Canadian national tree aboveground biomass equations

Abstract

The estimation of aboveground biomass density (organic dry mass per unit area) is required for balancing Canadian national forest carbon budgets. Tree biomass equations are the basic tool for converting inventory plot data into biomass density. New sets of national tree biomass equations have therefore been produced from archival biomass data collected at the beginning of the 1980s through the ENergy from the FORest research program (ENFOR) of the Canadian Forest Service. Since the sampling plan was not standardized among provinces and territories, data had to be harmonized before any biomass equation could be considered at the national level. Two features characterize the new equations: estimated biomass of the compartments (foliage, branch, wood, and bark) are constrained to equal the total biomass, and dependence among error terms for the considered compartments of the same tree is taken into account in the estimates of both the model parameters and the variance prediction. The estimation method known to economists as "seemingly unrelated regression" allowed the inclusion of dependencies among the error terms of the considered biomass compartments. Sets of equations based on diameter at breast height (dbh) and on dbh and height have been produced for 33 species, groups of hardwood and softwood, and for all species combined. Biomass predicted by the new equations was compared with that estimated from provincial equations to evaluate the loss of accuracy when scaling up from the regional to the national scale. Bias and error of prediction from the set of national equations based on dbh and height were generally more similar to those from provincial equations than to those of predictions from the set of equations based on dbh alone.Résumé : L'estimation de la densité de biomasse aérienne (matière organique sèche par unité de surface) est requise pour déterminer les bilans nationaux canadiens de carbone forestier. Les équations de biomasse d'arbre constituent l'outil de base pour convertir les données des placettes d'inventaire en densité de biomasse. C'est pour cette raison que les données de biomasse ont été récupérées d'archives nationales pour produire de nouvelles séries d'équations de biomasse d'arbre. Ces données ont été recueillies au début des années 80 par le Service canadien des forêts dans le cadre du programme de recherche appelé ÉNergie de la FORêt (ENFOR). Comme le plan d'échantillonnage n'était pas standardisé parmi les provinces et territoires, les données ont dû être uniformisées avant que les équations de biomasse puissent être considérées au niveau national. Deux propriétés caractérisent les nouvelles équations de biomasse : les estimés de composantes de biomasse (feuille, branche, bois et écorce) sont contraints à égaliser la biomasse totale et la dépen-dance parmi les termes d'erreurs pour les différentes composantes d'un même arbre est prise en considération dans les estimés des paramètres du modèle et dans la variance de la prédiction. La méthode d'estimation connue par les écono-mistes com...

Model effects on GLAS-based regional estimates of forest biomass and carbon

“…In the three southern zones, they measured 16 814, fixed area, 11.3 m radius, 400 m 2 , temporary sample plots between 1998 and 2004. Total above-ground dry biomass is calculated on each plot using equations available in Lambert et al (2005). A small portion of these 1362 R. Nelson plots, ones more recently measured in 2000 to 2004, are used to develop the models reported below.…”

Improving the robustness of biomass functions: from empirical to functional approaches

“…A point represents a measured ratio, the solid line represents the model fit, and the dotted line represents the asymptote. The statistical performances of each model are given in the text unexpected, though the much greater exponent value associated with d compared to h in the model of Cienciala et al (2008) suggests a prominent role of d over h. The same conclusion was drawn by Lambert et al (2005) in a systematic comparison of only dbh-, and dbh-and height-based set of equations.…”

“…Alternatively, biomass expansion factors can also be used (Lehtonen et al 2004;Longuetaud et al 2013). Since the early 1970s, many biomass equations have been established for a wide variety of tree species in different regions of the world (Ter-Mikaelian and Korzukhin 1997; Nelson et al 1999;Lambert et al 2005;Saint-André et al 2005;Wutzler et al 2008). Nevertheless, the users of these biomass equations are generally facing several problems:…”

“…The use of covariate models is another way to improve the validity range of biomass equations; this approach consists in fitting biomass equations site by site (or stand by stand) and to relate equation parameters to site/stand variables (Saint- André et al 2005;António et al 2007;Genet et al 2011). Finally, the problem of tree component additivity is now commonly solved by using joint-generalized least square regression, also called seemingly unrelated regression (Parresol 2001;Lambert et al 2005).…”

Influence of Fuel Load Dynamics on Carbon Emission by Wildfires in the Clay Belt Boreal Landscape

“…Tree fuel consumption takes place only when CanFIRE predicts a crown fire. Values are estimated as the sum of foliage and bark using tree biomass algorithms [62] and are comparable to overstory fuel consumption data recorded on experimental burns in the Canadian Forest Fire Behavior Prediction (FBP) System database [63]. DWD and duff consumption follows McRae et al's [64] and de Groot et al's [65] equations, respectively.…”