Publications

There is an increasing interest in forest biomass for energy throughout Europe, which is seen as a way of promoting forest mobilization and economic development locally, in particular in regions where forest biomass is available but its use is limited by lack of demand. This study was conducted to define, evaluate and select viable forest industry scenarios to increasing forest mobilisation in the North of Portugal using AppTitude ® , a Forest Management Decision Support Systems (FMDSS) considering spatially explicitly supply (biomass growth and yield), demand (industry), and supply-demand interactions (markets). The protocol followed combined a set of indicators of sustainable forest management to guide the selection of the best industry solutions in terms of location, dimension, forest biomass and other variables defined as objectives. The simulations allowed the selection of a small set of industry scenarios compatible with an existing plant outside the study area, increasing wood mobilization, preventing overexploitation and competition among industries but increasing value and price of forest biomass. The results of the application of this FMDSS showed that introducing new biomass plants in the region will increase sustainable forest mobilization and related local development. AppTitude ® revealed to be a powerful and reliable tool to assist forest planning.

Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.

Decision-making in protected areas is often difficult due to an unclear definition of management objectives and indicators for their monitoring. This is frequently related to the coexistence of systems of protected areas at national and regional levels for which management objectives are ambiguous or that are not directly related to protected areas classification standards. In this study, we propose a participative model based on PROMETHEE II to find consensual protected areas categories based on IUCN framework. This model involves the stakeholder’s assessments of management objectives in a protected area and their aggregation based on distance comparisons with objectives of international protected areas standards. The model was tested in the Albufera de Valencia Natural Park, a strongly human-modified wetland located in Eastern Spain and it allowed the identification of consensual management priorities and the IUCN category V as the equivalent international protected area category for this regional protected area.

The planning of protected rural areas is usually defined by institutional decision-makers without considering the preferences of the local communities that live on the land, which frequently leads to conflicts in land management. This paper proposes a voting method based on the Borda count to rank the management goals of a protected rural area. The method was applied in a Spanish-Portuguese reserve called Meseta Ibérica with the aim of collecting the preferences of institutional decision-makers (government and scientists) and rural landowners (farmers and businesspersons). Regarding the conservation and development objectives, the results show differences in spatial planning when only the preferences of institutional decision-makers are taken into consideration, as opposed to when the preferences of landowners are included within the analysis.

In this paper, we propose a sequentially participative model for planning in transboundary protected areas based on the Analytical Hierarchy Process, Goal Programming and Monte Carlo simulation. The model was developed with two scenarios: one determinist and another with simulations that provide a multi-level ranking of the most relevant goals according stakeholders’ preferences to establish priorities in the planning of protected areas. Moreover, the proposed methodology is capable of identifying conflicts, providing a comparison between the most likely priorities and the most consistent group priorities associated with each planning goal. The model was tested in a Portuguese-Spanish Reserve called Meseta Ibérica and it permitted the identification of the highest conflicts in conservation, agroforestry, local development, fire prevention, wildlife conservation and certification of local products. Moreover, it found strong intercountry conflicts related to development and planning goals associated with governance at the most specific level.

MCDM methods are useful to obtain information and generate knowledge useful for decision-making processes in multidisciplinary contexts. Particularly, when conflicts occur, knowledge is the key to start negotiation processes between stakeholders in order to achieve consensual solutions. The planning of protected areas is complex due to many competing uses of natural resources and the involvement of a large number of stakeholders. For the last ten years, participative MCDM methodologies have been carried out efficiently to reduce conflicts and allow to formalize stakeholder’s participation in the policy development processes. In this paper, we propose a MCDM participative methodology in three levels that seeks to define management plans in protected areas. This method allows for the definition of management plans based on three levels of criteria that define the use of the natural area and provide a ranking of the main goals according to the stakeholders’ priorities. The model was tested in two Spanish protected areas: Parque Natural de Lago de Sanabria y alrededores and Parque Natural de los Arribes del Duero. Firstly, the individual preferences of the most representative stakeholder groups were collected (Farmers, Business owners, Government and Scientists) and analysed using two multi-criteria methods: AHP and GP. Moreover, a conflict index between stakeholders’ groups was calculated. Results showed that conservation and development goals are the most preferred to consider for the planning of these areas. Furthermore, the most serious disagreement was found between farmers and scientists and between farmers and government about the wildlife conservation.

Forest management based on sustainability and multifunctionality requires reliable and user-friendly tools to address several objectives simultaneously. In this work we present FlorNExT Pro®, a multiple-criteria landscape-scale forest planning and management computer tool, and apply it in a region in the north of Portugal to find optimized management solutions according to objectives such as maximization of net present value (NPV), volume growth, and carbon storage, and minimization of losses due to fire. Comparisons made among single- and multi-objective solutions were made to explore the range of possible indicators provided by the tool such as carbon sequestered, volume growth, probability of fire occurrence, volume of wood extracted, and evenness of harvesting in the management period. Results show that FlorNExT Pro® is a reliable, flexible, and useful tool to incorporate multiple criteria and objectives into spatially explicit complex management problems and to prepare sustainable and multifunctional forest management plans at the landscape level. FlorNExT Pro® is also suited to guiding and adapting forest management for uncertainty scenarios for the assessment of ecosystem services and fire risk, therefore playing an important role in the maintenance of sustainable landscapes in the south of Europe.

Sustainable forest management requires decision support systems to evaluate possible scenarios and anticipate the consequences of decisions. Forest modellers typically develop complex systems of equations to predict the behaviour of forests which makes the use of forest models difficult for end-users in general, affecting transfer of knowledge and technology. To overcome these difficulties and facilitate their practical use, models can be integrated into software to generate user-friendly forest simulators. In this paper we introduce and describe ForestMTIS, a cloud computing compiled and editable open-source project to generate forest simulators which was developed for statistical, non-spatial, deterministic, disaggregated, single species even-aged stand growth and yield models. We demonstrate the use of ForestMTIS based on the development of FlorNExT®, its first practical application, based on a collaborative approach to make growth and yield modelling and sustainable forest management available to a large community of users in the Northeast of Portugal.

Rural settlements (RS) are references of the territorial reality of the town settlement and constitute a set of social and cultural constructions. Its delimitation is one of the most important and complicated tasks within territorial planning. This paper presents a comparison of methodologies to select the most important criteria for the delimitation of the rural settlements in Galicia (northwest of Spain), considering social, environmental and landscaping aspects. Due to the subjective nature of many of the criteria involved, an expert judgment was designed involving 12 decision-makers, experts from different disciplines related to rural planning, who evaluated the weight or importance of a scheme of 108 criteria spread over three levels, through two methodologies: the scoring method and the multicriteria decision making methodology analytical hierarchy process (AHP). The results indicated that the AHP method was the best methodology for obtaining weights in these criteria compared to the scoring method. The built structure criterion was the only one that had the best result according to the scoring method. The research allows concluding that in both methodologies social criteria obtained better weights than environmental and landscape criteria.

The Analytic Hierarchy Process (AHP) is one of the most widely used Multi-Criteria Decision-Making methods worldwide. As such, it is subject to criticisms that highlight some potential weaknesses. In this study, we present a new Multi-Criteria Decision-Making method denominated the “Triangular Assessment Method” (referred to by its Spanish abbreviation, MTC©). The MTC© aims to make use of the potential of AHP while avoiding some of its drawbacks. The main characteristics and advantages of the MTC© can be summarised as follows: (i) evaluation of criteria, and of the alternative options for each criterion, in trios instead of pairs; (ii) elimination of discrete scales and values involved in judgements; (iii) a substantial reduction in the number of evaluations (trios) relative to the corresponding number of pairs which would have to be considered when applying the AHP method; (iv) consistent decision-making; (v) introduction of closed cyclical series for comparing criteria and alternatives; and (vi) the introduction of opinion vectors and opinion surfaces. This new method is recommended for supporting decision-making with large numbers of subjective criteria and/or alternatives and also for group decisions where the consensus must be evaluated. The MTC© provides a different promising perspective in decision-making and could lead to new research lines in the field of information systems.