Comparison of the local pivotal method and systematic sampling for national forest inventories

• 作者: Minna R?ty Mikko Kuronen Mari Myllym?ki Annika Kangas Kai M?kisara Juha Heikkinen
• 来源期刊: 森林生态系统（英文版）
• 年，卷（期）: 2020,（4）
• 页码范围: 716-732
• 页数: 17页
• 语种: 英文

摘要

Background: The local pivotal method (LPM) utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories (NFIs). Its performance compared to simple random sampling (SRS) and LPM with geographical coordinates has produced promising results in simulation studies. In this simulation study we compared all these sampling methods to systematic sampling. The LPM samples were selected solely using the coordinates (LPMxy) or, in addition to that, auxiliary remote sensing-based forest variables (RS variables). We utilized field measurement data (NFI-field) and Multi-Source NFI (MS-NFI) maps as target data, and independent MS-NFI maps as auxiliary data. The designs were compared using relative efficiency (RE); a ratio of mean squared errors of the reference sampling design against the studied design. Applying a method in NFI also requires a proven estimator for the variance. Therefore, three different variance estimators were evaluated against the empirical variance of replications: 1) an estimator corresponding to SRS; 2) a Grafstrom-Schelin estimator repurposed for LPM; and 3) a Matern estimator applied in the Finnish NFI for systematic sampling design.Results: The LPMxy was nearly comparable with the systematic design for the most target variables. The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74-1.18, according to the studied target variable. The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly, the Grafstrom-Schelin estimator gave overestimates in the case of LPMxy. When the RS variables were utilized as auxiliary data, the Grafstrom-Schelin estimates tended to underestimate the empirical variance. In systematic sampling the Matern and Grafstrom-Schelin estimators performed for practical purposes equally.Conclusions: LPM optimized for a specific variable tended to be more efficient than systematic sampling, but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables. The Grafstrom-Schelin estimator could be used as such with LPMxy or instead of the Matern estimator in systematic sampling. Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.