Aboveground biomass quantification and tree-level prediction models for the Brazilian subtropical Atlantic Forest. Issue 3 (3rd July 2019)

• Record Type: Journal Article
• Authors: Uller, Heitor Felippe; Oliveira, Laio Zimermann; Klitzke, Aline Renata; Eleotério, Jackson Roberto; Fantini, Alfredo Celso; Vibrans, Alexander Christian
• Is Part Of: Southern forests. Volume 81:Issue 3(2019)
• Page Start: 261
• Page End: 271
• Publication Date: 2019-07-03
• Subjects: allometry -- REDD+ -- tree compartments; Forests and forestry -- Southern Hemisphere -- Periodicals; Forests and forestry -- Periodicals; 634.9091814
• DOI: 10.2989/00306525.2019.1581498
• Languages: English
• ISSNs: 2070-2620

Abstract

Abstract : Forests are the largest biological reservoir of biomass and carbon on the planet. This fact turns them into the main tool to neutralise the CO2 emitted by human activities. Despite such importance, the uncertainties associated with biomass estimates in forests, especially in (sub)tropical forests, are enormous. Facing this scenario, the objectives of this study were (1) to quantify through destructive sampling the aboveground biomass (AGB) of 105 trees of 47 species occurring in a secondary subtropical evergreen rainforest in Brazil; (2) to investigate the AGB distribution in different tree compartments; and (3) to fit tree-level models to improve biomass estimates for the referred forest type. The results revealed that most of the AGB was stored in the compartments stem and large branches (diameter≥ 5 cm). There was an increase in the proportion of biomass – in relation to the total tree AGB (kg) – allocated in the large branches as tree diameter at breast height (DBH) increased; this pattern was not observed for the compartments stem, thin branches (diameter < 5 cm), and leaves . The compartments thin branches and leaves represented between 5.4% and 17.0%, and 1.3% and 2.9% of the total tree AGB, respectively. From the 10 fitted biomass models, the linearised power models yielded the smallest errors. The best performance model, which returned a mean bias of 1.7%, may be written as AGB = exp(−8.9807 + 2.1642·ln[DBH] + 0.5072·ln[ h ] + 0.9999·ln[ρbas ]); Baskerville's factor = 1.0175. If there are no (reliable) data on tree total height ( h ; m), the following model, which embedded the DBH and wood basic specific gravity (ρbas ; kg m −3 ), may be employed: AGB = exp(−9.0086 + 2.4606·ln[DBH] + 1.0895·ln[ρbas ]); Baskerville's factor = 1.0206.