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Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data / Z.J. Bortolot in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 6 (November 2005)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 59 n° 6 (November 2005) . - pp 342 - 360 Titre : Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data Type de document : Article/Communication Auteurs : Z.J. Bortolot, Auteur ; R. Wynne, Auteur Année de publication : 2005 Article en page(s) : pp 342 - 360 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] couvert forestier [Termes IGN] données de terrain [Termes IGN] données lidar [Termes IGN] forêt tempérée [Termes IGN] houppier [Termes IGN] lasergrammétrie [Termes IGN] masse végétale [Termes IGN] optimisation (mathématiques) [Termes IGN] Pinus taeda [Termes IGN] régression [Termes IGN] sylviculture [Termes IGN] Virginie (Etats-Unis) Résumé : (Auteur) A new individual tree-based algorithm for determining forest biomass using small footprint LiDAR data was developed and tested. This algorithm combines computer vision and optimization techniques to become the first training data-based algorithm specifically designed for processing forest LiDAR data. The computer vision portion of the algorithm uses generic properties of trees in small footprint LiDAR canopy height models (CHMs) to locate trees and find their crown boundaries and heights. The ways in which these generic properties are used for a specific scene and image type is dependent on 11 parameters, nine of which are set using training data and the Nelder-Mead simplex optimization procedure. Training data consist of small sections of the LiDAR data and corresponding ground data. After training, the biomass present in areas without ground measurements is determined by developing a regression equation between properties derived from the LiDAR data of the training stands and biomass, and then applying the equation to the new areas. A first test of this technique was performed using 25 plots (radius = 15 m) in a loblolly pine plantation in central Virginia, USA (37.42N, 78.68W) that was not intensively managed, together with corresponding data from a LiDAR canopy height model (resolution = 0.5 m). Results show correlations (r) between actual and predicted aboveground biomass ranging between 0.59 and 0.82, and RMSEs between 13.6 and 140.4 t/ha depending on the selection of training and testing plots, and the minimum diameter at breast height (7 or 10 cm) of trees included in the biomass estimate. Correlations between LiDAR-derived plot density estimates were low (0.22 Numéro de notice : A2005-490 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2005.07.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2005.07.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27626 [article]

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