A new sampling strategy for forest inventories applied to the temporary clusters of the Swedish NFI

A new sampling strategy for forest inventories is presented. The most important difference to 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 (NFI) 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 NFI has a great potential to achieve large improvements in estimation of many important forest attributes.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author