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 Light Detection and Ranging (LiDAR) covariates improve model fit and can provide high spatial resolution stand table maps with associated estimates of uncertainty.
- Dynamic model
- Gaussian spatial process