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

Andrew O. Finley (), Hans-Erik Andersen, Chad Babcock, Bruce D. Cook, Douglas C. Morton and Sudipto Banerjee
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Andrew O. Finley: 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

Journal of Agricultural, Biological and Environmental Statistics, 2024, vol. 29, issue 4, No 3, 695-722

Abstract: 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

Date: 2024
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DOI: 10.1007/s13253-024-00611-3

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