Urbanization poses significant challenges to ecosystems, resources, and human well-being, necessitating sustainable planning. Urban vegetation, particularly trees, provides critical ecosystem services such as carbon sequestration, air quality improvement, and biodiversity conservation. Traditional tree assessments are resource-intensive and time-consuming. Recent advances in remote sensing, especially high-resolution multispectral imagery and object-based image analysis (OBIA), offer efficient alternatives for mapping urban vegetation. This study evaluates and compares the efficacy of Sentinel-2 and Pléiades satellite imagery in classifying tree species within historic urban parks in Rome—Villa Borghese, Villa Ada Savoia, and Villa Doria Pamphilj. Pléiades imagery demonstrated superior classification accuracy, achieving an overall accuracy (OA) of 89% and a Kappa index of 0.84 in Villa Ada Savoia, compared to Sentinel-2’s OA of 66% and Kappa index of 0.47. Specific tree species, such as Pinus pinea (Stone Pine), reached a user accuracy (UA) of 84% with Pléiades versus 53% with Sentinel-2. These insights underscore the potential of integrating high-resolution remote sensing data into urban forestry practices to support sustainable urban management and planning.

Livestock buildings in rural areas are increasingly recognized for their environmental impact, yet few studies provide applied, scenario-based evaluations to guide retrofit interventions. While the existing literature acknowledges the environmental burden of livestock facilities, it often lacks operationally grounded analyses applicable to real-world agricultural contexts. This paper proposes an original integration of experimental climatic monitoring and life cycle assessment (LCA) to evaluate retrofit scenarios for energy efficiency in real poultry farming contexts. Based on an accurate climatic monitoring campaign conducted on-site during the spring and summer periods, relevant data were collected on air temperature, humidity, wind speed, and solar radiation affecting two poultry tunnels in central Italy, highlighting the need for thermal mitigation. The comparison between the observed operational scenario and the hypothesized improved scenario, involving energy supply from photovoltaic sources, evaluated using the PVGIS tool, demonstrated a significant reduction in environmental impact, with a 33.4% decrease in global warming potential and a 26.1% reduction in energy consumption. This study combines experimental on-site climatic data collection with comparative environmental evaluation using LCA methodology. The LCA approach, which guided the entire study, highlighted how the energy efficiency gained through solar panels adequately offsets their production and maintenance costs over the long term. These findings offer a replicable model for energy retrofits in rural livestock facilities, contributing to both environmental goals and rural resilience.

The continuous development of satellite imagery, coupled with advancements in machine learning technologies, allows detailed mapping of terrestrial landscapes. This study evaluates the classification performance of tree typologies using Sentinel-2 and PRISMA data, focusing on central Italy’s different areas. The purpose is to assess the role of spectral and spatial resolution in land cover classification, contributing to forest management and conservation efforts. Random Forest Classifier was applied to classify tree typologies across two study areas: the Roman Coastal region and the Lake Vico Basin. Ground truth (GT) data, collected from a trial citizen survey campaign, were used for training and validation. PRISMA datasets, particularly when processed with PCA, consistently outperformed Sentinel-2. The PRISMA PCA dataset achieved the highest overall accuracy with 71.09% for the Roman Coastal region and 87.15% for the Lake Vico Basin, emphasizing the value of spectral resolution. However, Sentinel-2 showed comparative strength in spatially heterogeneous areas. Tree typologies with more uniform distribution, such as hazelnut and chestnut, achieved higher classification accuracy compared to mixed-species forests. The study assesses that Sentinel-2 remains a viable alternative where spatial resolution is critical also considering the limited PRISMA images’ availability. Moreover, the work explores the potential of combining satellites and accurate GT for improved land cover mapping.

Soil erosion, surface runoff, and nutrient losses are critical processes linking environmental degradation with social and economic pressures, particularly in multifunctional landscapes where agricultural production, ecosystem conservation, and local livelihoods coexist. In such contexts, the effectiveness of Nature-Based Solutions (NBS) depends not only on biophysical performance but also on their social feasibility and acceptance. This study explores how structured science–society interaction can support participatory planning of NBS in an erosion-prone socio-ecological system.The research is developed within the Horizon Europe EUROLakes project and focuses on the Lake Vico volcanic basin (Central Italy), a unique landscape where high natural value, hazelnut cultivation, and strong cultural, recreational, and identity-related ties to the lake coexist. Increasing erosion-driven runoff and nutrient transport are contributing to declining water quality and eutrophication, highlighting the urgent need to balance human pressures and ecosystem functioning to avoid further degradation of the lake’s water ecosystem.Environmental analyses of erosion processes and nutrient pathways were used as a shared knowledge base to support dialogue with local actors. Stakeholder mapping, workshops, and focus groups were adopted as key methodological steps to identify feasible management interventions and alternative scenarios aimed at improving water quality and erosion issues, while preserving community identity and agricultural productivity. Building on this process, a participatory workshop was conducted using a digital Participatory Multicriteria Analysis (PMCA), implemented through a tailored version of the consolidated MULINO Decision Support System (mDSS), and structured around the 4 Returns Framework to jointly evaluate NBS-oriented options across natural, social, financial, and inspirational returns.Preliminary results from the participatory assessment contributed to the identification of priority intervention themes and informed the evaluation of alternative management options within the EUROLakes project. By integrating scientific indicators with experiential and place-based knowledge within a single decision-support process, the approach makes trade-offs explicit and fosters collective learning. The study contributes to interdisciplinary debates by demonstrating how environmental and social sciences can jointly support the co-design of context-sensitive NBS in sensitive lake landscapes

Diffuse nutrient pollution during rainfall–runoff events is a major pressure on lake water quality, particularly in agricultural catchments. Short-lived runoff events can deliver disproportionally large nutrient loads, yet event-scale runoff observations are rare due to monitoring limitations. This restricts the design and evaluation of nature-based solutions (NBS) and catchment restoration measures.Within the Horizon Europe EUROLakes project, Lake Vico (Central Italy) is used as a pilot site to test innovative approaches for monitoring diffuse pollution. Lake Vico is a volcanic lake affected by agricultural nutrient inputs. To better capture event-driven nutrient transport, an experimental surface runoff monitoring setup was established in the Cerreto sub-catchment.A representative sub-basin was identified based on land use and topography, and an automatic runoff sampling system was installed to trigger autonomously during surface flow events. The system is integrated with a low-cost time-lapse camera acquiring images every 30 minutes, providing continuous visual information on soil moisture conditions and the occurrence of overland flow. Runoff samples are analyzed for key water-quality parameters, including nitrite, ammonium, reactive phosphorus, total nitrogen, and total phosphorus, allowing nutrient dynamics to be quantified at the event scale.To detect runoff directly from the image time series, a Python-based automated classification workflow is being developed. The method uses two regions of interest per image and simple grayscale features to distinguish dry soil from active runoff, accounting for day-night conditions. The workflow processes the full image archive and produces runoff flags and diagnostic indicators for further analysis.We present preliminary results from the first monitoring period and assess the performance of the image-based runoff detection. Key uncertainties related to illumination changes, vegetation dynamics, and night-time conditions are discussed. Ongoing work focuses on linking runoff occurrence to rainfall intensity and duration. Beyond site-specific insights, the resulting event-scale runoff and water-quality dataset provides a critical empirical basis for calibrating process-based hydrological models (e.g. SWAT) and supports the evaluation of NBS in data-limited lake catchments.