ObjectiveNeural network model can avoid the collinearity and heteroscedasticity of variables in modeling forest stand biomass. This paper aims to apply multi-layer perceptron networks to forest biomass model to provide methods for the calculation and prediction of forest biomass and carbon stocks at forest management unit and regional levels.

MethodBased on 917 observations from the sample plots of larch plantations from national forest inventory in Jilin Province of northeastern China, the aboveground and total biomass models by log-transformed linear regression, and multi-layer perceptron networks with and without site factors were established. AIC, adjusted R², RMSE, RMSE_r and MAE were used to evaluate the models.

ResultThe model with the highest prediction accuracy was the neural network one with the input unit quadratic mean diameters (D), stand mean height (H), stand density index (S), altitude (HB), slope (PD), slope aspect (PX), slope position (PW), two hidden layers and hidden unit number of 40−20. Compared with traditional log-transformed linear regression model, the adjusted R² of the aboveground and total biomass models was increased from 0.902 1 to 0.914 1, and from 0.897 9 to 0.908 9, RMSE_r was decreased from 6.330 5% to 5.992 2%, and from 6.490 1% to 6.153 6%, respectively. The neural network model with site factors had slightly higher estimation accuracy than that without site factors. The adjusted R² of the aboveground and total biomass models was increased by 0.88% and 0.99%, and RMSE_r decreased by 5.33% and 5.46%, respectively.

ConclusionBiomass model based on multi-layer perceptron networks had similar performance in terms of model accuracy, but it could avoid treating the traditional assumptions such as the collinearity and heteroscedasticity of variables, and had the ability to calculate aboveground and total biomass models at one time.