A novel application of small area estimation in loblolly pine forest inventory. (24th December 2019)

• Record Type: Journal Article
• Authors: Green, P Corey; Burkhart, Harold E; Coulston, John W; Radtke, Philip J
• Is Part Of: Forestry. Volume 93:Number 3(2020)
• Page Start: 444
• Page End: 457
• Publication Date: 2019-12-24
• Subjects: Small area estimation -- Forest inventory -- Auxiliary data -- Lidar -- Loblolly pine; Forests and forestry -- Periodicals; Forests and forestry -- Great Britain -- Periodicals; 634.9
• DOI: 10.1093/forestry/cpz073
• Languages: English
• ISSNs: 0015-752X

Abstract

Abstract: Loblolly pine ( Pinus taeda L.) is one of the most widely planted tree species globally. As the reliability of estimating forest characteristics such as volume, biomass and carbon becomes more important, the necessary resources available for assessment are often insufficient to meet desired confidence levels. Small area estimation (SAE) methods were investigated for their potential to improve the precision of volume estimates in loblolly pine plantations aged 9–43. Area-level SAE models that included lidar height percentiles and stand thinning status as auxiliary information were developed to test whether precision gains could be achieved. Models that utilized both forms of auxiliary data provided larger gains in precision compared to using lidar alone. Unit-level SAE models were found to offer additional gains compared with area-level models in some cases; however, area-level models that incorporated both lidar and thinning status performed nearly as well or better. Despite their potential gains in precision, unit-level models are more difficult to apply in practice due to the need for highly accurate, spatially defined sample units and the inability to incorporate certain area-level covariates. The results of this study are of interest to those looking to reduce the uncertainty of stand parameter estimates. With improved estimate precision, managers, stakeholders and policy makers can have more confidence in resource assessments for informed decisions.