arXiv subjects

George C. Gaines III

Publications and source records attributed to George C. Gaines III.

4 recordsLinked to original sources

arXiv2026

Within-fire estimates of biomass loss across national forests of the US West Coast: inventory-informed inference with uncertainty

Wildfire is reshaping forests in the western United States (US), yet estimates of wildfire impacts often rely on burned-area summaries or satellite-derived severity metrics that do not directly quantify changes in forest biomass or their uncertainty. We combine US Forest Inventory and Analysis plot measurements with annual 30 m tree canopy cover data in a Bayesian spatio-temporal modeling framework to estimate within-fire changes in live aboveground biomass (AGB) and forest mortality area across National Forest System lands in Washington, Oregon, and California. The model represents both live forest presence or absence and AGB conditional on live forest, allowing posterior predictive estimates of pre- and post-fire conditions at the pixel scale. Applied to 6,239 fires from 2000 to 2022, the analysis estimates 95.3 million Mg of wildfire-associated AGB loss and 2.39 million ha of forest mortality area, with the largest aggregate impacts occurring in 2020. Pixel-level estimates reveal substantial spatial heterogeneity within fire perimeters, including large differences between total burned area, forest mortality area, and associated biomass loss. This inventory-informed, uncertainty-aware approach provides a scalable framework for quantifying forest carbon impacts of disturbance across broad regions while retaining within-fire spatial detail.

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arXiv2025

Leveraging national forest inventory data to estimate forest carbon density status and trends for small areas

National forest inventory (NFI) data are often costly to collect, which inhibits efforts to estimate parameters of interest for small spatial, temporal, or biophysical domains. Traditionally, design-based estimators are used to estimate status of forest parameters of interest, but are unreliable for small areas where data are sparse. Additionally, design-based estimates constructed directly from the survey data are often unavailable when sample sizes are especially small. Traditional model-based small area estimation approaches, such as the Fay-Herriot (FH) model, rely on these direct estimates for inference; hence, missing direct estimates preclude the use of such approaches. Here, we detail a Bayesian spatio-temporal small area estimation model that efficiently leverages sparse NFI data to estimate status and trends for forest parameters. The proposed model bypasses the use of direct estimates and instead uses plot-level NFI measurements along with auxiliary data including remotely sensed tree canopy cover. We produce forest carbon estimates from the United States NFI over 14 years across the contiguous US (CONUS) and conduct a simulation study to assess our proposed model's accuracy, precision, and bias, compared to that of a design-based estimator. The proposed model provides improved precision and accuracy over traditional estimation methods, and provides useful insights into county-level forest carbon dynamics across the CONUS.

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arXiv2024

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

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.

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arXiv2024

Small area estimation of forest biomass via a two-stage model for continuous zero-inflated data

The United States (US) Forest Inventory & Analysis Program (FIA) collects data on and monitors the trends of forests in the US. FIA is increasingly interested in monitoring forest attributes such as biomass at fine geographic and temporal scales, resulting in a need for assessment and development of small area estimation techniques in forest inventory. We implement a small area estimator and parametric bootstrap estimator that account for zero-inflation in biomass data via a two-stage model-based approach and compare its performance to a post-stratified estimator and to the unit- and area-level empirical best linear unbiased prediction (EBLUP) estimators. For estimator comparison, we conduct a simulation study with counties in the US state Nevada as domains based on sampled plot data and remote sensing data products. Results show the zero-inflated estimator has the lowest relative bias and the smallest empirical root mean square error. Moreover, the 95% confidence interval coverages of the zero-inflated estimator and the unit-level EBLUP are more accurate than the other two estimators. To further illustrate the practical utility, we employ a data application across the 2019 measurement year in Nevada. We introduce the R package, saeczi, which efficiently implements the zero-inflated estimator and its mean squared error estimator.

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