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Incompatible spatial data modeling is a pervasive challenge in remote sensingdata analysis that involves field data. Typical approaches to addressing thischallenge aggregate information to a coarser common scale, i.e., compatibleresolutions. Such pre-processing aggregation to a common resolution simplifiesanalysis, but potentially causes information loss and hence compromisedinference and predictive performance. To incorporate finer information toenhance prediction performance, we develop a new Bayesian method aimed atimproving predictive accuracy and uncertainty quantification. The maincontribution of this work is an efficient algorithm that enables full Bayesianinference using finer resolution data while optimizing computational andstorage costs. The algorithm is developed and applied to a forest damageassessment for the 2018 Adrian storm in Carinthia, Austria, which uses fielddata and high-resolution LiDAR measurements. Simulation studies demonstratethat this approach substantially improves prediction accuracy and stability,providing more reliable inference to support forest management decisions.

Bayesian Modeling of Incompatible Spatial Data: A Case Study Involving Post-Adrian Storm Forest Damage Assessment