A critical challenge for urban forests is the arrival of Toumeyella parvicornis (or pine tortoise scale) in Italy, as this species damages stone pine (Pinus pinea L.), an emblematic Mediterranean species. The aim of this study is to evaluate the effectiveness of remote-sensing data for monitoring pest invasions in the urban area of Rome, using PlanetScope images with a 1-day revisit time and 3 m spatial resolution, making them ideal for detecting outbreaks in complex urban areas. First, we constructed a reference dataset, georeferencing 238 healthy trees in Tenuta San Rossore (Tuscany) and more than 2000 damaged trees in Rome’s green areas. In any case, this dataset of healthy trees—obtained from forest areas—was expected to exhibit higher photosynthetic activity compared to urban green areas. Second, more than 30,000 PlanetScope images were analyzed to test the effectiveness of the Renormalized Difference Vegetation Index in detecting this specific forest disturbance. Finally, different thresholds were examined, allowing for the identification of an optimal threshold to discriminate healthy trees from damaged trees. The index results showed a marked drop during the summer in the infested areas, compared to the healthy areas. The identified threshold provided 99% accuracy in detecting infested trees. The approach applied in this study demonstrated that PlanetScope imagery proved effective in detecting T. parvicornis, leading to promising results.

Urban areas across Europe are undergoing rapid morphological transformations driven by densification, redevelopment, and infrastructure expansion. Monitoring these urban changes requires operational, harmonized, and reproducible approaches grounded in Earth Observation. This study presents a Copernicus use case demonstrating how the High-Resolution Layer Imperviousness Change (2015–2018) and Urban Atlas datasets can be integrated with the Guidos Toolbox (GTB) to quantify structural urban change across six metropolitan areas (Milan, Sofia, Riga, Warsaw, Viseu, Santander). Morphological Spatial Pattern Analysis (MSPA) and entropy-based indicators were applied to characterize land take, fragmentation, compaction, and internal reorganization of impervious surfaces. The combined framework captured both configurational morphology and spatial disorder, revealing divergent development patterns: pronounced heterogeneity and fragmentation in Sofia, stabilization or compact growth in Milan, Warsaw, and Santander, controlled densification in Riga, and localized intensification without outward expansion in Viseu. All analyses rely on openly accessible Copernicus data and open-source tools, ensuring full reproducibility and transferability. Outputs were disseminated through a FAIR-compliant geoportal developed within a Copernicus FPCUP project, supporting transparency and reuse. The findings underscore the value of Copernicus services for operational urban monitoring and provide a scalable methodology to support European land-use policies, including the Zero Net Land Take 2050 target and the EU Soil Strategy.