# Detecting Symptoms and Dispersal of Pine Tortoise Scale Pest in an Urban Forest by Remote Sensing
## 5 Conclusions  
Multispectral remote sensing confirmed the onset of the _Toumeyella parvicornis_ pest outbreak in 2019 in a coastal urban forest, which led to an abrupt decline in near-infrared reflectance, indicating severe defoliation and a shift from evergreen to deciduous canopy phenology by 2021. Hyperspectral remote sensing, based on PRISMA sensors, enabled the accurate detection of stone pine forest stands bearing symptoms of infestations by pickingup a change in reflectance in the near-infrared and short-wave infrared regions of the spectrum. The detection model will be reusable for remote sensing monitoring of forests by hyperspectral satellite constellations that are planned to be operative in the coming years by taking advantage of their high spectral resolution and spectrum.  
The prevailing winds were not effective drivers of the cumulative airborne dispersal (2019-2021) of the pest that led to the complete dieback of the stone pine stands in the urban forest. Despite a previous study highlighting the role of the wind as a driver of scale dispersal in a vineyard at the plot level, terrain, canopy, and microclimate conditions could interact to varying degrees in different study sites at a site or regional level. Our results clearly call for the need for large-scale monitoring of the dispersal of pests regardless of the prevailing winds, even in the case of weak fliers such as the pine tortoise scale. This is particularly relevant, especially under climate change scenarios, with an increasing windstorm probability that could spread the pest randomly at unprecedented distances.  
The effect and magnitude of confounding factors on the role of the prevailing winds in scale pest dispersion are still unknown. Such factors may include augmenting drivers on pest dispersal, such as the presence of wind tunnels, flat terrains, and intensively trafficked routes, or hindering windbreak drivers, such as the presence of pest-resistant forests, specific terrains, or microclimates. Future work should factor in such effects to provide more effective dispersal models to be included in early warning monitoring systems.  
Conceptualization: S.B., A.T. and M.B.; Formal analysis: A.P. and M.B.; Investigation: S.B., A.T., E.M. and G.C.; Methodology: A.P. and M.B.; Project administration: M.B., Software: A.P., L.O. and E.M.; Visualization: L.O., E.M. and A.P.; Writing--original draft: M.B., A.P., E.M., L.O., S.B. and A.T.; Writing--review and editing: M.B. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by Comando Unita Forestali, Ambientali e Agroalimentari, Carabinieri (grant CREA PRJ4994m 2.99.99.92.00) \"Smart Forest Monitoring\".  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This research was made possible thanks to the personnel of the Presidential Estate of Castelporziano, who assisted the authors with the ground surveys and in the historical reconstruction of the _Toumeyella parvicornis_ infestation and who carried out using the ORIGINAL PRISMA Products-->Italian Space Agency (ASI); the products have been delivered under an ASI License to Use.  
The authors declare no conflict of interest.