Models to support forest inventory and small area estimation using sparsely sampled LiDAR: A case study involving G-LiHT LiDAR in Tanana, Alaska

TL;DR

This paper develops a hierarchical Bayesian model to estimate forest biomass using sparse LiDAR and inventory data, enabling accurate small area estimates in remote regions like Alaska.

Contribution

It introduces a novel two-stage Bayesian approach that combines sparse LiDAR and inventory data for forest biomass estimation in remote areas.

Findings

Model-based estimates outperform design-based methods in sparse data scenarios.

The approach provides reliable biomass estimates for small areas and large regions.

LiDAR data integration improves biomass prediction accuracy.

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 variables when inventory data are sparse or resources limit collection of enough data to achieve desired accuracy and precision using design-based methods.