'Lantana camara' L. (lantana) is a major problem globally and has been declared a weed of national significance (WoNS) in Australia due to its significant negative impacts on Australian biodiversity and agriculture. Development of remote sensing techniques and modelling approaches that can map lantana accurately and project its likely future distribution should be useful for formulation of more effective, long-term management plans. The research reported here comprises seven studies based on remote sensing and modelling techniques that should contribute to better mapping and projected modelling of lantana in an era of climate change. Four image fusion techniques, namely Brovey, Hue-Saturation-Value (HSV), Principal Components (PC) and Gram-Schmidt (GS) Spectral Sharpening, were investigated using Quickbird imagery to identify the most effective fusion algorithm for mapping lantana. The results identified GS and PC spectral sharpening techniques as the most effective for this purpose. Brovey transformation and HSV, on the other hand, performed poorly with much lower overall accuracies. Three commonly available satellite images of varying spectral, spatial and radiometric resolutions from Landsat TM, SPOT 5 and Quickbird were assessed for accuracy and cost effectiveness in lantana mapping. The most cost-effective option was provided by Landsat TM with no significant difference in overall accuracies between the three types of imagery.