We estimated forest biomass and growth in 169 plots across a range of forest types in northern Wisconsin, concurrent with small-footprint light detection and ranging (LiDAR) for both leaf-on and leaf-off conditions. We developed models to predict forest wood biomass and average annual wood biomass increment from LiDAR and digital soils data. We found strong relationships between LiDAR variables and forest woody biomass and biomass increment across a range of forest types and small differences between leaf-off and leaf-on LiDAR data when estimating biomass and increment. Stratification of models to coniferous, deciduous, and mixed stands improved accuracy while maintaining higher sample numbers per strata. Leaf-off LiDAR data, collected for other purposes and available at little or no cost, may be used to increase the accuracy and efficiency of estimating forest biomass and biomass increment across a range of forest types in the Great Lakes states.