Bruno Lasserre

Terrestrial laser scanning (TLS) technology characterizes standing trees with millimetric precision. An important step to accurately quantify tree volume and above-ground biomass using TLS point clouds is the discrimination between timber and leaf components. This study evaluates the performance of machine learning (ML)-derived models aimed at discriminating timber and leaf TLS point clouds, focusing on eight Mediterranean tree species datasets. The results show the best accuracies for random forests, gradient boosting machine, stacked ensemble model, and deep learning models with an average F1 score equal to 0.92. The top-performing ML-derived models showed well-balanced average precision and recall rates, ranging from 0.86 to 0.91 and 0.92 to 0.96 for precision and recall, respectively. Our findings show that Italian maple, European beech, hazel, and small-leaf lime tree species have more accurate F1 scores, with the best average F1 score of 0.96. The factors influencing the timber–leaf discrimination include phenotypic factors, such as bark surface (i.e., roughness and smoothness), technical issues (i.e., noise points and misclassification of points), and secondary factors (i.e., bark defects, lianas, and microhabitats). The top-performing ML-derived models report a time computation ranging from 8 to 37 s for processing 2 million points. Future studies are encouraged to calibrate, configure, and validate the potential of top-performing ML-derived models on other tree species and at the plot level.

The heterogeneous ecosystems in the Mediterranean Basin (MB) are becoming sensitive to water stress. To investigate the climatic stress, a water budget study was conducted over the basin using TerraClimate simulations for a long temporal range (1990–2020). According to the budget accounting, forested regions received the highest precipitation (P) on average compared to other land use types (annual mean ≈633 mm yr−1), and even then, they were in a water deficit state (−0.42 mm yr−1). Tree plantations in North Africa (Libya and Morocco) were also in a water deficit state; however, their average P was very low (≈12 mm yr−1) compared to that of northern parts of the MB, and the average Actual Evapotranspiration (AET) was ≈15 mm yr−1. Also, the water balance in other land use systems (rain-fed, irrigated croplands, and rangelands) was either negative or near zero. As a whole, the basin’s average annual P was ≈538 mm yr−1, the annual average AET was ≈415 mm yr−1, and the runoff (Q) was equivalent to 123 mm yr−1, which shows a strong influence of ET over the region. Since runoff was negligible in most dry areas, the AET’s large contribution was notable in the North African base of the Atlas Mountains including the Nile delta region. This indicates that precipitation and evaporation are the principal mechanisms of the water balance in the MB. The result shows strong climate variability over Southern Europe, Turkey, and the western Balkans in the recent years, affecting the AET and making their land use systems more vulnerable to water stress. This benchmark study signifies the consistent need for water storage in the Mediterranean vegetation systems of the basin. It also indicates two distinct climate clusters for water balance modeling.

Worldwide, national governments and private organizations are increasingly investing in Nature-Based Solutions (NBS) to foster both human well-being and biodiversity while achieving climate and environmental targets. Yet, investments in NBS remain uncoordinated among planning levels, their co-benefits underestimated, and their effectiveness undermined. This study aims to provide a spatially explicit approach to optimize the budget allocation for NBS implementation across Italian urban areas while maximizing their effectiveness in terms of environmental health. We explored three different NBS implementation scenarios oriented to (i) maximize the Ecosystem Services supply of NBS (Scenario BP), (ii) minimize costs of NBS (Scenario LC), and (iii) maximize Ecosystem Services supply of NBS at the lowest cost (Scenario CP). Once selected, we prioritized their allocation through the territory following an environmental risk index for population, and we explored the relationship between costs and effectiveness for the three scenarios. The implementation of Scenario BP costs EUR 777 billion while showing 31 billion of effectiveness. Scenario LC costs 70% less than scenario BP (EUR 206 billion) while losing 70% of its effectiveness. Scenario CP costs 60% less than Scenario BP (EUR 301 billion), offering just 20% less effectiveness. Our results show that employing the risk index for NBS allocation would allow for reducing the surface of interventions by saving 67% of the budget in the three scenarios with a negligible loss in terms of return for human health. The here-proposed approach can guide the national funds’ allocation system, improving its cost-effectiveness and equitableness.