Estimation of forest disturbance from retrospective observations in a broad-scale inventory

John W. Coulston, Christopher B. Edgar, James A. Westfall, Marcus E. Taylor

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Understanding the extent and timing of forest disturbances and their impacts is critical to formulating effective management and policy responses. Broad-scale inventory programs provide key estimates of forest parameters that indicate the extent and severity of disturbance impacts. Here, we review the use of a post-stratified estimator in a panelized design, in the context of disturbance observations that are collected retrospectively. We further develop a sample weight adjustment that is requisite for proper estimation of the extent and timing of disturbances. Using populations from areas of Arkansas, California, and Maine in the US, the weight adjustment technique was tested in a Monte Carlo simulation. We found that the estimated area of disturbance using the weight adjustment technique had satisfactory agreement with the true population values and performed considerably better than the conventional post-stratified estimation approach. The proliferation of panelized forest inventory designs globally suggests that accurate estimates of areal extent and timing of disturbances will often require that weighting adjustment techniques be employed in the estimation process.

Original languageEnglish (US)
Article number1298
Pages (from-to)1-14
Number of pages14
Issue number12
StatePublished - Dec 2020

Bibliographical note

Funding Information:
Funding: Funding to support CBE in this effort was provided by the USDA Forest Service Project 15 FIA–Forest Biometrics Program Support (RJVA 15-JV-11242305-100). J.W.C., J.A.W., and M.E.T. received no external funding in support of this research.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.


  • Forest inventory and analysis
  • Post-stratified estimation
  • Sample weights
  • Unequal probability


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