基于随机森林的杉木标准树高曲线

Generalized Height-diameter Model for Cunninghamia lanceolata Based on Random Forest

以湖北省赤壁市国有林场40块杉木人工林实测数据为例,运用随机森林方法,以胸径、优势树高、优势胸径为自变量,建立树高预测模型。首先根据随机森林的置换精度重要性筛选出建模的自变量,并确定决策树的数量和竞争节点变量数,得到决定系数R2为0.9450,均方误差MSE为2.6966的随机森林树高预测模型。利用检验数据对随机森林树高预测模型和传统树高预测模型分别进行精度检验。结果表明:随机森林模型的拟合效果与预测效果都优于该传统树高模型,随机森林模型可以作为有效的树高预测技术。

Taking the measured data of 40 Cunninghamia lanceolata plantation plots in the national 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.