The Horizon project ONEforest - Multi-criteria decision support system for a common forest management to strengthen forest resilience, harmonise stakeholder interests and ensure sustainable wood flows - has officially started on 1st June 2021. The project duration is set for 3 years and will end in June 2024. The project has an overall budget of around €5.2 million and consists of 19 partners in 8 countries throughout Europe. ONEforest will be led by Rosenheim Technical University of Applied Sciences (Germany).
Background
Forest ecosystems cover 42% of the EU’s total land area and strong efforts have been made to facilitate an increase of multiple forest ecosystem services. The forest-based sector can greatly contribute to climate change mitigation trough carbon storage and driving the bioeconomy transition and achieving the European Green Deal. However, all ecosystems recently have been hit by rapidly changing climatic conditions, e.g. long lasting droughts, heavy rain events, frequent and intensive storms, pests and forest fires. To address this within future silviculture management concepts, forest operations and wood supply, all stakeholders along the Forest Wood Value Chain will need to form a common idea of future forest management, while none of them can increase its benefit without harming another one.
Approach
ONEforest will address this challenge by developing a on multi-criteria decision support system that will enable different stakeholders in forestry to make long-term strategic decisions according to individual objectives, e.g. environmental, societal or economic aspects. Forest owners will be able to assess which way of forest management is advantageous for their objectives under current and future ecological and economic conditions. The overall idea is to achieve a multi-functional resilient forestry and sustainable wood supply levering wood-based products on a long-term perspective. ONEforest will provide solutions for harmonizing various Forest Ecosystem Services.
Within ONEforest, four Case Studies Regions will be established, following Europe’s biogeographical regions, which are Mediterranean forests, Alpine forests, Continental and Boreal/ Hemi-boreal forests. Through them the project will study climate-resilient
silvicultural management practices and new methods of seeding and planting by the application of an own engineered topsoil cover based on wood fibres. Corresponding forest operations and concepts of actions in case of disturbances will be developed under selected sustainability criteria. Stakeholders will be activated in the participative process of socio-economic studies. The information will be consolidated in a Dynamic Value Chain Model to assess the impact of the Forest Wood Value Chain on regional development quantified by a set of economic, environmental, and social indicators. The newly developed Multi-Criteria Decision Support System visualises decision-making by comparing Sustainable Forest Management, synergies and trade-offs of Forest Ecosystems, reliable wood supply, and stakeholder interests through Forest Wood Value Chain indicators of social, economic, and environmental dimensions. The application will be available for stakeholders. In the long run, all ONEforest results will be as far as possible implemented in the processes of establishing new Model Forests, being part of the International Model Forest Network for regionally adapted forest management concepts.
Biowood is a large project sponsored by the Research Foundation Flanders (FWO) and contributing to the development of a new wood-based bioeconomy in Flanders through multidisciplinary research in forest and chemical engineering and economics. This research will focus on (i) quantifying the current woody biomass feedstock supply in Flanders, (ii) predicting the future woody biomass in Flanders using the Sim4Tree decision support system (DSS), (iii) creating added value for wood flows based on a lignin-first process opening up opportunities for using the new derived molecules in the agro-industry and (iv) evaluating the sustainability of the lignin-first derived products including optimising locations of the biorefineries using the MooV DSS.
Estimating the current and future wood supply using the sim4tree DSS.
A first DSS-related task of the project is to accurately predict the woody biomass supply until 2050. To correctly estimate the current and future expected quantities of forest production in Flanders (Belgium), upscaling in space and time of the present forest resource and growth data is necessary. This will be done using the sustainable forest management DSS, Sim4Tree. This tool developed at KU Leuven, uses a combination of empirical yield tables different climate scenarios, and soil information as input data, Iterative Ideal Point Thresholding (IIPT) as the optimization algorithm, and an intuitive global GUI as a user interface, to guide forest managers to the optimal sustainable option to attain their tactical and strategic forest management goals. The software estimates the future age and species composition of the forest as well as the delivered ecosystem services in terms of wood production and nature conservation value of forest stands. Practically the DSS works in a similar way as the EFISCEN model but offers the significant advantage of having a higher spatial resolution of 1 hectare or 0.1 hectare depending the provision of additional input data (1 hectare for the whole of Flanders and 0.1 hectare for individual forest complexes if additional information is provided). An important drawback of the Sim4Tree model is that it is based on empirical growth models, making its applicability under climate change questionable. Consequently, a large part of this research will be dedicated to the incorporation of mechanistic or hybrid models that enable more precise estimations of forest growth and other ecosystem services under changing environmental conditions. The DSS will be designed in such a way that it allows for easy plug-in and plug-out options of different models (e.g. forest growth models, soil models, climate models, policy restrictions), making the core of the tool possibly more accessible to other parts of the world.
Optimising the bio-refinery value-chains with MooV DSS.
A second DSS-related task of this research is the coupling of the DSS MooV to Sim4Tree. MooV uses mixed integer linear programming to perform an integral value chain assessment at macroscale, taking into account the geographical fragmentation and temporal variations of the biomass supply. The major advantage of MooV compared to other value chain DSS is that it (i) allows for integral value chain assessment, (ii) includes spatial and temporal characteristics, both can be decisive for the long-term feasibility of wood-consuming bio-factories, and (iii) enables a straightforward representation of all kinds of resource-based value chains. Linking this value chain optimisation tool to the Sim4Tree DSS allows steering future wood supplies towards the most optimal applications, taking into account economic, environmental and energetic criteria.
Ensuring a practical implementation of the DSS
In the last DSS-related task of the project, several sessions will be organised with different stakeholders of the forestry and wood-based economy sector to illustrate the approach, functioning and advantages of the newly developed DSS. This will ensure smooth practical implementation of the DSS, in collaboration with the Flemish Agency of Nature and Forests (ANB), and guide stakeholders towards a blooming bio-economy while maintaining sustainable forestry practices.
The European Commission EIP-AGRI Focus Group ‘New forest practices and tools for adaptation and mitigation of climate change’ (Mini-paper #7 working group) is investigating the use of Decision Support Systems (DSS) and Tools which can be used by Forest Managers, practitioners and experts, to make informed decisions about climate change adaptation and mitigation.
The ForChange group works in the areas of forest resources inventory and modeling. As a result of several national and international research projects undergoing in these areas, the group has been developing several tools that aim to support forest management. Considerable efforts have been put into the development of several growth and yield models that have been integrated in stand and regional forest level simulators.
Being part of Centro de Estudos Florestais, the work developed has been mainly disseminated at academic level at Instituto Superior de Agronomia being sometimes difficult to extend its dissemination, which not only compromises the knowledge transfer to the end user, but also narrows the cooperation opportunities that could bring more added value to the technical and scientific development of forest management.
As a research group, ForChange is responsible for the constant development of new tools combined with the continuous improvement of the existing ones.
Forchange Tools website was created with the purpose of facilitating the access of all users to the existing tools allowing downloading its latest versions (registration required). In FcTools the user will find the descriptions of the models and simulators developed for the main tree species in Portugal.
There are several tools listed on the website so far:
Portuguese Cimate Normals 1960-1990 is an application that, by suppliyng a list of "ID, X , Y", detects the nearest weather station and calculates statistics of climate variables (sum, average, variance) for months chosen by the user.
Climate Picker is a tool that allows to obtain climatic variables (HadRM3Q0 ENSEMBLES) by selecting a location of interest on a map (Google maps), where the interface will search for the nearest climatic station .
WebCorky is a tool that allows the estimation of cork caliber evolution from different cork samples.
To obtain other tools user have to register first and access the Downloads menu.
Jason Papathanasiou, Nikolaos Ploskas and Isabelle Linden have recently published a new book on case studies of real world DSS applications. The book highlights best practices in each stage of a decision support system’s life cycle, from the initial requirements analysis and design phases to the final stages of the project. The authors presents both successful and unsuccessful decision support systems so that failures can be avoided and success repeated. All DSS are described in a constructive, coherent and deductive manner.
The book presents real-world decision support systems, that have been running for some time and as such have been tested in real environments and complex situations; the cases are from various application domains and highlight the best practices in each stage of the system’s life cycle, from the initial requirements analysis and design phases to the final stages of the project.
Each chapter provides decision-makers with recommendations and insights into lessons learned so that failures can be avoided and successes repeated. For this reason unsuccessful cases, which at some point of their life cycle were deemed as failures for one reason or another, are also included. All decision support systems are presented in a constructive, coherent and deductive manner to enhance the learning effect. It complements the many works that focus on theoretical aspects or individual module design and development by offering ‘good’ and ‘bad’ practices when developing and using decision support systems. Combining high-quality research with real-world implementations, it is of interest to researchers and professionals in industry alike.
Content of the book:
Computerized Decision Support Case Study Research: Concepts and Suggestions
ArgMed: A Support System for Medical Decision Making Based on the Analysis of Clinical Discussions
The Integration of Decision Analysis Techniques in High-Throughput Clinical Analyzer
Decision Support Systems for Energy Production Optimization and Network Design in District Heating Applications
Birth and Evolution of a Decision Support System in the Textile Manufacturing Field
A Decision Analytical Perspective on Public Procurement Processes
Evaluation Support System for Open Challenges on Earth Observation Topics
An Optimization Based Decision Support System for Strategic Planning in Process Industries: The Case of a Pharmaceutical Company
Decision Support in Water Resources Planning and Management: The Nile Basin Decision Support System
The AFM-ToolBox to Support Adaptive Forest Management Under Climate Change
SINGRAR—A Distributed Expert System for Emergency Management: Context and Design
SINGRAR—A Distributed Expert System for Emergency Management: Implementation and Validation
Crop Protection Online—Weeds: A Case Study for Agricultural Decision Support System
Some information about the editors:
Jason Papathanasiou is an Assistant Professor at the Department of Business Administration, University of Macedonia, Greece. His PhD was in Operational Research and Informatics and he has worked for a number of years at various institutes. He has organized and participated in many international scientific conferences and workshops. He has published more than 100 papers in international peer referred journals, conferences and edited volumes and has participated in various research projects in FP6, FP7, Interreg and COST; he served also as a member of the TDP Panel of COST and currently serves at the coordination board of the EURO Working Group of Decision Support Systems. His research interests include Decision Support Systems, Operational Research and Multicriteria Decision Making.
Nikolaos Ploskas is a Postdoctoral Researcher at the Department of Chemical Engineering, Carnegie Mellon University, USA. His primary research interests are in operations research, decision support systems, mathematical programming, linear programming, and parallel programming. He has participated in several international and national research projects. He is author of more than 40 publications in high-impact journals, book chapters and conferences. He has also served as reviewer in many scientific journals. He was awarded with an honorary award from HELORS (HELlenic Operations Research Society) for the best doctoral dissertation in operations research (2014).
Isabelle Linden is a Professor of Information Management at the University of Namur in Belgium, Department of Business Administration. She obtained her PhD in Computer Sciences from the University of Namur. She also holds Masters degrees in Philosophy and in Mathematics from the University of Liege, Belgium. She is member of the CoordiNam Laboratory and the FoCuS Research Group. Combining theoretical computer science and business administration, her main research domain regards information, knowledge and artificial intelligence. She explores their integration within systems as EIS, DSS and BI systems. Her works can be found in several international edited books, journals, books chapters and conferences. She serves as reviewer and program committee member in several international journals, conferences and workshops.
The Heureka system is a series of software tools developed at the Swedish University of Agricultural Sciences (SLU) that allows the user to perform a large amount of different analysis and to create forest management plans (see http://www.forestdss.org/wiki/index.php?title=Heureka and http://www.slu.se/heureka) . The system can perform short and long term projections of timber, economy, environmental conservation, recreation and carbon sequestration. It is used by all larger forest companies in Sweden (~40 % of productive forest land) and by many mid and small sized forest owners. A few news flashes:
It is now possible to add your own growth and yield and other models together with the Heureka system. You write your own ddl’s and the Heureka system will pick them up at program start.
Heurekas is now using DotSpatial's GIS-library (http://dotspatial.codeplex.com) for spatial computations and map rendering (already in version 2.4; version 2.5 now out).
The number of scientific articles based on Heureka analyses has now reached 30. The first article was written in 2009 and 30 articles reached at the end of 2015. They are written by a number of research groups, including outside SLU.
A larger work with forest scenarios covering the whole of Sweden and with a horizon of 100 years, SKA 15, has been completed with the Heureka system and incorporated into the Swedish National Forest Program process.
The system covers the entire analytical chain, from forest inventory plots, through predictive modeling and optimization, to tools to rank alternatives. The system consists of the following applications:
StandWise: For stand-level analysis, with 2D and 3D visualization. StandWise is an interactive simulator where the user specifies specifies treatmens period by period.
PlanWise: For forest-level and landscape planning. Enables the exploration on many different management options and has an inbuilt optimizer for solving problems. Includes a report builder for maps, tables and charts.
PlanEval: An application to compare and rank the plans created in PlanWise.
RegWise: The impact on the regional level. Hugin's successor.
PlanStart: Tool for importing forest data, and prepare for field inventories.
Ivent: Field computer application to inventory plots. Communicates with computer caliper, altimeter, GPS, and PosTex, a new instrument to determine tree coordinates.
More information can be found in the HEUREKA WIKI as well.
The Special Issue provides an overview on Forest Management Decision Support Systems currently designed and applied for the sustained provision of ecosystem services within the context of climate change based on the presentations given at the 24th World Congress of the International Union of Forest Research Organizations (IUFRO). The contributions demonstrate an overview on models, methods, techniques used in decision support and the proposed frameworks to support decision making. In this Special Issue, four different spatial decision support approaches allow reviewing the state of art in considering spatial and temporal dimensions in forest management planning.
Firstly Reynolds et al. (2014) discuss, in their review, the design features behind the success of the Ecosystem Management Decision Support System (EMDS) and propose the needs for the next generation of spatial decision support for adaptive management under climate change. Generality, transparency, simplification, abstraction and complexity, as well as the consideration of the spatial scale have been identified as critical for the success of the EMDS system, and deserve careful consideration in the design of decision-support systems for natural resource management. Following this recommendations, Frank et al. (2015) demonstrate the platform GISCAME to model spatio-temporal dynamics of biomass production at a regional scale in order to identify land use strategies that enhance biomass provision and avoid trade-offs for other ecosystem services. The results showed that forest conversion towards climate-change-adapted forest types had positive effects on ecological integrity and landscape aesthetics. Marušák et al. (2015) introduce the GIS tool Optimal for supporting spatial and temporal decisions in harvest scheduling. The DSS is designed and applied for silvicultural systems comprising clear-cuts and shelterwood systems with respect to the environmental and economic constraints. Forest managers are enabled to create and check spatial limits of harvest units and create various scenarios with Optimal. Daleman et al. (2015) introduce the concept, characteristics, functionalities, components and use of the Sim4Tree DSS, which supports strategic and tactical forestry planning by providing simulations of forest development, ecosystem services potential and economic performance through time, from a regional to a stand scale, under various management and climate regimes. Finally, Biber et al. (2015) compiled scenario runs from regionally tailored forest growth models and DSS from 20 case studies throughout Europe and analyzed whether the ecosystem service provision depends on management intensity and other co-variables, comprising regional affiliation, social environment, and tree species composition. They found a strong positive correlation between management intensity and wood production, but only weak correlation with protective and socioeconomic forest functions.
In addition to the presentation of various DSS, several advances in decision support techniques were identified, ranging from multi-attribute to multi-objective decision making methods. Garcia-Gonzalo et al. (2015) demonstrate the integration of an Interactive Decision Maps (IDM) technique within a DSS for an interactive visualization of the Pareto frontier. The multi-objective forest planning scenario of 1 million ha of cork and holm oak forest ecosystems in Southern Portugal is facilitated by stakeholder participation. Kašpar et al. (2015) present an approach where multiple criteria programming and integer programming techniques are used to find an optimal program of forest harvesting with respect to both economic and environmental constraints. Aldea et al. (2014) propose a goal programming procedure for integrating ecosystem services into forest management including a deterministic and a Monte Carlo modeling approach. Similar to other authors they highlight the variability of the results depending on the methodological approach taken and propose to critically evaluate the methodological approach taken. In this context also Estrella et al. (2014) introduced three different ideal point-based multi-criteria decision methods, i.e., iterative ideal point thresholding (IIPT), compromise programming (CP) and balanced compromise programming (BCP) to find an optimal distribution of land use types for regional land performance. For most ecosystem service analyzed, CP and BCP produced balanced results that were closer to the absolute optimal values when compared to IIPT. From the wide field of multi-criteria decision making techniques, Mendoza et al. (2015) compare different forest management schemes of Mexico in order to identify best management approaches considering multi-resources, environmental impacts and non-timber values. Considering the increased need for the involvement of preferences of decision makers, Rinaldi et al. (2015) provide an example of how forest owner specific characterization can be integrated in a DSS by linking a behavioral harvesting decision model to a forest resource dynamics model. They showed that the distribution of different owner types on forestland affects harvesting intensity and inter-temporal forest development. In the work of Nobre et al (2016) the material produced in the context of the COST Action FORSYS was used to generate quantitative summaries of (i) the types of problem where FMDSS are used, (ii) models and methods used to solve these problems, (iii) knowledge management techniques, and (iv) participatory planning techniques.
The Editorial of the Special Issue highlight that the development in decision support for forest management is set by decision theory, available technology, and methods. In addition to the technological and methodological advances, there is a strong need to include decision makers and DSS users in the design of the DSS from the very beginning in order to better meet their demands.
On behalf of the CoP on Forest Management Decision Support Systems (www.forestdss.org) we are pleased to invite you to contribute with your research to a special session of the ICDSST 2016 EWG-DSS 2016 International Conference on Decision Support System Technology ((https://icdsst2016.wordpress.com/) to be held in University of Plymouth, UK, during 23-25 May 2016.
Applications are invited for a doctoral fellowship (PhD) in the working group of Dr Verena Griess, Department of Forest Resources Management, Faculty of Forestry at the University of British Columbia in Vancouver, Canada.
We are looking for a high achieving student whose research will be guided by the question: “How to aid forest management decision makers in a field of increasing complexity?”
Applications are invited for a doctoral fellowship (PhD) in the working group of Dr Verena Griess, Department of Forest Resources Management, Faculty of Forestry at the University of British Columbia in Vancouver, Canada.
We are looking for a high achieving student whose research will be guided by the question: “How to aid forest management decision makers in a field of increasing complexity?”
