Evaluating the required scenario set size for stochastic programming in forest management planning: incorporating inventory and growth model uncertainty

Eyvindson, Kyle; Kangas, Annika

doi:10.1139/cjfr-2014-0513

Cited by 21 publications

(17 citation statements)

References 37 publications

(42 reference statements)

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“…The results of this study highlight the conclusion that stochastic programming models can be implemented into current forest management tools to be used in forest planning by forest owners and planners. Nevertheless, as stated by Eyvindson and Kangas [50], the value of using stochastic programming needs to be presented to both the forest owners and the forest planners. This may also be expanded to the need of moving from optimizing forest plans in a stochastic setting and accounting for the forest owners' preferences towards risk [23].…”

Section: Discussionmentioning

confidence: 99%

A Progressive Hedging Approach to Solve Harvest Scheduling Problem under Climate Change

García-Gonzalo

Pais

Bachmatiuk

et al. 2020

Forests

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Due to the long time horizon typically characterizing forest planning, uncertainty plays an important role when developing forest management plans. Especially important is the uncertainty related to recently human-induced global warming since it has a clear impact on forest capacity to contribute to biogenic and anthropogenic ecosystem services. If the forest manager ignores uncertainty, the resulting forest management plan may be sub-optimal, in the best case. This paper presents a methodology to incorporate uncertainty due to climate change into forest management planning. Specifically, this paper addresses the problem of harvest planning, i.e., defining which stands are to be cut in each planning period in order to maximize expected net revenues, considering several climate change scenarios. This study develops a solution approach for a planning problem for a eucalyptus forest with 1000 stands located in central Portugal where expected future conditions are anticipated by considering a set of climate scenarios. The model including all the constraints that link all the scenarios and spatial adjacency constraints leads to a very large problem that can only be solved by decomposing it into scenarios. For this purpose, we solve the problem using Progressive Hedging (PH) algorithm, which decomposes the problem into scenario sub-problems easier to solve. To analyze the performance of PH versus the use of the extensive form (EF), we solve several instances of the original problem using both approaches. Results show that PH outperforms the EF in both solving time and final optimality gap. In addition, the use of PH allows to solve the most difficult problems while the commercial solvers are not able to solve the EF. The approach presented allows the planner to develop more robust management plans that incorporate the uncertainty due to climate change in their plans.

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Section: Discussionmentioning

confidence: 99%

A Progressive Hedging Approach to Solve Harvest Scheduling Problem under Climate Change

García-Gonzalo

Pais

Bachmatiuk

et al. 2020

Forests

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“…This process of sorting determines the size of the second stage problem, and in this case, the total number of forest-level scenarios is fixed at 100. Based on previous research (Eyvindson and Kangas 2016), 100 scenarios were deemed to provide a sufficient solution quality for the sources of error considered in this example. The solution quality was determined through the use of the sample average approximation (Kleywegt et al 2002), and both insample stability and out-of-sample stability are evaluated (Chap.…”

Section: Methodsmentioning

confidence: 99%

Determining the appropriate timing of the next forest inventory: incorporating forest owner risk preferences and the uncertainty of forest data quality

Eyvindson

Petty

Kangas

2017

Annals of Forest Science

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& Key message The timing to conduct new forest inventories should be based on the requirements of the decision maker. Importance should be placed on the objectives of the decision maker and his/her risk preferences related to those objectives. & Context The appropriate use of pertinent and available information is paramount in any decision-making process. Within forestry, a new forest inventory is typically conducted prior to creating a forest management plan. The acquisition of new forest inventory data is justified by the simple statement of "good decisions require good data." & Aims By integrating potential risk preferences, we examine the specific needs to collect new forest information. & Methods Through a two-stage stochastic programming with recourse model, we evaluate the specific timing to conduct a holding level forest inventory. A Monte Carlo simulation was used to integrate both inventory and growth model errors, resulting in a large number of potential scenarios process to be used as data for the stochastic program. To allow for recourse, an algorithm to sort the simulations to represent possible updated forest inventories, using the same data was developed. & Results Risk neutral decision makers should delay obtaining new forest information when compared to risk averse decision makers. & Conclusion New inventory data may only need to be collected rather infrequently; however, the exact timing depends on the forest owner's objectives and risk preferences.

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“…It is intuitively clear that the smaller the uncertainty is, the smaller the number of scenarios needed to produce a good approximation of the uncertain variables is. Therefore, the use of DA can promote the tractability of the problems by reducing the uncertainty, which will be reflected in the appropriate scenario set size used (Eyvindson and Kangas 2016a).…”

Section: The Potential For Using Da In Forest Planningmentioning

confidence: 99%

Potential of using data assimilation to support forest planning

et al. 2017

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Uncertainty in forest information typically results in economic and ecological losses as a consequence of suboptimal management decisions. Several techniques have been proposed to handle such uncertainties. However, these techniques are often complex and costly. Data assimilation (DA) has recently been advocated as a tool that may reduce the uncertainty, thereby improving the quality of forest planning results. It offers an opportunity to make use of all new sources of information in a systematic way and thus provides more accurate and up-to-date information to forest planning. In this study, we refer to literature on handling uncertainties in forest planning, as well as related literature from other scientific fields, to assess the potential benefits of using DA in forest planning. We identify five major potential benefits: (i) the accuracy of the information will be improved; (ii) the information will be kept up to date; (iii) the DA process will provide information with estimated accuracy; (iv) stochastic decision making can be applied whereby the accuracy of the information can be utilized in the decision making process; and (v) DA data allows for the analysis of optimal data acquisition decisions.Key words: uncertainty, suboptimal loss, remote sensing, Bayesian statistics, stochastic optimization.Résumé : L'incertitude associée à l'information concernant la forêt est typiquement la cause de pertes économiques et écologiques attribuables à des décisions d'aménagement sous-optimales. Plusieurs techniques ont été proposées pour traiter ces incertitudes. Cependant, ces techniques sont souvent complexes et coûteuses. Un outil, l'assimilation des données (AD), susceptible de réduire l'incertitude et ainsi améliorer la qualité des résultats de la planification forestière a récemment été recommandé. Cet outil offre l'occasion de tirer profit de toutes les nouvelles sources d'information de façon systématique et fournit par conséquent une information plus précise et à jour pour la planification forestière. Dans cet article, nous présentons une étude de la littérature qui porte sur les façons de gérer les incertitudes en planification forestière ainsi que de la littérature provenant d'autres domaines scientifiques dans le but d'évaluer les bénéfices potentiels associés à l'utilisation de l'AD en planification forestière. Nous avons identifié cinq bénéfices potentiels majeurs : (i) la précision de l'information sera améliorée; (ii) l'information sera gardée à jour; (iii) le processus de l'AD fournira une information comportant une estimation de la précision; (iv) la prise de décision stochastique peut être appliquée de telle sorte que la précision de l'information puisse être utilisée dans le processus de prise de décision et (v) les données de l'AD permettent d'utiliser l'analyse de l'acquisition optimale de données. [Traduit par la Rédaction]

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Evaluating the required scenario set size for stochastic programming in forest management planning: incorporating inventory and growth model uncertainty

Cited by 21 publications

References 37 publications

A Progressive Hedging Approach to Solve Harvest Scheduling Problem under Climate Change

A Progressive Hedging Approach to Solve Harvest Scheduling Problem under Climate Change

Determining the appropriate timing of the next forest inventory: incorporating forest owner risk preferences and the uncertainty of forest data quality

Potential of using data assimilation to support forest planning

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