Detection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Data

Remote Sensing

Vasco Mantas¹  Luís Fonseca²  Jorge Canhoto²  Elsa Baltazar²  Isabel Abrantes² 

[1] CITEUC, Department of Earth Sciences, University of Coimbra, 3030-790 Coimbra, Portugal;Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, 3000-456 Coimbra, Portugal;

关键词: machine-learning;    pinewood nematode;    pine wilt disease;    remote sensing;    Sentinel-2;    tree decline;   

DOI  :  10.3390/rs14092028

来源: DOAJ

【 摘 要 】

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 with Worldview-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.

【 授权许可】

Unknown