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

Eyvindson, Kyle; Petty, Aaron D; Kangas, Annika

doi:10.1007/s13595-016-0607-9

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…The mechanism by which the interest rate affected the results was via different management intensity applied to stands with different structures. In other studies, the interest rate has been found to affect the optimal data accuracy (Kangas et al 2015) depending on risk aversion of a decision-maker (Eyvindson et al 2017). Because of interactions between inventory data and external factors that constrain the feasible set of management actions, it is recommended not to separate but to integrate the inventory and planning systems for well-informed decisions.…”

Section: Discussionmentioning

confidence: 99%

“…For example, according to a metaanalysis of five studies (Kangas et al 2018a), a unit increase in the relative root mean squared error of total volume increased losses due to suboptimal decisions by 4.4 €/ha, on average. Factors affecting optimal quality of inventory data have further been addressed by Mäkinen et al (2012), Kangas et al (2015), Eyvindson et al (2017), and Ruotsalainen et al (2019).…”

Section: Introductionmentioning

confidence: 99%

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Effects of diameter distribution errors on stand management decisions according to a simulated individual tree detection

Vauhkonen

2020

Annals of Forest Science

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& Key Message Tree-level forest inventory data are becoming increasingly available, which motivates the use of these data for decision-making. However, airborne inventories carried out tree-by-tree typically include systematic errors, which can propagate to objective function variables used to determine optimal forest management. Effects of under-detection focused on the smallest trees on predicted immediate harvest profits and future expectation values were assessed assuming different sites and interest rates. Management decisions based on the erroneous information caused losses of 0-17% of the total immediate and future expected income of Scots pine stands. & Context Optimal decisions on how to manage forest stands can depend on the absence or presence of intermediate and understory trees. Yet, these tree strata are likely prone to inventory errors. & Aims The aim of this study is to examine implications of making stand management decisions based on data that include systematic errors resembling those typically observed in airborne inventories carried out tree-by-tree. & Methods Stand management instructions were developed based on theoretical diameter distribution functions simulated to have different shape, scale, and frequency parameters corresponding to various degrees of under-detection focused on the smallest trees. Immediate harvest income and future expectation value were derived based on various management alternatives simulated. & Results Errors in diameter distributions affected the predicted harvest profits and future expectation values differently between the simulated alternatives and depending on site type and interest rate assumptions. As a result, different alternatives were considered as optimal management compared to the use of the error-free reference distributions. In particular, the use of no management or most intensive management alternatives became preferred over alternatives with intermediate harvesting intensities. Certain harvesting types such as thinning from below became preferred more often than what was optimal. The errors did not affect the selection of the management alternative in 71% of the simulations, whereas in the remaining proportion, relying on the erroneous information would have caused losing 2%, on average, and 17% at maximum, of the total immediate and future expected income. & Conclusion The effects above might not have been discovered, if the results were validated for inventory totals instead of separately considering the immediate and future income and losses produced by the erroneous decisions. It is recommended not to separate but to integrate the inventory and planning systems for well-informed decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of diameter distribution errors on stand management decisions according to a simulated individual tree detection

Vauhkonen

2020

Annals of Forest Science

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“…The economic losses denote how much more utility the forest owner could get by rescheduling the cuttings and other forest management operations. Regarding the forest owner, a new inventory or supplementing one can be worth it if new incomes from the forestry are greater than the increased inventory costs (Eyvindson et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Economic losses caused by tree species proportions and site type errors in forest management planning

Haara¹,

Kangas²,

Tuominen³

2019

Silva Fenn.

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The aim of this study was to estimate economic losses, which are caused by forest inventory errors of tree species proportions and site types. Our study data consisted of ground truth data and four sets of erroneous tree species proportions. They reflect the accuracy of tree species proportions in four remote sensing data sets, namely 1) airborne laser scanning (ALS) with 2D aerial image, 2) 2D aerial image, 3) 3D and 2D aerial image data together and 4) satellite data. Furthermore, our study data consisted of one simulated site type data set. We used the erroneous tree species proportions to optimise the timing of forest harvests and compared that to the true optimum obtained with ground truth data. According to the results, the mean losses of Net Present Value (NPV) because of erroneous tree species proportions at an interest rate of 3% varied from 124.4 â¬ ha to 167.7 â¬ ha. The smallest losses were observed using tree species proportions predicted using ALS data and largest using satellite data. In those stands, respectively, in which tree species proportion errors actually caused economic losses, they were 468 â¬ ha on average with tree species proportions based on ALS data. In turn, site type errors caused only small losses. Based on this study, accurate tree species identification seems to be very important with respect to operational forest inventory.â1â1â1

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“…Related to the issue of DA, the specific timing of when the data should be updated can be formulated as a multistage stochastic optimization problem. Based on the preferences of the decision maker and the optimization model used, the improvement of forest information could be timed specifically (Eyvindson et al 2017).…”

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

Cited by 8 publications

References 28 publications

Effects of diameter distribution errors on stand management decisions according to a simulated individual tree detection

Effects of diameter distribution errors on stand management decisions according to a simulated individual tree detection

Economic losses caused by tree species proportions and site type errors in forest management planning

Potential of using data assimilation to support forest planning

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