Principal Investigator: Dr. Stephen Prisley, Principal Research Scientist, and Dr. Holly Munro, Senior Research Scientist, NCASI

Collaborators: USDA Forest Service, Forest Inventory & Analysis Program (FIA)

Supported by: USDA Forest Service Rocky Mountain Research Station. Award 22-CA-11221638-201 for $2.57 million over five years.

Project Summary: The overarching objective of this project is to evolve FIA’s small area estimation capacity to meet resounding user demand. This goal will be achieved by collaboratively devising and implementing a blueprint for the transition of FIA-based SAE approaches from experimental to operational status through determination of user needs, research, and development.

This project will assemble and coordinate three panels of experts, designated as the User, Science, and Development Panels, to guide FIA’s adoption of new methods to increase the precision of estimates of target forest attributes in subpopulations spanning small geographic areas and short time intervals. The effort will be driven by user needs and will provide a lens through which historically independent research and development endeavors can be focused on unified production solutions.

The specific activities for resolving technical challenges include:

1. Develop a standardized framework for comparing competing estimation methods.
2. Test the range of small area techniques proposed to date, as well as new approaches, at operational scales and for a broad set of variables.
3. Explore strategies to dovetail with current direct estimates reported by the program.
4. Compare tools (online and otherwise) to determine those that most effectively deliver small area and improved model-assisted estimation techniques to our user base.

Call for PSAE RFP’s

Click here for RFP information and forms

Contact Steve Prisley at sprisley@ncasi.org or Holly Munro at hmunro@ncasi.org.

Related Reports and Publications

• Toward operational FIA model-based estimation of high-dimensional forest inventory parameters to support inference at user-defined spatial scales
  PI: Andrew O. Finley, Department of Forestry & Department of Statistics and Probability Michigan State University. Co-I: Paul B. May, Department of Mathematics, South Dakota School of Mines & Technology.
  • Progress reports: May 2024 and July 2024
• Incorporating spatial dependence and measurement error when estimating county level forest biomass.
  PI: Dr. Krishna P. Poudel Assistant Professor of Forest Biometrics, Department of Forestry, Mississippi State University.
  Co-PI: Dr. Curtis VanderSchaaf Assistant Extension Professor, Department of Forestry, Mississippi State University.
  • Progress reports: January 2024 and July 2024
• Spatial-temporal models for FIA data: Combining plots across time and space for time-specific and change estimates of forest biomass stocks.
  PI: Paul May, South Dakota School of Mines and Technology.
  Co-PI: Andrew Finley, Michigan State University.
  • Progress reports: February 2024 and July 2024
• Small-Area Estimation for FIA’s National Woodland Owner Survey.
  PI: Paul Catanzaro, UMass Amherst, Family Forest Research Center.
  Co-PI: Vance Harris, UMass Amherst, Family Forest Research Center.
  • Progress reports: January 2024 and July 2024
• Applications of Small Area Estimation over the Contiguous United States: Testing and Development of Alternative Methods.
  PI: Philip Radtke, Virginia Tech Department of Forest Resources and Environmental Conservation (FREC).
  Co-PI: Qianqian Cao, Corey Green, Valerie Thomas, Randolph Wynne, FREC.
  • Progress reports: January 2024 and August 2024
• Cloud-based small area estimation based on fast, on-demand processing of large-area data sets and mid- to high-resolution geospatial auxiliary remote sensing.
  PI: Aaron Weiskittel, Professor of Forest Biometrics and Director, Center for Research on Sustainable Forests, University of Maine.
  Co-PI:  Kasey Legaard, Research Associate of Geospatial Analytics and Machine Learning, University of Maine, Kenneth Bundy, Software and Machine Learning Engineer, University of Maine, Michael Premer, Assistant Professor of Forest Management, University of Maine.
  • Progress reports: Period 1 and Period 2
• Creating testbed populations for PSAE simulation studies.
  PI: Jerzy Wieczorek, Colby College.
  Co-PI: Kelly McConville, Harvard University, Grayson White, Michigan State University.
• A CONUS-wide application of KBAABB for formal assessment of PSAE-funded estimators.
  PI: Andrew O. Finley, Department of Forestry & Department of Statistics and Probability, Michigan State University. 
  Co-PI: Grayson W. White, Graduate Research Assistant, Michigan State University.
• Robust small-area estimation strategies for developing accurate stand-level diameter distributions.
  PI: Jaslam Poolakkal, University of Idaho.
• Integrating SAE methods with stand-level forest inventory and growth projection for southern pine plantations.
  PI: Sheng-I Yang, Assistant Professor, University of Georgia.
  Co-PI:  Bronson Bullock, Professor, University of Georgia.
• Using Small Area Estimation and 3D-NAIP/Sentinel-derived Variables for Multivariate Prediction of Stand Attributes.
  PI: Sukhyun Joo, Principal Investigator, Research Associate of Forest Biometrics, Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR.
  Co-PI: Temesgen Hailemariam, Co-Principal Investigator, Professor of Forest Biometrics, Department of Forest Engineering, Resources and Management, Oregon State University.
• The Interplay between Sampling Design and Small Area Estimation to Improve Stand- and Forest-level Estimates.
  PI: Temesgen Hailemariam, Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR.
  Co-PI: Sukhyun Joo, Research Associate of Forest Biometrics, Department of Forest Engineering, Resources and Management, Oregon State University.
• Stand-level small area estimation of tree size distribution.
  PI: Nicholas Nagle, Professor, Univ of Tennessee, Knoxville.
  Co-PI: Dingfan Xing, PhD, Postdoctoral Scholar, Univ of Tennessee, Knoxville.