Abstract: Forested areas are the largest carbon pool in terrestrial ecosystems. Thus, a key link in terrestrial carbon pool research is estimating the forest biomass accurately. In this study, canopy height and density indices were calculated from LiDAR point cloud data. Statistical models between the biomass calculated from field data and LiDAR-derived variables were built. A stepwise regression was used for variable selection and the maximum coefficient of determination (R²). Techniques for improving variable selection were applied to select the LiDAR-derived variables to be included in the models. Lastly, the forest aboveground biomass as estimated by field data and LiDAR data, was regarded as sample data. The forest aboveground biomass calculated from LiDAR data, band reflectance and vegetation indices of Landsat8 OLI were used to establish the regression model for estimating the forest aboveground biomass at a regional scale. The result shows that: the correlation (R²) between the biomass estimated by LiDAR data and the biomass calculated from field inventory data was 0.81, and the RMSE of biomass is 40.85 t/ha, which means canopy height indices and density indices of airborne LiDAR point cloud data has a strong relationship with biomass. The biomass was estimated by airborne LiDAR data and Landsat8 OLI for coniferous forest, broad-leaved forest and coniferous and broadleaf mixed forest. The estimated correlation results showed that R² was 0.51 (n=251), 0.58 (n=235) and 0.58 (n=241) respectively, and the RMSE for biomass was 24.1 t/ha, 31.3 t/ha and 29.9 t/ha respectively. The resulting estimated biomass for three different forest types is pretty much the same. On the whole, it is feasible and reliable to estimate forest aboveground biomass at regional scale based on remote sensing. The estimated biomass can provide useful data for the monitoring of forest ecosystem carbon fixation.