Detecting Symptoms and Dispersal of Pine Tortoise Scale Pest in an Urban Forest by Remote Sensing

Marco Bascietto, Gherardo Chirici, Emma Mastrogregori, Loredana Oreti (), Adriano Palma, Antonio Tiberini and Sabrina Bertin
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Marco Bascietto: Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Monterotondo, 00015 Rome, Italy
Gherardo Chirici: geoLAB—Laboratory of Forest Geomatics, Department of Agriculture, Food, Environment and Forestry, Università Degli Studi di Firenze, 50145 Firenze, Italy
Emma Mastrogregori: Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Monterotondo, 00015 Rome, Italy
Loredana Oreti: Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Monterotondo, 00015 Rome, Italy
Adriano Palma: Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Monterotondo, 00015 Rome, Italy
Antonio Tiberini: Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria (CREA)—Centro di Ricerca Difesa e Certificazione, 00156 Rome, Italy
Sabrina Bertin: Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria (CREA)—Centro di Ricerca Difesa e Certificazione, 00156 Rome, Italy

Land, 2025, vol. 14, issue 3, 1-17

Abstract: Forests provide essential ecosystem services but face increasing threats from invasive species like Toumeyella parvicornis (pine tortoise scale). Since its introduction to Italy in 2014, this pest has severely impacted Pinus pinea forests, with a major outbreak in 2019 affecting an urban forest in the Rome municipality area. This study aims to develop a tool for detecting forest dieback symptoms caused by the scale and assess the role of prevailing winds in its dispersal by integrating multispectral and hyperspectral earth observation systems, including Sentinel-2 and the Hyperspectral Precursor of the Application Mission (PRISMA). At a 6000-hectare protected area with diverse vegetation, a binary Random Forest classifier, trained on near-infrared and short-wave infrared reflectance data, identified symptomatic stands. A generalized linear mixed model compared uniform and wind-influenced probabilistic dispersal models, assessing the pest spread relative to the initial infestation hotspot. The results confirmed a sharp decline in near-infrared reflectance in 2019, indicating severe defoliation and a shift from evergreen to deciduous canopy phenology by 2021. The classifier achieved 82% accuracy, effectively detecting symptomatic pine forests (91% precision). The scale spread to 51% of the pine forest area by 2021, with no strong correlation to prevailing winds, suggesting other augmenting dispersal drivers, such as vehicles along congested routes, wind tunnels, pest-resistant forests, and the potential mitigating role of alternating coastal wind patterns that are effective in the study area.

Keywords: stone pine; reflectance; alien species; airborne dispersal; forest dieback (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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