Mapping the cover of invasive species using remotely sensed data alone is challenging, because many
invaders occur as mid-level canopy species or as subtle understorey species and therefore contribute
little to the spectral signatures captured by passive remote sensing devices. In this study, two common
non-parametric classifiers namely, the neural network and support vector machine were used to map
four cover classes of the invasive shrub Lantana camara in a protected game reserve and the adjacent
area under communal land management in Zimbabwe. These classifiers were each combined with a
geographic information system (GIS) expert system, in order to test whether the new hybrid classifiers
yielded significantly more accurate invasive species cover maps than the single classifiers. The neural
network, when used on its own,mapped the cover of L. camara with an overall accuracy of 71% and a Kappa
index of agreement of 0.61. When the neural network was combined with an expert system, the overall
accuracy and Kappa index of agreement significantly increased to 83% and 0.77, respectively. Similarly,
the support vector machine achieved an overall accuracy of 64% with a Kappa index of agreement of 0.52,
whereas the hybrid support vector machine and expert system classifier achieved a significantly higher
overall accuracy of 76% and a Kappa index of agreement of 0.67. These results suggest that integrating
conventional image classifiers with an expert system increases the accuracy of invasive species mapping.