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Area-level analysis of forest inventory variables

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Abstract

Small-area estimation is a subject area of growing importance in forest inventories. Modelling the link between a study variable Y and auxiliary variables X—in pursuit of an improved accuracy in estimators—is typically done at the level of a sampling unit. However, for various reasons, it may only be possible to formulate a linking model at the level of an area of interest (AOI). Area-level models and their potential have rarely been explored in forestry. This study demonstrates, with data (Y = stem volume per ha) from four actual inventories aided by aerial laser scanner data (3 cases) or photogrammetric point clouds (1 case), application of three distinct models representing the currency of area-level modelling. The studied AOIs varied in size from forest management units to forest districts, and municipalities. The variance explained by X declined sharply with the average size of an AOI. In comparison with a direct estimate mean of Y in an AOI, all three models achieved practically important reduction in the relative root-mean-squared error of an AOI mean. In terms of the reduction in mean-squared errors, a model with a spatial location effect was overall most attractive. We recommend the pursuit of a spatial model component in area-level modelling as promising within the context of a forest inventory.

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Acknowledgements

Francisco Mauro was supported by Oregon State University. Field data from Burgos was collected by TRAGSA S.L., and the LiDAR data for this area was provided by the regional Government of Castilla and Leon, Servicio Territorial de Medio Ambiente de Burgos, Junta de Castilla y Leon. Parts of this study were supported by the Horizon 2020 project DIABOLO (Grant Agreement No. 633464).

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Authors and Affiliations

  1. Canadian Forest Service, Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada

    Steen Magnussen

  2. Forest Engineering Resources and Management Department, Oregon State University, College of Forestry, 053 Peavy Hall, Corvallis, OR, 97331, USA

    Fransisco Mauro

  3. Norwegian Institute of Bioeconomy Research, P.O. Box 115, NO-1431, Ås, Norway

    Johannes Breidenbach

  4. Swiss Federal Research Institute, WSL, Zürcherstrasse 111, 8903, Birmensdorf ZH, Switzerland

    Adrian Lanz

  5. Forest Research Institute, Wonnhaldestraße 4, Baden-Württemberg, 79100, Freiburg, Germany

    Gerald Kändler

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  1. Steen Magnussen
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  2. Fransisco Mauro
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  3. Johannes Breidenbach
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  4. Adrian Lanz
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  5. Gerald Kändler
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Correspondence to Steen Magnussen.

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Communicated by Arne Nothdurft.

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Magnussen, S., Mauro, F., Breidenbach, J. et al. Area-level analysis of forest inventory variables. Eur J Forest Res 136, 839–855 (2017). https://doi.org/10.1007/s10342-017-1074-z

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