Pest-infected oak trees identify using remote sensing-based classification algorithms

Abstract Despite the rapidly increasing number of studies on methods to identify pest infestation using remote sensing techniques, novel methods are still emerging. Tortrix viridana and Tibicina plebejus are among the most destructive pests in oak forests in regions with Mediterranean climates. This study assessed classification models and compared their effectiveness in the detection of infestations by T. viridana and T. plebejus in western Iran using Sentinel-2 imagery. Seven classification algorithms—support vector machine (SVM), maximum likelihood (ML), Mahalanobis distance (MD), minimum distance of mean (MDM), parallel piped (PP), neural network (NN), and binary encoding (BE)—were compared in the region of the Zagros Forest. Employing 501 field sites at which the two pests were confirmed to be present or absent (316 locations in non-affected areas and 185 in infested areas), spatial distribution maps of T. viridana and T. plebejus were produced with each of the algorithms. The results demonstrate that the classification algorithms generate significantly (with 99% confidence) different predictions. SVM, with accuracies of 70.37% (of T. viridana predictions) and 86.73% (of T. plebejus predictions) was the most accurate classification algorithm. There is also no significant difference between the parametric and nonparametric classifiers for the pest-infestation mapping. The results can guide the improved and comprehensive management of pest detection and monitoring in forests using remote sensing techniques.