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

Abstract

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.

The continuous population approach to forest inventories and use of information in the design

“…The permanent sample will continue to serve as a permanent sample (at least in the near future), but the temporary sample has been redesigned to be more representative of a set of auxiliary variables derived from remote sensing via airborne laser scanning and from a digital elevation model. The results presented by Grafström et al () indicate a significant improvement over traditional designs. The increased representativity of the selected samples induced up to 95% reduction of variance of estimators for the included auxiliary variables.…”

“…The LPM with constant sampling intensity may be preferable in multipurpose inventories because it provides a representative sample, where the sample distribution of auxiliary variables is close to the population distribution. This theoretical framework has recently been used by Grafström, Zhao, Nylander, and Petersson () as a basis for the development of a new design for the national forest inventory of Sweden. The Swedish National Forest Inventory consists of two samples, a permanent and a temporary sample.…”

Remote sensing and forest inventories in Nordic countries – roadmap for the future

“…In Sweden and Finland, selecting the clusters using the socalled local pivotal method to ensure that a balanced sample is tested (Grafström et al 2014(Grafström et al , 2017, and plans to replace the systematic sampling design in the future exist. Model-assisted and post-stratified sampling methods to increase the accuracy are being tested (e.g.…”

Efficiency of post-stratification for a large-scale forest inventory—case Finnish NFI

“…A third possibility would be to utilize the auxiliary information already at the design phase, in stratification (e.g., Katila and Tomppo 2002;Tomppo et al 2014) or using a type of balanced sampling, e.g., a local pivotal method (Grafström et al 2017;Räty et al 2018). Katila and Tomppo (2002) used pre-stratification in the estimation of inventory results for small areas.…”