Uncertainty in forest simulators and forest planning systems

Mäkinen, Antti

doi:10.14214/df.97

Cited by 7 publications

(5 citation statements)

References 81 publications

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“…Indeed, previous studies on the effect of forest inventory errors on forest management planning [22] did not explicitly consider errors arising from the uncertainty in plot positioning due to limited GPS accuracy. Yet, previous research supports our results inasmuch as forest inventory errors other than plot positioning inaccuracy were also reported to modify the spatio-temporal allocation of forest treatments [19,20].…”

Section: Effect Of Plot Positioning Errors On Decision-making In Foresupporting

confidence: 91%

Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data

Pascual

Pukkala

de-Miguel

2018

Forests

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Forest management planning is increasingly relying on airborne laser scanning (ALS) in forest inventory. The area-based method to interpret ALS data requires sample plots measured in the field. The aim of this study was to assess and trace the impacts of the positioning errors of field plots along the entire forest management planning process, from their effect on forest inventory results to the outcome of forest management planning. This research links plot positioning errors with the spatio-temporal allocation of forest treatments and calculates the inoptimality losses arising from plot positioning errors in ALS-based forest inventory. The study area was a forest management unit in Central Spain. Growing stock attributes were predicted for a grid of square-shaped cells. Alternative management schedules were simulated for the grid cells by using growth and yield models. Then, a spatial forest planning problem aiming at maximizing timber production with even-flow cuttings was formulated. Spatial objective variables were used to cluster management prescriptions into dynamic treatment units. We used simulated annealing to conduct spatial optimization. First, the true plot locations were used and then the whole process was repeated with normally distributed random errors with standard deviation equal to 2.5, 5 and 10 m, resulting in an average error of 1.47, 3.06 and 8.34 m, respectively. Increasing the level of positioning errors resulted in higher variability in the estimated growing stock attributes and in the achieved values of management goals. Sub-optimal prescriptions caused by the tested plot positioning errors caused up to 4.62% losses in timber production and up to 3.35% losses in utility. The losses increased with increasing plot positioning error.

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Section: Effect Of Plot Positioning Errors On Decision-making In Foresupporting

confidence: 91%

Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data

Pascual

Pukkala

de-Miguel

2018

Forests

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“…However, the ILs for the ALS-based stand varia- bles were similar to the ILs (ca. €350Áha -1 , US$500Áha -1 ) of the traditional field data in Mäkinen (2010). On the other hand, the estimated ILs were much higher than the ILs based on traditional field data found by Eid (2000).…”

Section: Discussionmentioning

confidence: 55%

“…Therefore, it is important to take error trends into account in the analysis. In a recent study, Mäkinen et al (2010) employed error trends in their analysis. However, they used a limited number of treatment alternatives, which could squeeze the search space in optimization.…”

Section: Introductionmentioning

confidence: 99%

Inoptimality losses in forest management decisions caused by errors in an inventory based on airborne laser scanning and aerial photographs

et al. 2010

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Errors in inventory data may lead to inoptimal decisions that ultimately result in financial losses for forest owners. We estimated the expected monetary losses resulting from data errors that are similar to errors in laser-based forest inventory. The mean loss was estimated for 67 stands by simulating 100 realizations of inventory data for each stand with errors that mimic those in airborne laser scanning (ALS) based inventory. These realizations were used as input data in stand management optimization, which maximized the present value of all future net incomes (NPV). The inoptimality loss was calculated as the difference between the NPV of the optimal solution and the true NPV of the solution obtained with erroneous input data. The results showed that the mean loss exceeded €300Áha -1 (US$425Áha -1 ) in 84% of the stands. On average, the losses increased with decreasing stand age and mean diameter. Furthermore, increasing errors in the basal area weighted mean diameter and basal area of spruce were found to significantly increase the loss. It has been discussed that improvements in the accuracy of ALS-based inventory could be financially justified. Résumé :Les erreurs dans les données d'inventaire peuvent conduire à des décisions sous-optimales qui aboutissent en fin de compte à des pertes financières pour les propriétaires forestiers. Nous avons estimé les pertes monétaires escomptées dues à des erreurs de données semblables aux erreurs de l'inventaire forestier basé sur l'utilisation du laser. La perte moyenne a été estimée pour chacun des 67 peuplements en simulant les données de 100 inventaires dont les erreurs imitent celles de l'inventaire par balayage laser aéroporté. Les données de ces inventaires ont été utilisées comme données d'entrée pour optimiser l'aménagement des peuplements. L'objectif de cette optimisation consistait à maximiser la valeur actualisée de tous les revenus nets futurs (VAN). La perte due à la sous-optimalité a été calculée comme étant la diffé-rence entre la VAN de la solution optimale et la VAN réelle de la solution obtenue avec les données d'entrée erronées. Les résultats ont montré que la perte moyenne dépassait 300 €Áha -1 (425 $USÁha -1 ) dans 84 % des peuplements. En moyenne, les pertes augmentaient avec la diminution de l'âge du peuplement et du diamètre moyen. En outre, la perte augmentait de manière significative avec l'augmentation de l'erreur du diamètre moyen pondéré par la surface terrière et de l'erreur de la surface terrière de l'épinette. Des améliorations dans la précision de l'inventaire par balayage laser aéroporte pourraient être financièrement justifiées.[Traduit par la Rédaction]

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“…This improvement in data quality is especially significant in certain areas, such as the tropical countries [27], although several methodological issues persist. Natural variation in forest growth and estimation errors in forest inventories can cause uncertainty in forest growth and developmental predictions [28]. Even pixel-based comparisons of land cover maps built using remote sensing data can reveal spatial disagreement and uncertainty [29].…”

Section: Datamentioning

confidence: 99%

The Probability Distribution of Worldwide Forest Areas

González‐Val

2021

Sustainability

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This paper analyses the probability distribution of worldwide forest areas. We find moderate support for a Pareto-type distribution (power law) using FAO data from 1990 to 2015. Power laws are common features of many complex systems in nature. A power law is a plausible model for the world probability distribution of forest areas in all examined years, although the log-normal distribution is a plausible alternative model that cannot be rejected. The random growth of forest areas could generate a power law or log-normal distribution. We study the change in forest coverage using parametric and non-parametric methods. We identified a slight convergence of forest areas over the time reviewed; however, random forest area growth cannot be rejected for most of the distribution of forest areas. Therefore, our results give support to theoretical models of stochastic forest growth.

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Uncertainty in forest simulators and forest planning systems

Cited by 7 publications

References 81 publications

Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data

Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data

Inoptimality losses in forest management decisions caused by errors in an inventory based on airborne laser scanning and aerial photographs

The Probability Distribution of Worldwide Forest Areas

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