Tree- and Stand-Level Biomass Estimation in a Larix decidua Mill. Chronosequence

Abstract

Carbon pool assessments in forests is one of the most important tasks of forest ecology. Despite the wide cultivation range, and economical and traditional importance, the aboveground biomass of European larch (Larix decidua Mill.) stands is poorly characterized. To increase knowledge about forest biomass accumulation and to provide a set of tools for aboveground biomass estimation, we studied a chronosequence of 12 larch forest stands (7-120 years old). From these stands, we measured the biomass of 96 sample trees ranging from 1.9 to 57.9 cm in diameter at breast height. We provided age-specific and generalized allometric equations, biomass conversion and expansion factors (BCEFs) and biomass models based on forest stand characteristics. Aboveground biomass of stands ranged from 4.46 (7-year-old forest stand) to 445.76 Mg ha −1 (106-year-old). Stand biomass increased with increasing stand age, basal area, mean diameter, height and total stem volume and decreased with increasing density. BCEFs of the aboveground biomass and stem were almost constant (mean BCEFs of 0.4688 and 0.3833 Mg m −3 , respectively). Our generalized models at the tree and stand level had lower bias in predicting the biomass of the forest stands studied, than other published models. The set of tools provided fills the gap in biomass estimation caused by the low number of studies on larch biomass, which allows for better estimation of forest carbon pools.Forest stand biomass can be estimated at two levels of detail: tree level and stand level. For tree level the most frequent approach is usage of allometric equations (AEs)-regression models allowing for tree biomass estimation using simple measurements, i.e., diameter at breast height or height [17][18][19]. AEs based on individual tree measurements require time-and money-consuming inventories, but yield higher accuracy of biomass estimation. Stand level estimation is usually conducted using forest stand models (e.g., [20,21]) or biomass expansion and conversion factors (BCEFs; e.g., [12,[22][23][24]). BCEFs are literally proportions of forest stand biomass and tree stand volume, allowing for easy conversion of forest inventory data (growing stock) into biomass. However, this approach is double-biased by errors of statistical models and errors of forest inventory. For example, BCEFs are not useful in cases of young forest stands where volume of merchantable wood is underestimated [21].The need for elaboration of more accurate biomass models is connected with the variability of biomass allocation patterns. The proportions of carbohydrates invested in development of particular organs vary climatically [25], but also with increasing age (e.g., [26][27][28]). For that reason, younger trees need separate models both at the tree (e.g., [23,29,30]) and stand (e.g., [21,22,24]) levels. Moreover, according to metabolic scaling theory [31] and its expansion [32], biomass allocation is connected with tree dimensions. In cases of managed forests, initial spacing of forest stands also influence both bioma...

Tree and stand level estimations of Abies alba Mill. aboveground biomass

“…In contrast, increments of total aboveground biomass and stem biomass were more or less linear. Patterns of biomass accumulation through stand maturation were also reflected in values of BCEFs-the highest BCEF values were noted in the youngest tree stands and then BCEFs stabilized at more or less constant values, similar to other tree species (Lehtonen et al 2004;Teobaldelli et al 2009;Jagodziński et al 2018a). Biomass was most highly correlated with variables describing tree stand dimensions, i.e., volume, diameter, and height.…”

“…Overestimation caused by usage of coefficients provided by IPCC guidelines was especially high in the tree stands with the highest biomass. For that reason, usage of these models may lead to overestimation of forest biomass carbon pools (Jagodziński et al 2018a), especially in countries where other methods of biomass assessment are not well developed.…”

“…Precise assessment of carbon balance in forest ecosystems is possible due to detailed studies of particular species and site conditions, as biomass accumulation patterns depend on tree stand age (e.g., Peichl and Arain 2007;Donnelly et al 2016;Jagodziński et al 2018a), climate (e.g., Oleksyn et al 1999;Schepaschenko et al 2018), soil fertility (e.g., Rademacher et al 2009;Lehtonen et al 2016) or successional stage (e.g., Kuznetsova et al 2011;Jagodziński et al 2017). In recent decades, researchers developed very precise tools for biomass assessment for the main forest tree species (Zianis et al 2005;Teobaldelli et al 2009;Forrester et al 2017).…”

Aboveground Biomass Of Mongolian Larch (Larix Sibirica Ledeb.) Forests In The Eurasian Region

“…However, this is a solution to the problem only in the first approximation, because it is based on a limited amount of harvest data. If our database, containing 433 larch sample trees, may be supplemented with harvested (but not yet published) data on 600 larch trees in China (zeng et al 2017) and 96 trees in Poland (Jagodzinski et al 2018), the model will get more consistent.…”

Similar Impacts of Alien and Native Tree Species on Understory Light Availability in a Temperate Forest

“…We also provided SE for averaged values for species, to acknowledge intraspecific variability (Table S2). To calculate foliage biomass we used available allometric models, trying to use those developed in the most similar conditions, due to impact of climate on allometric trajectories [55,56].…”