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Ailanthus altissima mapping from multi-temporal very high resolution satellite images / Cristina Tarantino in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 147 (January 2019) . - pp 90 - 103 Titre : Ailanthus altissima mapping from multi-temporal very high resolution satellite images Type de document : Article/Communication Auteurs : Cristina Tarantino, Auteur ; Francesca Casella, Auteur ; Maria Adamo, Auteur ; Richard Lucas, Auteur ; Carl Beierkuhnlein, Auteur ; Palma Blonda, Auteur Année de publication : 2019 Article en page(s) : pp 90 - 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Ailanthus altissima [Termes IGN] analyse diachronique [Termes IGN] carte d'occupation du sol [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] espèce exotique envahissante [Termes IGN] filtrage optique [Termes IGN] filtre passe-bas [Termes IGN] image à très haute résolution [Termes IGN] image multitemporelle [Termes IGN] image Worldview [Termes IGN] indice de végétation [Termes IGN] Italie Résumé : (auteur) This study presents the results of multi-seasonal WorldView-2 (WV-2) satellite images classification for the mapping of Ailanthus altissima (A. altissima), an invasive plant species thriving in a protected grassland area of Southern Italy. The technique used relied on a two-stage hybrid classification process: the first stage applied a knowledge-driven learning scheme to provide a land cover map (LC), including deciduous vegetation and other classes, without the need of reference training data; the second stage exploited a data-driven classification to: (i) discriminate pixels of the invasive species found within the deciduous vegetation layer of the LC map; (ii) determine the most favourable seasons for such recognition. In the second stage, when a traditional Maximum Likelihood classifier was used, the results obtained with multi-temporal July and October WV-2 images, showed an output Overall Accuracy (OA) value of ≈91%. To increase such a value, first a low-pass median filtering was used with a resulting OA of 99.2%, then, a Support Vector Machine classifier was applied obtaining the best A. altissima User’s Accuracy (UA) and OA values of 82.47% and 97.96%, respectively, without any filtering. When instead of the full multi-spectral bands set some spectral vegetation indices computed from the same months were used the UA and OA values decreased. The findings reported suggest that multi-temporal, very high resolution satellite imagery can be effective for A. altissima mapping, especially when airborne hyperspectral data are unavailable. Since training data are required only in the second stage to discriminate A. altissima from other deciduous plants, the use of the first stage LC mapping as pre-filter can render the hybrid technique proposed cost and time effective. Multi-temporal VHR data and the hybrid system suggested may offer new opportunities for invasive plant monitoring and follow up of management decision. Numéro de notice : A2019-035 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.013 Date de publication en ligne : 20/11/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91972 [article]

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