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Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation
Qianqian Cao, Garret T. Dettmann, Philip J. Radtke, John W. Coulston, Jill Derwin, Valerie A. Thomas, Harold E. Burkhart, Randolph H. Wynne. Frontiers in Forests and Global Change, 2022-04-26
Topic: Risk, prevention and care in population studies
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.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Cao, Q., Dettmann, G. T., Radtke, P. J., Coulston, J. W., Derwin, J., Thomas, V. A., et al. (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. https://doi.org/10.3389/ffgc.2022.769917
Vancouver
Cao Q, Dettmann GT, Radtke PJ, Coulston JW, Derwin J, Thomas VA, et al. 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. 2022. doi:10.3389/ffgc.2022.769917.
BibTeX
@article{cao2022-68d90aa8,
author = {Cao, Qianqian and Dettmann, Garret T. and Radtke, Philip J. and Coulston, John W. and Derwin, Jill and Thomas, Valerie A. and Burkhart, Harold E. and Wynne, Randolph H.},
title = {{Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation}},
journal = {Frontiers in Forests and Global Change},
year = {2022},
doi = {10.3389/ffgc.2022.769917},
url = {https://doi.org/10.3389/ffgc.2022.769917}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- 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.
Why it matters
This study used small area estimation to improve the precision of county-level forest volume estimates in three U.S. states. It provides no information on treating, curing or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 32/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in Forests and Global Change) |
| Journal's citation record | 0 / 4 | Frontiers in Forests and Global Change: journal h-index 63 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 15 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.5 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 1 / 6 | Cited 1.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 11 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Randolph H. Wynne: h-index 40 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Virginia Tech: institution h-index 834 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 32 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/ffgc.2022.769917
- Original source →
- Free full text: Frontiers in Forests and Global Change →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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