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The continuous population approach to forest inventories and use of information in the design
Author(s) -
Grafström A.,
Schnell S.,
Saarela S.,
Hubbell S. P.,
Condit R.
Publication year - 2017
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2480
Subject(s) - estimator , variance (accounting) , sampling (signal processing) , selection (genetic algorithm) , sampling design , forest inventory , population , computer science , sample (material) , statistics , tree (set theory) , set (abstract data type) , population variance , data mining , mathematics , environmental science , forest management , machine learning , agroforestry , filter (signal processing) , business , mathematical analysis , chemistry , sociology , accounting , chromatography , computer vision , programming language , demography
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.