Multivariate spatial models for small area estimation of species-specific forest inventory parameters

12 S.

e-journal

200212887

Forest Ecology and Management ; Jrg. 597 ; 123112 (2025) , 12 S.

National Forest Inventories (NFIs) provide statistically reliable information on forest resources at national and other large spatial scales. As forest management and conservation needs become increasingly complex, NFIs are being called upon to provide forest parameter estimates at spatial scales smaller than current design-based estimation procedures can provide. This is particularly true when estimates are desired by species or species groups, which is often required to inform wildlife habitat management, sustainable forestry certifications, or timber product assessments. Here we propose a multivariate spatial model for small area estimation of species-specific forest inventory parameters. The hierarchical Bayesian modeling framework accounts for key complexities in species-specific forest inventory data, such as zero-inflation, correlations among species, and residual spatial autocorrelation. Importantly, by fitting the model directly to the individual plot-level data, the framework enables estimates of species-level forest parameters, with associated uncertainty, across any user-defined small area of interest. A simulation study revealed minimal bias and higher accuracy of the proposed model-based approach compared to the design-based estimator.
Keywords: Forest inventory and analysis; National forest inventory; Bayesian; Model-based inference