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Research article - Peer-reviewed, 2017

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

Grafstrom, Anton; Zhao, Xin; Nylander, Martin; Petersson, Hans

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.

Keywords

continuous population; double sampling; local pivotal method; remote sensing; sampling design

Published in

Canadian Journal of Forest Research
2017, volume: 47, number: 9, pages: 1161-1167
Publisher: CANADIAN SCIENCE PUBLISHING, NRC RESEARCH PRESS

Authors' information

Swedish University of Agricultural Sciences, Department of Forest Resource Management
Nylander, Martin
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Zhao, Xin
Swedish University of Agricultural Sciences, Department of Forest Resource Management

UKÄ Subject classification

Forest Science

Publication Identifiers

DOI: https://doi.org/10.1139/cjfr-2017-0095

URI (permanent link to this page)

https://res.slu.se/id/publ/93052