Development of an allometric model to estimate above-ground biomass of forests using MLPNN algorithm, case study: Hyrcanian forests of Iran

This research develops an allometric model for estimation of biomass based on the height and DBH of trees in the Hyrcanian forests of Iran. An accurate allometric model reduces the uncertainty of allometric equation in biomass estimation using radar images. In this study, 317 trees were selected randomly from the 4 different dominant tree species for the development of an allometric model covering the wide range of DBH and height classes. The selected trees were measured by fieldwork in different parts and then volumes of these parts were calculated separately. Total volume of tree is obtained by the summation of these volumes. Twelve commonly used allometric models, three generalized models and a proposed model were tested and the most suitable model was selected based on some of the commonly measured statistical parameters including coefficient of determination (R2), Root-Mean-Square Error (RMSE) and Mean Error (ME). We showed that the biomass estimation accuracy was improved in a multilayer perceptron neural network (MLPNN) when density of wood, DBH and height were used in combination compared to estimating the biomass by current allometric models. The RMSE value was decreased when the proposed method was used (RMSE =0.163 Mg and R2=0.986) in comparison with Chave model, as the best current method (RMSE =0.404 Mg and R2=0.957) in this paper.