Light Detection and Ranging （LiDAR） is one of the most promising technologies in forestry

which shows potential for timely and accurate measurements of forest biophysical properties over time.This study explores several regression models relating variables derived from airborne laser scanner for the estimation of various forest metrics

and discusses the results of prediction concluding accuracy.These prediction models use 78 plots with radius of 7.5 m or 15 m in Kunming

Yunnan province

China.Two series of variables are provided from the airborne laser scanner data

one is canopy height and to the other canopy density.These variables are used as independent variables in the regressions.The stepwise regression analysis has been used to select various independent variables.The results show high correlation between forest metrics and variables derived from airborne laser scanner.For the three different forest types （coniferous

broad-leaf and mixed）

all the prediction of mean heights are accurate.However

for the predictions of above ground biomass

the result of coniferous is better than broad-leaf

while there is no significant correlation between the biomass of mixed and the laser variables.Finally

the results of regression and factors affect the accuracy of prediction are discussed.The accuracy of prediction may be relate to the forest type

sampling time and density of laser scanning and position errors.