Combining forest growth models and remotely sensed data through a hierarchical model-based inferential framework

Large-area growth estimates can be obtained by coupling growth model predictions with wall-to-wall remotely sensed auxiliary variables through a generalized model-based (GHMB) inferential framework. So far, most GHMB variance estimators do not account for the residual errors of the submodels and their spatial correlations. This likely induces an underestimation of the true variance of the point estimator. In this study, we provide an example of large-area growth estimation obtained through the GHMB framework. To do this, we developed a new variance estimator that accounts for residual errors as well as potential spatial correlations among them. We tested this variance estimator through a simulation study and then used it to estimate the annual volume increment for a forest management unit in Quebec, Canada. Our results show that, contrary to our expectation, neglecting the residual errors of the different submodels leads to overestimating the true variance of the point estimator. We observed increases in the overestimation with small populations and spatially correlated residual errors. Our developed variance estimator corrected this overestimation and made it possible to derive reliable confidence intervals for annual volume increments at the population level.