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A Swedish case study on the prediction of detailed product recovery from individual stem profiles based on airborne laser scanning

  • Original Paper
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Abstract

• Context

Improved and cost-efficient predictions of detailed product recovery from logging operations may increase efficiency and improve value chains based on modern cut-to-length harvesting (CTL).

• Aims

The objective of this study was to investigate and evaluate the use of individual tree data estimates from two inventory techniques: (a) established airborne laser scanner inventory (ALS case) and (b) traditional field inventory (BAU case) for predicting product recovery in a Swedish case study.

• Methods

Statistics from previous harvester production files within the region were used to generate realistic levels of simulated stem defects. Bucking simulations were performed to optimise log products according to stem profiles, stem defects, and an operational price list expressing the demand of the industry customer. All simulation results at the stand level were compared to operational harvester production data that were used to provide an accurate measure of the ‘true’ product recovery. The total harvested area was 139 ha including 16 forest stands. Seven groups of log products were included in the analysis. The predicted versus real top diameter distributions of sawlogs were evaluated using an error index to express deviations.

• Results

At the stand level, the average error index values were 0.15 and 0.18 for the ALS and BAU approaches, respectively. As a consequence of an overall bias of the ALS tree lists the opposite was found at the total wood flow level, with the field-based data yielding a lower error index.

• Conclusions

The volume predictions for different log product groups were slightly more accurate in the ALS case than in the BAU case.

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Acknowledgments

BillerudKorsnäs and Bergvik Skog are acknowledged for providing forest data. Ingemar Gillgren (Bergvik skog), Göran Andersson, and Lars Ohlin (both BillerudKorsnäs) all provided vital support that made this project possible. The authors also thank the personnel and harvesting entrepreneurs who carried out the logging at the Uppland site (BillerudKorsnäs) for their assistance in recording and supplying data. Additional thanks are due to Hans Eskilsson (CGI), who provided the diameter distributions for the BAU case and to Magnus Eriksson and Per Knutsson (both CGI) for supporting and participating in the project planning. Ulf Söderman and Rikard Hedberg at Foran Remote Sensing AB delivered the ALS data and contributed to the project planning and the descriptions of the ALS case. All analyses were performed and all conclusions were drawn by the authors from Skogforsk.

Funding

This study was a part of the Flexwood project, Flexible Wood Supply Chain and partly financed by the European Union 7th Framework programme and Skogforsk. The data acquisition by remote sensing and harvesting was financed by Bergvik Skog, BillerudKorsnäs, CGI, Foran Remote Sensing, and Skogforsk, respectively.

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Authors and Affiliations

  1. Skogforsk—The Forestry Research Institute of Sweden, Uppsala Science Park, SE-75183, Uppsala, Sweden

    Andreas Barth, Johan J. Möller, Lars Wilhelmsson & John Arlinger

  2. Foran Remote Sensing AB, Linköping, Sweden

    Rikard Hedberg & Ulf Söderman

Authors
  1. Andreas Barth
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  2. Johan J. Möller
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  3. Lars Wilhelmsson
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  4. John Arlinger
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  5. Rikard Hedberg
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  6. Ulf Söderman
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Corresponding author

Correspondence to Lars Wilhelmsson.

Additional information

Handling editor: Barry Alan Gardiner

Contribution of the co-authors

Andreas Barth and Lars Wilhelmsson: writing the paper.

Ulf Söderman, Rikard Hedberg, Johan J. Möller, John Arlinger, and Andreas Barth: data delivery.

Andreas Barth, Lars Wilhelmsson, Johan J. Möller, and John Arlinger: data analysis.

Johan J. Möller and Andreas Barth: project coordination.

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Barth, A., Möller, J.J., Wilhelmsson, L. et al. A Swedish case study on the prediction of detailed product recovery from individual stem profiles based on airborne laser scanning. Annals of Forest Science 72, 47–56 (2015). https://doi.org/10.1007/s13595-014-0400-6

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  • DOI: https://doi.org/10.1007/s13595-014-0400-6

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