Analysis of the performance of different implementations of a heuristic method to optimize forest harvest scheduling

Research ArticleFinding an optimal solution of forest management scheduling problems with even flow constraints while addressing spatial concerns is not an easy task. Solving these combinatorial problems exactly with mixed-integer programming (MIP) methods may be infeasible or else involve excessive computational costs. This has prompted the use of heuristics. In this paper we analyze the performance of different implementations of the Simulated Annealing (SA) heuristic algorithm for solving three typical harvest scheduling problems. Typically SA consists of searching a better solution by changing one decision choice in each iteration. In forest planning this means that one treatment schedule in a single stand is changed in each iteration (i.e. one-opt move). We present a comparison of the performance of the typical implementation of SA with the implementation where up to three decision choices are changed simultaneously in each iteration (i.e. treatment schedules are changed in more than one stand). This may allow avoiding local optimal. In addition, the impact of SA – parameters (i.e. cooling schedule and initial temperature) are tested. We compare our heuristic results with a MIP formulation. The study case is tested in a real forest with 1000 stands and a total of 213116 decision choices. The study shows that when the combinatorial problem is very large, changing simultaneously the treatment schedule in more than one stand does not improve the performance of SA. Contrarily, if we reduce the size of the problem (i.e. reduce considerably the number of alternatives per stand) the two-opt moves approach performs betterinfo:eu-repo/semantics/publishedVersio

A progressive hedging approach to solve harvest scheduling problem under climate change

Due to the long time horizon typically characterizing forest planning, uncertainty plays an important role when developing forest management plans. Especially important is the uncertainty related to recently human-induced global warming since it has a clear impact on forest capacity to contribute to biogenic and anthropogenic ecosystem services. If the forest manager ignores uncertainty, the resulting forest management plan may be sub-optimal, in the best case. This paper presents a methodology to incorporate uncertainty due to climate change into forest management planning. Specifically, this paper addresses the problem of harvest planning, i.e., defining which stands are to be cut in each planning period in order to maximize expected net revenues, considering several climate change scenarios. This study develops a solution approach for a planning problem for a eucalyptus forest with 1000 stands located in central Portugal where expected future conditions are anticipated by considering a set of climate scenarios. The model including all the constraints that link all the scenarios and spatial adjacency constraints leads to a very large problem that can only be solved by decomposing it into scenarios. For this purpose, we solve the problem using Progressive Hedging (PH) algorithm, which decomposes the problem into scenario sub-problems easier to solve. To analyze the performance of PH versus the use of the extensive form (EF), we solve several instances of the original problem using both approaches. Results show that PH outperforms the EF in both solving time and final optimality gap. In addition, the use of PH allows to solve the most di cult problems while the commercial solvers are not able to solve the EF. The approach presented allows the planner to develop more robust management plans that incorporate the uncertainty due to climate change in their plansinfo:eu-repo/semantics/publishedVersio

Decision support approaches in adaptive forest management

Climate and social changes place strong demands on forest managers. Forest managers need powerful approaches and tools, which could help them to be able to react to the rapidly changing conditions. However, the complexity of quantifying forest ecosystems services as well as the complexity of current decision theories, technologies and operation research methods, complicate the creation of one general tool. The continuous research and development in this area is an indispensable part of the success of adaptive management as well as the sharing of knowledge and information between research teams around the world. The Community of Practice of Forest Management Decision Support Systems provides a platform for broad discussion among scientists, researchers as well as forest professionals. This special issue provides papers which resulted from a conference session of the International Union of Forest Research Organizations’ (IUFRO) 125th Anniversary Congress in Freiburg, Germany in 2017. The joint sessions and other meetings (and resulting publications) are appropriate opportunities for knowledge sharing on these important methods and systems for protecting and managing forest ecosystems in the future.This special issue was supported by the project “Advanced research supporting the forestry and wood-processing sector’s adaptation to global change and the 4th industrial revolution”, reg. No. CZ.02.1.01/0.0/0.0/16_019/0000803

A stochastic dynamic programming approach to optimize short-rotation coppice systems management scheduling: An application to eucalypt plantations under wildfire risk in Portugal

This article presents and discusses research with the aim of developing a stand-level management scheduling model for short-rotation coppice systems that may take into account the risk of wildfire. The use of the coppice regeneration method requires the definition of both the optimal harvest age in each cycle and the optimal number of coppice cycles within a full rotation. The scheduling of other forest operations such as stool thinning and fuel treatments (e.g., shrub removals) must be further addressed. In this article, a stochastic dynamic programming approach is developed to determine the policy (e.g., fuel treatment, stool thinning, coppice cycles, and rotation length) that maximizes expected net revenues. Stochastic dynamic programming stages are defined by the number of harvests, and state variables correspond to the number of years since the stand was planted. Wildfire occurrence and damage probabilities are introduced in the model to analyze the impact of the wildfire risk on the optimal stand management schedule policy. For that purpose, alternative wildfire occurrence and postfire mortality scenarios were considered at each stage. A typical Eucalyptus globulus Labill. stand in Central Portugal was used as a test case. Results suggest that the proposed approach may help integrate wildfire risk in short-rotation coppice systems management scheduling. They confirm that the maximum expected discounted revenue decreases with and is very sensitive to the discount rate and further suggest that the number of cycles within a full rotation is not sensitive to wildfire risk. Nevertheless, the expected rotation length decreases when wildfire risk is consideredinfo:eu-repo/semantics/publishedVersio

Addressing wildfire risk in a landscape-level scheduling model: an application in Portugal

Fundamental Research - Forest ManagementThe paper presents and discusses research aiming at the development of a forested landscape management scheduling model that may address the risk of wildfires. A general indicator is built from wildfire occurrence and damage probabilities to assess stand-level resistance to wildfires. This indicator is developed to further address the specificity of each stand configuration (e.g., shape and size) and spatial context (neighboring stands characteristics). The usefulness of the development of such an indicator is tested within a mixed integer programming (MIP) approach to find the location and timing of management options (e.g., fuel treatment, thinning, clearcut) that may maximize the forested landscape expected net revenues. The Leiria National Forest, a Portuguese forest in central Portugal, was used as a case study. Results suggest that the proposed approach may help integrate wildfire risk in forested landscape management planning and assess its impact on the optimal plan. Results further show that prescriptions that include fuel treatments are often chosen over others that do not include them, thus highlighting the importance of wildfire management efforts. Finally, they provide interesting insights about the role of thinnings and fuel treatment in mitigating wildfire riskinfo:eu-repo/semantics/publishedVersio

A mixed integer programming approach for multi-action planning for threat management

Planning for management actions that address threats to biodiversity is important for securing its long term persistence. However, systematic conservation planning (SCP) has traditionally overlooked this aspect and just focused on identiying priority areas without any recommendation on actions needed. This paper develops a mixed integer mathematical programming (MIP) approach for the multi-action management planning problem (MAMP), where the goal is to find an optimal combination of management actions that abate threats, in an efficent way while accounting for connectiivty. An extended version of the MAMP model (MAMP-E) is also proposed that adds an expression for minimizing fragmentation between different actions. To evaluate the efficiency of the two models, they were applied to a case study corresponding to a large area of the Mitchell River in Northern Asutralia, where 45 species of freshwater fish are exposed to the presence of four threats. The evaluation compares our exact MIP approach with the conservation planning software Marxan and the heuristic approach developed in Cattarino et al. (2015). The results obtained show that our MIP models have three advantages over their heuristic counterparts: shorter execution times, higher solutions quality, and a solution quality guarantee. Hence, the proposed MIP methodology provides a more effective framework for addressing the multi-action conservation problem.Peer ReviewedPostprint (published version

A model shrub biomass accumulation as a tool to support management of portuguese forests

Research ArticleAssessment of forest fuel loading is a prerequisite for most fire management activities. However, the inclusion of shrub biomass in forest planning has been hindered by the inability to predict its growth and accumulation. The main objective of this study was to model shrub biomass over time under a tree canopy with the aim of including shrub management in fire risk mitigation plans. To this purpose, data was obtained from the 4th and 5th Portuguese National Forest Inventories. Five biologically realistic models were built to describe shrub biomass accumulation in Portuguese forests. The selected model indicates that maximum biomass is affected by stand basal area and the percentage of resprouting shrub species in the stand. Biomass growth rate was clearly affected by the regeneration strategies of shrubs in combination with climatic conditions (mean annual temperature). The model can be used in the accumulation form for initialization purposes or in one of the two alternative difference forms to project observed shrub biomass. The model proposed in this study facilitates the inclusion of shrub biomass in forest growth simulations, and will contribute to more accurate estimates of fire behavior characteristics and stored carbon. This is essential to improve decision-making in forest management plans that integrate fire risk, namely to schedule understory fuel treatmentsinfo:eu-repo/semantics/publishedVersio

Case Study area: Leiria National Forest, Portugal

info:eu-repo/semantics/draf

A decision support system for assessing trade-offs between ecosystem management goals: an application in Portugal

Cork oak (Quercus suber L.) and holm oak (Quercus rotundifolia) ecosystems are characteristic of Mediterranean forestry in Portugal. Even though cork is the most valuable product, these ecosystems provide multiple products and services. Assessing trade-offs between multiple goals is thus critical for the effectiveness of oak ecosystem management planning. This paper focuses on the development of a decision support system for oak ecosystems’ scenario analysis including multiple criteria. It includes an innovative decision support systems (DSS) functionality to assess trade-offs between the criteria that may support negotiation and consensus building between decision-makers and forest stakeholders. Specifically, a module that encapsulates the Feasible Goals Method/Interactive Decision Maps (FGM/IDM) technique is developed for interactive visualization of the Pareto frontier. The Pareto frontier illustrates the degree to which improving one particular criterion requires accepting sacrifices in the achievements of others. It thus provides information about trade-offs between competing decision-makers’ preferences. Results are discussed for a large-scale application encompassing over 1 million ha of cork and holm oak forest ecosystems in Southern Portugal. This study demonstrates the potential of the new DSS functionality to enhance multi-objective forest planning, namely by facilitating participation by stakeholders and providing transparency to the decision-making processesinfo:eu-repo/semantics/publishedVersio