Bayesian Spatiotemporal Small Area Estimation for Forest Disturbance Monitoring

Topic description

Climate change is increasing the frequency and severity of forest disturbances (e.g., droughts, wildfires, storms, and insect outbreaks), which are inherently localized and evolve rapidly across space and time. While national forest inventories (NFIs) provide unbiased estimates of forest resources at large scales, they are not designed to quantify localized events when local sample sizes are small. Remote sensing technologies (LiDAR, satellite imagery) can detect and map disturbed areas with high precision, but they do not directly measure essential forest attributes such as the volume of timber affected.

Model-assisted estimation offers a partial solution: by incorporating remote sensing data as auxiliary covariates, it is possible to reduce the variance of design-based estimators without sacrificing their design-based validity. However, this approach relies on asymptotic guarantees and on auxiliary data that are strongly correlated wit...