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Modelling forest canopy gaps using LiDAR-derived variables / Leighton Lombard in Geocarto international, vol 34 n° 2 ([01/02/2019])  

Public [article]  inGeocarto international > vol 34 n° 2 [01/02/2019] . - pp 179 - 193 Titre : Modelling forest canopy gaps using LiDAR-derived variables Type de document : Article/Communication Auteurs : Leighton Lombard, Auteur ; Riyad Ismael, Auteur ; Nitesh Poona, Auteur Année de publication : 2019 Article en page(s) : pp 179 - 193 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse d'image orientée objet [Termes IGN] croissance des arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Eucalyptus grandis [Termes IGN] forêt privée [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de surface de la canopée Résumé : (auteur) Remote sensing has revolutionized forest management and has been widely employed to model canopy gaps. In this study, a canopy height model (CHM) and an intensity raster (IR) derived from light detection and ranging (LiDAR) data were used to model canopy gaps within a four-year-old Eucalyptus grandis forest using an object-based image analysis (OBIA) approach. Model thematic accuracies using the CHM, intensity raster and combined data set (CHM and IR) were all above 90%, with KHAT values ranging from 0.88 to 0.96. Independent test thematic accuracies were also above 90%, with KHAT values ranging from 0.82 to 0.91. A comparative area-based assessment yielded accuracies ranging from 70 to 90%, with the highest accuracies achieved using the combined data set. The results of this study show that using a CHM and intensity raster, and an OBIA approach, provides a viable framework to accurately detect and delineate canopy gaps within a commercial forest environment. Numéro de notice : A2019-221 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1377775 Date de publication en ligne : 12/10/2017 En ligne : https://doi.org/10.1080/10106049.2017.1377775 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92739 [article]