The tropical biomass & carbon project–An application for forest biomass and carbon estimates

Hassan C. David, Reinaldo I. Barbosa, Alexander C. Vibrans, Luciano F. Watzlawick, Jonathan W. Trautenmuller, Rafaelo Balbinot, Sabina C. Ribeiro, Laércio A.G. Jacovine, Ana Paula D. Corte, Carlos R. Sanquetta, Alessandra Calegari da Silva, Joberto Veloso de Freitas and David W. MacFarlane

Ecological Modelling, 2022, vol. 472, issue C

Abstract: This article introduces the Tropical Biomass & Carbon Application – the ‘TB&C App’, a web application available on the permanent link www.tropicalbiomass.com. The TB&C App requires as input attributes ‘the smallest and largest diameters’, ‘number of trees ha−1’, basal area ha−1, and ‘parameters of the diameter (beta) distribution’ describing stand structure. The App delivers outputs at two levels: (1) Stand level, including mean aboveground biomass (AGB) and carbon (AGC), in Mg ha−1, along with confidence intervals (CIs) as measures of uncertainty, and; (2) Tree level estimates, with AGB and diameter for every simulated tree. Phase 1 of the project TB&C comprises four Brazilian forest (and non-forest) formations: Campinarana, Floresta estacional, Floresta ombrofila, and Savana. This article aims to (i) describe the algorithm written for the TB&C App, and (ii) present results of Phase 1. This first phase counts on a standardized database of 1,428 trees with field-measured dry AGB, from plots across the different formations, which is the largest tree-biomass database compiled so far in Brazil. Model uncertainties were incorporated into the modeling process, allowing computation of CIs through an uncertainty approach. The total variance of residuals of AGB was also modeled, aiming at predicting CIs as a function of the quantity of AGB. An analysis of reliability of the equations implemented in the TB&C App indicates that more than 95% (n = 64,000) of the true AGB's fit into the CI outputted by the TB&C App. A comparison with other approaches in the literature shows significant agreement with previous estimates and more conservative estimates where previously-published estimates disagreed with the TB&C App. We cite as advantages of the TB&C App; (i) reliability of the outputs, (ii) a user-friendly layout, (iii) AGB and AGC estimates provided along with robust CIs, and (iv) estimates at the stand and tree levels with consistent totals. A biomass dataset containing information on 64,000 plots is also delivered as supplement of this paper.

Keywords: Web application; Amazon Forest; Forest Carbon; Aboveground biomass; Uncertainty analysis (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304380022001752
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:472:y:2022:i:c:s0304380022001752

DOI: 10.1016/j.ecolmodel.2022.110067

Access Statistics for this article

Ecological Modelling is currently edited by Brian D. Fath

More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().