Spatio-temporal areal models to support small area estimation: An application to national-scale forest carbon monitoring

TL;DR

This paper introduces a Bayesian spatio-temporal modeling framework for small area estimation of forest parameters, enabling accurate trend and change detection with quantified uncertainty, crucial for ecological monitoring and carbon tracking.

Contribution

It develops a flexible Bayesian model that accounts for spatio-temporal dependence, improving small area estimates of forest carbon dynamics over traditional methods.

Findings

Model outperforms traditional estimators in simulations.

Framework provides comprehensive uncertainty quantification.

Applied to US forest data, revealing detailed carbon trends.

Abstract

National Forest Inventory (NFI) programs can provide vital information on the status, trend, and change in forest parameters. These programs are being increasingly asked to provide forest parameter estimates for spatial and temporal extents smaller than their current design and accompanying design-based methods can deliver with desired levels of uncertainty. Many NFI designs and estimation methods focus on status and are not well equipped to provide acceptable estimates for trend and change parameters, especially over small spatial domains and/or short time periods. Fine-scale space-time indexed estimates are critical to a variety of environmental, ecological, and economic monitoring efforts. Estimates for forest carbon status, trend, and change are of particular importance to international initiatives to track carbon dynamics. Model-based small area estimation (SAE) methods for NFI and similar ecological monitoring data typically pursue inference on status within small spatial domains, with few demonstrated methods that account for spatio-temporal dependence needed for trend and change estimation. We propose a spatio-temporal Bayesian model framework that delivers statistically valid estimates with full uncertainty quantification for status, trend, and change. The framework accommodates a variety of space and time dependency structures, and we detail model configurations for different settings. Through analysis of simulated datasets, we compare the relative performance of candidate models and a traditional direct estimator. We then apply candidate models to a large-scale NFI dataset to demonstrate the utility of the proposed framework for providing unique quantification of forest carbon dynamics in the contiguous United States. We also provide computationally efficient algorithms, software, and data to reproduce our results and for benchmarking.