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Abstract

This thesis addresses uncertainty in information about the state of forests and the implications of this uncertainty for long-term forest planning. This is done by examining how forestry manages forest information uncertainty, including the effects of ignoring it. It also proposes a methodology to explicitly address this uncertainty using an optimisation approach. The primary motivation of this thesis is to enhance decision-making in forestry, which is why emphasis is placed on practical relevance. Paper I explores existing strategies for addressing forest information uncertainty at large forest companies. Notably, the use of analytical methods, such as optimisation, for planning under uncertainty was found to be rare. The effects of ignoring forest information uncertainty are analysed in Papers II and III. Paper II examines whether mismatches between strategic and tactical objectives lead to suboptimal decisions and how information uncertainty affects planning results in this context. Paper III examines how errors in remote sensing predictions, which stem largely from regression towards the mean, affect planning results. Here, regression towards the mean is the tendency to underestimate large true values and overestimate small ones. The results of Papers II and III show that objective fulfilment decreases when uncertainty is not addressed. Finally, Paper IV evaluates a stochastic programming model that explicitly incorporates uncertainty into long-term planning. The model was integrated into a forest decision support system and tested in a case study to assess the value of accounting for multiple uncertainty scenarios simultaneously. Feedback from users provided managerial insights, supporting further refinement and application of the model, including decision support system development. In conclusion, this thesis provides a deeper understanding of what strategies forestry currently employs to address information uncertainty. Furthermore, the thesis provides clear incentives why information uncertainty should be recognised and proposes a method to consider this uncertainty explicitly to improve the objective fulfilment of forest planning.

Keywords

errors; forest decision support systems; forest information uncertainty; forest inventory; forest management; optimisation; remote sensing; stochastic programming

Published in

Acta Universitatis Agriculturae Sueciae
2025, number: 2025:52
Publisher: Swedish University of Agricultural Sciences

SLU Authors

UKÄ Subject classification

Forest Science

Publication identifier

  • DOI: https://doi.org/10.54612/a.bkufh3equh
  • ISBN: 978-91-8046-561-8
  • eISBN: 978-91-8046-566-3

Permanent link to this page (URI)

https://res.slu.se/id/publ/141965