A new sampling strategy for forest inventories is presented. The most important difference from the traditional sampling strategies is that auxiliary variables from remote sensing are incorporated into the sampling design. The sample is selected to match population distributions of the auxiliary variables as well as possible. This is achieved by a double sampling approach, where auxiliary variables are extracted for a large first-phase sample. The second selection is done by the local pivotal method and produces an even thinning of the first-phase sample. Thus, we make sure that the selected second-phase sample becomes much more representative of the population than what is possible by the use of traditional designs. The potential of implementing the new strategy for the temporary clusters within the Swedish national forest inventory is evaluated with five auxiliary variables: the geographical coordinates, elevation, predicted tree height, and predicted basal area. The increased representativity that we achieve with the new strategy induces up to 95% reduction of the variance of the sample means of the remote sensing auxiliary variables compared with traditional designs. For this reason, we conclude that the new strategy that will be implemented in the forthcoming Swedish national forest inventory has a great potential to achieve large improvements in estimation of many important forest attributes.

For national and international reporting on forest available for wood supply (FAWS), harmonized data are needed. This information is required as forests provide goods for various sectors like the timber industry or the bioenergy sector. The effect of applying different thresholds to the three restriction classes (environmental, social and economic restriction) within the proposed reference definition for FAWS was evaluated. We applied the reference definition for FAWS to national data sets provided by five European National Forest Inventories using harmonized thresholds. The effects on FAWS area and growing stock were evaluated for each restriction and threshold. All countries within this study could report on protected areas. Social restrictions were not applied in any country, data on other restrictions are available but definitions vary. The application of common thresholds for restrictions proved difficult as effects vary between countries. The economic restriction is the most challenging to assess as many countries do not have corresponding data for direct calculation of, e.g., harvesting costs. Using proxies for harvesting costs was difficult, as common thresholds may not be applied in different countries. For standardized reporting, a FAWS definition should be developed that utilizes existing, harmonized indicators to describe restrictions.