Common walnut (Juglans regia L) is an economically important species cultivated worldwide for its high-quality wood and nuts. It is generally accepted that after the last glaciation J. regia survived and grew in almost completely isolated stands in Asia, and that ancient humans dispersed walnuts across Asia and into new habitats via trade and cultural expansion. The history of walnut in Europe is a matter of debate, however. In this study, we estimated the genetic diversity and structure of 91 Eurasian walnut populations using 14 neutral microsatellites. By integrating fossil pollen, cultural, and historical data with population genetics, and approximate Bayesian analysis, we reconstructed the demographic history of walnut and its routes of dispersal across Europe. The genetic data confirmed the presence of walnut in glacial refugia in the Balkans and western Europe. We conclude that human-mediated admixture between Anatolian and Balkan walnut germplasm started in the Early Bronze Age, and between western Europe and the Balkans in eastern Europe during the Roman Empire. A population size expansion and subsequent decline in northeastern and western Europe was detected in the last five centuries. The actual distribution of walnut in Europe resulted from the combined effects of expansion/contraction from multiple refugia after the Last Glacial Maximum and its human exploitation over the last 5,000 years.

Cities host more than half of the world’s population and due to global warming and land use change their vulnerability to deadly heat waves has increased. A healthy vegetated landscape can abate heat wave severity and diminish the related urban heat island through the process of evapotranspiration. This research aimed to develop a methodology for cities to use publicly available Copernicus land cover maps within the i-Tree Cool Air water and energy balance model to map air temperature and humidity. The manuscript presents proof of concept using Naples, Italy with its Mediterranean climate characterized by limited soil water for cooling via evapotranspiration. The approach achieved strong correlations between predicted and observed air temperatures across the city (r ≥ 0.89). During the warm season of 2020, forested land cover was 5°C cooler than land cover dominated by impervious cover. Simulated land cover change, limited to a 10% increase or decrease in tree cover, generated an inverse change of 0.2°C in maximum hourly air temperature, with more trees obtaining cooler air. Soil water limited the cooling, with the generally wetter spring season enabling greater cooling of air temperatures, and summer droughts without irrigation had constrained cooling. Sustainable urban design will likely require an increase in plant cover along with a reduction of impervious surfaces that absorb and reradiate heat in order to improve community resilience to heat waves.

Cities pursuing nature-based solutions to mitigate heatwaves need tools to estimate cooling benefits from increased tree canopy cover. This study applied the i-Tree Cool Air model and a heatwave degree day (HW_DD) metric to quantify reductions in heatwave severity if neighborhoods in 10 Italian cities achieved the recommended minimum 30% tree cover. The scenario focused on establishing functional urban forests, with additional canopy placed over permeable surfaces to enhance stormwater infiltration and evapotranspiration-based cooling. Despite dry summer conditions, the 30% tree cover scenario reduced HW_DD by a median of 34% (range: 16–84%), translating into comparable reductions (median 36%) in heatwave-related mortality for those aged 65 + . The tree cover generated new ecosystem service benefits valued at $10 million per city (range: $2–$62 million) through avoided stormwater runoff, air pollution removal, and carbon sequestration. Results consider drought constraints and potential irrigation trade-offs, including exacerbation of humid heat extremes.

This study proposes an automated method for distinguishing trees (T) from no-trees (NT) by means of optical data. We make use of an optical approach based on a statistical threshold to detect T areas on visible and near infrared bands. An object-based image classification allows to detect three kinds of tree out of forest (TOF) structures: forest patches (FP), isolated trees (IT), tree hedgerows (THR), distinguished from forest (F). Ground truth validation allows estimating the accuracy of classification. Four optical bands and six spectral indices are compared detecting images’ T areas: B2, B3, B4 and B8 bands, Negative Luminance (NL), Normalized Difference Vegetation index (NDVI), Green NDVI (GNDVI), Blue NDVI (BNDVI), Panchromatic NDVI (PNDVI) and Enhanced Vegetation Index (EVI). NL shows a relatively better capability for TOF detection and classification, with overall accuracy (OA) exceeding 92% and p-value = 10⁻⁵. Experiments were conducted on optical data acquired by Sentinel-2 in 2016 over the Alfina highland, central Italy. The tree characteristics were extracted exploiting GNU Octave Image Package. Our results show that this new approach could be extended to the detection and mapping of TOF within large areas of agroforestry landscape.