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Design‐based mapping of tree attributes by 3P sampling
Author(s) -
Fattorini Lorenzo,
Franceschi Sara,
Corona Piermaria
Publication year - 2020
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201900377
Subject(s) - estimator , sampling (signal processing) , weighting , consistency (knowledge bases) , statistics , sampling design , interpolation (computer graphics) , mathematics , population , mean squared error , inverse distance weighting , tree (set theory) , computer science , algorithm , data mining , multivariate interpolation , artificial intelligence , medicine , motion (physics) , demography , filter (signal processing) , sociology , bilinear interpolation , computer vision , radiology , mathematical analysis
The estimation of individual values (marks) in a finite population of units (e.g., trees) scattered onto a survey region is considered under 3P sampling. For each unit, the mark is estimated by means of an inverse distance weighting interpolator. Conditions ensuring the design‐based consistency of maps are considered under 3P sampling. A computationally simple mean squared error estimator is adopted. Because 3P sampling involves the prediction of marks for each unit in the population, prediction errors rather than marks can be interpolated. Then, marks are estimated by the predictions plus the interpolated errors. If predictions are good, prediction errors are more smoothed than raw marks so that the procedure is likely to better meet consistency requirements. The purpose of this paper is to provide theoretical and empirical evidence on the effectiveness of the interpolation based on prediction errors to prove that the proposed strategy is a tool of general validity for mapping forest stands.