Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation
This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, r...
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doaj-ee48da54d26e4ca993db810b64d77d542020-11-24T20:49:03ZengSpringerOpenForest Ecosystems2095-63552197-56202016-02-01310.1186/s40663-016-0064-9Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimationGöran Ståhl0Svetlana Saarela1Sebastian Schnell2 Sören Holm3Johannes Breidenbach4Sean P. Healey5Paul L. Patterson 6Steen Magnussen 7Erik Næsset8Ronald E. McRoberts 9 Timothy G. Gregoire10Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, SwedenDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, SwedenDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, SwedenDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, SwedenNorwegian Institute for Bioeconomy Research, Ås, NorwayUSDA Forest Service, Washington, D.C., USAUSDA Forest Service, Washington, D.C., USACanadian Forest Service, Pacific Forestry Centre, British Columbia, CanadaNorwegian University of Life Sciences, Ås, NorwayUSDA Forest Service, Washington, D.C., USASchool of Forestry and Environmental Studies, Yale University, New Haven, CT, USAThis paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters. Keywords: Design-based inference, Model-assisted estimation, Model-based inference, Hybrid inference, National forest inventory, Remote sensing, Samplinghttp://forestecosyst.springeropen.com/articles/10.1186/s40663-016-0064-9 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Göran Ståhl Svetlana Saarela Sebastian Schnell Sören Holm Johannes Breidenbach Sean P. Healey Paul L. Patterson Steen Magnussen Erik Næsset Ronald E. McRoberts Timothy G. Gregoire |
spellingShingle |
Göran Ståhl Svetlana Saarela Sebastian Schnell Sören Holm Johannes Breidenbach Sean P. Healey Paul L. Patterson Steen Magnussen Erik Næsset Ronald E. McRoberts Timothy G. Gregoire Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation Forest Ecosystems |
author_facet |
Göran Ståhl Svetlana Saarela Sebastian Schnell Sören Holm Johannes Breidenbach Sean P. Healey Paul L. Patterson Steen Magnussen Erik Næsset Ronald E. McRoberts Timothy G. Gregoire |
author_sort |
Göran Ståhl |
title |
Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation |
title_short |
Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation |
title_full |
Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation |
title_fullStr |
Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation |
title_full_unstemmed |
Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation |
title_sort |
use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation |
publisher |
SpringerOpen |
series |
Forest Ecosystems |
issn |
2095-6355 2197-5620 |
publishDate |
2016-02-01 |
description |
This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It
is motivated by the increasing availability of remotely sensed data, which facilitates the development of models
predicting the variables of interest in forest surveys. We present, review and compare three different estimation
frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are
well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased
and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the
target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased,
and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no
general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be
preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and
remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating
target variables such as growing stock volume or biomass, which are adequately related to commonly available
remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.
Keywords: Design-based inference, Model-assisted estimation, Model-based inference, Hybrid inference, National
forest inventory, Remote sensing, Sampling |
url |
http://forestecosyst.springeropen.com/articles/10.1186/s40663-016-0064-9 |
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