The reintroduction of livestock grazing to regulate biomass load is being tested for large-scale restoration in Mediterranean landscapes affected by rural abandonment. Concurrently, there is a need to develop cost-effective methods to monitor such interventions. Here, we investigate if satellite data can be used to monitor the response of vegetation phenology and productivity to grazing disturbance in a heterogenous forest mosaic with herbaceous, shrub, and tree cover. We identify which vegetation seasonal metrics respond most to grazing disturbances and are relevant to monitoring efforts. The study follows a BACI (Before-After-Control-Impact) design applied to a grazing intervention in a Pyrenean oak forest (Quercus pyrenaica) in central Portugal. Using NDVI time-series from Sentinel-2 imagery for the period between June 2016 and June 2021, we observed that each type of vegetation exhibited a distinct phenology curve. Herbaceous vegetation was the most responsive to moderate grazing disturbances with respect to changes in phenology and productivity metrics, namely an anticipation of seasonal events. Results for shrubs and trees suggest a decline in peak productivity in grazed areas but no changes in phenology patterns. The techniques demonstrated in this study are relevant to a broad range of use cases in the large-scale monitoring of fine-grained heterogeneous landscapes.

Sown Biodiverse Pastures (SBP) are the basis of a high-yield grazing system tailored for Mediterranean ecosystems and widely implemented in Southern Portugal. The application of precision farming methods in SBP requires cost-effective monitoring using remote sensing (RS). The main hurdle for the remote monitoring of SBP is the fact that the bulk of the pastures are installed in open Montado agroforestry systems. Sparsely distributed trees cast shadows that hinder the identification of the underlaying pasture using Unmanned Aerial Vehicles (UAV) imagery. Image acquisition in the Spring is made difficult by the presence of flowers that mislead the classification algorithms. Here, we tested multiple procedures for the geographical, object-based image classification (GEOBIA) of SBP, aiming to reduce the effects of tree shadows and flowers in open Montado systems. We used remotely sensed data acquired between November 2017 and May 2018 in three Portuguese farms. We used three machine learning supervised classification algorithms: Random Forests (RF), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). We classified SBP based on: (1) a single-period image for the maximum Normalized Difference Vegetation Index (NDVI) epoch in each of the three farms, and (2) multi-temporal image stacking. RF, SVM and ANN were trained using some visible (red, green and blue bands) and near-infrared (NIR) reflectance bands, plus NDVI and a Digital Surface Model (DSM). We obtained high overall accuracy and kappa index (higher than 79% and 0.60, respectively). The RF algorithm had the highest overall accuracy (more than 92%) for all farms. Multitemporal image classification increased the accuracy of the algorithms. as it helped to correctly identify as SBP the areas covered by tree shadows and flower patches, which would be misclassified using single image classification. This study thus established the first workflow for SBP monitoring based on remotely sensed data, suggesting an operational approach for SBP identification. The workflow can be applied to other types of pastures in agroforestry regions to reduce the effects of shadows and flowering in classification problems.

The escalating threat of climate-driven wildfires, land abandonment, wildland–urban interface expansion, and inadequate forest management poses an existential challenge to Mediterranean oak ecosystems, for which traditional fire suppression has proven insufficient. This paper presents a combination of integrated fire management (IFM) and closer-to-nature forest management (CTNFM) in a representative mixed Pyrenean oak (Quercus pyrenaica) forest at Quinta da França (QF), in Portugal. It is structured around three main objectives designed to evaluate this pioneer integrated approach: (1) to describe the integration of IFM and CTNFM within an agro-silvo-pastoral landscape; (2) to qualitatively assess its ecological, operational, and socio-economic outcomes; and (3) to quantitatively evaluate the effectiveness of two key nature-based solutions (NbSs), that is, prescribed burning and planned grazing, in reducing wildfire risk and enhancing forest resilience and biodiversity. By strategically combining proactive fuel reduction with biodiversity-oriented silviculture, the QF case provides a replicable model for managing analogous Mediterranean forested areas facing similar risks. This integrated approach supports forest multifunctionality, advancing both prevention and adaptation goals, and directly contributes to the ambitious targets set by the European Union’s New Forest and Biodiversity Strategies for 2030, marking a significant step towards a more sustainable and fire-resilient future for such Mediterranean landscapes.

Wildfires pose a growing threat to Mediterranean ecosystems. This study employs advanced classification techniques for shrub fractional cover mapping from satellite imagery in a fire-prone landscape in Quinta da França (QF), Portugal. The study area is characterized by fine-grained heterogeneous land cover and a Mediterranean climate. In this type of landscape, shrub encroachment after land abandonment and wildfires constitutes a threat to ecosystem resilience—in particular, by increasing the susceptibility to more frequent and large fires. High-resolution mapping of shrub cover is, therefore, an important contribution to landscape management for fire prevention. Here, a 20 cm resolution land cover map was used to label 10 m Sentinel-2 pixels according to their shrub cover percentage (three categories: 0%, >0%–50%, and >50%) for training and testing. Three distinct algorithms, namely Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forest (RF), were tested for this purpose. RF excelled, achieving the highest precision (82%–88%), recall (77%–92%), and F1 score (83%–88%) across all categories (test and validation sets) compared to SVM and ANN, demonstrating its superior ability to accurately predict shrub fractional cover. Analysis of confusion matrices revealed RF’s superior ability to accurately predict shrub fractional cover (higher true positives) with fewer misclassifications (lower false positives and false negatives). McNemar’s test indicated statistically significant differences (p value < 0.05) between all models, consolidating RF’s dominance. The development of shrub fractional cover maps and derived map products is anticipated to leverage key information to support landscape management, such as for the assessment of fire hazard and the more effective planning of preventive actions.

Forests, due to climate change, already face the impacts of drier conditions and prolonged fire seasons caused by rising global temperatures, that exceed 1.1°C above pre-industrial levels and reach the 1.5 threshold set by the Paris Agreement. In addition to human-induced climate change, other drivers that increase the risk of extreme wildfires include land abandonment, urban-wildland interface (WUI) expansion, and poor or inadequate forest and landscape management. Traditional firefighting methods, extensive fire suppression and exhaustive fire exclusion, are proving inadequate in the face of increasingly intense wildfires, necessitating a move towards Integrated Fire Management (IFM). IFM is a holistic and comprehensive proactive approach that encompasses a variety of tactics to manage wildfires effectively (proactive prevention measures, controlled response operations and proactive restoration efforts), while also promoting ecosystem resilience, social equity and viable economy. The EU Forest Strategy for 2030 recognizes the urgent need towards prevention, emphasizing Closer-to-Nature Forest Management (CTNFM) as a Nature-Based Solution (NbS) to reduce wildfire risk, to improve the quantity and quality of EU forests and fortify adaptive capacity in the context of climate change. In Quinta da França (Covilhā), Portugal, the implementation of IFM and CTNFM approaches has been implemented within a Sustainable Forestry 4.0 approach, in an observation – modelling – intervention – evaluation cycle, with the massive use of sensing and data modelling combined with activities including grazing, prescribed burning, controlled clear cuttings, pruning, thinning and mastication. These operations are tailored to enhance the sustainability and resilience of the landscape mosaics taking into account the local socioeconomic and environmental context as well. This approach fits within the implementation of IFM through the SILVANUS platform, by addressing the challenges outlined in the three phases of disaster risk management (A – Prevention and Preparedness, B – Early Detection and Response and D – Restoration). The evaluation of the approach shows namely its capacity to reduce fuel load and horizontal and vertical vegetation continuity, enhancing biomass carbon sequestration and soil protection, hence showing how the integration of these tools and methods allows the establishment of sustainable and resilient landscapes and communities.

Using locally produced forage and agro-industrial by-products can reduce dependence on imported feed and competition for human food sources, while improving meat quality. However, the overall effect of this feeding strategy on global greenhouse gas emissions must be evaluated to provide a comprehensive assessment of sustainability. This study aimed to test whether replacing the conventional concentrate finishing diet with a total mixed ration (TMR) diet based on maize silage and brewer’s spent grains (BSG) would improve meat quality without compromising productive performance, carcass composition, and the carbon footprint (CFp) of finishing beef cattle. Twenty crossbred young bulls were randomly distributed among 4 pens and randomly allocated to 2 treatments: Control—a conventional diet based on commercial concentrate and wheat straw or TMR—a maize silage-based diet with BSG, concentrate, and straw. Dry matter intake and average daily gain were 13% and 15%, respectively, lower in the TMR treatment than in the Control treatment. Daily methane emissions were 59% higher in the TMR treatment. However, life cycle assessment results revealed no differences in the CFp, and the beef from TMR treatment achieved higher meat quality. In conclusion, a maize silage-based diet offers a cost-effective alternative to conventional diets, with a lower environmental impact and improved beef quality.