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Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska

Author

Listed:
  • Andrew O. Finley

    (Michigan State University
    Michigan State University)

  • Hans-Erik Andersen

    (Pacific Northwest Research Station)

  • Chad Babcock

    (University of Minnesota)

  • Bruce D. Cook

    (Goddard Space Flight Center)

  • Douglas C. Morton

    (Goddard Space Flight Center)

  • Sudipto Banerjee

    (University of California)

Abstract

A two-stage hierarchical Bayesian model is developed and implemented to estimate forest biomass density and total given sparsely sampled LiDAR and georeferenced forest inventory plot measurements. The model is motivated by the United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) objective to provide biomass estimates for the remote Tanana Inventory Unit (TIU) in interior Alaska. The proposed model yields stratum-level biomass estimates for arbitrarily sized areas. Model-based estimates are compared with the TIU FIA design-based post-stratified estimates. Model-based small area estimates (SAEs) for two experimental forests within the TIU are compared with each forest’s design-based estimates generated using a dense network of independent inventory plots. Model parameter estimates and biomass predictions are informed using FIA plot measurements, LiDAR data that are spatially aligned with a subset of the FIA plots, and complete coverage remotely detected data used to define landuse/landcover stratum and percent forest canopy cover. Results support a model-based approach to estimating forest parameters when inventory data are sparse or resources limit collection of enough data to achieve desired accuracy and precision using design-based methods. Supplementary materials accompanying this paper appear on-line

Suggested Citation

  • Andrew O. Finley & Hans-Erik Andersen & Chad Babcock & Bruce D. Cook & Douglas C. Morton & Sudipto Banerjee, 2024. "Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 695-722, December.
    • Handle: RePEc:spr:jagbes:v:29:y:2024:i:4:d:10.1007_s13253-024-00611-3
    DOI: 10.1007/s13253-024-00611-3
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    References listed on IDEAS

    as
    1. Finley, Andrew O. & Banerjee, Sudipto & MacFarlane, David W., 2011. "A Hierarchical Model for Quantifying Forest Variables Over Large Heterogeneous Landscapes With Uncertain Forest Areas," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 31-48.
    2. Qian Ren & Sudipto Banerjee, 2013. "Hierarchical Factor Models for Large Spatially Misaligned Data: A Low-Rank Predictive Process Approach," Biometrics, The International Biometric Society, vol. 69(1), pages 19-30, March.
    3. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
    4. Little R.J., 2004. "To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 546-556, January.
    5. Alec M. Chan‐Golston & Sudipto Banerjee & Mark S. Handcock, 2020. "Bayesian inference for finite populations under spatial process settings," Environmetrics, John Wiley & Sons, Ltd., vol. 31(3), May.
    6. Lu Zhang & Sudipto Banerjee, 2022. "Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data," Biometrics, The International Biometric Society, vol. 78(2), pages 560-573, June.
    7. Malay Ghosh, 2012. "Finite population sampling: a model-design synthesis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 235-242, June.
    8. Paul B. May & Andrew O. Finley & Ralph O. Dubayah, 2024. "A Spatial Mixture Model for Spaceborne Lidar Observations Over Mixed Forest and Non-forest Land Types," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 671-694, December.
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