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

Authors
  • Cao, Qianqian1
  • Dettmann, Garret T.1
  • Radtke, Philip J.1
  • Coulston, John W.2
  • Derwin, Jill1
  • Thomas, Valerie A.1
  • Burkhart, Harold E.1
  • Wynne, Randolph H.1
  • 1 Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, VA , (United States)
  • 2 Southern Research Station, Forest Service, United States Forest Service, Asheville, NC , (United States)
Type
Published Article
Journal
Frontiers in Forests and Global Change
Publisher
Frontiers Media S.A.
Publication Date
Apr 26, 2022
Volume
5
Identifiers
DOI: 10.3389/ffgc.2022.769917
Source
Frontiers
Keywords
Disciplines
  • Forests and Global Change
  • Original Research
License
Green

Abstract

Many National Forest Inventory (NFI) stakeholders would benefit from accurate estimates at 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 cost-effective as a means for increasing the accuracy of county-level forest volume estimates in the United States NFI.

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