# Detecting Symptoms and Dispersal of Pine Tortoise Scale Pest in an Urban Forest by Remote Sensing
## 3 Results  
The onset of the PTS pest outbreak in 2019 on the stone pine forests in the study site, reported by visual inspection by the Estate's personnel, was confirmed by analyzing the long-term phenology reflectance data in the wavelength ranges of near-infrared (NIR) and visible red, most commonly used to derive common vegetation indexes (Figure 3). On the one hand, the steady NIR phenology of evergreen canopy cover [18] clearly showed an abrupt drop in June 2019 and a further decline in 2020, whereas the visible red phenology could not mirror any similar change pattern. By 2021, the NIR phenology changed to a yearly cycle where the original evergreen canopy died or was harvested by alternating photosynthetically active (from spring to autumn) to nonactive seasons (winter) typical of a deciduous broadleaf forest land cover. As a result, to perform detection of the cumulative dispersal of PTS symptoms in a stone pine forest, archive-available PRISMA imagery for 15 June 2021, 10:07 CET, was retrieved. The map of detected infestation (Figure 4) confirms that, by 2021, most of the pine forests in the study site were irreversibly attacked by PTS. The total outbreak area was estimated to be 270 hectares, or approximately 51% of the entire pine forest area. The RF classifier revealed an overall good accuracy (82%), specifically in correctly identifying the pine forest pixels affected by PTS (94% user accuracy in the \"symptomatic\" class, -UA- in Table 1). Despite that, a certain amount of variability affects the estimated predictions (80% producer accuracy in the \"symptomatic\" class, -PA- in Table 1). Thus, the classifier metric shows the potential for monitoring and correctly detecting PTS outbreaks in pine forests at the cost of misclassifying asymptomatic forest patches as being attacked by PTS once every five predictions of infestation presence. Figure 3: Boxplot of reflectance value distributions per class in each of the relevant bands for symptom detection. The stone pine vegetative status (presence/absence of symptoms), picked up by the reflectance spectra (Figure 5), showed higher reflectance for healthy vegetation in the NIR region where leaf internal scattering, foliar biochemical components, and water absorption contribute to the shape of the spectra, and lower reflectance in the SWIR region, where mostly proteins and cell wall components such as lignin and starch share reflective potential. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**UAs**} & \multicolumn{4}{c}{**Predicted Symptom Condition**} \\ \cline{2-5}  & **Total** & **Absence** & **Presence** & \\ \hline
91\% & 48 & 4 & 44 & **Presence** & **Actual** \\
82\% & 62 & 51 & 11 & **Absence** & **symptom** \\  & 110 & 54 & 56 & **Total** & **condition** \\
**ACC:** 87\% & & & & & \\
**K:** 0.72 & & 92\% & 80\% & & PAs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Random Forest classification confusion matrix. Figure 4: Binary detection map of PTS infestation in Castelporziano during June 2021 (yellow: presence of symptoms; cyan: absence of symptoms). Coordinate Reference System: WGS84/UTM zone 33N. The most useful diagnostic wavelengths used by the RF classifier (Table 1) were found chiefly in the SWIR region (specifically in the 1970-2080 nm and 2350-2500 nm windows) and two more bands in the NIR region (817 nm and 1317 nm). Consistent with previous studies, these results showed that asymptomatic stone pines can be differentiated from symptomatic and stressed vegetation according to their higher reflectance in the NIR regions and lower reflectance in the SWIR region (Figure 5). Figure 6 shows that the prevailing wind direction during the study period was NE-SW. Such a dynamic confirms the local sea-land breeze regime with mild winds from the sea to the land during daytime hours and from the land to sea at night.