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Full waveform-based analysis for forest type information derivation from large footprint spaceborne lidar data / Junjie Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 3 (March 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 3 (March 2011) . - pp 281 - 290 Titre : Full waveform-based analysis for forest type information derivation from large footprint spaceborne lidar data Type de document : Article/Communication Auteurs : Junjie Zhang, Auteur ; A. De Gier, Auteur ; Y. Xing, Auteur ; Gunho Sohn, Auteur Année de publication : 2011 Conférence : SilviLaser 2010, 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems 14/09/2010 17/09/2010 Fribourg Allemagne Article en page(s) : pp 281 - 290 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] canopée [Termes IGN] capteur spatial [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] couvert forestier [Termes IGN] décomposition de Gauss [Termes IGN] données lidar [Termes IGN] feuillu [Termes IGN] lidar à retour d'onde complète [Termes IGN] Pinophyta [Termes IGN] signal laser Résumé : (Auteur) This study developed a new method to derive forest type information from large-footprint lidar data based on full waveform analysis. For this purpose, the raw waveform was decomposed into Gaussian components, and canopy return and ground return of the waveforms were separated. Two types of metrics hypothesized to have relationship with forest types were derived from the canopy return part of the waveform. The first type of metrics is quantile-based metrics reflecting the vertical distribution of canopy return energy, and the second type is statistical characteristics of the Gaussian components of canopy return part. Support Vector Machine classification was applied to different combinations of the metrics to find their relationship with different forest types. The results showed that the second type of metrics, indicating the canopy stratum characteristics, showed great promise in separating broad-leaved and needle-leaved forests with the accuracy ranging from 88.68 percent to 90.57 percent and Kappa statistic from 0.7406 to 0.7868. Numéro de notice : A2011-081 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.77.3.281 En ligne : https://doi.org/10.14358/PERS.77.3.281 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30862 [article]