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Pine Wilt Disease is one of the most destructive pests affecting coniferous forests. After being infected by the harmful Bursaphelenchus xylophilus nematode, most trees die within one year. The complex spreading pattern of the disease and the tedious hard labor process of diagnosis involving field wood sampling followed by laboratory analysis call for alternative methods to detect and manage the infected areas. Remote sensing comes naturally into play owing to the possibility of covering relatively large areas and the ability to discriminate healthy from sick trees based on spectral characteristics. This paper presents the development of machine learning classification algorithms for the detection of Pine Wilt Disease in Pinus pinaster, performed in the framework of the European Commission’s Horizon 2020 project “Operational Forest Monitoring using Copernicus and UAV Hyperspectral Data” (FOCUS) in two provinces of central Portugal. Five flight campaigns have been carried out in two consecutive years in order to capture a multitemporal variation of disease distribution. Classification algorithms based on a Random Forest approach were separately designed for the acquired very-high-resolution multispectral and hyperspectral data, respectively. Both algorithms achieved overall accuracies higher than 0.91 in test data. Furthermore, our study shows that the early detection of decaying trees is feasible, even before symptoms are visible in the field.

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Pine Wilt Disease is one of the forest pests with high destructive potential, due to its random spreading and the fast evolution of the symptoms. The correct identification of infected trees is critical for the containment of the pest in affected areas. This paper exploits the capabilities of Random Forest classification algorithms designed to spot the infected trees based on remote sensing images. We use as input both multi-and hyperspectral imagery with high spatial resolution, acquired via remotely piloted airborne systems in infected Portuguese forests. For both imagery types, the classification schemes achieve accuracies higher than 0.91. We conclude that Random Forest classification is a feasible method to detect the Pine Wilt Disease in spectral images acquired over wild forests, even at early stages of the infestation.