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Forest Ecosystems 2016, 3(2): 5 https://doi.org/10.1186/s40663-016-0064-9
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Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

Show Author's Information Göran Ståhl1,  Svetlana Saarela1 ( ),  Sebastian Schnell1,  Sören Holm1,  Johannes Breidenbach2,  Sean P. Healey3,  Paul L. Patterson3,  Steen Magnussen4,  Erik Næsset5,  Ronald E. McRoberts3,  Timothy G. Gregoire6
Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden
Norwegian Institute for Bioeconomy Research, Ås, Norway
USDA Forest Service, Washington, D.C., USA
Canadian Forest Service, Pacific Forestry Centre, British Columbia, Canada
Norwegian University of Life Sciences, Ås, Norway
School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA
Keywords:
Design-based inference, Model-assisted estimation, Model-based inference, Hybrid inference, National forest inventory, Remote sensing, Sampling
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Ståhl G, Saarela S, Schnell S, et al. Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation. Forest Ecosystems, 2016, 3(2): 5. https://doi.org/10.1186/s40663-016-0064-9
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Abstract Full text About this article

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 design-based 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, model-based, 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.


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About this article

Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

Show Author's information Göran Ståhl1, Svetlana Saarela1 ( ), Sebastian Schnell1, Sören Holm1, Johannes Breidenbach2, Sean P. Healey3, Paul L. Patterson3, Steen Magnussen4, Erik Næsset5, Ronald E. McRoberts3, Timothy G. Gregoire6
Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden
Norwegian Institute for Bioeconomy Research, Ås, Norway
USDA Forest Service, Washington, D.C., USA
Canadian Forest Service, Pacific Forestry Centre, British Columbia, Canada
Norwegian University of Life Sciences, Ås, Norway
School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA

Abstract

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 design-based 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, model-based, 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

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Received: 12 November 2015
Accepted: 17 February 2016
Published: 18 February 2016
Issue date: June 2016

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© 2016 Ståhl et al.

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