Abstract:

Based on the airborne LiDAR data,the influence of dummy variables on the estimation accuracy of stand volume is analyzed.In this study,Guangxi Gaofeng Forest Farm is taken as the research object,and based on the airborne lidar point cloud data and 96 plot data,the plot data is randomly divided into training samples and test samples are at a ratio of 7∶3.The samples and the corresponding point cloud features are estimated by regression modeling through the use of random forest model(RFR) and support vector machine model(SVR),and the tree species group(coniferous forest and broad-leaved forest) and age group as dummy variables are introduced into the regression model.The study uses the estimation accuracy of the test samples to evaluate the estimation accuracy of the model,and introduces tree species group as dummy variables.The RFR determination coefficient R² increases from 0.59 to 0.64,and the SVR determination coefficient R ² increases from 0.49 to 0.50.With the introduction of dummy variables in the age group,the RFR determination coefficient R ² increases from 0.59 to 0.65 and the SVR determination coefficient R ² increases from 0.45 to 0.55.According to the modeling accuracy and verification accuracy results of the model,the introduction of dummy variables is relatively effective in improving the accuracy of the accumulation estimation model.The dummy variables of age group have better effect on model accuracy improvement than dummy variables of tree species group.

Key words: ALS, dummy variables, volume, RFR, SVR