Numéro de notice : A2015--031 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1371/journal.pone.0141006 En ligne : http://dx.doi.org/10.1371/journal.pone.0141006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81106 [article]