Allometric equation choice impacts lidar-based forest biomass estimates: A case study from the Sierra National Forest, CA

Regression modeling of biomass estimates from an airborne, multiple return lidar system using regional biomass allometric equations differs significantly from those using national scale Jenkins allometric equations with respect to the amount of variation explained, variables selected and variables of importance. Our discrete return lidar data were collected in September 2007 at 121 plots in a conifer dominated forest site in the Sierra Nevada mountains that include a full range of forest density. We regressed field plot-level estimates of biomass derived from field data and two different allometric equations with a range of lidar metrics. We compared regression performance across eight models: (1) point clouds alone, (2) point clouds with an empirical relationship between DBH and height (i.e., volume), (3) individual tree-level metrics, and (4) all data combined, and across two allometric equations – (A) Forest Inventory Analysis (FIA), and (B) Jenkins. In lower biomass plots, the reference above ground biomass (AGB) estimates from regional allometric equations and Jenkins equations were closely related; in plots with large biomass they were different. This finding suggests that published equations from large biomass plots are either not readily available or less represented in national scale allometric equation compiling. Models using reference AGBs calculated from regional allometric equations performed much better than those using reference AGBs calculated from Jenkins allometric equations. In these cases adjusted R2 improvement ranged from 0.07 to 0.11. The regression model that used regional allometric equations with lidar metrics and individual tree data provided the best overall R2 (0.79) with lowest RMSE suggesting that in most conditions regional biomass equations should be preferred over national equations. The inclusion of volumetric metrics shows that lidar variables are more sensitive to the reference AGBs calculated from regional allometric equations, and care should be taken when substituting regional equations using national scale compiled allometric equations in regional biomass studies. In addition, consistent with previous studies, the mean height of individual trees identified was chosen by both models with both reference AGBs calculated from regional allometric equations and those calculated from Jenkins equations, supporting the need to identify individual trees for biomass prediction. Based on these results, we conclude that the selection of allometric equations can influence the capacity of lidar data to estimate biomass significantly, and a careful selection of allometric equations is required for regional lidar biomass studies.

► Plot-level estimates of biomass were derived from field data and two different allometric equations.
► Estimates differed between allometric equations, especially in plots with high biomass.
► Selection of allometric equations can influence the capacity of lidar data to estimate biomass.
► The best fit between field data and lidar data were found using a regional allometric equation and a combination of lidar metrics and individual tree data.