International audienceAbstractKey messageKnowing the uncertainty for biomass equations is critical for their use and error propagation of biomass estimates. Presented here is a method to estimate uncertainty for equations where only n and R2 values from the original equations are available.ContextTree allometric equations form the basis of research and assessments of forest biomass. Frequently, uncertainty estimations do not propagate errors from these equations since the necessary information about sampling and tree measurements is not included in the original publication. Many biomass studies were conducted decades ago and the original, raw data is unavailable.AimsBecause of this information deficiency, and to improve error estimates in applications, a system to estimate the error structures of such equations is presented.MethodsA pseudo-data approach involving the creation of possible (pseudo) data using only R2 and n with a simple Monte-Carlo process generates probable error structures that can be used to propagate errors.ResultsIn a test of five different species with varying n input data and population variability, the original error structures were successfully recreated.ConclusionThis method of creating pseudo-data is simple and extensible and requires commonly published information about the original dataset. The method can be employed to create new ecosystem-level equations from species-specific equations, implemented in systems to select allometric equations to reduce uncertainty, and aid in the design of large-scale campaigns to generate new allometric equations for improving local to national scale estimates of forest biomass. The R code will be made freely available to anyone upon request to the authors
This report describes the database used to compile, store, and manage intensive ground-based biometric data collected at research sites in Colorado, Minnesota, New Hampshire, New Jersey, North Carolina, and Wyoming, supporting research activities of the U.S. North American Carbon Program (NACP). This report also provides details of each site, the sampling design and collection standards for biometric measurements, the database design, data summary examples, and the uses of intensive ground-based biometric data. Additional information on location descriptions, data, databases, and documentation may be accessed at http://www.nrs.fs.fed.us/data/lcms.
Forest inventories are commonly used to estimate total tree biomass of forest land even though they are not traditionally designed to measure biomass of trees outside forests (TOF). The consequence may be an inaccurate representation of all of the aboveground biomass, which propagates error to the outputs of spatial and process models that rely on the inventory data. An ideal approach to fill this data gap would be to integrate TOF measurements within a traditional forest inventory for a parsimonious estimate of total tree biomass. In this study, Light Detection and Ranging (LIDAR) data were used to predict biomass of TOF in all "nonforest" Forest Inventory and Analysis (FIA) plots in the state of Maryland. To validate the LIDAR-based biomass predictions, a field crew was sent to measure TOF on nonforest plots in three Maryland counties, revealing close agreement at both the plot and county scales between the two estimates. Total tree biomass in Maryland increased by 25.5 Tg, or 15.6%, when biomass of TOF were included. In two counties (Carroll and Howard), there was a 47% increase. In contrast, counties located further away from the interstate highway corridor showed only a modest increase in biomass when TOF were added because nonforest conditions were less common in those areas. The advantage of this approach for estimating biomass of TOF is that it is compatible with, and explicitly separates TOF biomass from, forest biomass already measured by FIA crews. By predicting biomass of TOF at actual FIA plots, this approach is directly compatible with traditionally reported FIA forest biomass, providing a framework for other states to follow, and should improve carbon reporting and modeling activities in Maryland.
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