Abstract:Taking the measured data of 40 Cunninghamia lanceolata plantation plots in the state-owned forest farm of Chibi City, Hubei Province as an example, a tree height prediction model was established by using the random forest method and taking the DBH, dominant tree height and dominant DBH as independent variables. First, the independent variable for modeling was selected, then, number of trees and number of predictors sampled for spliting at each node were determined, then, an optimum random forest model was developed, with a determinate coefficient of 0.9450 and error of mean square of 2.6966. And then, it was compared with one traditional generalized height-diameter equation, the validation datasets were used to test the models, respectively. The fitting effect and prediction effect of Random forest are better than the traditional equation, and Random forest model can be used as effective tree height prediction technology.