Abstract

An extended theoretical framework for the continuous population approach to forest inventories is derived. Here, we treat a simultaneous selection of sample points with any prescribed sampling intensity over a continuous population. Different ways to use available auxiliary information, for example, from remote sensing, by selection of approximately balanced or spatially balanced samples are considered. A large data set of spatially continuous individual tree-level data is used to demonstrate the potential of these theoretical approaches. This study shows new ways to integrate remote sensing information in designs for forest inventory applications, which can significantly reduce the variance of the Horvitz-Thompson estimator for target variables related to the auxiliary information.

Keywords

balanced sampling; cube method; Horvitz-Thompson estimator; local pivotal method; Monte Carlo; representative samples

Published in

Environmetrics
2017, Volume: 28, number: 8, article number: e2480
Publisher: WILEY

SLU Authors

• Grafström, Anton

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Schnell, Sebastian

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Saarela, Svetlana

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Forest Science


Publication identifier

DOI: https://doi.org/10.1002/env.2480

Permanent link to this page (URI)

https://res.slu.se/id/publ/93332