Assessing the importance of detailed forest inventory information using stochastic programming
Publication: Canadian Journal of Forest Research
14 November 2024
Abstract
Errors in forest inventory data can lead to sub-optimal management decisions and dramatic economic losses. Forest inventory approaches are typically evaluated by their levels of precision and accuracy; however, this overlooks the specific usefulness of the data in decision-making. By evaluating the value of information (VoI), we can assess the usefulness of the data for specific decision-making problems. We evaluated the VoI through stochastic programming for four airborne laser scanning-based inventory approaches. The stochastic programming model explored the trade-off between the maximal net present value and the minimal conditional value at risk of meeting specified periodic income targets. We evaluated a range of periodic targets and risk aversion preference levels. To compare the performance of the inventory approaches, we used a reference dataset that was acquired using a forest harvester with precise positioning. For a wide range of the trade-offs, inventory approaches with higher-quality information provided the best overall performance. If only one of the extreme objectives was desired, less precise inventory approaches were sufficient to produce high-quality solutions.
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Information & Authors
Information
Published In
Canadian Journal of Forest Research
Volume 54 • Number 12 • December 2024
Pages: 1486 - 1499
History
Received: 15 September 2023
Accepted: 8 August 2024
Accepted manuscript online: 14 August 2024
Version of record online: 14 November 2024
Copyright
© 2024 The Author(s). Permission for reuse (free in most cases) can be obtained from copyright.com.
Data Availability Statement
Data used in this study are available upon reasonable request.
Key Words
Authors
Author Contributions
Conceptualization: ON, TG, KE
Data curation: ON, LN, TG, KE
Formal analysis: ON, KE
Funding acquisition: TG
Investigation: ON, LN, TG, KE
Methodology: ON, LN, KE
Software: ON, KE
Supervision: TG, KE
Visualization: ON
Writing – original draft: ON
Writing – review & editing: ON, LN, TG, KE
Competing Interests
The authors declare that there are no competing interests.
Funding Information
Norges Forskningsråd: SmartForest, [project, no., 309671]
This work was supported by the Research Council of Norway under the project SmartForest (project No. 309671).
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Olha Nahorna, Lennart Noordermeer, Terje Gobakken, and Kyle Eyvindson. 2024. Assessing the importance of detailed forest inventory information using stochastic programming. Canadian Journal of Forest Research.
54(12): 1486-1499. https://doi.org/10.1139/cjfr-2023-0218
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