Balancing multiple forest management objectives requires integrating ecological, economic, and social priorities among diverse stakeholders with conflicting interests. Despite advances in optimization and participatory approaches, limited attention has been given to combining stakeholder-informed multi-criteria decision analysis with linear programming (LP) optimization to evaluate landscape-level forest management scenarios. To address this gap, this study aims to evaluate and rank landscape-level management planning scenarios in Vale do Sousa, a region in northwestern Portugal. The evaluation is based on stakeholders’ preferences using a hybrid decision-support framework that combines optimization and participatory approaches. Five management scenarios were developed using LP, each maximizing or minimizing a single ecosystem service. Stakeholder preferences were elicited through an Analytic Hierarchy Process (AHP) survey, in which 25 participants weighed stand-level forest management models and associated ecosystem services. These weights were incorporated into a Multi-Criteria Decision Analysis (MCDA) implemented in Criterium Decision Plus (CDP). The results show that stakeholders’ preferences strongly influence the ranking of landscape-level scenarios. The scenario maximizing timber production ranked highest under stakeholder-weight evaluations, whereas maximizing wildfire resistance emerged as the top-ranked under equal weighting conditions. These findings demonstrate the value of integrating stakeholder-informed preferences with optimization-based scenario evaluation. This study is among the first to integrate AHP-based stakeholder preferences with LP optimization to rank landscape scenarios.

Greece is facing a wildfire crisis that parallels many other countries in fire-prone regions around the globe. Recent wildfire data for Greece point to an alarming trend of increasing fire size and severity catalyzed by climate change, lack of forest and fuel management, urban expansion into wildlands around major population centers, and rural exodus from areas that traditionally supported fire-resilient land uses. Fire management in Greece has long emphasized suppression with relatively little attention to prevention and coordination. In this paper, we identify key factors that are slowing progress towards a solution to the Greek wildfire crisis, including the current legislative framework around wildfire management that has contributed to conflicts and inefficiency. We then discuss specific policies to rebalance the current suppression emphasis by integrating new prevention strategies aiming to create fire-resilient landscapes and reduce wildfire impacts, widely adopt the use of technology, and enhance stakeholder cooperation for more efficient fire suppression. We also highlight how optimizing landscape scale management of fuels is contributing solutions to the wildfire crisis, specifically from the EU-funded FIRE-RES project.

Acorn (Quercus spp.) and pine nut (Pinus pinea L.) value chains in Portugal reflect contrasting development paths of non-timber forest products (NTFPs). Using a mixed-methods approach, combining stakeholder surveys, participatory workshops, and literature review, our study analyzes their structure, governance, and challenges. We map the network of actors and activities, highlighting key bottlenecks in both value chains. We found that the pine nut chain is industrialized and commercially consolidated but faces persistent issues such as yield variability, price volatility, and limited innovation. In contrast, the acorn chain remains largely artisanal, constrained by underdeveloped processing infrastructure and scalability barriers, but driven by cultural values and grassroots innovation. In terms of governance models, pine nuts operate within top-down, actor-driven systems, while acorns are shaped by bottom-up, community-led initiatives embedded in territorial identity. Our findings highlight that these NTFP value chains function within broader socio-ecological systems, shaped by land-use histories and cultural contexts. We suggest that for unlocking their full potential it is required adaptive governance, cross-sector collaboration, and investment in innovation. Supporting such integrated value chains could enhance rural livelihoods and a transition to a bio-based economy.

With the increasing number of wildfire events, people living close to the wildland–urban interface (WUI) are more likely to be exposed to these events. To mitigate the hazards related to wildfires, it is of great importance to identify areas where human settlements are at a greater risk. Remote sensing-based techniques for mapping and quantifying the inhabitants possibly affected by these events are crucial to reduce the loss of life as well as reduce the negative impact that wildfires pose to the people living in WUIs, the surrounding areas, and the environment. Fine-scale mapping is a suitable auxiliary tool to indicate areas at greater risk. Hence, the dasymetric method was applied to generate a high-resolution map of the study area’s population, using products generated from Sentinel-2 imagery, a census, and Light Detection and Ranging (LiDAR) data. The findings of the proposed methodology show that around 59% of the population in the study area currently lives inside the WUI, while in 2025, most of the people affected by wildfires—77%—lived outside the WUI. This is expected, since wildfires vary in space and time, and they are seen as spatial–temporal processes. In addition, the results demonstrated that women are slightly more exposed to wildfires than other population groups. These results showed that the proposed methodology could not only help identify high-risk areas but also the number of people living in these areas due to the high-resolution dasymetric methodology. The proposed methodology described in this work shows that fine-scale mapping could enrich forest management in order to protect the populations susceptible to the negative impacts of wildfires, consequently protecting the environment.

Long-term planning is a crucial tool for addressing forest harvest scheduling problems, particularly when dealing with complex future scenarios. When the planning horizon extends beyond the rotation period, stands can be harvested multiple times. The complexity of models and solution techniques for harvest scheduling problems, particularly those with constraints on clearcut areas (referred to as area-restricted model, ARM), increases significantly in a multiple-harvest context. This study aims to solve the ARM for a highly combinatorial case study where a 100-year planning horizon allows, on average, six harvests per stand. The forest, located in northern Portugal, consists of 1926 stands spread across two non-contiguous regions. Existing studies on multiple harvests often either fail to assess the quality of the solutions or are generally unsuitable for the case study. We propose a branch-and-cut-based heuristic, that incorporates a variable fixing process, to provide a measure of solution's quality. While this method cannot solve the problem for the entire forest, it yields good solutions for the two regions.

Cultural ecosystem services (CES), which encompass recreational and aesthetic values, contribute to human well-being and yet are often underrepresented in forest management planning due to challenges in quantifying these services. This study introduces the Recreational and Aesthetic Values of Forested Landscapes (RAFL) index, a novel framework combining six measurable recreational and aesthetic components: Stewardship, Naturalness, Complexity, Visual Scale, Historicity, and Ephemera. The RAFL index was integrated into a Linear Programming (LP) Resource Capability Model (RCM) to assess trade-offs between CES and other ecosystem services, including timber production, wildfire resistance, and biodiversity. The approach was applied in a case study in Northern Portugal, comparing two forest management scenarios: Business as Usual (BAU), dominated by eucalyptus plantations, and an Alternative Scenario (ALT), focused on the conversion to native species: cork oak, chestnut, and pedunculate oak. Results revealed that the ALT scenario consistently achieved higher RAFL values, reflecting its potential to enhance CES, while also supporting higher biodiversity and wildfire resilience compared to the BAU scenario. Results highlighted further that management may maintain steady timber production and wildfire regulatory services while addressing concerns with CES. This study provides a replicable methodology for quantifying CES and integrating them into forest management frameworks, offering actionable insights for decision-makers. The findings highlight the effectiveness of the approach in designing landscape mosaics that provide CES while addressing the need to supply provisioning and regulatory ecosystem services.

Extreme wildfire events (EWEs) are becoming increasingly frequent in Mediterranean regions, posing significant threats to ecosystems. This study aimed to support post-fire restoration planning by developing a prioritization framework that categorizes areas according to different levels of vulnerability to the adverse impacts of EWEs. We developed a multi-criteria decision analysis (MCDA) approach to classify these areas within a fire perimeter. The process begins with the collection of available spatial data to assess the pre- and post-fire conditions. Following this, a set of criteria and sub-criteria was established through a participatory approach with local stakeholders. The analytic hierarchy process (AHP) was used to determine stakeholders’ preferences, which were then processed using the Criterium Decision Plus (CDP) version 4 software to support problem modeling. A combined consistency check was applied to ensure both individual coherence and group agreement. Finally, the methodology was integrated using the Ecosystem Management Decision Support (EMDS) software version 9, resulting in a spatial prioritization map that visually represents the levels of restoration priority and serves as a decision-support tool for post-fire restoration planning. Both the process and its results are discussed for an application to a large fire perimeter in the Vale do Sousa forested landscape.

We present two mixed integer linear programming (MILP) formulations for a well-known integrated network, timber landing location, and routing problem that arises in forest management. The models seek to jointly optimize the construction and maintenance schedule of forest road networks with landing site selection and transportation routing for timber production. This problem is, in general, difficult to solve as it contains the so-called fixed charge network flow problem, which is known to be NP-hard. One of the proposed MILP formulations considers 3-index continuous variables to represent timber flows on road segments in each period. The presence of Big-M constraints leads to weak linear relaxation bounds. Disaggregating flow variables, according to timber origin, results in a novel 4-index formulation with very tight linear relaxation bounds. Nevertheless, the number of variables increases prohibitively. This research makes use of spatial constraints common to Smallholding Forested Landscapes to develop a solution approach that reduces the number of flow variables in the new 4-index model. Results from a real-world case study located in Northwest Portugal show that, with the 4-index formulation, the proposed solution approach makes it possible to obtain optimal solutions in a short computational time.

In this work, we address a forest management problem for timber production with fire concerns, employing a novel simulation-based optimization approach wherein forest management is iteratively guided by the feedback from fire spread simulations.The forest management problem involves selecting an alternative prescription for each stand, subject to various restrictions (e.g. bounds on ecosystems services), to maximize the net present value. For each stand, prescriptions involve projecting forest conditions and outcomes using species-specific growth and yield models, combined with different fuel treatment scenarios. In each iteration, the optimization problem is solved. Fire travel times between adjacent points in a grid representing the forest are calculated, based on fuel models associated with the selected prescriptions and other conditions as wind and slopes. Fire spread is simulated for all potential ignitions. Fire paths with a rate of spread greater than a given threshold are identified and constraints are added to the forestry problem to exclude their associated prescriptions to be jointly selected. This problem is re-optimized and the process is repeated until there are no such paths.We describe computational experiments in a Portuguese forest showing how trade-offs between the net present value and the maximum fire rate of spread can be obtained. When too restrictive conditions are imposed on fire, the approach suggests a set of stands to become fire breaks. We also conducted experiments to demonstrate how the impact of the forest surroundings, as well as bounds on ecosystem services, can be evaluated with respect to these trade-offs.

Forest fires are becoming a more common occurrence in Portugal as well as worldwide. To extinguish or reduce them more quickly and effectively, it is crucial to understand how they spread. This paper presents a study and a model that shows how wildfires spread, assuming the forest can be represented by a graph, where the nodes correspond to forest stands and the arcs to the path between them. In order to do this, algorithms were developed in Python, using discrete event simulation, that allow modelling the progression of the fire on the graph. This fire propagation model takes into account several aspects of the forest, the wind being the most influential one. Some tests were performed, considering different ignition points, wind directions and wind speeds.

Forest managers need inventory data and information to address sustainability concerns over extended temporal horizons. In situ information is usually derived from field data and computed using appropriate equations. Nonetheless, fieldwork is time-consuming and costly. Thus, new technologies like Light Detection and Ranging (LiDAR) have emerged as an alternative method for forest assessment. In this study, we evaluated the accuracy of geostatistical methods in predicting the Site Index (SI) using LiDAR metrics as auxiliary variables. Since primary variables, which were obtained from forestry inventory data, were used to calculate the SI, secondary variables obtained from LiDAR surveying were considered and multivariate kriging techniques were tested. The ordinary cokriging (CK) method outperformed the simple cokriging (SK) and Inverse Distance Weighted (IDW) methods, which was interpolated using only the primary variable. Aside from having fewer SI sample points, CK was proven to be a trustworthy interpolation method, minimizing interpolation errors due to the highly correlated auxiliary variables, highlighting the significance of the data’s spatial structure and autocorrelation in predicting forest stand attributes, such as the SI. CK increased the SI prediction accuracy by 36.6% for eucalyptus, 62% for maritime pine, 72% for pedunculate oak, and 43% for cork oak compared to IDW, outperforming this interpolation approach. Although cokriging modeling is challenging, it is an appealing alternative to non-spatial statistics for improving forest management sustainability since the results are unbiased and trustworthy, making the effort worthwhile when dense secondary variables are available.

Forests provide multiple ecosystem services, some of which are competitive, while others are complementary. Pareto frontier approaches are often used to assess the trade-offs among these ecosystem services. However, when dealing with spatial optimization problems, one is faced with problems that are computationally complex. In this paper, we study the sources of this complexity and propose an approach to address adjacency conflicts while analyzing trade-offs among wood production, cork, carbon stock, erosion, fire resistance and biodiversity. This approach starts by sub-dividing a large landscape-level problem into four smaller sub-problems that do not share border stands. Then, it uses a Pareto frontier method to get a solution to each. A fifth sub-problem included all remaining stands. The solution of the latter by the Pareto frontier method is constrained by the solutions of the four sub-problems. This approach is applied to a large forested landscape in Northwestern Portugal. The results obtained show the effectiveness of using Pareto frontier approaches to analyze the trade-offs between ecosystem services in large spatial optimization problems. They highlight the existence of important trade-offs, notably between carbon stock and wood production, alongside erosion, biodiversity and wildfire resistance. These trade-offs were particularly clear at higher levels of these optimized services, while spatial constraints primarily affected the magnitude of the services rather than the underlying trade-off patterns. Moreover, in this paper, we study the impact of the size and complexity of the spatial optimization problem on the accuracy of the Pareto frontiers. Results suggest that the number of stands, and the number of adjacency conflicts do not affect accuracy. They show that accuracy decreases in the case of spatial optimization problems but it is within an acceptable range of discrepancy, thus showing that our approach can effectively support the analysis of trade-offs between ecosystem services.

Characterizing Management Units (MUs) with tree-level data is instrumental for a comprehensive understanding of forest structure and for providing information needed to support forest management decision-making. Airborne Laser Scanning (ALS) data may enhance this characterization. While some studies rely on Individual Tree Detection (ITD) methods using ALS data to estimate tree diameters within stands, these methods often face challenges when the goal is to characterize MUs in dense forests. This study proposes a methodology that simulates diameter distributions from LiDAR data using an Area-Based Approach (ABA) to overcome these limitations. Focusing on maritime pine (Pinus pinaster Ait.) MUs within a forest intervention zone in northern Portugal, the research initially assesses the suitability of two highly flexible Probability Density Functions (PDFs), Johnson’s SB and Weibull, for simulating diameter distribution in maritime pine stands in Portugal using the PINASTER database. The selected PDF is then used in conjunction with ABA to derive the variables needed for parameter recovery, enabling the simulation of diameter distributions within each MU. Monte Carlo Simulation (MCS) is applied to generate a sample list of tree diameters from the simulated distributions. The results indicate that this methodology is appropriate to estimate diameter distributions within maritime pine MUs by using ABA combined with Johnson’s SB and Weibull PDFs.

Climate change is driving worldwide efforts to mitigate and reverse the increasing anthropogenic emissions of greenhouse gases. Forests can uptake considerable amounts of carbon from the atmosphere, but management decisions and resultant silvicultural practices can largely influence these ecosystems’ carbon balance. This research presents an approach to help land managers cope with the need to ensure the provision of forest products and services while contributing to mitigating climate change via carbon sequestration. The emphasis is on combining a landscape-level resource capability model with a mathematical programming (LP) optimization method to model and solve a land management problem involving timber production, carbon sequestration, and resistance to wildfire targets. The results of an application on a forested landscape in Northwest Portugal showed that this approach may contribute to analyzing and discussing synergies and trade-offs between these targets. They revealed important trade-offs between carbon sequestration and both timber production and fire resistance.

This research aims at presenting landscape management planning methods to help stakeholders select forest ecosystem management plans that may address concerns with wildfire risk and with the environmental impacts of clearcuts. Specifically, we develop mixed integer programming models for spatial optimization that incorporate a wildfire resistance index as well as constraints on the size of clearcut openings. The former is used to enforce a minimum level of resistance to wildfire while the latter limits the size of openings, in each period of the planning horizon. Timber volume even flow is another concern that is also taken into account. This research is applied to the Zonas de Intervenção Florestal (ZIF) de Paiva and de Entre-Douro e Sousa (ZIF_VS) which are located in northwestern Portugal.

In this paper we propose an new approach to multivariate collective models based on asymptotic distibutions, since the modeling problems posed have large samples. Collective risk models play an important part in Risk Theory and in Actuarial Mathematics. Inference based on these models is centered on claims totals. However, the new approach has in mind a different kind of risk, the risk of forest fires, where the variables of interest are the number fires and the total burnt areas. As a result, a special case bivariate risk model is derived with this intent. Besides single models, structured families whose models correspond to the treatments of a base linear model are considered. This leads to an ANOVA-like situation where we don't have to estimate the error, which enable to test the influence of several factors on the multivariate collective models mean vectors. Instead of F tests, we use chi 2 tests, availing us of asymptotic distributions that lightens the treatment. To illustrate the approach, an application to forest fires in Portugal with real data is presented.