Development and assessment of regeneration imputation models for National Forests of Oregon and Washington

Karin Kralicek, Andrew J Sanchez Meador, Leah C. Rathbun

Research output: Contribution to journalArticle

Abstract

Imputation models were developed to predict seedling regeneration density and composition on National Forest System (NFS) lands in Oregon and Washington. The models were based on Forest Inventory and Analysis and Pacific Northwest Regional NFS Monitoring data. Individual models were developed based on broad forest plant association groups (FPAGs) with all model development and analysis conducted in R using a most similar neighbor-like imputation approach. Model performance was evaluated based on bias, mean absolute deviation, root mean-squared error (RMSE), and error rate in correctly predicting the total presence or absence of any regenerating species regardless of species (Total ER). Low to moderate RMSE (≤7400 regeneration stems ha−1) and low to moderate Total ER (≤50%) were observed for 25 out of 58 FPAG-specific models. The regeneration imputation models produced in this study represent a large first step towards developing flexible, expandable, and adaptable regeneration models that can be easily incorporated into existing growth models like the Forest Vegetation Simulator.

Original languageEnglish (US)
Pages (from-to)667-682
Number of pages16
JournalForest Ecology and Management
Volume409
DOIs
StatePublished - Feb 1 2018

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national forests
regeneration
forest inventory
growth models
simulator
stem
seedling
vegetation
stems
seedlings
monitoring

Keywords

  • Forest inventory and analysis
  • Most similar neighbor
  • Multi-species
  • Pacific Northwest
  • Plant associations

ASJC Scopus subject areas

  • Forestry
  • Nature and Landscape Conservation
  • Management, Monitoring, Policy and Law

Cite this

Development and assessment of regeneration imputation models for National Forests of Oregon and Washington. / Kralicek, Karin; Sanchez Meador, Andrew J; Rathbun, Leah C.

In: Forest Ecology and Management, Vol. 409, 01.02.2018, p. 667-682.

Research output: Contribution to journalArticle

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