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Title: Detection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Data
Authors: Mantas, Vasco 
Fonseca, Luís 
Baltazar, Elsa 
Canhoto, Jorge 
Abrantes, Isabel 
Keywords: machine-learning; pinewood nematode; pine wilt disease; remote sensing; Sentinel-2; tree decline
Issue Date: 2022
Project: Horizon 2020 Grant Agreement 776026 
‘Monitorizar para Decidir e Valorizar’, funded by Programa PROMOVE of BPI/Fundação La Caixa 
ReNATURE (Centro-01-0145-FEDER-000007). 
Serial title, monograph or event: Remote Sensing
Volume: 14
Issue: 9
Abstract: Moderate-resolution satellite imagery is essential to detect conifer tree decline on a regional scale and address the threat caused by pinewood nematode (PWN), (Bursaphelenchus xylophilus. This is a quarantine organism responsible for pine wilt disease (PWD), which has caused substantial ecological and economic losses in the maritime pine (Pinus pinaster) forests of Portugal. This study describes the first instance of a pre-operational algorithm applied to Sentinel-2 imagery to detect PWD-compatible decline in maritime pine. The Random Forest model relied on a pre-wilting and an in-season image, calibrated with data from a 24-month long field campaign enhanced withWorldview- 3 data and the analysis of biological samples (hyperspectral reflectance, pigment quantification in needles, and PWN identification). Independent validation results attested to the good performance of the model with an overall accuracy of 95%, particularly when decline affects more than 30% of the 100 m2 pixel of Sentinel-2. Spectral angle mapper applied to hyperspectral measurements suggested that PWN infection cannot be separated from other drivers of decline in the visible-near infrared domain. Our algorithm can be employed to detect regional decline trends and inform subsequent aerial and field surveys, to further investigate decline hotspots.
ISSN: 2072-4292
DOI: 10.3390/rs14092028
Rights: openAccess
Appears in Collections:I&D CITEUC - Artigos em Revistas Internacionais

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