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Using FIA data to predict forest understory vegetation structure

Date: September 03, 2019

Assessing forest understory vegetation structure using national inventory data


Background

Vegetation treatments that promote a mix of individual trees, groups and clumps like the one picture above near Red Feather Lakes, Colorado promote conditions more typical of Front Range forest structure in the mid-1800s. (Photo credit: P. Brown)
Ponderosa pine stand near Red Feather Lakes, Colorado (Photo credit: P. Brown).
Understory vegetation is a significant component of terrestrial carbon stocks and play an important role in determining fuel loading and wildlife habitat. Given the paucity of regional to national-scale studies aimed at describing relationships between overstory and understory vegetation attributes and an increasing need to understand ecosystems, it is critical to evaluate the ability of such national datasets to reveal these relationships. 

Research

Understory vegetation structure and its relationship with forest canopies and site conditions are important determinants of carbon stocks, wildlife habitat, and fuel loading for wildland fire assessments, and comprehensive studies are needed to better assess these relationships. One approach is to make use of preexisting forest inventory data to estimate understory vegetation height and cover from site and overstory attributes.

In this study, scientists at the Rocky Mountain Research Station obtained overstory, understory, and site condition data from over 6,700 Forest Inventory and Analysis plots to assess how understory vegetation cover and height vary with overstory attributes and site characteristics for four common forest types of the western United States: lodgepole pine, Douglas-fir, ponderosa pine, and grand fir. They found that forest overstory attributes played an important role in influencing vegetation structure and corroborating much previous work demonstrating this at different scales. Models developed from this study were weak to moderate in their ability to predict understory cover and height but nonetheless suggest that predicting understory vegetation attributes to aid assessments of carbon, fuel, and wildlife habitat may be more generalizable across forests of the western United States using standardized national inventory data in conjunction with improved measurements. 

Key Findings

  • Overstory tree variables are most influential in predicting understory vegetation.
  • Predictive models of understory cover were generally better than associated height models.
  • Predictive models of shrub height performed best overall while forb height models were least predictive.

Additional Resources

Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler A., Hess, K.T, Gibson, J.C., Lawler, J.J. 2007. Random forests for classification in ecology. Ecology 88 (11), 2783–2792. https://doi.org/10.1890/07-0539.1

Ffolliott, P.F., Clary, W.P. 1982. Understory-overstory vegetation relationships: an annotated bibliography. Gen. Tech. Rep. INT-136. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 39 p.

Johnson, K., Domke, G., Russell, M., Walters, B., Hom J., Peduzzi A., Birdsey R., Dolan, K., Huang, W. 2017. Estimating aboveground live understory vegetation carbon in the United States. Environmental Research Letters 12, 125010. https://doi.org/10.1088/1748-9326/aa8fdb

Suchar, V.A., Crookston, N.L. 2010. Understory cover and biomass indices predictions for forest ecosystems of the Northwestern United States. Ecological Indicators 10, 602–609. https://doi.org/10.1016/j.ecolind.2009.10.004

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Principal Investigators: 
Principal Investigators - External: 
Michael Krebs - Contractor with RMRS
Research Location: 
USFS Rocky Mountain Research Station, Missoula, MT 59801