Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation
| Authors: | Qianqian Cao, Garret T. Dettmann, Philip J. Radtke, John W. Coulston, Jill Derwin, Valerie A. Thomas, Harold E. Burkhart, Randolph H. Wynne |
| Year: | 2022 |
| Type: | Scientific Journal |
| Station: | Southern Research Station |
| DOI: | https://doi.org/10.3389/ffgc.2022.769917 |
| Source: | Frontiers in Forests and Global Change |
Abstract
Many National Forest Inventory (NFI) stakeholders would benefit from accurate estimatesat finer geographic scales than most currently implemented in operational estimates
using NFI sample data. In the past decade small area estimation techniques have
been shown to increase precision in forest inventory estimates by combining field
observations and remote-sensing.We sought to demonstrate the potential for improving
the precision of forest inventory growing stock volume estimates for counties in
United States of North Carolina, Tennessee, and Virginia, by pairing canopy height
models from digital aerial photogrammetry (DAP) and field plot data from the
United States NFI. Area-level Fay-Herriot estimators were used to avoid the need for
precise (GPS) coordinates of field plots. Reductions in standard errors averaging 30%
for North Carolina county estimates were observed, with 19% average reductions in
standard errors in both Tennessee and Virginia. Accounting for spatial autocorrelation
among adjacent counties provided further gains in precision when the three states
were treated as a single forest land population; however, analyses conducted one state
at a time showed that good results could be achieved without accounting for spatial
autocorrelation. Apparent gains in sample sizes ranged from about 65% in Virginia to
128% in North Carolina, compared to the current number of inventory plots. Results
should allow for determining whether acquisition of statewide DAP would be costeffective
as a means for increasing the accuracy of county-level forest volume estimates
in the United States NFI.