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Mangrove tree crown delineation from high-resolution imagery / Muditha K. Heenkenda in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 6 (June 2015) . - pp 471 - 479 Titre : Mangrove tree crown delineation from high-resolution imagery Type de document : Article/Communication Auteurs : Muditha K. Heenkenda, Auteur ; Karen E. Joyce, Auteur ; Stefan W. Maier, Auteur Année de publication : 2015 Article en page(s) : pp 471 - 479 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] croissance des arbres [Termes IGN] houppier [Termes IGN] image à haute résolution [Termes IGN] image Worldview [Termes IGN] mangrove [Termes IGN] modèle numérique de surface [Termes IGN] objet géographique Résumé : (auteur) Mangroves are very dense, spatially heterogeneous, and have limited height variations between neighboring trees. Delineating individual tree crowns is thus very challenging. This study compared methods for isolating mangrove crowns using object based image analysis. A combination of WorldView-2 imagery, a digital surface model, a local maximum filtering technique, and a region growing approach achieved 92 percent overall accuracy in extracting tree crowns. The more traditionally used inverse watershed segmentation method showed low accuracy (35 percent), demonstrating that this method is better suited to homogeneous forests with reasonable height variations between trees. The main challenges with each of the methods tested were the limited height variation between surrounding trees and multiple upward pointing branches of trees. In summary, mangrove tree crowns can be delineated from appropriately parameterized objectbased algorithms with a combination of high-resolution satellite images and a digital surface model. We recommend partitioning the imagery into homogeneous species stands for best results. Numéro de notice : A2015-977 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.81.6.471 En ligne : https://doi.org/10.14358/PERS.81.6.471 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80060 [article]