Forest Above-Ground Biomass（ AGB） is a critical indicator of carbon cycle in the ecosystem. In this study

we estimated AGB at tree and plot levels using airborne Li DAR point clouds

together with numerous field-measured structure information of tree. We compared methodologies and analyzed uncertainties in the estimation of AGB at different spatial levels

to provide supportive suggestions for the management and monitoring of forest resources. First

Li DAR metrics were calculated at plot and tree levels. Tree-level Li DAR metrics were calculated on the basis of the results from individual tree delineation using local maxima algorithm. Second

stepwise regression models were established between Li DAR metrics and AGB and their logarithm-transformed data. Apart from regression models established by combining different tree species

regression models were also developed for two dominant tree species in our study area. Finally

uncertainty analyses were conducted on AGB estimation at plot and tree levels.Li DAR points with different point densities were generated using thinning strategy to analyze the effects of point density variation on Canopy Height Model（ CHM）

the basic data source for individual tree delineation. The number of plots with specific point density was counted. Results were as follows.（ 1） Li DAR-estimated AGB was highly correlated with the field-estimated ones at plot and individual tree levels. The logarithm-transformed models obtained higher estimation accuracy than the non-logarithm-transformed models.（ 2） Regression model developed at the plot level（ R2= 0. 84

rRMSE = 0. 23） showed better performance than that at the tree level（ R2= 0. 61

rRMSE = 0. 46）.（ 3） Regression models built for the two dominant tree species improved the estimation accuracy of individual tree AGB; they obtained higher R2（ 0. 67 and 0. 69） and lower rRMSE（ 0. 29 and 0. 21） than the combined models.（ 4） AGB estimation at plot and tree levels suffered from uncertainty problems. Not all the plots used in this study could obtain the same point density. CHM was seriously influenced by point density when the density was lower than seven points per square meter（ pts / m2）. The influence from point density decreased when the density reached 9 pts/m2. The influence can be simply identified from the visual appearance of CHMs and directly affected the calculation of Li DAR metrics and results of individual tree delineation. The conclusions were as follows.（ 1） Logarithm-transformed model can improve the estimation accuracy of AGB at plot and tree levels.（ 2） The estimation accuracy of tree AGB can be improved when regression models are established for different tree species.（ 3） Larger uncertainties exist in AGB estimation at the tree level compared with those at the plot level.These uncertainties mainly come from the process of individual tree delineation.