  Many forest management planning decisions are based on information about the
number of trees by species and diameter per unit area. This information is
commonly summarized in a stand table, where a stand is defined as a group of
forest trees of sufficiently uniform species composition, age, condition, or
productivity to be considered a homogeneous unit for planning purposes.
Typically information used to construct stand tables is gleaned from observed
subsets of the forest selected using a probability-based sampling design. Such
sampling campaigns are expensive and hence only a small number of sample units
are typically observed. This data paucity means that stand tables can only be
estimated for relatively large areal units. Contemporary forest management
planning and spatially explicit ecosystem models require stand table input at
higher spatial resolution than can be affordably provided using traditional
approaches. We propose a dynamic multivariate Poisson spatial regression model
that accommodates both spatial correlation between observed diameter
distributions and also correlation between tree counts across diameter classes
within each location. To improve fit and prediction at unobserved locations,
diameter specific intensities can be estimated using auxiliary data such as
management history or remotely sensed information. The proposed model is used
to analyze a diverse forest inventory dataset collected on the United States
Forest Service Penobscot Experimental Forest in Bradley, Maine. Results
demonstrate that explicitly modeling the residual spatial structure via a
multivariate Gaussian process and incorporating information about forest
structure from LiDAR covariates improve model fit and can provide high spatial
resolution stand table maps with associated estimates of uncertainty.
