Identification of invasive trees in a Brazilian subtropical forest using remotely piloted aircraft systems and machine learning

We aimed to combine the use of images obtained from remotely piloted aircraft systems (RPAS) and machine learning (ML) to identify the invasive alien species Psidium guajava in a protected area in southern Brazil. Field data were obtained in a sampling area, where the species’ geographic coordinates were collected with a global positioning system device. Remote data were collected with the Parrot Sequoia® multispectral camera onboard the Phantom 4® Pro platform. Image processing was used to generate reflectance maps and vegetation indices, after which four classes of interest were defined for model training. The supervised classification involved two approaches (pixel-based—BP and object-based image analysis—OBIA) and two ML algorithms compared (random forest—RF and support vector machine—SVM). For performance analysis, confusion matrices with user and producer accuracies, Kappa values and overall accuracy (OA) were calculated. The results demonstrate that the multispectral composition was excellent in identifying the invasive P. guajava, in an OBIA approach with the RF algorithm (0.90 Kappa and 93% OA). Thus, considering the priority of biodiversity conservation and the importance of the Brazilian Atlantic Forest for the maintenance of endemic and endangered species, we present a robust methodology to identify the invasive species P. guajava in subtropical forest, which can be applied in management strategies for the species control and eradication.