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Articles containing the keyword 'uncertainty'

Category : Special section

article id 289, category Special section

Thomas Wutzler, Martina Mund. (2007). Modelling mean above and below ground litter production based on yield tables. Silva Fennica vol. 41 no. 3 article id 289. https://doi.org/10.14214/sf.289

Keywords: uncertainty; thinnings; soil carbon; biomass turnover; carbon accounting; harvest residues; sensitivity analysis

Abstract | View details | Full text in PDF | Author Info

Estimates of litter production are a prerequisite for modeling soil carbon stocks and its changes at regional to national scale. However, the required data on biomass removal is often available only for the recent past. In this study we used yield tables as a source of probable past forest management to drive a single tree based stand growth model. Next, simulated growth and timber volume was converted to tree compartment carbon stocks and biomass turnover. The study explicitly accounted for differences in site quality between stands. In addition we performed a Monte Carlo uncertainty and sensitivity analysis. We exemplify the approach by calculating long-term means of past litter production for 10 species by using yield tables that have been applied in Central Germany during the last century. We found that litter production resulting from harvest residues was almost as large as the one from biomass turnover. Differences in site quality caused large differences in litter production. At a given site quality, the uncertainty in soil carbon inputs were 14%, 17%, and 25% for beech, spruce, and pine stands, respectively. The sensitivity analysis showed that the most influential parameters were associated with foliage biomass and turnover. We conclude that rates of mean past litter production and their uncertainties can reliably be modeled on the basis of yield tables if the model accounts for 1) full rotation length including thinning and final harvest, 2) differences in site quality, and 3) environmental dependency of foliage biomass and foliage turnover.

Wutzler, Max Planck Institute for Biogeochemistry, Jena, Germany E-mail: tm@nn.de
Mund, Max Planck Institute for Biogeochemistry, Jena, Germany E-mail: mm@nn.de

article id 287, category Special section

Mikko Peltoniemi, Juha Heikkinen, Raisa Mäkipää. (2007). Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils. Silva Fennica vol. 41 no. 3 article id 287. https://doi.org/10.14214/sf.287

Keywords: uncertainty; soil carbon; anticipated variance; forest soil; monitoring; repeated measurement; soil survey; stratified sampling

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Monitoring changes in soil C has recently received interest due to reporting under the Kyoto Protocol. Model-based approaches to estimate changes in soil C stocks exist, but they cannot fully replace repeated measurements. Measuring changes in soil C is laborious due to small expected changes and large spatial variation. Stratification of soil sampling allows the reduction of sample size without reducing precision. If there are no previous measurements, the stratification can be made with model-predictions of target variable. Our aim was to present a simulation-based stratification method, and to estimate how much stratification of inventory plots could improve the efficiency of the sampling. The effect of large uncertainties related to soil C change measurements and simulated predictions was targeted since they may considerably decrease the efficiency of stratification. According to our simulations, stratification can be useful with a feasible soil sample number if other uncertainties (simulated predictions and forecasted forest management) can be controlled. For example, the optimal (Neyman) allocation of plots to 4 strata with 10 soil samples from each plot (unpaired repeated sampling) reduced the standard error (SE) of the stratified mean by 9–34% from that of simple random sampling, depending on the assumptions of uncertainties. When the uncertainties of measurements and simulations were not accounted for in the division to strata, the decreases of SEs were 2–9 units less. Stratified sampling scheme that accounts for the uncertainties in measured material and in the correlates (simulated predictions) is recommended for the sampling design of soil C stock changes.

Peltoniemi, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: mikko.peltoniemi@metla.fi
Heikkinen, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jh@nn.fi
Mäkipää, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: raisa.makipaa@metla.fi

Category : Research article

article id 10089, category Research article

Arto Haara, Annika Kangas, Sakari Tuominen. (2019). Economic losses caused by tree species proportions and site type errors in forest management planning. Silva Fennica vol. 53 no. 2 article id 10089. https://doi.org/10.14214/sf.10089

Keywords: forest inventory; value of information; uncertainty; sub-optimality loss

Highlights: Errors in tree species proportions caused more economic losses for forest owners than site type errors; Economic losses due to sub-optimal treatments were observed from 26.5% to 31.7% of plots, depending on the remote sensing data set used; Even with the most accurate remote sensing data set, namely ALS data set, NPV losses were on average 124.4 € ha^–1 with 3% interest rate.

Abstract | Full text in HTML | Full text in PDF | Author Info

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 € ha^–1 to 167.7 € ha^–1. 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 € ha^–1 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.

Haara, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: arto.haara@luke.fi
Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 2, FI-00791 Helsinki, Finland https://orcid.org/0000-0001-5429-3433 E-mail: sakari.tuominen@luke.fi

article id 9923, category Research article

Annika Kangas, Terje Gobakken, Stefano Puliti, Marius Hauglin, Erik Naesset. (2018). Value of airborne laser scanning and digital aerial photogrammetry data in forest decision making. Silva Fennica vol. 52 no. 1 article id 9923. https://doi.org/10.14214/sf.9923

Keywords: forest inventory; value of information; uncertainty; remote sensing; cost-plus-loss; data quality

Highlights: Airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) are nearly equally valuable for harvest scheduling decisions even though ALS data is more precise; Large underestimates of stand volume are most dangerous errors for forest owner because of missed cutting probabilities; Relative RMSE of stand volume and the mean volume in a test area explain 77% of the variation between the expected losses due to errors in the data in the published studies; Increasing the relative RMSE of volume by 1 unit, increased the losses in average by 4.4 € ha^–1.

Abstract | Full text in HTML | Full text in PDF | Author Info

Airborne laser scanning (ALS) has been the main method for acquiring data for forest management planning in Finland and Norway in the last decade. Recently, digital aerial photogrammetry (DAP) has provided an interesting alternative, as the accuracy of stand-based estimates has been quite close to that of ALS while the costs are markedly smaller. Thus, it is important to know if the better accuracy of ALS is worth the higher costs for forest owners. In many recent studies, the value of forest inventory information in the harvest scheduling has been examined, for instance through cost-plus-loss analysis. Cost-plus-loss means that the quality of the data is accounted for in monetary terms through calculating the losses due to errors in the data in the forest management planning context. These costs are added to the inventory costs. In the current study, we compared the losses of ALS and DAP at plot level. According to the results, the data produced using DAP are as good as data produced using ALS from a decision making point of view, even though ALS is slightly more accurate. ALS is better than DAP only if the data will be used for more than 15 years before acquiring new data, and even then the difference is quite small. Thus, the increased errors in DAP do not significantly affect the results from a decision making point of view, and ALS and DAP data can be equally well recommended to the forest owners for management planning. The decision of which data to acquire, can thus be made based on the availability of the data on first hand and the costs of acquiring it on the second hand.

Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80170 Joensuu, Finland E-mail: annika.kangas@luke.fi
Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no
Puliti, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: stefano.puliti@nibio.no
Hauglin, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: marius.hauglin@nmbu.no
Naesset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no

article id 449, category Research article

Thomas Wutzler, Ingolf Profft, Martina Mund. (2011). Quantifying tree biomass carbon stocks, their changes and uncertainties using routine stand taxation inventory data. Silva Fennica vol. 45 no. 3 article id 449. https://doi.org/10.14214/sf.449

Keywords: forest management; forest inventory; uncertainty; carbon stocks

Abstract | View details | Full text in PDF | Author Info

For carbon (C) trading or any other verifiable C reports, it would be reasonable to identify and quantify continuous changes in carbon stocks at regional scales without high investments into additional C-specific, time- and labor-intensive inventories. Our study demonstrates the potential of using routine stand taxation data from large scale forestry inventories for verifiable quantification of tree biomass C stocks, C stock change rates, and associated uncertainties. Empirical models, parameters, and equations of uncertainty propagation have been assembled and applied to data from a forest management unit in Central Germany (550 000 ha), using stand taxation inventories collected between 1993 and 2006. The study showed: 1) The use of stand taxation data resulted in a verifiable and sufficiently precise (cv = 7%) quantification of tree biomass carbon stocks and their changes at the level of growth-regions (1700 to 140 000 ha). 2) The forest of the test region accumulated carbon in tree biomass at a mean annual rate of 1.8 (–0.9 to 4.5) tC/ha/yr over the studied period. 3) The taxation inventory data can reveal spatial patterns of rates of C stock changes, specifically low rates of 0.4 tC/ha/yr in the northwest and high rates of 3.0 tC/ha/yr in the south of the study region.

Wutzler, Max Planck Institute for Biogeochemistry, Hans-Knöll-Strasse 10, 07745 Jena, Germany E-mail: twutz@bgc-jena.mpg.de
Profft, Max Planck Institute for Biogeochemistry, Hans-Knöll-Stra§e 10, 07745 Jena, Germany E-mail: ip@nn.de
Mund, Max Planck Institute for Biogeochemistry, Hans-Knöll-Stra§e 10, 07745 Jena, Germany E-mail: mm@nn.de

article id 111, category Research article

Ilona Pietilä, Annika Kangas, Antti Mäkinen, Lauri Mehtätalo. (2010). Influence of growth prediction errors on the expected losses from forest decisions. Silva Fennica vol. 44 no. 5 article id 111. https://doi.org/10.14214/sf.111

Keywords: growth prediction; uncertainty; forest information; updating; inoptimality loss

Abstract | View details | Full text in PDF | Author Info

In forest planning, forest inventory information is used for predicting future development of forests under different treatments. Model predictions always include some errors, which can lead to sub-optimal decisions and economic loss. The influence of growth prediction errors on the reliability of projected forest variables and on the treatment propositions have previously been examined in a few studies, but economic losses due to growth prediction errors is an almost unexplored subject. The aim of this study was to examine how the growth prediction errors affected the expected losses caused by incorrect harvest decisions, when the inventory interval increased. The growth models applied in the analysis were stand-level growth models for basal area and dominant height. The focus was entirely on the effects of growth prediction errors, other sources of uncertainty being ignored. The results show that inoptimality losses increased with the inventory interval. Average relative inoptimality loss was 3.3% when the inventory interval was 5 years and 11.6% when it was 60 years. Average absolute inoptimality loss was 230 euro ha^–1 when the inventory interval was 5 years and 860 euro ha^–1 when it was 60 years. The average inoptimality losses varied between development classes, site classes and main tree species.

Pietilä, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ip@nn.fi
Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
Mäkinen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: am@nn.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi

article id 219, category Research article

Arto Haara, Pekka Leskinen. (2009). The assessment of the uncertainty of updated stand-level inventory data. Silva Fennica vol. 43 no. 1 article id 219. https://doi.org/10.14214/sf.219

Keywords: uncertainty; measurement error; simulation; non-parametric methods; observed error; stand-level inventory

Abstract | View details | Full text in PDF | Author Info

Predictions of growth and yield are essential in forest management planning. Growth predictions are usually obtained by applying complex simulation systems, whose accuracy is difficult to assess. Moreover, the computerised updating of old inventory data is increasing in the management of forest planning systems. A common characteristic of prediction models is that the uncertainties involved are usually not considered in the decision-making process. In this paper, two methods for assessing the uncertainty of updated forest inventory data were studied. The considered methods were (i) the models of observed errors and (ii) the k-nearest neighbour method. The derived assessments of uncertainty were compared with the empirical estimates of uncertainty. The practical utilisation of both methods was considered as well. The uncertainty assessments of updated stand-level inventory data using both methods were found to be feasible. The main advantages of the two studied methods include that bias as well as accuracy can be assessed.

Haara, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: arto.haara@joensuu.fi
Leskinen, Finnish Environment Institute, Research Programme for Production and Consumption, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pl@nn.fi

article id 496, category Research article

Hampus Holmström, Hans Kallur, Göran Ståhl. (2003). Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data. Silva Fennica vol. 37 no. 3 article id 496. https://doi.org/10.14214/sf.496

Keywords: uncertainty; data acquisition; imputation; forestry planning

Abstract | View details | Full text in PDF | Author Info

The usefulness of kNN (k Nearest Neighbour)-assigned reference sample plot data as a basis for forest management planning was studied. Cost-plus-loss analysis was applied, whereby the inventory cost for a specific method is added to the expected loss due to non-optimal forestry activities caused by erroneous descriptions of the forest state. Four different strategies for data acquisition were evaluated: 1) kNN imputation of sample plots based on traditional stand record information, 2) imputation based on plot-wise aerial photograph interpretation in combination with stand record information, 3) sample plot inventory in the field with 5 plots per stand, and 4) sample plot inventory with 10 plots per stand. Expected losses were derived as mean values of differences between the maximum net present value and the corresponding value obtained when the treatment schedule believed to be optimal (based on data simulated according to method 1–4) was selected. The optimal choice of method was found to depend on factors such as stand maturity, stand area, and time to next treatment (thinning or clearcutting). In general, the field sample plot methods were competitive in large mature stands, especially when the time to the next (optimal) treatment was short. By in each stand (within an estate) employing the method with the lowest cost-plus-loss rather than choosing the method that performed best on average for the entire estate, the total cost for inventory at the estate level could be decreased by 15–50%. However, it was found difficult to identify what method should optimally be employed in a stand based on general stand descriptions.

Holmström, Regional Board of Forestry of Västra Götaland, P.O. Box 20008, SE-50420 Borås, Sweden E-mail: hampus.holmstrom@svsvg.svo.se
Kallur, ÖKA Skogsplan, Kopparvägen 45 O, SE-90750 Umeå, Sweden E-mail: hk@nn.se
Ståhl, Swedish Univ. of Agricultural Sciences, Dept. of Forest Resource Management and Geomatics, SE-90183 Umeå, Sweden E-mail: gs@nn.se

article id 597, category Research article

Annika Kangas, Jyrki Kangas, Jouni Pykäläinen. (2001). Outranking methods as tools in strategic natural resources planning. Silva Fennica vol. 35 no. 2 article id 597. https://doi.org/10.14214/sf.597

Keywords: uncertainty; fuzzy relations; multicriteria decision support; multiple-use planning

Abstract | View details | Full text in PDF | Author Info

Two outranking methods, ELECTRE III and PROMETHEE II, commonly used as decision-aid in various environmental problems, and their applications to decision support for natural resources management are presented. These methods represent ‘the European school’ of multi-criteria decision making (MCDM), as opposed to ‘the American school’, represented by, for instance, the AHP method. On the basis of a case study, outranking methods are compared to so far more usually applied techniques based on the ideas of multi attribute utility theory (MAUT). The outranking methods have been recommended for situations where there is a finite number of discrete alternatives to be chosen among. The number of decision criteria and decision makers may be large. An important advantage of outranking methods, when compared to decision support techniques most often applied in today’s natural resources management, is the ability to deal with ordinal and more or less descriptive information on the alternative plans to be evaluated. Furthermore, the uncertainty concerning the values of the criterion variables can be taken into account using fuzzy relations, determined by indifference and preference thresholds. The difficult interpretation of the results, on the other hand, is the main drawback of the outranking methods.

Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: annika.kangas@metla.fi
Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi
Pykäläinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: jp@nn.fi

article id 595, category Research article

Kenneth Nyström, Göran Ståhl. (2001). Forecasting probability distributions of forest yield allowing for a Bayesian approach to management planning. Silva Fennica vol. 35 no. 2 article id 595. https://doi.org/10.14214/sf.595

Keywords: basal area growth model; mixed-model; uncertainty of predictions; Monte Carlo simulation

Abstract | View details | Full text in PDF | Author Info

Probability distributions of stand basal area were predicted and evaluated in young mixed stands of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and birch (Betula pendula Roth and Betula pubescens Ehrh.) in Sweden. Based on an extensive survey of young stands, individual tree basal area growth models were estimated using a mixed model approach to account for dependencies in data and derive the variance/covariance components needed. While most of the stands were reinventoried only once, a subset of the stands was revisited a second time. This subset was used to evaluate the accuracy of the predicted stand basal area distributions. Predicting distributions of forest yield, rather than point estimates, allows for a Bayesian approach to planning and decisions can be made with due regard to the quality of the information.

Nyström, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: kenneth.nystrom@resgeom.slu.se
Ståhl, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: gs@nn.se

article id 606, category Research article

Pekka Leskinen, Jyrki Kangas. (2001). Modelling future timber price development by using expert judgments and time series analysis. Silva Fennica vol. 35 no. 1 article id 606. https://doi.org/10.14214/sf.606

Keywords: forest planning; uncertainty; expert knowledge; forecasting; price peaks; price trend; price variation

Abstract | View details | Full text in PDF | Author Info

Timber prices belong to the most important variables affecting the optimality of forest management. On the other hand, forecasting of timber prices is very uncertain. One difficulty when using past time series data in forecasting future timber price development is the possibility of changes in the markets and in the society at large. Expert knowledge can be applied in forecasting of timber prices as information additional to that provided by time series modelling. This paper presents an approach utilising both time series data and expert judgments in modelling future timber prices. A time series model is used as the basis for the approach. Parameters describing future timber price trends, variation in future timber prices, and the probabilities of price peaks taking place in the future are estimated with expert judgments as the basis. A case study involving 12 experts was carried out in Finland, and models were estimated for all the six major timber assortments in the country. The model produced can be utilised in the optimisation calculations of forest planning.

Leskinen, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: pekka.leskinen@metla.fi
Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi

article id 651, category Research article

Annika S. Kangas, Jyrki Kangas. (1999). Optimization bias in forest management planning solutions due to errors in forest variables. Silva Fennica vol. 33 no. 4 article id 651. https://doi.org/10.14214/sf.651

Keywords: forest planning; uncertainty; prediction; decision analysis

Abstract | View details | Full text in PDF | Author Info

The yield of various forest variables is predicted by means of a simulation system to provide information for forest management planning. These predictions contain many kinds of uncertainty, for example, prediction and measurement errors. Inevitably, this has an effect on forest management planning. It is well known that uncertainty in the forest yields causes optimistic bias in the observed values of the objective function. This bias increases with the error variances. The amount of bias, however, also depends on the error structure and the relations between the objective variables. In this paper, the effect of uncertainty in forest yields on optimization is studied by simulation. The effect of two different sources of error, the correlation structure of these errors and relations among the objective variables are considered, as well as the effect of two different optimization approaches. The relations between the objective variables and the error structure had a notable effect on the optimization results.

Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: annika.kangas@metla.fi
Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi

article id 677, category Research article

Pekka Leskinen, Jyrki Kangas. (1998). Analysing uncertainties of interval judgment data in multiple-criteria evaluation of forest plans. Silva Fennica vol. 32 no. 4 article id 677. https://doi.org/10.14214/sf.677

Keywords: forest planning; uncertainty; decision analysis; expert judgment; pairwise comparisons

Abstract | View details | Full text in PDF | Author Info

The use of interval judgments instead of accurate pairwise comparisons has been proposed as a solution to facilitate the analysis of uncertainties in the widely applied pairwise comparisons technique. A method is presented for deriving probability distributions for the pairwise comparisons and for utilizing the distributions in the analysis of uncertainties in the evaluation process. The first step is that the expert or the decision-maker is queried as to the best guess of the priority ratio of the attributes compared. This is followed by an adjusting query concerning the uncertainty in the comparison: what is the probability of the priority ratio being between the best guess ± 1 unit of the pairwise comparison scale? An application of the method is presented in the form of multiple-criteria evaluation of alternative management plans for a forest area.

Leskinen, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: pekka.leskinen@metla.fi
Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi

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