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Deriving a tree growth model from any existing stand growth model / Quang V. Cao in Canadian Journal of Forest Research, Vol 52 n° 2 (February 2022)  

Public [article]  inCanadian Journal of Forest Research > Vol 52 n° 2 (February 2022) . - pp 137 - 147 Titre : Deriving a tree growth model from any existing stand growth model Type de document : Article/Communication Auteurs : Quang V. Cao, Auteur Année de publication : 2022 Article en page(s) : pp 137 - 147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] désagrégation [Termes IGN] Etats-Unis [Termes IGN] méthode des moindres carrés [Termes IGN] modèle de croissance végétale [Termes IGN] modélisation de la forêt [Termes IGN] Pinus taeda [Termes IGN] régression [Termes IGN] surface terrière Résumé : (auteur) In this study, a new method was developed to derive a tree survival and diameter growth model from any existing stand-level model, without the need for individual-tree growth data. Predictions from the derived tree model are constrained to match the number of trees and the basal area per hectare as outputted by the stand model. The tree models derived from three different stand models were evaluated against a tree model, in both unadjusted and disaggregated forms. For the same stand-level model, the derived tree model outperformed its counterpart, the disaggregated tree model. Furthermore, except for one stand model with poor performance, the tree models derived from the remaining two stand models delivered results comparable to those obtained from the unadjusted tree model. The tree model derived from one stand model even performed slightly better than the unadjusted tree model. This result is significant because the coefficients of the unadjusted and disaggregated tree models had to be estimated from tree-level growth data, whereas the derived tree model required no tree growth data at all. The methodology presented in this study should be applicable when there is no ingrowth or recruitment of new trees. Numéro de notice : A2022-311 Affiliation des auteurs : non IGN Autre URL associée : Draft Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1139/cjfr-2021-0106 En ligne : https://doi.org/10.1139/cjfr-2021-0106 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100408 [article]