Techniques

Status
Ongoing
Start Date
February, 2023

Project Description

A primary focus of the Resource Monitoring and Assessment program research is to improve monitoring techniques and develop new applications for the Forest Inventory and Analysis Program.

For example, we are working to expand forest health metrics using indicator species, improve estimates of canopy cover and biomass from standard inventory measurements.

Purpose and Scope

Improving variance estimators

  • We are working to improve variance estimators with alternative methods of incorporating remote sensing.

Forest health metrics

  • This project entails improving forest health metrics with a national vegetation diversity indicator and national lichen community indicator.

Canopy cover estimates

  • We are working to improve estimates of tree canopy cover from standard inventory measurements.

Equation development

  • PNW-FMA researchers and statisticians are continually working to improve monitoring techniques and improves estimates of forest attributes such as canopy cover and biomass.
  • Development of analytical and visualization software tools to manipulate and process data from large-area lidar acquisitions to produce information useful for resource analyses.

SVS: Stand Visualization System

EnVision: Environmental Visualization System

Key Personnel

Project Contact

  • Person

    Vicente Monleon

    Mathematical Statistician

Investigators

  • Person

    Olaf Kuegler

    Mathematical Statistician
  • Person

    Andrew Gray, PhD

    Research Ecologist, Team Leader
  • Person

    Hans Andersen, PhD

    Research Forester/VMaRS Team Leader
  • Person

    Demetrios Gatziolis

    Research Forester
  • Person

    Robert McGaughey

    Research Forester
  • Person

    Vicente Monleon

    Mathematical Statistician

Collaborators

  • Geospatial Technology and Applications Center

Data and Tools

Publications

External Publications

    • Mauro, F., Monleon, V.J., Gray, A.N., Kuegler, O., Temesgen, H., Hudak, A.T., Fekety, P.A., Yang, Z. 2022. Comparison of model assisted endogenous poststratification methods for estimation of aboveground biomass change in Oregon, USA. Remote Sensing 14: 6024
    • Strunk, J.L., Bell, D.M., Gregory, M.J., 2022. Pushboom photogrammetic heights enhance state-level forest attribute mapping with Landsat and environmental gradients. Remote Sensing 14(14): 3433.
    • Gray, A.N., McIntosh, A.C.S., Garman, S.L., Shettles, M.A. 2021. Predicting canopy cover of diverse forest types from individual tree measurements. Forest Ecology and Management 501: 119682.
    • Kimsey Jr, M.J., Strimbu, B.M., McGaughey, R.J. 2021. Advancements in forest mensuration and biometrics in the artificial intelligence era. Canadian Journal of Forest Research.
    • Mauro, F., Frank, B., Monleon, V.J., Temesgen, H., Ford, K.R. 2019. Generation of tree lists for stands in Southwest Oregon using LiDAR and stand level auxiliary information. Canadian Journal of Forest Research 49: 775-787.
    • Strunk, J.L., Gould, P.J., Packalen, P., Poudel, K.P., Andersen, H.E., Temesgen, H. 2017. An examination of diameter density prediction with k-NN and airborne lidar. Forests 8: 444.

     

Last updated January 19, 2024