Skip to Main Content
Spatial-temporal models for improved county-level annual estimatesAuthor(s): Francis Roesch
Source: In: McWilliams, Will; Moisen, Gretchen; Czaplewski, Ray, comps. Forest Inventory and Analysis (FIA) Symposium 2008; October 21-23, 2008; Park City, UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 20 p.
Publication Series: Proceedings (P)
Station: Rocky Mountain Research Station
PDF: View PDF (413.59 KB)
DescriptionThe consumers of data derived from extensive forest inventories often seek annual estimates at a finer spatial scale than that which the inventory was designed to provide. This paper discusses a few model-based and model-assisted estimators to consider for county level attributes that can be applied when the sample would otherwise be inadequate for producing low-variance estimates in the smaller counties. I present and demonstrate simple spatial and/or temporal estimators that draw strength from neighboring counties and/or years in order to increase confidence in the county level annual estimates. The spatial estimators are restricted to those that do not require knowledge of exact plot locations in order to enable their use with privacy protected, publicly available data. A series of simulations is used to compare and contrast the performance of these estimators relative to position in the time series of interest under various variance prescriptions. Although none of the estimators is shown to be superior in terms of minimum mean squared error (MSE) overall, a few general conclusions are drawn. The first is that estimators that draw strength through consecutive measurements of the same set of field plots show a significant reduction in MSE under a wider variety of circumstances than those that draw strength from plots in neighboring counties. The second conclusion is that of the estimators that rely on a temporal model, a simple, centralized weight-adjusted moving average (with weights specific to time-series position) often was the most robust.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationRoesch, Francis A. Spatial-temporal models for improved county-level annual estimates. 2009. In: McWilliams, Will; Moisen, Gretchen; Czaplewski, Ray, comps. Forest Inventory and Analysis (FIA) Symposium 2008; October 21-23, 2008; Park City, UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 20 p.
Keywordssmall-area estimation, weighted moving average, mixed estimation, forest inventory
- How much can natural resource inventory benefit from finer resolution auxiliary data?
- Opportunities to improve monitoring of temporal trends with FIA panel data
- Improving FIA trend analysis through model-based estimation using landsat disturbance maps and the forest vegetation simulator
XML: View XML