Mapping Solanum mauritianum plant invasions using WorldView-2 imagery and unsupervised random forests

Abstract

Abstract The accurate detection and mapping of plant invasions is important for an effective weed management strategy in forest plantations. In this study, the utility of WorldView-2 was investigated to automatically map the occurrence of Solanum mauritianum (bugweed) found as an anomaly in forest margins, open areas and riparian zones. The unsupervised methodology developed, proved to be an effective and an accurate framework in detecting and mapping the invasive alien plant (IAP). Using the random forest (RF) proximity matrix, similarity measures between pixels were successfully transformed into scores (Eigen weights) for each pixel using eigenvector analysis. Neighbourhood windows with minimum variance revealed the most important information from localized surrounding pixels to detect potential anomalous pixels. Bugweed occurrence in forest margins, open areas and riparian zones were successfully mapped at accuracies of 91.33%, 85.08%, and 67.90%, respectively. This research has demonstrated the unique capability of using an automated unsupervised RF approach for mapping IAPs using new generation multispectral remotely sensed data.