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The effects of combining the variables in allometric biomass models on biomass estimates over large forest areas: A european beech case study / Erick O. Osewe in Forests, vol 12 n° 10 (October 2021)  

Public [article]  inForests > vol 12 n° 10 (October 2021) . - n° 1428 Titre : The effects of combining the variables in allometric biomass models on biomass estimates over large forest areas: A european beech case study Type de document : Article/Communication Auteurs : Erick O. Osewe, Auteur ; Ioan Dutca, Auteur Année de publication : 2021 Article en page(s) : n° 1428 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] biomasse forestière [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données allométriques [Termes IGN] Fagus sylvatica [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de croissance végétale [Termes IGN] modélisation de la forêt [Termes IGN] Roumanie [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Effective initiatives for forest-based mitigation of climate change rely on continuous efforts to improve the estimation of forest biomass. Allometric biomass models, which are nonlinear models that predict aboveground biomass (AGB) as a function of diameter at breast height (D) and tree height (H), are typically used in forest biomass estimations. A combined variable D2H may be used instead of two separate predictors. The Q-ratio (i.e., the ratio between the parameter estimates of D and parameter estimates of H, in a separate variable model) was proposed recently as a measure to guide the decision on whether D and H can be safely combined into D2H, being shown that the two model forms are similar when Q = 2.0. Here, using five European beech (Fagus sylvatica L.) biomass datasets (of different Q-ratios ranging from 1.50 to 5.05) and an inventory dataset for the same species, we investigated the effects of combining the variables in allometric models on biomass estimation over large forest areas. The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean AGB per hectare for Q > 2.0 (i.e., by 6.3% for Q = 5.05), underestimation for Q Numéro de notice : A2021-864 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12101428 Date de publication en ligne : 19/10/2021 En ligne : https://doi.org/10.3390/f12101428 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99080 [article]