  Incompatible spatial data modeling is a pervasive challenge in remote sensing
data analysis that involves field data. Typical approaches to addressing this
challenge aggregate information to a coarser common scale, i.e., compatible
resolutions. Such pre-processing aggregation to a common resolution simplifies
analysis, but potentially causes information loss and hence compromised
inference and predictive performance. To incorporate finer information to
enhance prediction performance, we develop a new Bayesian method aimed at
improving predictive accuracy and uncertainty quantification. The main
contribution of this work is an efficient algorithm that enables full Bayesian
inference using finer resolution data while optimizing computational and
storage costs. The algorithm is developed and applied to a forest damage
assessment for the 2018 Adrian storm in Carinthia, Austria, which uses field
data and high-resolution LiDAR measurements. Simulation studies demonstrate
that this approach substantially improves prediction accuracy and stability,
providing more reliable inference to support forest management decisions.
