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Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations / Kun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 465 - 482 Titre : Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations Type de document : Article/Communication Auteurs : Kun Liu, Auteur ; Xin Shen, Auteur ; Lin Cao, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 465 - 482 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de sensibilité [Termes IGN] biomasse aérienne [Termes IGN] canopée [Termes IGN] Chine [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] drone [Termes IGN] échelle des données [Termes IGN] erreur moyenne quadratique [Termes IGN] Ginkgo biloba [Termes IGN] plantation forestière [Termes IGN] semis de points [Termes IGN] structure de la végétation Résumé : (auteur) Estimating forest structural attributes in planted forests is crucial for sustainably management of forests and helps to understand the contributions of forests to global carbon storage. The Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) has become a promising technology and attempts to be used for forest management, due to its capacity to provide highly accurate estimations of three-dimensional (3D) forest structural information with a lower cost, higher flexibility and finer resolution than airborne LiDAR. In this study, the effectiveness of plot-level metrics (i.e., distributional, canopy volume and Weibull-fitted metrics) and individual-tree-summarized metrics (i.e., maximum, minimum and mean height of trees and the number of trees from the individual tree detection (ITD) results) derived from UAV-LiDAR point clouds were assessed, then these metrics were used to fit estimation models of six forest structural attributes by parametric (i.e., partial least squares (PLS)) and non-parametric (i.e., k-Nearest Neighbors (k-NN) and Random Forest (RF)) approaches, within a Ginkgo plantation in east China. In addition, we assessed the effects of UAV-LiDAR point cloud density on the derived metrics and individual tree segmentation results, and evaluated the correlations of these metrics with aboveground biomass (AGB) by a sensitivity analysis. The results showed that, in general, models based on both plot-level and individual-tree-summarized metrics (CV-R2 = 0.66–0.97, rRMSE = 2.83–23.35%) performed better than models based on the plot-level metrics only (CV-R2 = 0.62–0.97, rRMSE = 3.81–27.64%). PLS had a relatively high prediction accuracy for Lorey’s mean height (CV-R2 = 0.97, rRMSE = 2.83%), whereas k-NN performed well for predicting volume (CV-R2 = 0.94, rRMSE = 8.95%) and AGB (CV-R2 = 0.95, rRMSE = 8.81%). For the point cloud density sensitivity analysis, the canopy volume metrics showed a higher dependence on point cloud density than other metrics. ITD results showed a relatively high accuracy (F1-score > 74.93%) when the point cloud density was higher than 10% (16 pts·m−2). The correlations between AGB and the metrics of height percentiles, lower height level of canopy return densities and canopy cover appeared stable across different point cloud densities when the point cloud density was reduced from 50% (80 pts·m−2) to 5% (8 pts·m−2). Numéro de notice : A2018-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.001 Date de publication en ligne : 08/11/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91570 [article]

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