Abstract:

Based on the airborne laser scanning(ALS) data and field measurement data of 1 947 sample plots distributed across the spruce & fir(Picea spp.& Abies spp.),larch(Larix spp.),Mongolian scotch pine(Pinus sylvestris var mongolica),Korean pine(Pinus koraiensis),oak(Quercus spp.),birch(Betula spp.),poplar(Populus spp.),elm(Ulmus spp.),linden(Tilia spp.),and other three precious broadleaved species(Fraxinus mandshurica,Juglans mandshurica & Phellodendron amurense) forest stands in Northeast China,the stand volume regression models were developed through multiple linear regression and nonlinear regression methods,and the base regression model was determined with comparison and analysis.Then,the integrated stand volume models with stand type-specific parameters and the same ALS variables were developed,using the dummy variable modeling approach.The results showed that multiple linear volume regression models developed in this study for the 10 stand types have 2~7 explainable variables and the coefficients of determination(R ²) are between 0.460~0.858;and the nonlinear models have 2~4 explainable variables and the R ² are between 0.461~0.846.The two-variable nonlinear volume models based on mean height and mean intensity of point clouds are defined as standard volume models,of which the R ² are between 0.440~0.815,the mean prediction errors(MPEs) are between 2.88%~4.42%,and the mean percent standard errors(MPSEs) are between 16.76%~25.52%,which almost meet the need for precision requirements of the regulation on forest management inventory.According to the stand volume models developed in this study for 10 forest types,aerial stand volume tables based on ALS data can be established,which may be applied in forest management inventory.

Key words: airborne laser scanning, linear model, nonlinear model, dummy variable model, stand volume table