Enhancing the precision of broad-scale forestland removals estimates with small area estimation techniques. (4th January 2021)

• Record Type: Journal Article
• Authors: Coulston, John W; Green, P Corey; Radtke, Philip J; Prisley, Stephen P; Brooks, Evan B; Thomas, Valerie A; Wynne, Randolph H; Burkhart, Harold E
• Is Part Of: Forestry. Volume 94:Number 3(2021)
• Page Start: 427
• Page End: 441
• Publication Date: 2021-01-04
• Subjects: Forests and forestry -- Periodicals; Forests and forestry -- Great Britain -- Periodicals; 634.9
• DOI: 10.1093/forestry/cpaa045
• Languages: English
• ISSNs: 0015-752X

Abstract

Abstract: National Forest Inventories (NFI) are designed to produce unbiased estimates of forest parameters at a variety of scales. These parameters include means and totals of current forest area and volume, as well as components of change such as means and totals of growth and harvest removals. Over the last several decades, there has been a steadily increasing demand for estimates for smaller geographic areas and/or for finer temporal resolutions. However, the current sampling intensities of many NFI and the reliance on design-based estimators often leads to inadequate precision of estimates at these scales. This research focuses on improving the precision of forest removal estimates both in terms of spatial and temporal resolution through the use of small area estimation techniques (SAE). In this application, a Landsat-derived tree cover loss product and the information from mill surveys were used as auxiliary data for area-level SAE. Results from the southeastern US suggest improvements in precision can be realized when using NFI data to make estimates at relatively fine spatial and temporal scales. Specifically, the estimated precision of removal volume estimates by species group and size class was improved when SAE methods were employed over post-stratified, design-based estimates alone. The findings of this research have broad implications for NFI analysts or users interested in providing estimates with increased precision at finer scales than those generally supported by post-stratified estimators.