  Spatially explicit quantification of forest biomass is important for
forest-health monitoring and carbon accounting. Direct field measurements of
biomass are laborious and expensive, typically limiting their spatial and
temporal sampling density and therefore the precision and resolution of the
resulting inference. Satellites can provide biomass predictions at a far
greater density, but these predictions are often biased relative to field
measurements and exhibit heterogeneous errors. We developed and implemented a
coregionalization model between sparse field measurements and a predictive
satellite map to deliver improved predictions of biomass density at a 1-by-1 km
resolution throughout the Pacific states of California, Oregon and Washington.
The model accounts for zero-inflation in the field measurements and the
heterogeneous errors in the satellite predictions. A stochastic partial
differential equation approach to spatial modeling is applied to handle the
magnitude of the satellite data. The spatial detail rendered by the model is
much finer than would be possible with the field measurements alone, and the
model provides substantial noise-filtering and bias-correction to the satellite
map.
