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United States Forest Service Use of Forest Inventory Data: Examples and Needs for Small Area Estimation

  • Wiener, Sarah S.1
  • Bush, Renate2
  • Nathanson, Amy3
  • Pelz, Kristen4
  • Palmer, Marin5
  • Alexander, Mara L.1
  • Anderson, David6
  • Treasure, Emrys3
  • Baggs, Joanne3
  • Sheffield, Ray7
  • 1 Ecosystem Management Coordination, USDA Forest Service, Washington, DC , (United States)
  • 2 Northern Region, USDA Forest Service, Missoula, MT , (United States)
  • 3 Southern Region, USDA Forest Service, Atlanta, GA , (United States)
  • 4 Rocky Mountain Research Station, USDA Forest Service, Santa Fe, NM , (United States)
  • 5 Pacific Northwest Region, USDA Forest Service, Portland, OR , (United States)
  • 6 Southwestern Region, USDA Forest Service, Albuquerque, NM , (United States)
  • 7 Retired, USDA Forest Service, Hendersonville, NC , (United States)
Published Article
Frontiers in Forests and Global Change
Frontiers Media S.A.
Publication Date
Dec 06, 2021
DOI: 10.3389/ffgc.2021.763487
  • Forests and Global Change
  • Perspective


Forest Inventory and Analysis (FIA) data provides robust information for the United States Forest Service’s (USFS) mid-to-broad-scale planning and assessments, but ecological challenges (i.e., climate change, wildfire) necessitate increasingly strategic information without significantly increasing field sampling. Small area estimation (SAE) techniques could provide more precision supported by a rapidly growing suite of landscape-scale datasets. We present three Regional case studies demonstrating current FIA uses, how SAE techniques could enhance existing uses, and steps FIA could take to enable SAE applications that are user-friendly, comprehensive, and statistically appropriate. The Northern Region uses FIA data for planning and assessments, but SAE techniques could provide more specificity to guide vegetation management activities. State and transition simulation models (STSM) are run with FIA data in the Southwestern Region to predict effects of treatments and disturbances, but SAE could support model validation and more precision to identify treatable areas. The Southern Region used FIA to identify existing longleaf pine stands and evaluate condition, but SAE techniques within FIA tools would streamline analyses. Each case study demonstrates a desire to have FIA data on non-forested conditions and non-tree variables. Additional tools to measure statistical confidence would help maximize utility. FIA’s SAE techniques could add value to a widely used data set, if FIA can support key supplements to basic data and functionality.

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