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Auteur Chien-Shun Lo |
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Growth-competition-based stem diameter and volume modeling for tree-level forest inventory using airborne LiDAR data / Chien-Shun Lo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 2 (April 2013)
[article]
Titre : Growth-competition-based stem diameter and volume modeling for tree-level forest inventory using airborne LiDAR data Type de document : Article/Communication Auteurs : Chien-Shun Lo, Auteur ; Chinsu Lin, Auteur Année de publication : 2013 Article en page(s) : pp 2216 - 2226 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] croissance des arbres
[Termes IGN] données lidar
[Termes IGN] estimation statistique
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modélisation spatiale
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] tronc
[Termes IGN] volume (grandeur)Résumé : (Auteur) An individual tree within a forest stand will have its height and diameter growth restricted by the influence of neighboring trees. This is because trees in close proximity compete for resources and space to enable growth. In this paper, the position of trees, tree height (LH), tree crown radius (LCR), and growth competition index (LCI) were extracted from a light-detection-and-ranging (LiDAR)-based rasterized canopy height model using the multilevel morphological active-contour algorithm. The diameter and volume of individual trees are tested and validated to be an exponential function of those LiDAR-derived tree parameters. The best LiDAR-based diameter estimation model and volume estimation model were tested as significant with an R2 value of 0.84 and 0.9 and evaluated with an estimation bias of 8.7 cm and 0.9m3, respectively. Results also showed that LH and LCR are positively related to the LiDAR-derived diameter at breast height (DBH) and the LiDAR-derived volume of individual trees in a forest stand, whereas LCI is negatively related. The proposed algorithm of individual tree volume estimation was further applied to predict the volume of three sample plots in mountainous forest stands. It was found that the LVM could be used to predict an acceptable volume estimate of old-aged forest stands. The estimation bias, i.e., percentage RMSE (RMSE%), is averaged at around 4% using the LiDAR metrics lnLH, LCI, and LCR, whereas the RMSE% increases to 50% if only lnLH is applied. Results suggest that LCI is an important regulation factor in the estimation of forest volume stocks using LiDAR remote sensing. Numéro de notice : A2013-223 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2211023 En ligne : https://doi.org/10.1109/TGRS.2012.2211023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32361
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 4 Tome 2 (April 2013) . - pp 2216 - 2226[article]Réservation
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