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Assessing post-fire regeneration in a Mediterranean mixed forest using lidar data and artificial neural networks / Haifa Debouk in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 12 (December 2013) . - pp 1121 - 1130 Titre : Assessing post-fire regeneration in a Mediterranean mixed forest using lidar data and artificial neural networks Type de document : Article/Communication Auteurs : Haifa Debouk, Auteur ; Ramon Riera-Tatché, Auteur ; Cristina Vega-Garcia, Auteur Année de publication : 2013 Article en page(s) : pp 1121 - 1130 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Catalogne (Espagne) [Termes IGN] classification par réseau neuronal [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt méditerranéenne [Termes IGN] hauteur de la végétation [Termes IGN] hauteur des arbres [Termes IGN] impact sur l'environnement [Termes IGN] incendie de forêt Résumé : (Auteur) Post-wildfire management practices can greatly influence vegetation condition and dynamics, and are crucial in Mediterranean erosion-prone poor soil sites. Acquiring accurate ground inventory data is time-consuming, expensive and limited to small areas; but lidar data can be used to assess the impact of fires, and also to determine the convenient silvicultural measurements which should be carried out for site restoration. The aim of this paper was to assess the post-fire regeneration status of the vegetation in Sant Llorenç del Munt massif after a wildfire in summer 2003 by modeling the relationship between lidar height bins and canopy height model (CHM) with field data. Artificial Neural Network (ANN) prediction models provided estimations of vegetation fraction cover, average height (HM) over 1.30 m and number of stems over 1.30 m, with Pearson r values between 0.18 and 0.83. Classification models built with the same variables allowed separating two ground-based regeneration classes (good and scarce regeneration) with an approximate accuracy of 83 to 76 percent (model building and validation data, respectively). Numéro de notice : A2013-690 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.79.12.1121 En ligne : https://doi.org/10.14358/PERS.79.12.1121 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32826 [article]