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Using lidar for large-scale forest management can improve operational and management decisions. Using multi-temporal lidar sampling and remeasured field inventory data collected from 78 plots in the Panther Creek Watershed, Oregon, USA, we evaluated the performance of different fixed and mixed models in estimating change in aboveground biomass ( ∆ AGB ) and cubic volume including top and stump ( ∆ CVTS ) over a five-year period. Actual values of CVTS and AGB were obtained using newly fitted volume and biomass equations or the equations used by the Pacific Northwest unit of the Forest Inventory and Analysis program. Estimates of change based on fixed and mixed-effect linear models were more accurate than change estimates based on differences in LIDAR-based estimates. This may have been due to the compounding of errors in LIDAR-based estimates over the two time periods. Models used to predict volume and biomass at a given time were, however, more precise than the models used to predict change. Models used to estimate ∆ CVTS were not as accurate as the models employed to estimate ∆ AGB . Final models had cross-validation root mean squared errors as low as 40.90% for ∆ AGB and 54.36% for ∆ CVTS .

Predicting Volume and Biomass Change from Multi-Temporal Lidar Sampling and Remeasured Field Inventory Data in Panther Creek Watershed, Oregon, USA