Benchmarking and calibration of Forest Vegetation Simulator individual tree attribute predictions across the northeastern United States

Matthew B. Russell, Aaron R. Weiskittel, John A. Kershaw

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This study used permanent sample plot data from the USDA Forest Service's Forest Inventory and Analysis database to benchmark and calibrate three key submodels of the Forest Vegetation Simulator-Northeast variant (FVS-NE). Model predictions for total tree height (ht) and 5-year diameter (Δdbh 5) and height increment (δht5) for the 20 most abundant species did not indicate any serious spatial trends. FVS-NE predictions for total ht performed moderately well, as mean bias averaged -0.9 ± 5.2 ft (mean ± SD) across all species. FVS-NE Δdbh5 predictions fell within 15% of observed values between 8.4 and 17.3% of the time and performed best for shade-tolerant species and worst for intermediate shade intolerants. For Δht5, the number of predictions that fell within 15% of observed values averaged 7.7%. Submodel performance generally improved after calibrating FVS-NE predictions using tree size, site, and climate variables. After employing a calibrated Δdbh5, 5-year basal area growth continued to be underpredicted across all ecoregions and forest types. Results indicate that (1) an assessment of overall model performance should be conducted if calibrated submodels are used and (2) alternative modeling strategies be explored to better represent the allometry and growth of the important trees species across the northeastern United States.

Original languageEnglish (US)
Pages (from-to)75-84
Number of pages10
JournalNorthern Journal of Applied Forestry
Volume30
Issue number2
DOIs
StatePublished - Jun 2013

Keywords

  • Diameter increment
  • Forest inventory and analysis (fia)
  • Height increment
  • Height-diameter
  • Model validation

Fingerprint

Dive into the research topics of 'Benchmarking and calibration of Forest Vegetation Simulator individual tree attribute predictions across the northeastern United States'. Together they form a unique fingerprint.

Cite this