Background: Leaf area index (LAI) is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration. For this reason, the LAI is often used as a key input parameter in ecosystem services’ modeling, which is emerging as a critical tool for steering upcoming urban reforestation strategies. However, LAI field measures are extremely time-consuming and require remarkable economic and human resources. In this context, spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8, may represent a feasible and economic solution for estimating the LAI at the city scale. Nonetheless, as far as we know, only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems. To fill such a gap, we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI, using field measurements collected with the LI-COR LAI 2200c as a reference. We hypothesized that Sentinel-2 data, owing to their finer spatial and spectral resolution, perform better in estimating vegetation’s structural parameters compared to Landsat 8. Results: We found that Landsat 8-derived models have, on average, a slightly better performance, with the best model (the one based on NDVI) showing an R² of 0.55 and NRMSE of 14.74%, compared to R² of 0.52 and NRMSE of 15.15% showed by the best Sentinel-2 model, which is based on the NBR. All models were affected by spectrum saturation for high LAI values (e.g., above 5). Conclusion: In Mediterranean ecosystems, Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season. Therefore, the uncertainty introduced using satellite-derived LAI in ecosystem services’ assessments should be systematically accounted for.

Ecosystem services delivered by natural ecosystems are increasingly important for climate change adaptation and mitigation and play a huge role in biodiversity conservation. For this reason, the EU has the ambitious goal of protecting at least 30% of land by 2030. Member states are called to improve and expand the network of protected areas within the next few years; to do so, scientific studies aimed at identifying areas with high ecological value, as well as at defining best management practices, are highly needed. In this study, we used the InVEST suite of models to spatially assess three regulating ecosystem services, that is, carbon storage, seasonal water yield, and urban flood risk mitigation in three administrative regions of central Italy. Using overlay analysis, we found areas with the highest delivery in each of the considered ESs; based on these findings, we eventually proposed four new protected areas, which combine for 888 km², that is, 2.73% of the study area. Interestingly, each of the newly proposed protected areas has somehow been discussed and hypothesized by stakeholders, but only one is presumably going to be part of the national network of protected areas within the next years. Hopefully, by prioritizing areas according to the production of ecosystem services, this study can be intended as a step towards the systematic inclusion of ecosystem services studies for enhancing the network of areas under national protection schemes and achieving the goal of protecting at least 30% of land in Europe by 2030.

Maritime Spatial Planning is a pivotal tool for the sustainable development of coastal areas and the implementation of the blue economy. It requires a spatially explicit environmental risk assessment, including risk identification, analysis and evaluation, following the principles of Ecosystem-Based Management. This study aims to test an Environmental Risk Assessment approach using the InVEST Habitat Risk Assessment model to enhance Maritime Spatial Planning in the northern Mediterranean Sea. The model requires spatial data representing the distribution of habitats and stressors, and user-provided tabular data describing their interactions; it then produces quantitative, spatially explicit risk estimates, useful to prioritize actions for risk mitigation. The approach was applied in the Gulf of La Spezia (Italy), one of the most critical areas for Maritime Spatial Planning implementation in the Northwestern Mediterranean. Indeed, the gulf is a hotspot for marine biodiversity, hosting three Marine Protected Areas with valuable habitats like the Mediterranean coral Cladocora caespitosa , Posidonia oceanica meadows, Cymodocea nodosa seagrass beds, and animal forests. Nonetheless, several economically important human activities are located in the area, such as shipping, shipbuilding industries, Navy bases and arsenals, aquaculture plants, fishing and diving centres, as well as ports and other nautical facilities. Results show that risk is concentrated near coastlines (within the first 250 metres from the coast) and around islands, reflecting the spatial overlap between susceptible habitats and anthropogenic activities. The major stressors are activities/infrastructures connected with nautical tourism and coastal urbanization, such as leisure boat anchorage areas, sewage pipelines, tourist ports, and nautical facilities. A set of management actions is discussed for risk reduction, including stricter local enforcement of existing regulations and the installation of eco-friendly moorings. The proposed approach has proven to be suitable to understand habitat vulnerabilities and criticalities in a Mediterranean marine test site, providing relevant evidence-based information that could eventually steer stakeholders in defining actions for risk management. Its transparency and ease of replication make it a sound choice to build ecosystem-based knowledge, which is essential for balancing socio-economic targets with the preservation of biodiversity, ecosystem functions, and ecosystem services.