Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation

Abstract

Many National Forest Inventory (NFI) stakeholders would benefit from accurate estimatesat finer geographic scales than most currently implemented in operational estimatesusing NFI sample data. In the past decade small area estimation techniques havebeen shown to increase precision in forest inventory estimates by combining fieldobservations and remote-sensing.We sought to demonstrate the potential for improvingthe precision of forest inventory growing stock volume estimates for counties inUnited States of North Carolina, Tennessee, and Virginia, by pairing canopy heightmodels from digital aerial photogrammetry (DAP) and field plot data from theUnited States NFI. Area-level Fay-Herriot estimators were used to avoid the need forprecise (GPS) coordinates of field plots. Reductions in standard errors averaging 30%for North Carolina county estimates were observed, with 19% average reductions instandard errors in both Tennessee and Virginia. Accounting for spatial autocorrelationamong adjacent counties provided further gains in precision when the three stateswere treated as a single forest land population; however, analyses conducted one stateat a time showed that good results could be achieved without accounting for spatialautocorrelation. Apparent gains in sample sizes ranged from about 65% in Virginia to128% in North Carolina, compared to the current number of inventory plots. Resultsshould allow for determining whether acquisition of statewide DAP would be costeffectiveas a means for increasing the accuracy of county-level forest volume estimatesin the United States NFI.

Citation

Cao, Qianqian; Dettmann, Garret T.; Radtke, Philip J.; Coulston, John W.; Derwin, Jill; Thomas, Valerie A.; Burkhart, Harold E.; Wynne, Randolph H. 2022. Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation. Frontiers in Forests and Global Change. 5: 28-. https://doi.org/10.3389/ffgc.2022.769917.