8Data obtained from airborne laser scanning (ALS) are frequently used for acquiring forest data. 9Using a relatively low number of laser pulses per unit area (≤ 5 pulses
Local pivotal method sampling design combined with micro stands utilizing airborne laser scanning data in a long term forest management planning setting Saad R., Wallerman J., Holmgren J., Lämås T. (2016). Local pivotal method sampling design combined with micro stands utilizing airborne laser scanning data in a long term forest management planning setting. Silva Fennica vol. 50 no. 2 article id 1414. 13 p. http://dx.doi.org/10.14214/ sf.1414. Highlights• Most similar neighbor imputation was used to estimate forest variables using airborne laser scanning data as auxiliary data. • For selecting field reference plots the local pivotal method (LPM) was compared to systematic sampling design. • The LPM sampling design combined with a micro stand approach showed potential for improvement and has the potential to be a competitive method when considering cost efficiency. AbstractA new sampling design, the local pivotal method (LPM), was combined with the micro stand approach and compared with the traditional systematic sampling design for estimation of forest stand variables. The LPM uses the distance between units in an auxiliary space -in this case airborne laser scanning (ALS) data -to obtain a well-spread sample. Two sets of reference plots were acquired by the two sampling designs and used for imputing data to evaluation plots. The first set of reference plots, acquired by LPM, made up four imputation alternatives (varying number of reference plots) and the second set of reference plots, acquired by systematic sampling design, made up two alternatives (varying plot radius). The forest variables in these alternatives were estimated using the nonparametric method of most similar neighbor imputation, with the ALS data used as auxiliary data. The relative root mean square error (RelRMSE), stem diameter distribution error index and suboptimal loss were calculated for each alternative, but the results showed that neither sampling design, i.e. LPM vs. systematic, offered clear advantages over the other. It is likely that the obtained results were a consequence of the small evaluation dataset used in the study (n = 30). Nevertheless, the LPM sampling design combined with the micro stand approach showed potential for improvement and might be a competitive method when considering the cost efficiency.
Aim of study: To examine methods of incorporating risk and uncertainty to stand level forest decisions.Area of study: A case study examines a small forest holding from Jönköping, Sweden.Material and methods: We incorporate empirically estimated uncertainty into the simulation through a Monte Carlo approach when simulating the forest stands for the next 100 years. For the iterations of the Monte Carlo approach, errors were incorporated into the input data which was simulated according to the Heureka decision support system. Both the Value at Risk and the Conditional Value at Risk of the net present value are evaluated for each simulated stand.Main results: Visual representation of the errors can be used to highlight which decision would be most beneficial dependent on the decision maker’s opinion of the forest inventory results. At a stand level, risk preferences can be rather easily incorporated into the current forest decision support software.Research highlights: Forest management operates under uncertainty and risk. Methods are available to describe this risk in an understandable fashion for the decision maker.
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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