Based on my previous work experience for Russian and Finnish NFIs, I focused my research interest on developing new methods for forest resources surveys on large remote areas using remotely sensed data as auxiliary information. Field-based forest inventories have many advantages. However, they become expensive when large sample size is required to reach the needed levels of precision. Sparse road networks or other conditions in a country may prevent easy access to the plots. Also, NFI information from field plots alone often leads to imprecise estimates for small regions within a country. This has stimulated the development of solutions where field plots and remotely sensed data are combined in order to provide the required information. Over the previous decades the interest in large-scale forest inventories utilizing several sources of data has increased considerably. In case design-based samples are selected, the model-assisted estimation framework allows combining several sources of auxiliary information through, e.g., multi-stage or multi-phase surveys. In this case model relationships between auxiliary and target variables are used to improve the precision of the estimators.
Model-based inference is an alternative mode of inference that can be applied in case auxiliary data are available. Described briefly, model-based inference relies more heavily on the correctness of the model(s) applied in the estimators. While the dependence on the model is a drawback, this mode of inference also has advantages over design-based approaches; e.g., in some cases smaller sample sizes might be applied for reaching a certain level of accuracy.