Lidar aboveground vegetation biomass estimates in shrublands: prediction, uncertainties and application to coarser scales

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Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the Random Forests (RF) regression models. The Stepwise Multiple Regression (SMR) results were similar but slightly better than RF. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem.