Publications

We introduce a Knowledge Transfer Portal (KTP) which supports knowledge transfer among researchers and forest managers. The KTP will be used for supporting transfer of knowledge generated in the FunDivEUROPE (FUNctional significance of forest bioDIVersity in EUROPE) after project life. It uses semantic web technologies to achieve a common understanding throughout a knowledge representation based on an expert elicitation process. Knowledge transfer tools (KTTs) take use of knowledge elements within the knowledge base and implement various knowledge transfer functionalities. The knowledge base shows interactions of biodiversity effects on the sustainable provision of ecosystem services. In this contribution we focus on the ongoing knowledge base engineering process and show first results that were based on a series of workshops with domain experts to generate a common understanding about terms, definitions and their relations. Relations were generated upon FunDivEUROPE project hypothesis with respect to project results and expert beliefs. We use a web-based, collaborative knowledge base engineering cycle and create a thesaurus which was initiated with terms from these expert workshops. Copyright ©2013 SCITEPRESS.

Aim of study: Knowledge Management (KM) tools facilitate the implementation of knowledge processes by identifying, creating, structuring, and sharing knowledge through use of information technology in order to improve decision-making. In this contribution, we review the way in which KM tools and techniques are used in forest management, and categorize a selected set of them according to their contribution to support decision makers in the phases of problem identification, problem modelling, and problem solving. Material and methods: Existing examples of cognitive mapping tools, web portals, workflow systems, best practices, and expert systems as well as intelligent agents are screened for their applicability and use in the context of decision support for sustainable forest management. Evidence from scientific literature and case studies is utilized to evaluate the contribution of the different KM tools to support problem identification, problem modelling, and problem solving. Main results: Intelligent agents, expert systems and cognitive maps support all phases of the forest planning process strongly. Web based tools have good potential to support participatory forest planning. Based on the needs of forest management decision support and the thus-far underutilized capabilities of KM tools it becomes evident that future decision analysis will have to consider the use of KM more intensively. Research highlights: As the problem-solving process is the vehicle for connecting both knowledge and decision making performance, the next generation of DSS will need to better encapsulate practices that enhance and promote knowledge management. Web based tools will substitute desktop applications by utilizing various model libraries on the internet.

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.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.

The forest planning system of large Swedish forest owners follows a three step procedure: long-term, medium-term, and short-term planning. The system is sequential and hierarchical in the sense that longer-term plans form the framework for shorter-term plans, and that top-level management prepares the long range plans and the lower management levels develop plans with successively shorter horizons. Studies indicate that this approach does not fully use existing knowledge within the organization. Problems associated with the top-down approach are also recognized in the general literature on organization and management. A proposal for a bottom-up approach is developed that aim at the use of local level knowledge to enhance accuracy and applicability of the forest plans. After top-level management has issued some fundamental planning directives, medium-term planning is conducted by the districts. Then the district plans are consolidated at the top-level for coordination and revision. A simulated planning process provides an illustration of the approach. The Heureka system is used here to optimize harvests and road costs with a mixed integer programming model of the problem, spanning 10 years with three seasons per year. The importance of detailed local knowledge to the outcome of planning is indicated, and needs for continued decision support systems development is discussed.The forest planning system of large Swedish forest owners follows a three step procedure: long-term, medium-term, and short-term planning. The system is sequential and hierarchical in the sense that longer-term plans form the framework for shorter-term plans, and that top-level management prepares the long range plans and the lower management levels develop plans with successively shorter horizons. Studies indicate that this approach does not fully use existing knowledge within the organization. Problems associated with the top-down approach are also recognized in the general literature on organization and management. A proposal for a bottom-up approach is developed that aim at the use of local level knowledge to enhance accuracy and applicability of the forest plans. After top-level management has issued some fundamental planning directives, medium-term planning is conducted by the districts. Then the district plans are consolidated at the top-level for coordination and revision. A simulated planning process provides an illustration of the approach. The Heureka system is used here to optimize harvests and road costs with a mixed integer programming model of the problem, spanning 10 years with three seasons per year. The importance of detailed local knowledge to the outcome of planning is indicated, and needs for continued decision support systems development is discussed.

Decision support systems (DSSs) are important in decision-making environments with conflicting interests. Many DSSs developed have not been used in practice. Experts argue that these tools do not respond to real user needs and that the inclusion of stakeholders in the development process is the solution. However, it is not clear which features of participatory development of DSSs result in improved uptake and better outcomes. A review of papers, reporting on case studies where DSSs and other decision tools (information systems, software and scenario tools) were developed with elements of participation, was carried out. The cases were analysed according to a framework created as part of this research; it includes criteria to evaluate the development process and the outcomes. Relevant aspects to consider in the participatory development processes include establishing clear objectives, timing and location of the process; keeping discussions on track; favouring participation and interaction of individuals and groups; and challenging creative thinking of the tool and future scenarios. The case studies that address these issues show better outcomes; however, there is a large degree of uncertainty concerning them because developers have typically neither asked participants about their perceptions of the processes and resultant tools nor have they monitored the use and legacy of the tools over the long term.Decision support systems (DSSs) are important in decision-making environments with conflicting interests. Many DSSs developed have not been used in practice. Experts argue that these tools do not respond to real user needs and that the inclusion of stakeholders in the development process is the solution. However, it is not clear which features of participatory development of DSSs result in improved uptake and better outcomes. A review of papers, reporting on case studies where DSSs and other decision tools (information systems, software and scenario tools) were developed with elements of participation, was carried out. The cases were analysed according to a framework created as part of this research; it includes criteria to evaluate the development process and the outcomes. Relevant aspects to consider in the participatory development processes include establishing clear objectives, timing and location of the process; keeping discussions on track; favouring participation and interaction of individuals and groups; and challenging creative thinking of the tool and future scenarios. The case studies that address these issues show better outcomes; however, there is a large degree of uncertainty concerning them because developers have typically neither asked participants about their perceptions of the processes and resultant tools nor have they monitored the use and legacy of the tools over the long term.

This study developed an integrated decision support system (DSS) to assist land managers in taking a long-term holistic approach to integrated land-use decisions. ?MyLand? is a unique combination of existing methods and techniques: meta-modelling calibrated off-productivity surfaces for spatial application, a decision tree for selecting options, multiple land-use analysis, multiple outputs and a mapping interface deployed over the Web. The design provides visualisation of geospatial information and enables multiple stakeholders to contribute to a more collaborative land-use planning process. Techniques to solve forestry modelling challenges have been generalised and applied in modelling pastoral and forestry land-use types. Forestry yield modelling is accommodated by a two-stage approach of spatial modelling of a productivity index followed by meta-modelling output from forest stand growth models. Livestock farming is modelled using the property owner's estimates of livestock carrying capacity of land management units in a whole property stock reconciliation model. The environmental performance of the property is calculated from the land-use type and management regime. A case study is described to demonstrate the use of ?MyLand? and results of user evaluation of the DSS are presented.This study developed an integrated decision support system (DSS) to assist land managers in taking a long-term holistic approach to integrated land-use decisions. ?MyLand? is a unique combination of existing methods and techniques: meta-modelling calibrated off-productivity surfaces for spatial application, a decision tree for selecting options, multiple land-use analysis, multiple outputs and a mapping interface deployed over the Web. The design provides visualisation of geospatial information and enables multiple stakeholders to contribute to a more collaborative land-use planning process. Techniques to solve forestry modelling challenges have been generalised and applied in modelling pastoral and forestry land-use types. Forestry yield modelling is accommodated by a two-stage approach of spatial modelling of a productivity index followed by meta-modelling output from forest stand growth models. Livestock farming is modelled using the property owner's estimates of livestock carrying capacity of land management units in a whole property stock reconciliation model. The environmental performance of the property is calculated from the land-use type and management regime. A case study is described to demonstrate the use of ?MyLand? and results of user evaluation of the DSS are presented.

Decision support systems (DSSs) are indispensable tools in preparing a forest management plan for a better combination of multiple forest values. This study attempted to develop and explain a stand-based forest management DSS (Ecosystem-based multiple-use forest planning [ETÇAP]) comprising a traditional simulation, linear programming (LP), metaheuristics and geographic information system. The model consists of five submodels; traditional management approach to handle inventory data, an empirical growth and yield model, a simulation to conceptualize management actions, a LP technique to optimize resource allocation and a simulated annealing approach to directly create a spatially feasible harvest schedule. The ETÇAP model has been implemented in a comparative two case study areas; Denizli?Honaz and Akseki?Ibrad?. Both simulation and optimization models outperformed to the traditional management plan. The periodical change of growing stock, allowable cuts, carbon sequestration and water production are used as performance indicators. The results showed that more amount of wood could be harvested over time compared to traditional level of harvesting. It could be concluded that various management strategies allowed managers to stimulate more decision options for better outputs through intertemporal trade-offs of management interventions as the model provided tools to quantify forest dynamics over time and space. Challenges exist to establish the functional relationships between forest structure and values for better quantification and integration into the management plans.Decision support systems (DSSs) are indispensable tools in preparing a forest management plan for a better combination of multiple forest values. This study attempted to develop and explain a stand-based forest management DSS (Ecosystem-based multiple-use forest planning [ETÇAP]) comprising a traditional simulation, linear programming (LP), metaheuristics and geographic information system. The model consists of five submodels; traditional management approach to handle inventory data, an empirical growth and yield model, a simulation to conceptualize management actions, a LP technique to optimize resource allocation and a simulated annealing approach to directly create a spatially feasible harvest schedule. The ETÇAP model has been implemented in a comparative two case study areas; Denizli?Honaz and Akseki?Ibrad?. Both simulation and optimization models outperformed to the traditional management plan. The periodical change of growing stock, allowable cuts, carbon sequestration and water production are used as performance indicators. The results showed that more amount of wood could be harvested over time compared to traditional level of harvesting. It could be concluded that various management strategies allowed managers to stimulate more decision options for better outputs through intertemporal trade-offs of management interventions as the model provided tools to quantify forest dynamics over time and space. Challenges exist to establish the functional relationships between forest structure and values for better quantification and integration into the management plans.

We present a decision support tool for guiding the selection of marked stands based on airborne laser scanning (ALS) data. We describe three stages, namely (1) wall-to-wall mapping of the stands matured for cutting using low-density ALS data; (2) tree-level inventory of these stands using high-density ALS data and (3) theoretical bucking of the imputed tree stems to produce detailed information on their characteristics. We tested them in a Scots pine dominated boreal forest area in Eastern Finland, where 79 sample plots were measured in the field. The detection of the stands matured for cutting had a success rate of 95% and our results demonstrated a further potential to limit the result towards stands dominated by certain species by means of intensity values derived from the low-density ALS data. The applied single-tree detection and estimation chain produced detailed tree-level information and realistic diameter distributions, yet the detection was highly emphasised on the dominant tree layer. The error levels in the estimates were generally less than standard deviations of the field attributes. Finally, plot-level accumulations of saw-log volumes were found rather similar, whether the input was based on the imputed tree data or trees measured in the field. The results are considered useful for ranking the stands based on their properties, whether the aim in the wood procurement is to focus on certain species or to select stands suitable for production needs.We present a decision support tool for guiding the selection of marked stands based on airborne laser scanning (ALS) data. We describe three stages, namely (1) wall-to-wall mapping of the stands matured for cutting using low-density ALS data; (2) tree-level inventory of these stands using high-density ALS data and (3) theoretical bucking of the imputed tree stems to produce detailed information on their characteristics. We tested them in a Scots pine dominated boreal forest area in Eastern Finland, where 79 sample plots were measured in the field. The detection of the stands matured for cutting had a success rate of 95% and our results demonstrated a further potential to limit the result towards stands dominated by certain species by means of intensity values derived from the low-density ALS data. The applied single-tree detection and estimation chain produced detailed tree-level information and realistic diameter distributions, yet the detection was highly emphasised on the dominant tree layer. The error levels in the estimates were generally less than standard deviations of the field attributes. Finally, plot-level accumulations of saw-log volumes were found rather similar, whether the input was based on the imputed tree data or trees measured in the field. The results are considered useful for ranking the stands based on their properties, whether the aim in the wood procurement is to focus on certain species or to select stands suitable for production needs.

Over the last decade, researchers have developed a range of decision support systems (DSS) which seek to improve the evidence-base for decision-making in the forestry sector in Great Britain. Many are now integral to the systems of forest management and planning used. However, in some cases, levels of adoption have been lower than expected. This problem is neither unique to Great Britain nor to forestry, and increasingly it is being explained in terms of the quality of stakeholder engagement during DSS development and implementation. Thus, social research was undertaken to understand the factors affecting DSS uptake. The methods included an online survey completed by 81 members of the Institute of Chartered Foresters and Forestry Commission staff and 30 semi-structured interviews with stakeholders. Four sets of factors were seen to influence uptake: professional judgement and cultural resistance; communication and access; training, support and consolidation; and meeting user requirements. More generally, our conclusions highlight the need for a shift in the quality of interactions at the science?policy?practice interface: from knowledge-transfer (a unidirectional ?bridging of gaps?) to knowledge-exchange (dialogue between collaborating partners) and knowledge-interaction (shared cultures and institutions).Over the last decade, researchers have developed a range of decision support systems (DSS) which seek to improve the evidence-base for decision-making in the forestry sector in Great Britain. Many are now integral to the systems of forest management and planning used. However, in some cases, levels of adoption have been lower than expected. This problem is neither unique to Great Britain nor to forestry, and increasingly it is being explained in terms of the quality of stakeholder engagement during DSS development and implementation. Thus, social research was undertaken to understand the factors affecting DSS uptake. The methods included an online survey completed by 81 members of the Institute of Chartered Foresters and Forestry Commission staff and 30 semi-structured interviews with stakeholders. Four sets of factors were seen to influence uptake: professional judgement and cultural resistance; communication and access; training, support and consolidation; and meeting user requirements. More generally, our conclusions highlight the need for a shift in the quality of interactions at the science?policy?practice interface: from knowledge-transfer (a unidirectional ?bridging of gaps?) to knowledge-exchange (dialogue between collaborating partners) and knowledge-interaction (shared cultures and institutions).

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.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.