Classifying Serrated Tussock Cover from Aerial Imagery Using RGB Bands, RGB Indices, and Texture Features

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Monitoring the location and severity of invasive plant infestations is critical to the management of their spread. Remote sensing can be an effective tool for mapping invasive plants due to its capture speed, continuous coverage, and low cost, compared to ground-based surveys. Serrated tussock (Nassella trichotoma) is a highly problematic invasive plant in Victoria, Australia, as it competes with the species in the communities that it invades. In this study, a workflow was developed and assessed for classifying the cover of serrated tussock in a mix of grazing pastures and grasslands. Using high-resolution RGB aerial imagery and vegetation field survey plots, random forest models were trained to classify the plots based on their fractional coverage of serrated tussock. Three random forest classifiers were trained by utilising spectral features (RGB bands and indices), texture features derived from the Grey-Level Co-occurrence Matrix, and a combination of all the features. The model trained on all the features achieved an overallaccuracy of 67% and a kappa score of 0.52 against a validation dataset. Plots with high and low infestation levels were classified more accurately than plots with moderate or no infestation. Notably, texture features proved more effective than spectral features for classification. The developed random forest model can be used for producing classified maps to depict the spatial distribution of serrated tussock infestation, thus supporting land managers in managing the infestation.