Reducing the error in biomass estimates strongly depends on model selection

Nicolas Picard , Faustin Boyemba Bosela , Vivien Rossi

Improving the precision of forest biomass estimates requires prioritizing the different sources of errors. In a tropical moist forest in central Africa, the choice of the allometric equation was found to be the main source of error. Context: When estimating the forest biomass at the landscape level using forest inventory data and allometric models, there is a chain of propagation of errors including the measurement errors, the models' prediction error, the error due to the model choice, and the sampling error. Aims: This study aims at comparing the contributions of these different sources of error to the total error, to prioritize them, and improve the precision of biomass estimates. Methods: Using a 9-ha permanent sample plot in a moist forest near Kisangani in the Democratic Republic of Congo and seven competing allometric models, we estimated the contributions of the different sources of error to the total error of the per hectare biomass estimate, for plot sizes ranging from 0.04 to 1 ha. Results: When there was no a priori on which model being the best and for 1-ha plots, the error due to the model choice was the largest source of error (76 % of the total error). Using weights to combine the predictions of the different models into a single ensemble prediction strongly reduced this error. Conclusion: Collecting training data sets on tree biomass at many sites would be needed to improve the precision of forest biomass estimates in central Africa.

Mots clés: Aboveground biomass; African moist forest; Allometric equation; Bayesian model averaging; Error propagation; Prediction error

Domaines: Sciences du Vivant [q-bio]

Nicolas Picard, Faustin Boyemba Bosela, Vivien Rossi. Reducing the error in biomass estimates strongly depends on model selection. Annals of Forest Science, 2015, 72 (6), pp.811-823. ⟨10.1007/s13595-014-0434-9⟩. ⟨hal-01284209⟩