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Decision Support Systems in Forest Management
Abstract
Numerous decision support systems have been developed for forest management over the past 20 years or more. In this chapter, the authors briefly review some of the more important and recent developments, including examples from North America, Europe, and Asia. In addition to specific systems, we also review some of the more-significant methodological approaches such as artificial neural networks, knowledge-based systems, and multicriteria decision models. A basic conclusion that emerges from this review
is that the availability of DSSs in forest management has enabled more-effective analysis of the options for and implications of alternative management approaches for all components of forest ecosystems. The variety of tools described herein, and the approaches taken by the different systems, provide a sample of the possible methods that can be used to help stakeholders and decision makers arrive at reasoned and reasonable decisions.
... The peculiarities of forestry scenario modelling systems are related to the peculiarities of country forests, forestry, and forest inventories. There are many detailed references summarizing the methods used for forestry scenario modelling and their implementation in forestry decision support systems [6][7][8][9][10][11][12]. There are also guidelines for standardized evaluation and documentation of forestry decision support tools proposed [1], which are usually followed in reporting on the solutions used. ...
... But would those models be closer to real modelling? If so, it would be really excellent" (8); "First, it would make things easier, but it would also add quality or objectivity. Now, although we consider the alternatives when making decisions, we propose to the extent [allowed by] the available information, subjective understanding. ...
... It would certainly help to optimise land uses." (8); "There was a project where we deciphered land use from the time of the war, how forest coverage was changing. [Forest coverage] was changing more differently than we thought, the land was afforested in some places and deforested in another ones" (18). ...
This paper aims to demonstrate the use of qualitative research methods, specifically in-depth interviews, to explore the intangible and often difficult-to-quantify needs for forestry scenario modelling in Lithuania, which are frequently not adequately perceived. The study involved informants representing key actors in forest policy, forest management, research, and education. A total of 21 informants from 11 different institutions, which hold significant power and expertise in forest decision making, were interviewed. The purpose of these interviews was to gather their perspectives on the potential forest decision support system in the country, aiming to address most of their needs. The interview questions explored various aspects, including the requirements for forestry scenario modelling, the desired level of detail and information content for decision making, and both functional and nonfunctional requirements for the scenario modelling system. It is worth noting that the expected functionality of the planned forest DSSs aligns with modern international standards. Nevertheless, the diversity of perspectives, wishes, visions, and intentions of key Lithuanian forestry actors regarding the aims, objectives, and essential functionality of forestry scenario modelling tools were identified. The understanding of the requirements for modern forest DSSs was greatly influenced by the current forestry paradigms in the country and the professional experiences of individual informants. In conclusion, our findings demonstrate that the utilization of qualitative research, particularly through in-depth interviews, has proven to be a highly effective tool for accurately specifying the requirements of a modern forest DSS. It helped mitigate preconceived notions and address gaps in the envisioned product, specifically by developing a framework of core solutions for the national forestry and land-use scenario modelling system.
... Most of the existing DSSs are softwares designed to support decision-making within an organization and are generally based on (i) a graphical user interface (GUI), (ii) a knowledge management system that stores all the information, and (iii) a problem processing system (Burstein & Holsapple 2008). Forestry is a long-established area of DSS and GIS application, and an early adopter of Spatial DSS (SDSS) since 1980s (Reynolds et al. 2008, Keenan & Jankowski 2019. Some first FDSSs were mainly aimed at supporting the wood production, while subsequent and more detailed systems have been recently developed with the aim of helping multifunctional forest management, so that to consider all the other ecosystem services (Reynolds et al. 2008, Bagstad et al. 2013. ...
... Forestry is a long-established area of DSS and GIS application, and an early adopter of Spatial DSS (SDSS) since 1980s (Reynolds et al. 2008, Keenan & Jankowski 2019. Some first FDSSs were mainly aimed at supporting the wood production, while subsequent and more detailed systems have been recently developed with the aim of helping multifunctional forest management, so that to consider all the other ecosystem services (Reynolds et al. 2008, Bagstad et al. 2013. Most FDSSs consider specific operational aspects related to the management of existing forests or plantations, such as forest management planning (e.g., what, when and where to plant/harvest), estimation of potential tree growth, economic estimates of logged wood, optimization of harvest operations (Reynolds et al. 2008, Borges et al. 2014, Segura et al. 2014, Nobre et al. 2016) . ...
... Some first FDSSs were mainly aimed at supporting the wood production, while subsequent and more detailed systems have been recently developed with the aim of helping multifunctional forest management, so that to consider all the other ecosystem services (Reynolds et al. 2008, Bagstad et al. 2013. Most FDSSs consider specific operational aspects related to the management of existing forests or plantations, such as forest management planning (e.g., what, when and where to plant/harvest), estimation of potential tree growth, economic estimates of logged wood, optimization of harvest operations (Reynolds et al. 2008, Borges et al. 2014, Segura et al. 2014, Nobre et al. 2016) . Others have been implemented for specific purposes such as fire management (Sakellariou et al. 2017). ...
... The Multi-criteria decision-making (MCDM) technique in forest management (Ananda and Herath, 2009) refers to the implementation of decision-making techniques that consider multiple criteria related to forest resources. Forest management comprises many complicated and interconnected aspects, and MCDM offers a systematic decision-making framework for analyzing criteria to assess forest management policy alternatives (Kangas and Kangas, 2005;Reynolds et al., 2008). Traditional decision-making assumes precise Content courtesy of Springer Nature, terms of use apply. ...
This article aims to establish a sustainable, steady-state forest that can provide ongoing environmental and economic benefits. A sustainable forest management model (SFMM) must be established to achieve this objective, incorporating a systematic policy to deal with environmental factors, forest ecosystem dynamics, human factors, and several others. This paper develops SFMMs that integrate multiple objectives, such as maximizing wood production, maximizing landscape diversity, and transforming into regulated forests. Eight SFMMs are constructed focusing on four objectives and two types of period length. Four distinct edge classes are considered for each criterion: timber volume during the conversion period, wood output during the final phase, and harvesting cost. The cost of re-planting is also considered a crucial criterion for model construction. The qualitative nature of the SFMMs’ criteria is quantified using triangular Pythagorean fuzzy numbers. We propose a Pythagorean extension of the Method Based on the Removal Effects of criterion (MEREC) to find the fuzzy weights of the criterion to overcome the decision-maker’s lack of knowledge in calculating the criteria weight. Along with these fuzzy weights, we developed Pythagorean measurement alternatives and ranking according to compromise solution (MARCOS) approach to solve uncertain MCDM issues. The integrated Pythagorean MEREC–MARCOS technique is then used to rank the SFMMs. The proposed approach is compared to existing crisp methodologies to demonstrate merit and consistency. The sensitivity analysis involves using rank-sum and rank-exponent techniques to ascertain the criteria weights, hence establishing the ranking of the SFMMs. Also, several closeness parameters are used to analyze the behaviour of the suggested approach in order to assist the decision-maker. The first model, which has a ten-year periodic length, achieves the highest level of acceptance. The objective of “maximize the total volume produced throughout the planning period” is essential in determining the ranking of SFMMs. The seventh model, with its six-year cycles and goal of maximizing landscape diversity, has the lowest level of acceptance. The objectives for constructing SFMMs are more crucial than the periodic length when determining ranking. This article is a first attempt at applying the Pythagorean MARCOS method to determine the best SFMM. Also, the fuzzy criteria weights are, for the first time, derived by the Pythagorean MEREC approach.
Graphical abstract
... Understanding the long-term forest dynamics focusing on the effects of different rotation lengths on the levels of ecosystem services with quantitative indicators and decision support systems (DSS) are indispensable to design and implement forest ecosystem management scenarios (von Gadow, 2004;Eriksson et al., 2014;Baskent 2020;Mozgeris et al., 2021;Roces-Díaz et al., 2021). In these regard, various types of DSS have been developed and used to explore the trade-offs between ecosystems services based on a number of management strategies (Reynolds et al., 2008;Nordström. et al., 2011;Pukkala. 2014;Vacik et al., 2015;Borges et al., 2017;Cristal et al., 2019;. ...
... For example, the community of practice ForestDSS 1 gathered information and experiences on forest DSS and on its Wiki page more than 80 systems are listed. While early DSS were designed to address relatively narrow, well-defined problems, more recent systems tend to be used for more general purposes and are multifunctional, allowing for the assessment of multiple forest ecosystem services (Reynolds et al., 2008). Today's DSS can therefore be used for diverse decision-making problems at different temporal and spatial scales (Nobre et al., 2016). ...
For several decades, computerized forest decision support systems (DSS) have helped managers and decision makers to analyze different management options and supported the search for preferred management alternatives. In Sweden, a country rich in forests and with a long tradition in intensive forest management, such systems have been developed and available since the 1970s. Changes in societal as well as in forest owners’ preferences and objectives in the 1990s led to a need for forest DSS handling broader perspectives compared to precedent single-objective timber-oriented systems. In Sweden, this led to the initiation of a research programme in the beginning of the 2000s aiming at developing a versatile and multi-objective forest DSS, resulting in the first version of the Heureka forest DSS released in 2009. The system handles several forest values, such as timber and biofuel production, carbon sequestration, dead wood dynamics, habitat for species, recreation and susceptibility to forest damages (spruce bark beetle, wind-throw and root rot). It contains a suite of software for different problem settings and geographical scales and uses simulation as well as optimization techniques. Three software handle projections of the forest using a common core of growth and yield models for simulating forest dynamics. A fourth software, built for multi-criteria decision analysis and including a web-version, enables also group decision making and participatory planning. For more than 10 years, the Heureka system has been used in teaching, environmental analysis, research and as decision support in practical forestry. For example, several research groups using the system for analyses in different problem areas have so far published more than 80 scientific papers. The system is used for nation-wide forest impact analysis for policy support and all large and many medium-sized forest owners use it for their long-term forest planning, meaning that it directly influences forest management decisions and activities on more than 50% of the Swedish forest area. Besides presenting the present system and its use, we also discuss lessons learned and potential future development.
... They are often integrated within web platforms or GIS (geographic information systems), facilitating a dialogue and the exchange of information, and thus providing insights to decision-makers, which can support them in exploring, for example, potential outcomes of different policy options. Numerous DSS have been developed for forest management over the past 40 years [7,8]. More recently, DSS have also been introduced in natural hazard risk management with the goal to communicate hazard and risk modeling results to the public, supported by improved visuals and graphical user interfaces (GUI) [6]. ...
Protective forests are an effective Forest-based Solution (FbS) for Ecosystem-based Disaster Risk Reduction (Eco-DRR) and are part of an integrated risk management (IRM) of natural hazards. However, their utilization requires addressing conflicting interests as well as considering relevant spatial and temporal scales. Decision Support Systems (DSS) can improve the quality of such complex decision-making processes regarding the most suitable and accepted combinations of risk mitigation measures. We introduce four easy-to-apply DSS to foster an ecosystem-based and integrated management of natural hazard risks as well as to increase the acceptance of protective forests as FbS for Eco-DRR: (1) the Flow-Py simulation tool for gravitational mass flows that can be used to model forests with protective functions and to estimate their potential for reducing natural hazards' energy, (2) an exposure assessment model chain for quantifying forests' relevance for reducing natural hazard risks, (3) the Rapid Risk management Appraisal (RRA), a participatory method aiming to identify IRM strengths and points for improvement, and (4) the Protective Forest Assessment Tool (FAT), an online DSS for comparing different mitigation measures. These are only a few examples covering various aims and spatial and temporal scales. Science and practice need to collaborate to provide applied DSS for an IRM of natural hazards.
This chapter discusses digital mechanisms for optimizing the management system in the forest industry, which includes organizational, legal, socio-economic, and environmental aspects. Efficient forest management is considered as an integral part of efficient nature management and includes the use of forest resources, their protection, and reproduction of forests. Digital management mechanisms in forest management in general and in the forest industry in particular are based on platform solutions. Platform solutions are based on the formation and processing of data on the basis of a single automated information system, which acts as the foundation for the development of digitalization in forestry. Such a digital platform is designed to provide informational, analytical, consulting, and other support to the activities of all subjects of relations in the field of use, conservation, protection, and reproduction of forest resources.
Scenario tools are widely used to support policymaking and strategic planning. Loss of biodiversity, climate change, and increase in biomass demand ways to project future forest resources considering e.g. various protection schemes, alterations to forest management, and potential threats like pests, wind, and drought. The European Forestry Dynamics Model (EFDM) is an area-based matrix model that can combine all these aspects in a scenario, simulating large-scale impacts. The inputs to the EFDM are the initial forest state and models for management activities such as thinning, felling or other silvicultural treatments. The results can be converted into user-defined outputs like wood volumes, the extent of old forests, dead wood, carbon, or harvest income. We present here a new implementation of the EFDM as an open-source R package. This new implementation enables the development of more complex scenarios than before, including transitions from even-aged forestry to continuous cover forestry, and changes in land use or tree species. Combined with a faster execution speed, the EFDM can now be used as a building block in optimization systems. The new user interface makes the EFDM more approachable and usable, and it can be combined with other models to study the impact of climate change, for example.
Understanding forest dynamics under varying management intensification is a crucial step for designing and implementing sustainable forest management scenarios. One way to assess the sustainability is to evaluate the long-term supply of ecosystem services (ES) with some performance indicators. This research focuses on exploring the effects of management intensification on several ESs such as habitat for biodiversity conservation, wood production, carbon stock, cultural values, water provision and soil protection. Forest development was simulated over time with the ETCAP forest management decision support system (DSS) to investigate the effects of intensified forest management activities, representing different treatment rates, rotation periods and afforestation levels, on the selected ecosystem services. Hamidiye forest planning unit was used as a case study area with 19,009 ha forests in southeastern Turkey.
The management scenarios with intensified forest interventions such as high rate of thinning and afforestation areas with medium rotation ages led to increased harvest level, carbon storage, soil protection, deadwood and forest area, and reductions in largest stand volume, understory, basal area, ground water and cultural values. The same intensified scenarios with short rotation ages, however, resulted in again higher harvest levels, yet a more regulated forest structure due mainly to the increasing afforestation areas and productivity. Extension of rotation periods, however, appear to have marginal impact on carbon storage, positive effect on soil protection and significant effect on harvest level. Scenarios with low intensified interventions only resulted in high values of biodiversity conservation and cultural values. Intensive treatments and larger afforestation areas had significant impact on the overall results. Overall, the analysis of the modeling approach with varying management scenarios led to better and wider understanding of forest development over time by allowing the assessment of the impacts of management interventions on the sustainable supply of the ecosystem services that highly depend on the afforestation level, thinning rate and rotation period.
This chapter discusses digital mechanisms for optimizing the management system in the forest industry, which includes organizational, legal, socio-economic, and environmental aspects. Efficient forest management is considered as an integral part of efficient nature management and includes the use of forest resources, their protection, and reproduction of forests. Digital management mechanisms in forest management in general and in the forest industry in particular are based on platform solutions. Platform solutions are based on the formation and processing of data on the basis of a single automated information system, which acts as the foundation for the development of digitalization in forestry. Such a digital platform is designed to provide informational, analytical, consulting, and other support to the activities of all subjects of relations in the field of use, conservation, protection, and reproduction of forest resources.
Winds over topography and inside forests produce mechanical reactions in trees, and eventually failure in stems and roots when stressed by storms. The mechanics of these reactions and the physiological responses to wind in leaves, stems and root systems, and the important ecological consequences of wind-throw are described in this book. Management techniques of forests in windy climates are detailed, including the use of models predicting risk of wind damage. It is clear that the whole field of wind effects on trees has benefited from recent multi-disciplinary research, and significant advances in knowledge of most parts of the subject have been made in the last decade. This book brings the up-to-date theories, methodologies and results together, and gives the reader a sense of coherence in this complex but fascinating field.
A red pine (Pinus resinosa) forest management advisory system (RP-FMAS) was developed to capture, organize, and disseminate the available expertise on managing red pine forests in the upper Great Lakes region. A unified theory of the red pine forest management consisting of 12 major software components and their interactions was developed. The model has not yet undergone silvicultural evaluation and must therefore be regarded as more of a research model than a management model at this time. A blackboard framework is used to provide strategic system control. -from Authors
The UK has larger energy resources than any other member of the European Union and is self-sufficient in energy. Coal and gas are currently the country's largest sources of energy supply while nuclear accounts for some 10%. However, with the privatization of the electricity supply industry, including some of the nuclear reactors, a new competitive climate has been introduced. The nuclear operators responded to the challenge and last year nuclear accounted for over 28% of electricity supplied.
The purpose of forest planning is to support forestry decision-making by suggesting management alternatives, providing information about their consequences, and helping the decision maker to rank the alternatives. In multi-objective forest planning, forest plans are evaluated using various multiple criteria decision support methods and multi-objective optimisation algorithms. Multiple criteria comparison methods help to systematise subjective evaluations whereas multi; objective optimisation seeks the best plan among a huge number of alternatives using automated computer-based search methods. The ranking of alternatives depends on the preferences of the decision maker, both in multiple criteria comparison and in multi-objective optimisation. A careful analysis of preferences is an important step of any multi-objective planning case. The quantitative approach to decision-making suggests that a specific planning model be developed for every planning situation. This model is then solved, the result being a candidate plan that must pass various post-optimisation tests and analyses. There are several ways to prepare a multi-objective planning model, based on linear programming, goal programming, penalty functions or multi-attribute utility theory. The planning model may be solved using mathematical programming techniques or various heuristics. The use of heuristic optimisation has gained popularity in forest planning along the increasing importance of ecological forest management goals, which are often described with spatial variables. Examples of heuristics available to multi-objective forest planning are random ascent heuristics, simulated annealing, tabu search and genetic algorithm. Practical forest plans are produced in a computerised system, which includes subsystems for data management, simulation of stand development, planning model generation and optimisation, and subjective evaluation of alternative plans.
The guide describes use of the NetWeaver knowledge base development system. Knowledge representation in NetWeaver is based on object-oriented fuzzy-logic networks that offer several significant advantages over the more traditional rule-based representation. Compared to rule-based knowledge bases, NetWeaver knowledge bases are easier to build, test, and maintain because the underlying object-based representation makes them modular. The modularity of NetWeaver knowledge bases, in turn, allows designers to gradually evolve complex knowledge bases from simpler ones in small, simple steps. Modularity also allows interactive knowledge base debugging at any and all stages of knowledge base development, which expedites the development process. Finally, fuzzy logic provides a formal and complete calculus for knowledge representation that is less arbitrary than the confidence factor approach used in rule-based systems and much more parsimonious than bivalent rules.
Perhaps at no other time has the dredging industry been as competitive as it is today. Some contractors, in a vain attempt to steal a march over their competitors, have invested in large capacity dredges - a move which is at variance with market conditions. Instead this action has only served to increase competition, forcing contractors to cut their margins to the bare bone. These contractors recklessly engaging in this price war are as never before exposed to the risks that are inherent in many of the world's dredging contracts. One company, Royal Boskalis Westminister nv of Papendrecht, the Netherlands, has however chosen to sharpen its competitive edge in other ways. Its participation together with Rotterdam's Erasmus University, in a research project - aimed at reducing its exposure to contract risk through improved tendering procedures - is one such move in the direction of increased competitiveness.
Forest ecosystems are subject to a variety of natural and anthropogenic disturbances that extract a penalty from human population values. Such value losses (undesirable effects) combined with their likelihoods of occurrence constitute risk. Assessment or prediction of risk for various events is an important aid to forest management. Artificial intelligence (AI) techniques have been applied to risk analysis owing to their ability to deal with uncertainty, vagueness, incomplete and inexact specifications, intuition, and qualitative information. This paper examines knowledge-based systems, fuzzy logic, artificial neural networks, and Bayesian belief networks and their application to risk analysis in the context of forested ecosystems. Disturbances covered are: fire, insects/diseases, meteorological, and anthropogenic. Insect/disease applications use knowledge-based system methods exclusively, whereas meteorological applications use only artificial neural networks. Bayesian belief network applications are almost nonexistent, even though they possess many theoretical and practical advantages. Embedded systems -that use AI alongside traditional methods-are, not unexpectedly, quite common.
