A novel application of small area estimation in loblolly pine forest inventory

Abstract

Loblolly pine (Pinus taeda L.) is one of the most widely planted tree species globally. As the reliability of estimatingforest characteristics such as volume, biomass and carbon becomes more important, the necessary resourcesavailable 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 pineplantations aged 9–43. Area-level SAE models that included lidar height percentiles and stand thinning status asauxiliary informationwere developed to test whether precision gains could be achieved. Models that utilized bothforms of auxiliary data provided larger gains in precision compared to using lidar alone. Unit-level SAE modelswere found to offer additional gains compared with area-level models in some cases; however, area-level modelsthat incorporated both lidar and thinning status performed nearly as well or better. Despite their potential gainsin precision, unit-level models are more difficult to apply in practice due to the need for highly accurate, spatiallydefined sample units and the inability to incorporate certain area-level covariates. The results of this study areof interest to those looking to reduce the uncertainty of stand parameter estimates. With improved estimateprecision, managers, stakeholders and policy makers can have more confidence in resource assessments forinformed decisions.

Citation

Green, P Corey; Burkhart, Harold E; Coulston, John W; Radtke, Philip J. 2019. A novel application of small area estimation in loblolly pine forest inventory. Forestry: An International Journal of Forest Research. 93(3): 444-457. https://doi.org/10.1093/forestry/cpz073.