The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories
Dublin Core
Title
The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories
Subject
: forest inventory, design-based, infinite population approach, two- and three-phase
sampling, regression estimators, small area estimation
sampling, regression estimators, small area estimation
Description
Forest inventories provide reliable evidence-based information to assess the state and
development of forests over time. They typically consist of a random sample of plot locations in the forest that are assessed individually by field crews. Due to the high costs
of these terrestrial campaigns, remote sensing information available in high quantity and
low costs is frequently incorporated in the estimation process in order to reduce inventory
costs or improve estimation precision. With respect to this objective, the application of
multiphase forest inventory methods (e.g., double- and triple-sampling regression estimators) has proved to be efficient. While these methods have been successfully applied in
practice, the availability of open-source software has been rare if not non-existent. The
R package forestinventory provides a comprehensive set of global and small area regression estimators for multiphase forest inventories under simple and cluster sampling. The
implemented methods have been demonstrated in various scientific studies ranging from
small to large scale forest inventories, and can be used for post-stratification, regression
and regression within strata. This article gives an extensive review of the mathematical
theory of this family of design-based estimators, puts them into a common framework of
forest inventory scenarios and demonstrates their application in the R environment.
development of forests over time. They typically consist of a random sample of plot locations in the forest that are assessed individually by field crews. Due to the high costs
of these terrestrial campaigns, remote sensing information available in high quantity and
low costs is frequently incorporated in the estimation process in order to reduce inventory
costs or improve estimation precision. With respect to this objective, the application of
multiphase forest inventory methods (e.g., double- and triple-sampling regression estimators) has proved to be efficient. While these methods have been successfully applied in
practice, the availability of open-source software has been rare if not non-existent. The
R package forestinventory provides a comprehensive set of global and small area regression estimators for multiphase forest inventories under simple and cluster sampling. The
implemented methods have been demonstrated in various scientific studies ranging from
small to large scale forest inventories, and can be used for post-stratification, regression
and regression within strata. This article gives an extensive review of the mathematical
theory of this family of design-based estimators, puts them into a common framework of
forest inventory scenarios and demonstrates their application in the R environment.
Creator
Andreas Hill
Source
https://www.jstatsoft.org/article/view/v097i04
Publisher
ETH Zürich
Date
Januari 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Andreas Hill, “The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8179.