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Data acquisition for sustainable forest management has focused on obtaining high quality information to estimate biomass. Improving the quality of non-timber sustainability indicators, like deadwood volume, has been a minor interest. To explore how inventory approaches could be improved, we applied a Global Uncertainty and Sensitivity Analysis (GUSA) to evaluate which factors propagate more errors in deadwood modelling and how better data collection can minimize them. The impact of uncertainty on deadwood characteristics (diameter, collapse ratio, decay class, tree species, and position) was explored under stakeholders´ preferences, management actions, and climate change scenarios. GUSA showed that removing the prediction error in deadwood tree species and diameter would alter the most the total uncertainty in deadwood volume. We found that assessment of high deadwood volume was less uncertain for the scenarios where small deadwood items were left decaying on the forest floor (BAU) and for high-end climate change scenario (RCP8.5) which resulted in lower deadwood accumulation in forest stands and therefore also in lower likelihood of erroneous estimates. Reduced uncertainty in tree species and diameter class will elevate the certainty of deadwood volume to a similar level achieved in living biomass estimation. Our uncertainty and sensitivity analysis was successful in ranking factors propagating errors in estimate of deadwood and identified a strategy to minimize uncertainty in predicting deadwood characteristics. The estimation of uncertainty in deadwood levels under the scenarios developed in our study can help decision makers to evaluate risk of decreasing deadwood value for biodiversity conservation and climate change mitigation. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The aim of this study was to construct a nationwide stand age model by using National Forest Inventory (NFI) data and nationwide airborne laser scanning (ALS) data. In plantation forestry, age is usually known. While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories. Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning (ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age. Results: While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error (RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice. Conclusions: The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but still possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field. [ABSTRACT FROM AUTHOR]

To study whether, why, and how forestry decision-makers in Southeastern Norway adapt to climate change, we conducted a series of semi-structured interviews in focus groups consisting of non-industrial private forest owners, forest managers, and forest advisors. Our results show that a majority of the participants believed in climate change as a phenomenon, and had experienced events or observed changes that they attributed to climate change. However, we found little evidence of concern regarding climate change impacts on forest ecosystems and forestry among the participants. Instead, the majority regarded climate change more as an opportunity for the Norwegian forest-based sector than a threat. A minority had implemented proactive practices motivated by climate change but in all but one case, the adjustments were adaptation of forest infrastructure. In general, the participants agreed that the uncertainty associated with the effects of climate change and the (economical) uncertainty associated with adaptation of forest ecosystems were too large to change forest management practices at present. However, many participants, in particular the managers, are already adapting in response to experienced problems, such as increased frequency and duration of periods with low carrying capacity of the ground implying reduced or no accessibility within and to stands. [ABSTRACT FROM AUTHOR]

Managing forest resources occurs under various sources of uncertainty. Depending on the management problem, this uncertainty may have a substantial impact on the quality of the solution. As our knowledge on the sources and magnitude of uncertainty improves, integrating this knowledge into the development of management plans becomes increasingly useful, as additional information can improve the decision-making process. This adjustment requires a fundamental shift in how planning problems are viewed: instead of interpreting risk management as a technique needed only for addressing problems with natural hazards, risk management should be an integral part of most planning problems. Managing risks can be linked to a variety of adaptive planning methods: to help mitigate risk, either plans should be revised as new information becomes available or the possibility of adaptation should be accounted for in preparing the plans. We conduct a brief examination of the key topics in risk management and highlight how risk management implies trade-offs. Several decision problems that incorporate risk management are analyzed, and alternative perspectives for the problem are suggested to better address risk management issues. We then provide a decision framework for considering how to integrate risk management practices into the forest planning process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Key message : Through a stochastic programming framework, risk preferences can be included in forest planning. The value of utilizing stochastic programming is always positive; however, the value depends on the information quality and risk preferences of the decision maker. Context : Harvest scheduling requires decisions be taken based on imperfect information and assumptions regarding the future state of the forest and markets. Aims : The aim of this study is to incorporate elements of risk management into forest management, so that the decision maker can understand the risks associated with utilizing the imperfect data. Methods : Incorporation of uncertainty is done through stochastic programming. This allows for the decision maker's attitude towards risk to be incorporated into the development of a solution. By means of a simple even-flow problem formulation, a method of using stochastic programming to incorporate explicit trade-off between objective function value and risk of not meeting the constraints has been developed. Results : The different models highlight the importance of including uncertainty in management of forest resources. In general, as the decision maker becomes more risk averse, the incorporation of uncertainty into the model becomes more important. Conclusions : The use of stochastic programming allows for additional information to be included in the formulation, and this allows for the decision maker to account for downside risk. [ABSTRACT FROM AUTHOR]

Developing a plan of action for the future use of forest resources requires a way to predict the development of the forest through time. These predictions require the use of inventory data and growth models that contain a large number of uncertainties. These uncertainties impact the quality of the predictions, and if not accounted for, they can lead to the selection of a suboptimal management plan. To account for and manage the uncertainties and associated risk, we have explored the use of stochastic programming. Stochastic programming can integrate uncertainty into the optimization process by solving the problem for a large number of potential scenarios of the forests future development. The selection of an appropriately sized set of scenarios involves a trade-off between tractability issues and problem representation issues. In this paper, an analysis of the trade-offs is conducted. Two cases are studied, one in which only the uncertainty of the inventory data is included and a second in which both growth model and inventory data uncertainties are included. The impact of increasing the number of scenarios on the problem representation is examined through a simple even-flow problem. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The last 30 years has seen an increase in environmental, socio-economic, and recreational objectives being considered throughout the forest planning process. In the Finnish context these are considered mainly at the regional level potentially missing out on more local issues and problems. Such local information would be most efficiently collected with a participatory GIS approach. A mobile participatory GIS application called Tienoo was developed as a tool for collecting location-specific opinions of recreational and aesthetical characteristics of forests and forest management. The application also contains information the user can access regarding the practical details of the area, for instance about the recreational infrastructure. The application was tested in Ruunaa National Hiking Area, North Karelia, Eastern Finland. Through this application it is possible to continuously collect geolocated preference information. As a result, the collected opinions have details which can be located in both time and space. This allows for the possibility to monitor the changes in opinions when the stands are treated, and it also allows for easily analyzing the effect of time of year on the opinions. It is also possible to analyze the effect of the spatial location and the forest characteristics on the opinions using GIS analysis. [ABSTRACT FROM AUTHOR]

The development of forest management plans is a multi-criteria problem that usually involves multiple stakeholders. In forested recreational areas or natural parks, stakeholders could include government officials, forest companies and visitors to the area. With the progress of mobile phone technology, preference information (including aesthetic values) can be conveniently obtained from stakeholders while they are in the forest. This paper develops a methodological approach that incorporates place-specific opinion preference into the development of alternative forest management plans. Through the use of a modified Compromise Programming approach, prospective alternatives are developed, which optimize the opinions of stakeholders and the quantity of timber over time depending on specified parameters. The analysis is based on a small simulated forest holding and place-specific simulated opinion data. To allow for a comparison between potential stakeholder groups, the opinion data was generated by a method that produced three separate groups. The results indicate the practical use of place-specific preferences and provide a means to incorporate the information to generate possible alternative plans. In addition, the analysis highlights that by segregating the preference information into groups, the planning of more specific courses of actions can be made. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The Finnish state forest enterprise, Metsähallitus, defines the regional harvest levels for a 10-year period in a strategic-level natural resources plan. Although this plan defines stand-level harvest schedules for all stands, in practice, it cannot be used, as the harvests need to be clustered in time and in space. It is applied by giving each subregion goals they need to fulfill in a tactical level planning process, and the harvests are manually clustered into predefined groups of adjacent stands (departments). In this study, we developed a hierarchical optimization process making use of departments for clustering the harvests. For each of the departments, 91 different stand-level harvest schedules (plans) were determined using incomes from one period and the forest value at the end as objectives. The department-level plans were then used as alternatives in a region-level goal optimization problem. The resulting hierarchic plan was compared to the stand-level solution of the strategic-level plan which served as a benchmark plan. The hierarchical plan clustered the harvests and achieved the goals set better than the benchmark plan, but the net present income was 3.3% lower. The approach turned out usable, but further developing of the approach is needed to reduce the costs of clustering. [ABSTRACT FROM PUBLISHER]

Multi-objective forest planning is a multi-methodological endeavor whose success largely depends on how well the combined use of different methods contributes to the goals of the planning. This review assessed the benefits of mixing methods in natural resources planning. A sample of 30 peer-reviewed research articles was analyzed using an evaluation framework, designed based on democracy and planning theories, and participatory planning literature, including four dimensions: transparency, flexibility, consensus building, and operability. According to analyses, mixing different types (i.e. qualitative and quantitative) of methods generally yields greater benefits than the combination of similar methods. The subsample of 12 planning cases that utilized simulation-optimization software (SOS) appeared operable and moderately transparent, whereas flexibility and consensus building were often lacking. In comparison to the wide scholarly discussion on multi-methodology and mixing methods, it was observed that successful mixing examples in natural resource planning are still scarce and there are weaknesses in bridging the methods together. There is an evident need to pursue and to better communicate the benefits of mixing. Some good mixing examples utilizing SOS provided evidence that forest planning processes would make an excellent venue for studying the benefits and caveats of using mixed methods. [ABSTRACT FROM PUBLISHER]

Developing a forest management plan in a multicriteria perspective is traditionally accomplished utilizing simulation and optimization tools as a means to predict and optimize a variety of criteria under consideration. Goal programming is a useful tool to balance conflicting aspects of the competing criteria. When information regarding an aspect of uncertainty is available, stochastic programming should be utilized to efficiently integrate this additional information. Research has been conducted into determining the accuracy of forest inventory methods; however, the measurement error is typically ignored when generating forest management plans. Through integrating the uncertainty in a systematic fashion, the forest management plan can be improved by describing the potential uncertainty in the plan and by managing the influences of this uncertainty. This paper develops three stochastic goal programming formulations and highlights the usefulness of the approach on a small forest holding. [ABSTRACT FROM AUTHOR]

Participatory approaches and computerised tools such as decision support systems (DSS) represent conflicting tendencies in state-of the-art sustainable forest management. As a result, there may be considerable tension between these two developments in practice. The objective of this paper is to explore how participatory approaches and DSS could be brought together to improve planning processes and to explore how DSS could be adapted in their use or combined with other tools to enable successful participatory planning. From a review of the literature, we identified criteria related to successful participatory planning. From these criteria, we selected those a DSS can influence and created a short list of the criteria that could be used to evaluate participatory processes where DSS are applied. The evaluation criteria with particular relevance for DSS that we identified are as follows: fairness, opportunity to influence outcome, quality and selection of information, cost-effectiveness, challenging status quo and fostering creative thinking, structured decision-making process, transparency, and independence and neutrality of process. We also scrutinised existing forest DSS and identified features that may enable DSS to address these criteria. The features of DSS we identified that may support participatory processes are as follows: group decision support, possibilities to include other values than timber production, flexibility of system to include non-traditional forest data and management options, and multi-criteria decision analysis tools. We argue that the DSS to be used should be assessed to clarify, how it can be used in the specific planning situation and how it should be complemented with other available and non-computerised tools. [ABSTRACT FROM AUTHOR]

The development of new alternative plans based on applying multicriteria decision making (MCDM) techniques in discrete choice situations has received little attention in the context of forest planning. This article proposes a two-stage approach to be applied in participatory decision-making situations in which a specific number of initial alternatives are evaluated by the decision makers (DMs) using MCDM analysis. The preference information, obtained from these analyses in the form of target values, is then used for generating still more efficient forest plans. This paper concentrates on the latter stage and tests nine different goal programming (GP) formulations. This paper uses the formulas and preference information obtained from a case study of three forest owners to generate new forest plans. Among the tested techniques, formulas with a penalty function provided the most appropriate plans. These GP formulations could enhance the participatory planning processes in which a discrete number of alternatives are evaluated. With further development, this process could be applied to a variety of forest ownership types and could be a useful tool in supporting group decision making. This proposed approach could facilitate an increase in the DMs' satisfaction and an increased commitment towards the derived decision. [ABSTRACT FROM AUTHOR]

Errors in forest planning data are known to have various undesired effects, which have been examined previously by simulating their impact on forest planning systems. In most cases, the simulation of forest inventory errors has been simplified by assuming the error distribution to be Gaussian, possibly with a constant bias, and neglecting possible correlations between the errors in various attributes. The first aim here was to examine the distributions, correlations, and trends in errors when using alternative forest inventory methods, and the second was to analyse how different error simulation methods affect the estimated economic losses caused by suboptimal harvest timing on account of errors. We found that the errors were not normally distributed, had notable trends, and showed significant correlations between the errors for the various attributes. The most important factor affecting the inoptimality losses was the powerful tendency to underestimate the growing stock properties of mature stands. The error simulation method clearly makes a difference when analysing the effects of errors, and it is therefore important to use a simulation method that generates realistic errors. [ABSTRACT FROM AUTHOR]

• Damage from wind, bark beetle, wildfire, root rot, snow and ice were considered. • Links between natural disturbances and climate change were reviewed. • Forty disturbance models were categorized and assessed. • Modelling approaches estimating disturbance's occurrences and effects were assessed. • Appropriate modelling approaches for large scale management planning are recommended. It is expected that European Boreal and Temperate forests will be greatly affected by climate change, causing natural disturbances to increase in frequency and severity. To detangle how, through forest management, we can make forests less vulnerable to the impact of natural disturbances, we need to include the risks of such disturbances in our decision-making tools. The present review investigates: i) how the most important forestry-related natural disturbances are linked to climate change, and ii) different modelling approaches that assess the risks of natural disturbances and their applicability for large-scale forest management planning. Global warming will decrease frozen soil periods, which increases root rot, snow, ice and wind damage, cascading into an increment of bark beetle damage. Central Europe will experience a decrease in precipitation and increase in temperature, which lowers tree defenses against bark beetles and increases root rot infestations. Ice and wet snow damages are expected to increase in Northern Boreal forests, and to reduce in Temperate and Southern Boreal forests. However, lack of snow cover may increase cases of frost-damaged seedlings. The increased temperatures and drought periods, together with a fuel increment from other disturbances, likely enhance wildfire risk, especially for Temperate forests. For the review of European modelling approaches, thirty-nine disturbance models were assessed and categorized according to their required input variables and to the models' outputs. Probability models are usually common for all disturbance model approaches, however, models that predict disturbance effects seem to be scarce. [ABSTRACT FROM AUTHOR]

Forest management planning is based on stand-level information. The stands are typically visually delineated based on aerial photographs. Because of visual interpretation, the estimation of stand boundaries is always subjective. Moreover, there are no definite criteria for the delineation. For one forest area, several different solutions could be obtained. Automated segmentation of digital imagery offers a possible solution to these problems, by producing more objective delineation, reducing time and costs, and increasing consistency of stand delineation. The aim of this study was to evaluate the applicability of a canopy height model (CHM) in automatic segmentation of forest stands. In addition, the mosaic produced with CHM was compared with one that was produced automatically using an aerial photograph (RGB). The usefulness of combining CHM and an aerial photograph in the automatic segmentation process was also examined. The results showed that delineation based on CHM is a good option compared with aerial photographs, when aiming for homogeneity of the delineated stands. The explained proportion of the whole variability of mean diameter was 74% and of mean height 83% in the CHM mosaic, compared with 65% and 73% in the RGB mosaic, and 60% and 73% in the reference. However, a visual interpreter was able to produce delineation almost as homogeneous as the reference with respect to volume, and better with respect to mean diameter and height. [ABSTRACT FROM AUTHOR]

Abstract: Predicting forest development under varying treatment schedules forms the basis of forest management planning. The actual growth predictions are made with a forest simulator which includes growth equations and additional models for predicting a number of varying tree, forest and site properties. Forest growth simulators typically include either tree-level or stand-level growth models, but these two approaches have not been thoroughly compared. We set out here to compare these two approaches with the SIMO simulator framework in a small data set from southern Finland based on 60 sample plots in 30 stands, the development of which was known for 20 years. The stands chosen were very dense, so that the simulators could be tested under extreme conditions. The results show that the stand-level model is more accurate in almost all cases and its computational burden is much lower. It could therefore be advisable to use tree-level models for short-term predictions, which would ensure detailed information on forest structure for planning the near-future operations. Stand-level models would be more advisable in longer term predictions, especially when accurate volume estimates are considered more important than the forest structure. The errors observed in these simulators were analysed further by quantile regression, which allows empirical estimates of confidence intervals to be obtained for the simulator. [Copyright &y& Elsevier]

Abstract: We discuss the benefits of using multiple criteria decision support (MCDS) methods in forest management, briefly present some MCDS methods recently applied in forestry, and summarize experiences gained from MCDS applications in forestry. Applications of MCDS methods of varying characteristics can be found in the management planning of multiple-purpose forestry. However, the tool to be used should be chosen to fit the planning process at hand. When choosing a method, compromises must often be made. For instance, simple and easily understandable methods may mean loss of attainable information and, correspondingly, deficient analyses. More versatile methods enable deeper analyses and more complete exploitation of available data, but typically they are hard to use and understand. Simple and straightforward MCDS methods are needed in participatory approaches and in planning via information networks. Some recent studies indicate that, especially for behavioural reasons, it would be useful to use more than just one MCDS method, or hybrid approaches, in many planning situations. A further conclusion has been that interactive use of the methods greatly improves the efficiency of the planning process. [Copyright &y& Elsevier]

In Finland, forest management planning is based on standwise assessment of forest variables. The data is traditionally gathered partly by (subjective) sampling and partly by visual assessment, which makes the accuracy assessments difficult. This study consists of an experiment where the visual assessments of field technicians were compared with the accurately measured values. The data consists of assessments from 18 sample plots made by 19 technicians. Each technician assessed four forest characteristics from each stand, for each tree species and each tree class. Basal area was observed in all cases; the other three variables varied according to 18 different measurement strategies. From these observations, mixed models were estimated to analyze to what extent the assessment errors depend on forest characteristics. Variation among both sample plots and field technicians was also considered. The results show that some of the variables could be interpreted as Berkson cases. The assessment errors were also often highly hetero sce dastic. Therefore, variance was explicitly modeled, and the final error models were estimated with weighted mixed regression using the variance estimates as weights. The results show clear variation among technicians, especially in characteristics that include personal judgment. The effect of training could be detected from variation between the technician groups. Furthermore, the broad-leaved tree classes were generally more difficult to assess than conifers. [ABSTRACT FROM AUTHOR]

Presents a study that demonstrated the effect of calibrating the initial diameter distribution on the growth and yield predictions when using single-tree growth models. Information on diameter distribution; Methodology; Discussion on the application of calibration system on diameter distribution.

The forests in Finland have been under intensive planning for decades. Currently, mathematical programming is widely used in planning of wood production. Today's multi-functional forestry, however, calls for more flexible decision support methods. MCDM tools have been used in responding to fresh planning challenges. For example, the Finnish Forest and Park Service, entrusted with the care of the vast majority of state-owned natural resources in Finland, endeavours to produce large-scale natural resource plans satisfying the needs of both economic, social, and ecological sustainability. Participatory approach is applied in the process. Several forestry applications of MCDM methods, particularly those making use of the AHP or the HIPRE program, have been presented. Also, the outranking methods ELECTRE and PROMETHEE have been tested. Due to the nature of forestry applications, statistical techniques for analysing uncertainties in pairwise comparisons and for utilizing interval judgement data have been developed to improve the usability of the AHP. Recently, a hybrid method called A 'WOT. making use of the AHP and SWOT, was also introduced into strategic forest planning. This paper summarizes the experiences gained in applying a MAVT and two outranking methods in connection with a participatory natural resource planning process in Finland. In addition, some results of the method development work related to application needs are briefly presented. The details of the planning cases reviewed here have previously been presented in forestry journals. The purpose of this paper is not only lo show how MCDM methods have been applied in forestry, but also to discuss the usability and usefulness of MCDM methods from the viewpoint of supporting forestry decision making -- and how they might further be improved. Also, some perspectives for the future development work of MCDM applications in the field of natural resource management are focused on. As a conclusion, the use of more than just one MCDM method in a single planning process is seen usually recommendable. In addition, developing hybrid MCDM methods is regarded as a potential direction for future research. Also, closer co-operation between method developers and appliers is called for to produce more useful applications. [ABSTRACT FROM AUTHOR]

• Forest changes were automatically modelled from multitemporal Sentinel-2 images. • Errors were evaluated based on visually interpreted VHR images. • Extraction of clear-cuts was accurate whereas thinnings had more variation. • Image quality and translucent clouds had most significant effect on errors. • Results were regarded applicable for operational change monitoring. The majority of the boreal forests in Finland are regularly thinned or clear-cut, and these actions are regulated by the Forest Act. To generate a near-real time tool for monitoring management actions, an automatic change detection modelling chain was developed using Sentinel-2 satellite images. In this paper, we focus mainly on the error evaluation of this automatized workflow to understand and mitigate incorrect change detections. Validation material related to clear-cut, thinned and unchanged areas was collected by visual evaluation of VHR images, which provided a feasible and relatively accurate way of evaluating forest characteristics without a need for prohibitively expensive fieldwork. This validation data was then compared to model predictions classified in similar change categories. The results indicate that clear-cuts can be distinguished very reliably, but thinned stands exhibit more variation. For thinned stands, coverage of broadleaved trees and detections from certain single dates were found to correlate with the success of the modelling results. In our understanding, this relates mainly to image quality regarding haziness and translucent clouds. However, if the growing season is short and cloudiness frequent, there is a clear trade-off between the availability of good-quality images and their preferred annual span. Gaining optimal results therefore depends both on the targeted change types, and the requirements of the mapping frequency. [ABSTRACT FROM AUTHOR]

Background: We investigated how the precision and costs of forest resource estimates for sample plots of different type and size depend on the spatial structure of forests and jointly studied the effects of tree density and size distribution. Statistically thinking, the trees in a forest can be regarded as a point pattern. Based on the spatial properties of the point pattern, we classified the forests into clustered, random, and regular. We used empirical data from 396 mapped forest plots from Finland. The variance of the unbiased Horvitz-Thompson estimator and expected costs of the basal area and tree density estimation were calculated for 99 different sample plots of different type and size in each of the 396 forest plots. Further, we considered the estimation of the change between two time points for a subset of the data.Results: The precision and expected cost depended on the tree size distribution and spatial pattern of trees. While large sample plots are advisable for clustered forests or the monitoring of young forests with small trees, we see potential for measuring smaller sample plots in regular forests. The choice of sample plot was more important in clustered forests, where also the variability of the expected costs was higher.Conclusions: If the spatial structure of forests could be predicted accurately and precisely prior to field measurements, for instance from remote sensing data, the precision of forest inventories could potentially be improved or costs decreased by allowing the sample plot size and type to vary from one forest stand to another. When using a compromise sample plot over a large region and a long inventory rotation, optimizing the sample plot for one time point ignores possible changes in forest structures caused by changes in forest management practices. [ABSTRACT FROM AUTHOR]

Economically-oriented forestry aims to sustain timber harvest revenues, while ecologically-oriented management supplies suitable habitat for species using deadwood as primary habitat. As these objectives are conflicting, planning for economic and ecological sustainability involves compromise and trade-offs. We analyze the spatial trade-offs between the economic value from timber harvesting and the volume of deadwood in the boreal forest. We assess these trade-offs from three perspectives: (1) landscape characteristics, affected by conservation strategies; (2) forest management promoting either economic or ecological values; (3) uncertainty in inventory errors undermining the estimate of the two sustainability objectives. To reveal the tradeoffs between the forest economic and ecological values we simulated and optimized a production landscape in Finland 30 years into the future accounting for uncertainty in biomass and deadwood inventories. We found that, with a limited reduction in timber harvesting (7%), (i) the amount of deadwood increased more in non-aggregated (45%) than in aggregated (16%) stands, (ii) constraining stands in adjacent areas further increased deadwood (21%) respect to the matrix and (iii) 7% of connected stand area harbored ≥20 m3/ha deadwood supporting survival of near-threatened species. Our results demonstrate that the structure of the landscape for biodiversity can be improved with limited economic losses. However, improving habitat configuration requires larger economic losses than only increasing habitat amount, but its ecological benefits are larger both for common and red-listed species. We found that management oriented towards stand aggregation not only creates connected areas with high deadwood of high value biodiversity but also improves the value of the whole matrix by decreasing intensive timber harvesting and energy wood collection. Finally, we found that uncertainties alter the estimate of the potential of the forest landscape to supply deadwood, and this can affect the choice of management actions to allocate over the landscape. To conclude, our results demonstrate the trade-offs between economic forest use and conservation are affected differently by landscape characteristics, forest management and uncertainty in inventory errors. As such these drivers should be considered when optimizing the forest for multiple uses. • Deadwood volume increased with a very limited reduction in timber harvesting. • Adjacent stands supplied higher deadwood volume than non-aggregated stands. • Reducing by 7% total NPV supplied 7% connected stands 'area with deadwood ≥20 m3/ha. • Management for "deadwood islands" decreased harvesting and energywood collection. • Uncertainties alter the estimate of the forest landscape for biodiversity. [ABSTRACT FROM AUTHOR]

Good estimates of the precision and accuracy of stand growth and yield predictions are needed in the decision-making process. Future growth and yield are often projected with a complex simulation system, sothat these assessments are not easy. The precision of long-term growth and yield predictions has often been estimated through Monte Carlosimulation, combining several error sources with different variances. A simple method to assess the uncertainty of stand growth and yieldpredictions is to model the observed (past) errors obtained by comparing the observed stand characteristic with the characteristic predicted using the simulation system. Using this model, the uncertainty offuture predictions can then be anticipated. Another possibility is to form an elementary model for stand growth (or yield) and use its variance as an assessment of the uncertainty for the simulation system.In this paper, the above-mentioned three methods were used for assessing the uncertainty of a simulation system. These assessments were then compared with the empirical estimates of uncertainty. The three methods were also compared with respect to their data requirements andthe capabilities of the methods. [ABSTRACT FROM AUTHOR]

Connecting Multiple Criteria Decision Support (MCDS) methods with SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis yields analytical priorities for the factors included in SWOT analysis and makes them commensurable. In addition, decision alternatives can be evaluated with respect to each SWOT factor. In this way, SWOT analysis provides the basic frame within which to perform analyses of decision situations. MCDS methods, in turn, assist in carrying out SWOT more analytically and in elaborating the results of the analyses so that alternative strategic decisions can be prioritized also with respect to the entire SWOT. The A''WOT analysis is an example of such hybrid methods. It makes combined use of the Analytic Hierarchy Process (AHP) and SWOT. In this study, a hybrid method of the Stochastic Multicriteria Acceptability Analysis with Ordinal criteria (SMAA-O) and SWOT is developed as an elaboration of the basic ideas of A''WOT. The method is called S-O-S (SMAA-O in SWOT). SMAA-O enables the handling of ordinal preference information as well as mixed data consisting of both ordinal and cardinal information. Using SMAA-O is enough to just rank decision elements instead of giving them cardinal preference or priority ratios as required by the most commonly used MCDS methods. Using SMAA-O, in addition to analyzing what the recommended action is under certain priorities of the criteria, enables one to analyze what kind of preferences would support each action. The S-O-S approach is illustrated by a case study, where the shareholders of a forest holding owned by a private partnership prepared the SWOT analysis. Six alternative strategies for the management of their forest holding and of old cottage located on the holding were formed. After S-O-S analyses were carried out, one alternative was found to be the most recommendable. However, different importance orders of the SWOT groups would lead to different recommendations, since three of the six alternatives were efficient according to S-O-S analyses. [Copyright &y& Elsevier]