Knowledge of the uncertainty in estimates of forest live and dead biomass is critically important for understanding and interpreting forest inventory data used in applications such as national greenhouse gas inventories and biodiversity assessments. The Forest Inventory and Analysis (FIA) program of the USDA Forest Service conducts and maintains a comprehensive inventory of the forests of the US. The Northern Research Station FIA unit is responsible for inventorying forests in 24 states in the northeastern and north central US. As part of the inventory, the FIA program implements quality assurance (QA) procedures for the data it produces. Around the time the standard “production” crews inventory plots, highly trained and experienced “QA” crews, comprised of field crew supervisors and trainers, make independent inventories (i.e., blind remeasurement) on a subset of the same plots using identical methods. The difference between the measurements made by these two crews is a rich source of data on measurement uncertainty. Here we report the magnitude of measurement uncertainty in trees and downed coarse woody debris (DCWD).
Results/Conclusions
For trees, diameter measurements had better agreement between the two crews (0.14 cm or 1% of diameter) than did height (0.96 m or 7% of height), with hardwoods, especially Quercus and Juglandaceae, being more difficult to identify than conifers. Tree status (live, dead, or cut), crown class, crown ratio, grade, and decay class were also subject to error. Aggregated at the scale of a region or a forest type, differences between estimates based on production or QA crew measurement were 0.1 - 3% of volume or biomass. In contrast, estimates of DCWD volume, averaging 31.2 m3 ha-1 across the 79 plots evaluated, were highly uncertain (with a 95% CI of 15.9 m3 ha-1). Estimates of carbon storage (and mass) were more uncertain, due to poorly constrained estimates of wood density. Accounting for the collapse of dead wood as it decomposes would improve estimates. On the other hand, measuring the hollowness of DCWD could be eliminated in this region, with little effect. This study demonstrates how uncertainty analysis can be used to quantify confidence in estimates and to help identify where best to allocate resources to improve monitoring designs.