Aboveground biomass of Atlantic Forest: modeling and strategies for carbon estimate

• Ano de defesa: 2018
• Autor(a) principal: Colmanetti, Michel Anderson Almeida
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Biomass; Biomassa; Carbon; Carbono; Floresta tropical; Modelos preditivos; Predictive models; Tropical forest
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/91/91131/tde-02082018-095010/

The current concerning on potential effect of CO2 on climate change has assigned to the biomass of the tropical forest the importance as a sink of carbon. However, the heterogeneity of the natural ecosystems in tropics has significant implications for biomass estimation. This study proposed different biomass models using destructive sampling for the highly diverse Atlantic Forest. Models from two different approaches: generalized and species-specific were fitted and had the performance compared. Regarding the generalized models, it was proposed different covariates including diameter at breast height (dbh), height to the crown base, woody specific gravity (wsg) and functional plant traits. The species-specific models were fitted by linear mixed-models (LME) using species as a random effect and ordinary least square (OLS). The performance of all models and approaches were compared to existing models from the literature. Also, different estimates of biomass in stand- and forest-level, and the implications for carbon quantification were verified. Additionally, two methods for calibration for individual tree-level biomass model were proposed, and different strategies for tree selection were tested. The primary results show that the species-specific model using LME had better performance and can be used for the most abundant species, and models that include dbh, wsg, and plant traits are suitable for less abundant species. The calibration using the LME method in some cases can be used as an alternative for species that do not have a random effect presented here being a reasonable alternative for diverse tropical forests such as Atlantic Forest.