Estimation of aboveground biomass using the BIOMASS package in R: A case study in the Elías Meneses Experimental Forest, Santa Cruz, Bolivia
DOI: 10.03670/rft.v5i1.698
Keywords:
aboveground biomass, BIOMASS, Elías Meneses Experimental Forest, local model, error propagationAbstract
Estimating aboveground biomass in trees demonstrates significant spatial variability, and various methods are employed for its assessment, leading to uncertainty in the quantification of carbon and carbon dioxide. This study aims to implement an automated approach to quantify aboveground biomass with minimal uncertainty. Data collection was conducted in a 2.25 ha plot within the Elías Meneses Experimental Forest (BEEM), situated in the El Chore Forest Reserve. The methodology utilizes the functions of the BIOMASS package in the R environment to rectify plant taxonomy and subsequently assign wood density from a database, estimate total height using a local model (derived from a sample of diameter and height measurements), a model based on geographic region, and a model based on coordinates, thereby quantifying aboveground biomass per tree. The findings revealed 19 orders, 40 families, and 116 forest species, with 88.3% of individuals accurately classified and an aboveground biomass of 227.91 t/ha. The families Moraceae, Euphorbiaceae, Annonaceae, Chrysobalanaceae, Phyllanthaceae, and Meliaceae displayed the highest aboveground biomass, while the species Hura crepitans, Clarisia racemosa, Pseudolmedia laevis, Licania oblongifolia, Richeria grandis, and Ficus boliviana exhibited the greatest aboveground biomass. Through Monte Carlo simulation, the allometric model revealed the highest uncertainty, whereas the diameter presented the least uncertainty. This research concludes that the functions of the BIOMASS package in R facilitate the automated estimation of aboveground biomass in tropical forests, thereby reducing errors and enhancing accuracy.
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