Using spatially balanced sampling utilizing auxiliary information in the design phase can enhance the design efficiency of national forest inventory. These gains decreased with increasing proportion of permanent plots in the sample. Using semi-permanent plots, changing every nth inventory round, instead of permanent plots, reduced this phenomenon. Further studies for accounting the permanent sample when selecting temporary sample are needed.
Context National forest inventories (NFIs) produce national- and regional-level statistics for sustainability assessment and decision-making. Using an interpreted satellite image as auxiliary information in the design phase improved the relative efficiency (RE). Spatially balanced sampling through local pivotal method (LPM) used for selection of clusters of sample plots is designed for temporary sample; thus, the method was tested in a NFI design with both permanent and temporary clusters.
Aims We estimated LPM method and stratified sampling for a NFI designed for successive occasions, where the clusters are permanent, semi-permanent, or temporary being replaced: never, every nth, and every inventory round, respectively.
Methods REs of sampling designs against systematic sampling were studied with simulations of inventory sampling.
Results The larger the proportion of permanent clusters the smaller benefits gained with LPM. REs of stratified sampling were not depending on the proportion of permanent clusters. The semi-permanent sampling with LPM removed the previously described decrease and resulted in the largest REs.
Conclusion Sampling strategies with semi-permanent clusters were the most efficient, yet not necessarily optimal for all inventory variables. Further development of method to simultaneously take into account the distribution of permanent sample when selecting temporary or semi-temporary sample is desired since it could increase the design efficiency.
Auxiliary information, Local pivotal method, Permanent cluster, Relative efficiency, Sampling design, Semi-permanent cluster
Open Access Publication
Räty, M. & Kangas, A.S. Annals of Forest Science (2019) 76: 20. https://doi.org/10.1007/s13595-019-0802-6
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.