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

  • Authors: Green, P Corey; Burkhart, Harold; Coulston, John; Radtke, Philip J.
  • Publication Year: 2019
  • Publication Series: Scientific Journal (JRNL)
  • Source: Forestry: An International Journal of Forest Research
  • DOI: 10.1093/forestry/cpz073

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 informationwere 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.

  • 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.
  • Keywords: Small area estimation, Forest inventory, Auxiliary data, Lidar, Loblolly pine
  • Posted Date: October 23, 2020
  • Modified Date: November 18, 2020
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