Single Tree Biomass Prediction Model of Eucalyptus Plantations Based on Random Forest Algorithm
Single-tree biomass models serve as foundations for estimating forest biomass. In order to develop such a model,biomass data from 90 Eucalyptus trees in the Leizhou Peninsula area were measured using the standard wood method. Date from sixty of these sample trees were randomly selected as the training set,and the other 30 sample trees were used for model validation. The model used ridge regression,heteroscedastic growth,and random forest models with stand age,tree height,and diameter at breast height as independent variables and single tree biomass as the dependent variable. The model's performance was evaluated using the coefficient of determination (R2),root mean square error (RMSE),and mean absolute error (MAE). The study found that the random forest model outperformed the ridge regression model and the heteroskedastic growth model in both the training and validation sets,as evidenced by higher values of the R2,RMSE,and MAE. The factor importance values of the random forest model indicated that diameter at breast height was the most significant factor affecting single tree biomass. In summary,the introduction of the forest age factor can improve the prediction accuracy of single-tree biomass in the random forest model. This provides basic data and model support for carbon sink measurement.
Eucalyptussingle tree biomassridge regression modelallometric growth modelrandom forest algorithm
万方数据
维普
