13: A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans

Music City Center 

Increasingly, it has become common for data containing records about overlapping individuals to be distributed across multiple sources, making it necessary to identify which records refer to the same individual. The goal of record linkage is to estimate this unknown structure in the absence of a unique identifiable attribute. We introduce a Bayesian record linkage model for spatial location data motivated by the estimation of individual growth-size curves for conifers using overlapping LiDAR scans. Annual tree growth may be estimated upon correctly identifying unique individuals across scans in the presence of noise. We formalize a two-stage modeling framework, connecting the record linkage and downstream individual tree growth models, that provides uncertainty propagation through both stages of the modeling pipeline. In this work, we discuss the two-stage formulation, outline computational strategies to achieve scalability, assess model performance on simulated data, and fit the model to bi-temporal LiDAR scans of the Upper Gunnison Watershed to assess the impact of key topographic covariates on the growth behavior of conifer species in the Southern Rocky Mountains (USA).