Estimating tree aboveground biomass in an Atlantic Forest remnant using different modeling methods
DOI:
https://doi.org/10.24221/jeap.9.4.2024.6499.325-339Palavras-chave:
Generalized linear model, random forest, remote sensingResumo
The Atlantic Forest stores vast amounts of aboveground biomass (AGB), yet estimating these stocks is still challenging. We aimed to predict the AGB stock of the largest biodiversity remnant of Serra da Tiririca State Park (Rio de Janeiro, Brazil) by comparing the accuracy of generalized linear models (GLM) and random forest (RF) models. The results derived from field plots showed an AGB of 371 t.ha-1. The comparison between the modeling methods revealed that the GLM is more accurate; still, the RF is also fit to estimate the AGB of the remnant. The most accurate GLM predicted an AGB of 405 t.ha-1. We observed that the accuracy of the models improved when all predictor variables were combined. This study allowed us to improve the AGB estimates and produce an AGB map useful for managing and conserving the remnants.Downloads
Referências
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