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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

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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

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Acknowledgements

Funding was provided by: Finley was supported by NASA Carbon Monitoring System (CMS) grants Hayes (CMS 2020) and Cook (CMS 2018); National Science Foundation (NSF) DMS-1916395; joint venture agreement with the USDA Forest Service Forest Inventory and Analysis 22-CA-11221638-201; USDA Forest Service, Region 9, Forest Health Protection, Northern Research Station; and Michigan State University AgBioResearch. Sudipto Banerjee was supported, in part, by National Science Foundation (NSF) under grants DMS-2113778, by the National Institute of Environmental Health Sciences (NIEHS) under grants R01ES030210 and 5R01ES027027 and by the National Institute of General Medical Sciences (NIGMS) under grant R01GM148761.

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Authors and Affiliations

  1. Department of Forestry, Michigan State University, East Lansing, MI, USA

    Andrew O. Finley

  2. Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA

    Andrew O. Finley

  3. USDA Forest Service, Pacific Northwest Research Station, Seattle, WA, USA

    Hans-Erik Andersen

  4. Department of Forest Resources, University of Minnesota, St Paul, MN, USA

    Chad Babcock

  5. National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, MD, USA

    Bruce D. Cook & Douglas C. Morton

  6. Department of Biostatistics, University of California, Los Angeles, CA, USA

    Sudipto Banerjee

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Finley, A.O., Andersen, HE., Babcock, C. et al. Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska. JABES (2024). https://doi.org/10.1007/s13253-024-00611-3

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