The relationship between lidar-derived metrics and biomass could vary across different vegetation types. However, in many studies, there are usually a limited number of field plots associated with each vegetation type, making it difficult to fit reliable statistical models for each vegetation type. To address this problem, this study used mixed-effects modeling to integrate airborne lidar data and vegetation types derived from aerial photographs for biomass mapping over a forest site in the Sierra Nevada mountain range in California, USA. It was found that the incorporation of vegetation types via mixed-effects models can improve biomass estimation from sparse samples. Compared to the use of lidar data alone in multiplicative models, the mixed-effects models could increase the R2 from 0.77 to 0.83 with RMSE (root mean square error) reduced by 10% (from 80.8 to 72.2 Mg/ha) when the lidar metrics derived from all returns were used. It was also found that the SAF (Society of American Forest) cover types are as powerful as the NVC (National Vegetation Classification) alliance-level vegetation types in the mixed-effects modeling of biomass, implying that the future mapping of vegetation classes could focus on dominant species. This research can be extended to investigate the synergistic use of high spatial resolution satellite imagery, digital image classification, and airborne lidar data for more automatic mapping of vegetation types, biomass, and carbon.